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| created_time
timestamp[ns, tz=UTC]date 2022-03-02 23:29:04
2025-03-18 02:34:30
| last_modified
stringdate 2021-02-13 00:06:56
2025-03-18 09:30:19
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15.6M
| likes
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4.86k
| README
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|---|---|---|---|---|---|---|---|---|---|
RichardErkhov/Undi95_-_Dawn-v2-70B-gguf
|
RichardErkhov
| null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | 2024-05-28T07:40:02Z |
2024-05-29T03:24:59+00:00
| 30 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Dawn-v2-70B - GGUF
- Model creator: https://huggingface.co/Undi95/
- Original model: https://huggingface.co/Undi95/Dawn-v2-70B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Dawn-v2-70B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.Q2_K.gguf) | Q2_K | 23.71GB |
| [Dawn-v2-70B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.IQ3_XS.gguf) | IQ3_XS | 26.37GB |
| [Dawn-v2-70B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.IQ3_S.gguf) | IQ3_S | 27.86GB |
| [Dawn-v2-70B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.Q3_K_S.gguf) | Q3_K_S | 27.86GB |
| [Dawn-v2-70B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.IQ3_M.gguf) | IQ3_M | 28.82GB |
| [Dawn-v2-70B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.Q3_K.gguf) | Q3_K | 30.99GB |
| [Dawn-v2-70B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.Q3_K_M.gguf) | Q3_K_M | 30.99GB |
| [Dawn-v2-70B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.Q3_K_L.gguf) | Q3_K_L | 33.67GB |
| [Dawn-v2-70B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.IQ4_XS.gguf) | IQ4_XS | 34.64GB |
| [Dawn-v2-70B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.Q4_0.gguf) | Q4_0 | 36.2GB |
| [Dawn-v2-70B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.IQ4_NL.gguf) | IQ4_NL | 36.55GB |
| [Dawn-v2-70B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/blob/main/Dawn-v2-70B.Q4_K_S.gguf) | Q4_K_S | 36.55GB |
| [Dawn-v2-70B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q4_K | 38.58GB |
| [Dawn-v2-70B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q4_K_M | 38.58GB |
| [Dawn-v2-70B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q4_1 | 40.2GB |
| [Dawn-v2-70B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q5_0 | 44.2GB |
| [Dawn-v2-70B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q5_K_S | 44.2GB |
| [Dawn-v2-70B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q5_K | 45.41GB |
| [Dawn-v2-70B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q5_K_M | 45.41GB |
| [Dawn-v2-70B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q5_1 | 48.2GB |
| [Dawn-v2-70B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q6_K | 52.7GB |
| [Dawn-v2-70B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Dawn-v2-70B-gguf/tree/main/) | Q8_0 | 68.26GB |
Original model description:
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
<center>[<a href="https://huggingface.co/Undi95/Dawn-v2-70B">fp16</a> - <a href="https://huggingface.co/Undi95/Dawn-v2-70B-GGUF">gguf</a> - exl2 : <a href="https://huggingface.co/Undi95/Dawn-v2-70B-2.55bpw-h6-exl2">2.55bpw</a>]</center>
</br>
<div style="width: 100%;">
<img src="https://hf.fast360.xyz/production/uploads/63ab1241ad514ca8d1430003/Cxcfqi4WdtXCNLnaIqSRB.png" style="width: 75%; min-width: 200px; display: block; margin: auto;">
</div>
<!-- description start -->
## Description
This repo contains fp16 files of Dawn-70B, a merge I have done with the new [layer shuffle](https://github.com/cg123/mergekit/blob/main/mergekit/scripts/layershuffle.py) method from mergekit.
[UtopiaXL](https://huggingface.co/Undi95/UtopiaXL-13B) was a huge success for me, I really liked it, so I took the same path to do this 70B: A good base, some psychologic data, some medical data, a little bit of this, of that, and LimaRP at the end as always.
NOTE: This repo contain the file [measurement.json](https://huggingface.co/Undi95/Dawn-v2-70B/blob/main/measurement.json) needed to do your own exl2 quant (I use [wikitext](https://huggingface.co/datasets/wikitext/resolve/refs%2Fconvert%2Fparquet/wikitext-2-raw-v1/train/0000.parquet)).
<!-- description end -->
<!-- description start -->
## Models and loras used
- [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B)
- [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1)
- [ehartford/Samantha-1.11-70b](https://huggingface.co/ehartford/Samantha-1.11-70b)
- [NousResearch/Nous-Hermes-Llama2-70b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-70b)
- [augtoma/qCammel-70-x](https://huggingface.co/augtoma/qCammel-70-x)
- [jondurbin/airoboros-l2-c70b-3.1.2](https://huggingface.co/jondurbin/airoboros-l2-c70b-3.1.2)
- [fangloveskari/ORCA_LLaMA_70B_QLoRA](https://huggingface.co/fangloveskari/ORCA_LLaMA_70B_QLoRA)
- [Doctor-Shotgun/limarpv3-llama2-70b-qlora](https://huggingface.co/Doctor-Shotgun/limarpv3-llama2-70b-qlora)
<!-- description end -->
## The sauce
```
!mergekit-layershuffle ./Dawn-v2-70B \
--model Sao10K/Euryale-1.3-L2-70B --weight 0.3 \
--model Xwin-LM/Xwin-LM-70B-V0.1 --weight 0.2 \
--model ehartford/Samantha-1.11-70b --weight 0.1 \
--model NousResearch/Nous-Hermes-Llama2-70b --weight 0.05 \
--model augtoma/qCammel-70-x --weight 0.05 \
--model jondurbin/airoboros-l2-c70b-3.1.2 --weight 0.2 \
--model fangloveskari/ORCA_LLaMA_70B_QLoRA --weight 0.1 \
--write-yaml Dawn-v2-70B.yaml
=========================
merge_method: passthrough
slices:
- sources:
- layer_range:
- 0
- 1
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 1
- 2
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 2
- 3
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 3
- 4
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 4
- 5
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 5
- 6
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 6
- 8
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 8
- 9
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 9
- 10
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 10
- 11
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 11
- 12
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 12
- 13
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 13
- 14
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 14
- 15
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 15
- 16
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 16
- 17
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 17
- 18
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 18
- 19
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 19
- 20
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 20
- 21
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 21
- 22
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 22
- 23
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 23
- 24
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 24
- 25
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 25
- 27
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 27
- 28
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 28
- 29
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 29
- 30
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 30
- 32
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 32
- 33
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 33
- 34
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 34
- 35
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 35
- 37
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 37
- 38
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 38
- 39
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 39
- 40
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 40
- 41
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 41
- 42
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 42
- 43
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 43
- 44
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 44
- 45
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 45
- 46
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 46
- 48
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 48
- 49
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 49
- 50
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 50
- 51
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 51
- 54
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 54
- 55
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 55
- 56
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 56
- 58
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 58
- 59
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 59
- 60
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 60
- 62
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 62
- 63
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 63
- 64
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 64
- 65
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 65
- 66
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 66
- 67
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 67
- 68
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 68
- 70
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 70
- 71
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 71
- 72
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 72
- 73
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 73
- 75
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 75
- 76
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 76
- 77
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 77
- 78
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 78
- 79
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 79
- 80
model: Xwin-LM/Xwin-LM-70B-V0.1
=========================
=> Applying Doctor-Shotgun/limarpv3-llama2-70b-qlora x 0.35
```
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
A big thanks to [Charles](https://huggingface.co/chargoddard) for adding the layer shuffle method to his tool [mergekit](https://github.com/cg123/mergekit/tree/main) and [Henky/KoboldAI](https://koboldai.org/) for the machine he let me use.
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
[
"MEDICAL DATA"
] |
sileod/deberta-v3-xsmall-tasksource-nli
|
sileod
|
zero-shot-classification
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"smol",
"zero-shot-classification",
"en",
"dataset:glue",
"dataset:super_glue",
"dataset:anli",
"dataset:metaeval/babi_nli",
"dataset:sick",
"dataset:stanfordnlp/snli",
"dataset:scitail",
"dataset:hans",
"dataset:alisawuffles/WANLI",
"dataset:metaeval/recast",
"dataset:sileod/probability_words_nli",
"dataset:joey234/nan-nli",
"dataset:pietrolesci/nli_fever",
"dataset:pietrolesci/breaking_nli",
"dataset:pietrolesci/conj_nli",
"dataset:pietrolesci/fracas",
"dataset:pietrolesci/dialogue_nli",
"dataset:pietrolesci/mpe",
"dataset:pietrolesci/dnc",
"dataset:pietrolesci/recast_white",
"dataset:pietrolesci/joci",
"dataset:pietrolesci/robust_nli",
"dataset:pietrolesci/robust_nli_is_sd",
"dataset:pietrolesci/robust_nli_li_ts",
"dataset:pietrolesci/gen_debiased_nli",
"dataset:pietrolesci/add_one_rte",
"dataset:metaeval/imppres",
"dataset:hlgd",
"dataset:paws",
"dataset:medical_questions_pairs",
"dataset:conll2003",
"dataset:Anthropic/model-written-evals",
"dataset:truthful_qa",
"dataset:nightingal3/fig-qa",
"dataset:tasksource/bigbench",
"dataset:blimp",
"dataset:cos_e",
"dataset:cosmos_qa",
"dataset:dream",
"dataset:openbookqa",
"dataset:qasc",
"dataset:quartz",
"dataset:quail",
"dataset:head_qa",
"dataset:sciq",
"dataset:social_i_qa",
"dataset:wiki_hop",
"dataset:wiqa",
"dataset:piqa",
"dataset:hellaswag",
"dataset:pkavumba/balanced-copa",
"dataset:12ml/e-CARE",
"dataset:art",
"dataset:tasksource/mmlu",
"dataset:winogrande",
"dataset:codah",
"dataset:ai2_arc",
"dataset:definite_pronoun_resolution",
"dataset:swag",
"dataset:math_qa",
"dataset:metaeval/utilitarianism",
"dataset:mteb/amazon_counterfactual",
"dataset:SetFit/insincere-questions",
"dataset:SetFit/toxic_conversations",
"dataset:turingbench/TuringBench",
"dataset:trec",
"dataset:tals/vitaminc",
"dataset:hope_edi",
"dataset:strombergnlp/rumoureval_2019",
"dataset:ethos",
"dataset:tweet_eval",
"dataset:discovery",
"dataset:pragmeval",
"dataset:silicone",
"dataset:lex_glue",
"dataset:papluca/language-identification",
"dataset:imdb",
"dataset:rotten_tomatoes",
"dataset:ag_news",
"dataset:yelp_review_full",
"dataset:financial_phrasebank",
"dataset:poem_sentiment",
"dataset:dbpedia_14",
"dataset:amazon_polarity",
"dataset:app_reviews",
"dataset:hate_speech18",
"dataset:sms_spam",
"dataset:humicroedit",
"dataset:snips_built_in_intents",
"dataset:hate_speech_offensive",
"dataset:yahoo_answers_topics",
"dataset:pacovaldez/stackoverflow-questions",
"dataset:zapsdcn/hyperpartisan_news",
"dataset:zapsdcn/sciie",
"dataset:zapsdcn/citation_intent",
"dataset:go_emotions",
"dataset:allenai/scicite",
"dataset:liar",
"dataset:relbert/lexical_relation_classification",
"dataset:tasksource/crowdflower",
"dataset:metaeval/ethics",
"dataset:emo",
"dataset:google_wellformed_query",
"dataset:tweets_hate_speech_detection",
"dataset:has_part",
"dataset:wnut_17",
"dataset:ncbi_disease",
"dataset:acronym_identification",
"dataset:jnlpba",
"dataset:SpeedOfMagic/ontonotes_english",
"dataset:blog_authorship_corpus",
"dataset:launch/open_question_type",
"dataset:health_fact",
"dataset:commonsense_qa",
"dataset:mc_taco",
"dataset:ade_corpus_v2",
"dataset:prajjwal1/discosense",
"dataset:circa",
"dataset:PiC/phrase_similarity",
"dataset:copenlu/scientific-exaggeration-detection",
"dataset:quarel",
"dataset:mwong/fever-evidence-related",
"dataset:numer_sense",
"dataset:dynabench/dynasent",
"dataset:raquiba/Sarcasm_News_Headline",
"dataset:sem_eval_2010_task_8",
"dataset:demo-org/auditor_review",
"dataset:medmcqa",
"dataset:RuyuanWan/Dynasent_Disagreement",
"dataset:RuyuanWan/Politeness_Disagreement",
"dataset:RuyuanWan/SBIC_Disagreement",
"dataset:RuyuanWan/SChem_Disagreement",
"dataset:RuyuanWan/Dilemmas_Disagreement",
"dataset:lucasmccabe/logiqa",
"dataset:wiki_qa",
"dataset:metaeval/cycic_classification",
"dataset:metaeval/cycic_multiplechoice",
"dataset:metaeval/sts-companion",
"dataset:metaeval/commonsense_qa_2.0",
"dataset:metaeval/lingnli",
"dataset:metaeval/monotonicity-entailment",
"dataset:metaeval/arct",
"dataset:metaeval/scinli",
"dataset:metaeval/naturallogic",
"dataset:onestop_qa",
"dataset:demelin/moral_stories",
"dataset:corypaik/prost",
"dataset:aps/dynahate",
"dataset:metaeval/syntactic-augmentation-nli",
"dataset:metaeval/autotnli",
"dataset:lasha-nlp/CONDAQA",
"dataset:openai/webgpt_comparisons",
"dataset:Dahoas/synthetic-instruct-gptj-pairwise",
"dataset:metaeval/scruples",
"dataset:metaeval/wouldyourather",
"dataset:metaeval/defeasible-nli",
"dataset:metaeval/help-nli",
"dataset:metaeval/nli-veridicality-transitivity",
"dataset:metaeval/natural-language-satisfiability",
"dataset:metaeval/lonli",
"dataset:metaeval/dadc-limit-nli",
"dataset:ColumbiaNLP/FLUTE",
"dataset:metaeval/strategy-qa",
"dataset:openai/summarize_from_feedback",
"dataset:tasksource/folio",
"dataset:metaeval/tomi-nli",
"dataset:metaeval/avicenna",
"dataset:stanfordnlp/SHP",
"dataset:GBaker/MedQA-USMLE-4-options-hf",
"dataset:sileod/wikimedqa",
"dataset:declare-lab/cicero",
"dataset:amydeng2000/CREAK",
"dataset:metaeval/mutual",
"dataset:inverse-scaling/NeQA",
"dataset:inverse-scaling/quote-repetition",
"dataset:inverse-scaling/redefine-math",
"dataset:metaeval/puzzte",
"dataset:metaeval/implicatures",
"dataset:race",
"dataset:metaeval/race-c",
"dataset:metaeval/spartqa-yn",
"dataset:metaeval/spartqa-mchoice",
"dataset:metaeval/temporal-nli",
"dataset:riddle_sense",
"dataset:metaeval/clcd-english",
"dataset:maximedb/twentyquestions",
"dataset:metaeval/reclor",
"dataset:metaeval/counterfactually-augmented-imdb",
"dataset:metaeval/counterfactually-augmented-snli",
"dataset:metaeval/cnli",
"dataset:metaeval/boolq-natural-perturbations",
"dataset:metaeval/acceptability-prediction",
"dataset:metaeval/equate",
"dataset:metaeval/ScienceQA_text_only",
"dataset:Jiangjie/ekar_english",
"dataset:metaeval/implicit-hate-stg1",
"dataset:metaeval/chaos-mnli-ambiguity",
"dataset:IlyaGusev/headline_cause",
"dataset:metaeval/logiqa-2.0-nli",
"dataset:tasksource/oasst2_dense_flat",
"dataset:sileod/mindgames",
"dataset:universal_dependencies",
"dataset:metaeval/ambient",
"dataset:metaeval/path-naturalness-prediction",
"dataset:civil_comments",
"dataset:AndyChiang/cloth",
"dataset:AndyChiang/dgen",
"dataset:tasksource/I2D2",
"dataset:webis/args_me",
"dataset:webis/Touche23-ValueEval",
"dataset:tasksource/starcon",
"dataset:PolyAI/banking77",
"dataset:tasksource/ConTRoL-nli",
"dataset:tasksource/tracie",
"dataset:tasksource/sherliic",
"dataset:tasksource/sen-making",
"dataset:tasksource/winowhy",
"dataset:mediabiasgroup/mbib-base",
"dataset:tasksource/robustLR",
"dataset:CLUTRR/v1",
"dataset:tasksource/logical-fallacy",
"dataset:tasksource/parade",
"dataset:tasksource/cladder",
"dataset:tasksource/subjectivity",
"dataset:tasksource/MOH",
"dataset:tasksource/VUAC",
"dataset:tasksource/TroFi",
"dataset:sharc_modified",
"dataset:tasksource/conceptrules_v2",
"dataset:metaeval/disrpt",
"dataset:conll2000",
"dataset:DFKI-SLT/few-nerd",
"dataset:nlpaueb/finer-139",
"dataset:tasksource/zero-shot-label-nli",
"dataset:tasksource/com2sense",
"dataset:tasksource/scone",
"dataset:tasksource/winodict",
"dataset:tasksource/fool-me-twice",
"dataset:tasksource/monli",
"dataset:tasksource/corr2cause",
"dataset:lighteval/lsat_qa",
"dataset:tasksource/apt",
"dataset:zeroshot/twitter-financial-news-sentiment",
"dataset:tasksource/icl-symbol-tuning-instruct",
"dataset:tasksource/SpaceNLI",
"dataset:sihaochen/propsegment",
"dataset:HannahRoseKirk/HatemojiBuild",
"dataset:tasksource/regset",
"dataset:tasksource/esci",
"dataset:lmsys/chatbot_arena_conversations",
"dataset:neurae/dnd_style_intents",
"dataset:hitachi-nlp/FLD.v2",
"dataset:tasksource/SDOH-NLI",
"dataset:allenai/scifact_entailment",
"dataset:tasksource/feasibilityQA",
"dataset:tasksource/simple_pair",
"dataset:tasksource/AdjectiveScaleProbe-nli",
"dataset:tasksource/resnli",
"dataset:tasksource/SpaRTUN",
"dataset:tasksource/ReSQ",
"dataset:tasksource/semantic_fragments_nli",
"dataset:MoritzLaurer/dataset_train_nli",
"dataset:tasksource/stepgame",
"dataset:tasksource/nlgraph",
"dataset:tasksource/oasst2_pairwise_rlhf_reward",
"dataset:tasksource/hh-rlhf",
"dataset:tasksource/ruletaker",
"dataset:qbao775/PARARULE-Plus",
"dataset:tasksource/proofwriter",
"dataset:tasksource/logical-entailment",
"dataset:tasksource/nope",
"dataset:tasksource/LogicNLI",
"dataset:kiddothe2b/contract-nli",
"dataset:AshtonIsNotHere/nli4ct_semeval2024",
"dataset:tasksource/lsat-ar",
"dataset:tasksource/lsat-rc",
"dataset:AshtonIsNotHere/biosift-nli",
"dataset:tasksource/brainteasers",
"dataset:Anthropic/persuasion",
"dataset:erbacher/AmbigNQ-clarifying-question",
"dataset:tasksource/SIGA-nli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-03T16:19:20Z |
2024-07-04T09:08:35+00:00
| 30 | 3 |
---
datasets:
- glue
- super_glue
- anli
- metaeval/babi_nli
- sick
- stanfordnlp/snli
- scitail
- hans
- alisawuffles/WANLI
- metaeval/recast
- sileod/probability_words_nli
- joey234/nan-nli
- pietrolesci/nli_fever
- pietrolesci/breaking_nli
- pietrolesci/conj_nli
- pietrolesci/fracas
- pietrolesci/dialogue_nli
- pietrolesci/mpe
- pietrolesci/dnc
- pietrolesci/recast_white
- pietrolesci/joci
- pietrolesci/robust_nli
- pietrolesci/robust_nli_is_sd
- pietrolesci/robust_nli_li_ts
- pietrolesci/gen_debiased_nli
- pietrolesci/add_one_rte
- metaeval/imppres
- hlgd
- paws
- medical_questions_pairs
- conll2003
- Anthropic/model-written-evals
- truthful_qa
- nightingal3/fig-qa
- tasksource/bigbench
- blimp
- cos_e
- cosmos_qa
- dream
- openbookqa
- qasc
- quartz
- quail
- head_qa
- sciq
- social_i_qa
- wiki_hop
- wiqa
- piqa
- hellaswag
- pkavumba/balanced-copa
- 12ml/e-CARE
- art
- tasksource/mmlu
- winogrande
- codah
- ai2_arc
- definite_pronoun_resolution
- swag
- math_qa
- metaeval/utilitarianism
- mteb/amazon_counterfactual
- SetFit/insincere-questions
- SetFit/toxic_conversations
- turingbench/TuringBench
- trec
- tals/vitaminc
- hope_edi
- strombergnlp/rumoureval_2019
- ethos
- tweet_eval
- discovery
- pragmeval
- silicone
- lex_glue
- papluca/language-identification
- imdb
- rotten_tomatoes
- ag_news
- yelp_review_full
- financial_phrasebank
- poem_sentiment
- dbpedia_14
- amazon_polarity
- app_reviews
- hate_speech18
- sms_spam
- humicroedit
- snips_built_in_intents
- hate_speech_offensive
- yahoo_answers_topics
- pacovaldez/stackoverflow-questions
- zapsdcn/hyperpartisan_news
- zapsdcn/sciie
- zapsdcn/citation_intent
- go_emotions
- allenai/scicite
- liar
- relbert/lexical_relation_classification
- tasksource/crowdflower
- metaeval/ethics
- emo
- google_wellformed_query
- tweets_hate_speech_detection
- has_part
- wnut_17
- ncbi_disease
- acronym_identification
- jnlpba
- SpeedOfMagic/ontonotes_english
- blog_authorship_corpus
- launch/open_question_type
- health_fact
- commonsense_qa
- mc_taco
- ade_corpus_v2
- prajjwal1/discosense
- circa
- PiC/phrase_similarity
- copenlu/scientific-exaggeration-detection
- quarel
- mwong/fever-evidence-related
- numer_sense
- dynabench/dynasent
- raquiba/Sarcasm_News_Headline
- sem_eval_2010_task_8
- demo-org/auditor_review
- medmcqa
- RuyuanWan/Dynasent_Disagreement
- RuyuanWan/Politeness_Disagreement
- RuyuanWan/SBIC_Disagreement
- RuyuanWan/SChem_Disagreement
- RuyuanWan/Dilemmas_Disagreement
- lucasmccabe/logiqa
- wiki_qa
- metaeval/cycic_classification
- metaeval/cycic_multiplechoice
- metaeval/sts-companion
- metaeval/commonsense_qa_2.0
- metaeval/lingnli
- metaeval/monotonicity-entailment
- metaeval/arct
- metaeval/scinli
- metaeval/naturallogic
- onestop_qa
- demelin/moral_stories
- corypaik/prost
- aps/dynahate
- metaeval/syntactic-augmentation-nli
- metaeval/autotnli
- lasha-nlp/CONDAQA
- openai/webgpt_comparisons
- Dahoas/synthetic-instruct-gptj-pairwise
- metaeval/scruples
- metaeval/wouldyourather
- metaeval/defeasible-nli
- metaeval/help-nli
- metaeval/nli-veridicality-transitivity
- metaeval/natural-language-satisfiability
- metaeval/lonli
- metaeval/dadc-limit-nli
- ColumbiaNLP/FLUTE
- metaeval/strategy-qa
- openai/summarize_from_feedback
- tasksource/folio
- metaeval/tomi-nli
- metaeval/avicenna
- stanfordnlp/SHP
- GBaker/MedQA-USMLE-4-options-hf
- sileod/wikimedqa
- declare-lab/cicero
- amydeng2000/CREAK
- metaeval/mutual
- inverse-scaling/NeQA
- inverse-scaling/quote-repetition
- inverse-scaling/redefine-math
- metaeval/puzzte
- metaeval/implicatures
- race
- metaeval/race-c
- metaeval/spartqa-yn
- metaeval/spartqa-mchoice
- metaeval/temporal-nli
- riddle_sense
- metaeval/clcd-english
- maximedb/twentyquestions
- metaeval/reclor
- metaeval/counterfactually-augmented-imdb
- metaeval/counterfactually-augmented-snli
- metaeval/cnli
- metaeval/boolq-natural-perturbations
- metaeval/acceptability-prediction
- metaeval/equate
- metaeval/ScienceQA_text_only
- Jiangjie/ekar_english
- metaeval/implicit-hate-stg1
- metaeval/chaos-mnli-ambiguity
- IlyaGusev/headline_cause
- metaeval/logiqa-2.0-nli
- tasksource/oasst2_dense_flat
- sileod/mindgames
- universal_dependencies
- metaeval/ambient
- metaeval/path-naturalness-prediction
- civil_comments
- AndyChiang/cloth
- AndyChiang/dgen
- tasksource/I2D2
- webis/args_me
- webis/Touche23-ValueEval
- tasksource/starcon
- PolyAI/banking77
- tasksource/ConTRoL-nli
- tasksource/tracie
- tasksource/sherliic
- tasksource/sen-making
- tasksource/winowhy
- mediabiasgroup/mbib-base
- tasksource/robustLR
- CLUTRR/v1
- tasksource/logical-fallacy
- tasksource/parade
- tasksource/cladder
- tasksource/subjectivity
- tasksource/MOH
- tasksource/VUAC
- tasksource/TroFi
- sharc_modified
- tasksource/conceptrules_v2
- metaeval/disrpt
- conll2000
- DFKI-SLT/few-nerd
- nlpaueb/finer-139
- tasksource/zero-shot-label-nli
- tasksource/com2sense
- tasksource/scone
- tasksource/winodict
- tasksource/fool-me-twice
- tasksource/monli
- tasksource/corr2cause
- lighteval/lsat_qa
- tasksource/apt
- zeroshot/twitter-financial-news-sentiment
- tasksource/icl-symbol-tuning-instruct
- tasksource/SpaceNLI
- sihaochen/propsegment
- HannahRoseKirk/HatemojiBuild
- tasksource/regset
- tasksource/esci
- lmsys/chatbot_arena_conversations
- neurae/dnd_style_intents
- hitachi-nlp/FLD.v2
- tasksource/SDOH-NLI
- allenai/scifact_entailment
- tasksource/feasibilityQA
- tasksource/simple_pair
- tasksource/AdjectiveScaleProbe-nli
- tasksource/resnli
- tasksource/SpaRTUN
- tasksource/ReSQ
- tasksource/semantic_fragments_nli
- MoritzLaurer/dataset_train_nli
- tasksource/stepgame
- tasksource/nlgraph
- tasksource/oasst2_pairwise_rlhf_reward
- tasksource/hh-rlhf
- tasksource/ruletaker
- qbao775/PARARULE-Plus
- tasksource/proofwriter
- tasksource/logical-entailment
- tasksource/nope
- tasksource/LogicNLI
- kiddothe2b/contract-nli
- AshtonIsNotHere/nli4ct_semeval2024
- tasksource/lsat-ar
- tasksource/lsat-rc
- AshtonIsNotHere/biosift-nli
- tasksource/brainteasers
- Anthropic/persuasion
- erbacher/AmbigNQ-clarifying-question
- tasksource/SIGA-nli
language:
- en
pipeline_tag: zero-shot-classification
tags:
- smol
---
`deberta-v3-xsmall` fine-tuned for 100k steps on the tasksource collection
Model size: 22M backbone + 48M vocabulary parameters
Refer to the this page for documentation :[https://huggingface.co/sileod/deberta-v3-base-tasksource-nli]
|
[
"HEAD-QA",
"JNLPBA",
"MEDQA",
"NCBI DISEASE",
"SCICITE",
"SCIFACT",
"SCIQ",
"SCITAIL"
] |
MohamedAhmedAE/Llama3-8B-Medical-Finetune-Merged
|
MohamedAhmedAE
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"medical",
"mmlu",
"medalpaca",
"medmcqa",
"conversational",
"dataset:cais/mmlu",
"dataset:medalpaca/medical_meadow_medqa",
"dataset:medalpaca/medical_meadow_wikidoc",
"dataset:openlifescienceai/medmcqa",
"dataset:bigbio/med_qa",
"dataset:GBaker/MedQA-USMLE-4-options",
"dataset:medalpaca/medical_meadow_mmmlu",
"dataset:medalpaca/medical_meadow_wikidoc_patient_information",
"dataset:qiaojin/PubMedQA",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | 2024-06-04T18:56:40Z |
2024-06-04T22:19:23+00:00
| 30 | 1 |
---
datasets:
- cais/mmlu
- medalpaca/medical_meadow_medqa
- medalpaca/medical_meadow_wikidoc
- openlifescienceai/medmcqa
- bigbio/med_qa
- GBaker/MedQA-USMLE-4-options
- medalpaca/medical_meadow_mmmlu
- medalpaca/medical_meadow_wikidoc_patient_information
- qiaojin/PubMedQA
pipeline_tag: text-generation
tags:
- medical
- mmlu
- medalpaca
- medmcqa
---
### Evaluation results
| Dataset | GPT-3.5 | Tuned Llama 3 |
|:-------------:|:-----:|:----:|
| MMLU Clinical Knowledge | 69.8| 74.34 |
| MMLU College Biology | 72.2| 72.92 |
| MMLU College Medicine | 61.3| 61.85 |
| MMLU Medical Genetics | 70.0| 76.0 |
| MMLU Professional Medicine| 70.2| 72.43 |
| MMLU Anatomy | 56.3| 61.48 |
|
[
"MEDQA",
"PUBMEDQA"
] |
RinaChen/nomic-embed-text-v1.5-Q4_K_M-GGUF
|
RinaChen
|
sentence-similarity
|
[
"sentence-transformers",
"gguf",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:nomic-ai/nomic-embed-text-v1.5",
"base_model:quantized:nomic-ai/nomic-embed-text-v1.5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-08-05T08:11:29Z |
2024-08-05T08:11:33+00:00
| 30 | 0 |
---
base_model: nomic-ai/nomic-embed-text-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- mteb
- transformers
- transformers.js
- llama-cpp
- gguf-my-repo
model-index:
- name: epoch_0_model
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.20895522388058
- type: ap
value: 38.57605549557802
- type: f1
value: 69.35586565857854
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.8144
- type: ap
value: 88.65222882032363
- type: f1
value: 91.80426301643274
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.162000000000006
- type: f1
value: 46.59329642263158
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.253
- type: map_at_10
value: 38.962
- type: map_at_100
value: 40.081
- type: map_at_1000
value: 40.089000000000006
- type: map_at_3
value: 33.499
- type: map_at_5
value: 36.351
- type: mrr_at_1
value: 24.609
- type: mrr_at_10
value: 39.099000000000004
- type: mrr_at_100
value: 40.211000000000006
- type: mrr_at_1000
value: 40.219
- type: mrr_at_3
value: 33.677
- type: mrr_at_5
value: 36.469
- type: ndcg_at_1
value: 24.253
- type: ndcg_at_10
value: 48.010999999999996
- type: ndcg_at_100
value: 52.756
- type: ndcg_at_1000
value: 52.964999999999996
- type: ndcg_at_3
value: 36.564
- type: ndcg_at_5
value: 41.711999999999996
- type: precision_at_1
value: 24.253
- type: precision_at_10
value: 7.738
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.149000000000001
- type: precision_at_5
value: 11.593
- type: recall_at_1
value: 24.253
- type: recall_at_10
value: 77.383
- type: recall_at_100
value: 98.009
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 45.448
- type: recall_at_5
value: 57.965999999999994
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.69069567851087
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.35185490976283
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.71274951450321
- type: mrr
value: 76.06032625423207
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.73980520022269
- type: cos_sim_spearman
value: 84.24649792685918
- type: euclidean_pearson
value: 85.85197641158186
- type: euclidean_spearman
value: 84.24649792685918
- type: manhattan_pearson
value: 86.26809552711346
- type: manhattan_spearman
value: 84.56397504030865
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.25324675324674
- type: f1
value: 84.17872280892557
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.770253446400886
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.94307095497281
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.164
- type: map_at_10
value: 42.641
- type: map_at_100
value: 43.947
- type: map_at_1000
value: 44.074999999999996
- type: map_at_3
value: 39.592
- type: map_at_5
value: 41.204
- type: mrr_at_1
value: 39.628
- type: mrr_at_10
value: 48.625
- type: mrr_at_100
value: 49.368
- type: mrr_at_1000
value: 49.413000000000004
- type: mrr_at_3
value: 46.400000000000006
- type: mrr_at_5
value: 47.68
- type: ndcg_at_1
value: 39.628
- type: ndcg_at_10
value: 48.564
- type: ndcg_at_100
value: 53.507000000000005
- type: ndcg_at_1000
value: 55.635999999999996
- type: ndcg_at_3
value: 44.471
- type: ndcg_at_5
value: 46.137
- type: precision_at_1
value: 39.628
- type: precision_at_10
value: 8.856
- type: precision_at_100
value: 1.429
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 21.268
- type: precision_at_5
value: 14.649000000000001
- type: recall_at_1
value: 32.164
- type: recall_at_10
value: 59.609
- type: recall_at_100
value: 80.521
- type: recall_at_1000
value: 94.245
- type: recall_at_3
value: 46.521
- type: recall_at_5
value: 52.083999999999996
- type: map_at_1
value: 31.526
- type: map_at_10
value: 41.581
- type: map_at_100
value: 42.815999999999995
- type: map_at_1000
value: 42.936
- type: map_at_3
value: 38.605000000000004
- type: map_at_5
value: 40.351
- type: mrr_at_1
value: 39.489999999999995
- type: mrr_at_10
value: 47.829
- type: mrr_at_100
value: 48.512
- type: mrr_at_1000
value: 48.552
- type: mrr_at_3
value: 45.754
- type: mrr_at_5
value: 46.986
- type: ndcg_at_1
value: 39.489999999999995
- type: ndcg_at_10
value: 47.269
- type: ndcg_at_100
value: 51.564
- type: ndcg_at_1000
value: 53.53099999999999
- type: ndcg_at_3
value: 43.301
- type: ndcg_at_5
value: 45.239000000000004
- type: precision_at_1
value: 39.489999999999995
- type: precision_at_10
value: 8.93
- type: precision_at_100
value: 1.415
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 20.892
- type: precision_at_5
value: 14.865999999999998
- type: recall_at_1
value: 31.526
- type: recall_at_10
value: 56.76
- type: recall_at_100
value: 75.029
- type: recall_at_1000
value: 87.491
- type: recall_at_3
value: 44.786
- type: recall_at_5
value: 50.254
- type: map_at_1
value: 40.987
- type: map_at_10
value: 52.827
- type: map_at_100
value: 53.751000000000005
- type: map_at_1000
value: 53.81
- type: map_at_3
value: 49.844
- type: map_at_5
value: 51.473
- type: mrr_at_1
value: 46.833999999999996
- type: mrr_at_10
value: 56.389
- type: mrr_at_100
value: 57.003
- type: mrr_at_1000
value: 57.034
- type: mrr_at_3
value: 54.17999999999999
- type: mrr_at_5
value: 55.486999999999995
- type: ndcg_at_1
value: 46.833999999999996
- type: ndcg_at_10
value: 58.372
- type: ndcg_at_100
value: 62.068
- type: ndcg_at_1000
value: 63.288
- type: ndcg_at_3
value: 53.400000000000006
- type: ndcg_at_5
value: 55.766000000000005
- type: precision_at_1
value: 46.833999999999996
- type: precision_at_10
value: 9.191
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 23.448
- type: precision_at_5
value: 15.862000000000002
- type: recall_at_1
value: 40.987
- type: recall_at_10
value: 71.146
- type: recall_at_100
value: 87.035
- type: recall_at_1000
value: 95.633
- type: recall_at_3
value: 58.025999999999996
- type: recall_at_5
value: 63.815999999999995
- type: map_at_1
value: 24.587
- type: map_at_10
value: 33.114
- type: map_at_100
value: 34.043
- type: map_at_1000
value: 34.123999999999995
- type: map_at_3
value: 30.45
- type: map_at_5
value: 31.813999999999997
- type: mrr_at_1
value: 26.554
- type: mrr_at_10
value: 35.148
- type: mrr_at_100
value: 35.926
- type: mrr_at_1000
value: 35.991
- type: mrr_at_3
value: 32.599000000000004
- type: mrr_at_5
value: 33.893
- type: ndcg_at_1
value: 26.554
- type: ndcg_at_10
value: 38.132
- type: ndcg_at_100
value: 42.78
- type: ndcg_at_1000
value: 44.919
- type: ndcg_at_3
value: 32.833
- type: ndcg_at_5
value: 35.168
- type: precision_at_1
value: 26.554
- type: precision_at_10
value: 5.921
- type: precision_at_100
value: 0.8659999999999999
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.861
- type: precision_at_5
value: 9.605
- type: recall_at_1
value: 24.587
- type: recall_at_10
value: 51.690000000000005
- type: recall_at_100
value: 73.428
- type: recall_at_1000
value: 89.551
- type: recall_at_3
value: 37.336999999999996
- type: recall_at_5
value: 43.047000000000004
- type: map_at_1
value: 16.715
- type: map_at_10
value: 24.251
- type: map_at_100
value: 25.326999999999998
- type: map_at_1000
value: 25.455
- type: map_at_3
value: 21.912000000000003
- type: map_at_5
value: 23.257
- type: mrr_at_1
value: 20.274
- type: mrr_at_10
value: 28.552
- type: mrr_at_100
value: 29.42
- type: mrr_at_1000
value: 29.497
- type: mrr_at_3
value: 26.14
- type: mrr_at_5
value: 27.502
- type: ndcg_at_1
value: 20.274
- type: ndcg_at_10
value: 29.088
- type: ndcg_at_100
value: 34.293
- type: ndcg_at_1000
value: 37.271
- type: ndcg_at_3
value: 24.708
- type: ndcg_at_5
value: 26.809
- type: precision_at_1
value: 20.274
- type: precision_at_10
value: 5.361
- type: precision_at_100
value: 0.915
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 11.733
- type: precision_at_5
value: 8.556999999999999
- type: recall_at_1
value: 16.715
- type: recall_at_10
value: 39.587
- type: recall_at_100
value: 62.336000000000006
- type: recall_at_1000
value: 83.453
- type: recall_at_3
value: 27.839999999999996
- type: recall_at_5
value: 32.952999999999996
- type: map_at_1
value: 28.793000000000003
- type: map_at_10
value: 38.582
- type: map_at_100
value: 39.881
- type: map_at_1000
value: 39.987
- type: map_at_3
value: 35.851
- type: map_at_5
value: 37.289
- type: mrr_at_1
value: 34.455999999999996
- type: mrr_at_10
value: 43.909
- type: mrr_at_100
value: 44.74
- type: mrr_at_1000
value: 44.786
- type: mrr_at_3
value: 41.659
- type: mrr_at_5
value: 43.010999999999996
- type: ndcg_at_1
value: 34.455999999999996
- type: ndcg_at_10
value: 44.266
- type: ndcg_at_100
value: 49.639
- type: ndcg_at_1000
value: 51.644
- type: ndcg_at_3
value: 39.865
- type: ndcg_at_5
value: 41.887
- type: precision_at_1
value: 34.455999999999996
- type: precision_at_10
value: 7.843999999999999
- type: precision_at_100
value: 1.243
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 18.831999999999997
- type: precision_at_5
value: 13.147
- type: recall_at_1
value: 28.793000000000003
- type: recall_at_10
value: 55.68300000000001
- type: recall_at_100
value: 77.99000000000001
- type: recall_at_1000
value: 91.183
- type: recall_at_3
value: 43.293
- type: recall_at_5
value: 48.618
- type: map_at_1
value: 25.907000000000004
- type: map_at_10
value: 35.519
- type: map_at_100
value: 36.806
- type: map_at_1000
value: 36.912
- type: map_at_3
value: 32.748
- type: map_at_5
value: 34.232
- type: mrr_at_1
value: 31.621
- type: mrr_at_10
value: 40.687
- type: mrr_at_100
value: 41.583
- type: mrr_at_1000
value: 41.638999999999996
- type: mrr_at_3
value: 38.527
- type: mrr_at_5
value: 39.612
- type: ndcg_at_1
value: 31.621
- type: ndcg_at_10
value: 41.003
- type: ndcg_at_100
value: 46.617999999999995
- type: ndcg_at_1000
value: 48.82
- type: ndcg_at_3
value: 36.542
- type: ndcg_at_5
value: 38.368
- type: precision_at_1
value: 31.621
- type: precision_at_10
value: 7.396999999999999
- type: precision_at_100
value: 1.191
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 17.39
- type: precision_at_5
value: 12.1
- type: recall_at_1
value: 25.907000000000004
- type: recall_at_10
value: 52.115
- type: recall_at_100
value: 76.238
- type: recall_at_1000
value: 91.218
- type: recall_at_3
value: 39.417
- type: recall_at_5
value: 44.435
- type: map_at_1
value: 25.732166666666668
- type: map_at_10
value: 34.51616666666667
- type: map_at_100
value: 35.67241666666666
- type: map_at_1000
value: 35.78675
- type: map_at_3
value: 31.953416666666662
- type: map_at_5
value: 33.333
- type: mrr_at_1
value: 30.300166666666673
- type: mrr_at_10
value: 38.6255
- type: mrr_at_100
value: 39.46183333333334
- type: mrr_at_1000
value: 39.519999999999996
- type: mrr_at_3
value: 36.41299999999999
- type: mrr_at_5
value: 37.6365
- type: ndcg_at_1
value: 30.300166666666673
- type: ndcg_at_10
value: 39.61466666666667
- type: ndcg_at_100
value: 44.60808333333334
- type: ndcg_at_1000
value: 46.91708333333334
- type: ndcg_at_3
value: 35.26558333333333
- type: ndcg_at_5
value: 37.220000000000006
- type: precision_at_1
value: 30.300166666666673
- type: precision_at_10
value: 6.837416666666667
- type: precision_at_100
value: 1.10425
- type: precision_at_1000
value: 0.14875
- type: precision_at_3
value: 16.13716666666667
- type: precision_at_5
value: 11.2815
- type: recall_at_1
value: 25.732166666666668
- type: recall_at_10
value: 50.578916666666665
- type: recall_at_100
value: 72.42183333333334
- type: recall_at_1000
value: 88.48766666666667
- type: recall_at_3
value: 38.41325
- type: recall_at_5
value: 43.515750000000004
- type: map_at_1
value: 23.951
- type: map_at_10
value: 30.974
- type: map_at_100
value: 31.804
- type: map_at_1000
value: 31.900000000000002
- type: map_at_3
value: 28.762
- type: map_at_5
value: 29.94
- type: mrr_at_1
value: 26.534000000000002
- type: mrr_at_10
value: 33.553
- type: mrr_at_100
value: 34.297
- type: mrr_at_1000
value: 34.36
- type: mrr_at_3
value: 31.391000000000002
- type: mrr_at_5
value: 32.525999999999996
- type: ndcg_at_1
value: 26.534000000000002
- type: ndcg_at_10
value: 35.112
- type: ndcg_at_100
value: 39.28
- type: ndcg_at_1000
value: 41.723
- type: ndcg_at_3
value: 30.902
- type: ndcg_at_5
value: 32.759
- type: precision_at_1
value: 26.534000000000002
- type: precision_at_10
value: 5.445
- type: precision_at_100
value: 0.819
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 12.986
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.951
- type: recall_at_10
value: 45.24
- type: recall_at_100
value: 64.12299999999999
- type: recall_at_1000
value: 82.28999999999999
- type: recall_at_3
value: 33.806000000000004
- type: recall_at_5
value: 38.277
- type: map_at_1
value: 16.829
- type: map_at_10
value: 23.684
- type: map_at_100
value: 24.683
- type: map_at_1000
value: 24.81
- type: map_at_3
value: 21.554000000000002
- type: map_at_5
value: 22.768
- type: mrr_at_1
value: 20.096
- type: mrr_at_10
value: 27.230999999999998
- type: mrr_at_100
value: 28.083999999999996
- type: mrr_at_1000
value: 28.166000000000004
- type: mrr_at_3
value: 25.212
- type: mrr_at_5
value: 26.32
- type: ndcg_at_1
value: 20.096
- type: ndcg_at_10
value: 27.989000000000004
- type: ndcg_at_100
value: 32.847
- type: ndcg_at_1000
value: 35.896
- type: ndcg_at_3
value: 24.116
- type: ndcg_at_5
value: 25.964
- type: precision_at_1
value: 20.096
- type: precision_at_10
value: 5
- type: precision_at_100
value: 0.8750000000000001
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 11.207
- type: precision_at_5
value: 8.08
- type: recall_at_1
value: 16.829
- type: recall_at_10
value: 37.407000000000004
- type: recall_at_100
value: 59.101000000000006
- type: recall_at_1000
value: 81.024
- type: recall_at_3
value: 26.739
- type: recall_at_5
value: 31.524
- type: map_at_1
value: 24.138
- type: map_at_10
value: 32.275999999999996
- type: map_at_100
value: 33.416000000000004
- type: map_at_1000
value: 33.527
- type: map_at_3
value: 29.854000000000003
- type: map_at_5
value: 31.096
- type: mrr_at_1
value: 28.450999999999997
- type: mrr_at_10
value: 36.214
- type: mrr_at_100
value: 37.134
- type: mrr_at_1000
value: 37.198
- type: mrr_at_3
value: 34.001999999999995
- type: mrr_at_5
value: 35.187000000000005
- type: ndcg_at_1
value: 28.450999999999997
- type: ndcg_at_10
value: 37.166
- type: ndcg_at_100
value: 42.454
- type: ndcg_at_1000
value: 44.976
- type: ndcg_at_3
value: 32.796
- type: ndcg_at_5
value: 34.631
- type: precision_at_1
value: 28.450999999999997
- type: precision_at_10
value: 6.241
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 14.801
- type: precision_at_5
value: 10.280000000000001
- type: recall_at_1
value: 24.138
- type: recall_at_10
value: 48.111
- type: recall_at_100
value: 71.245
- type: recall_at_1000
value: 88.986
- type: recall_at_3
value: 36.119
- type: recall_at_5
value: 40.846
- type: map_at_1
value: 23.244
- type: map_at_10
value: 31.227
- type: map_at_100
value: 33.007
- type: map_at_1000
value: 33.223
- type: map_at_3
value: 28.924
- type: map_at_5
value: 30.017
- type: mrr_at_1
value: 27.668
- type: mrr_at_10
value: 35.524
- type: mrr_at_100
value: 36.699
- type: mrr_at_1000
value: 36.759
- type: mrr_at_3
value: 33.366
- type: mrr_at_5
value: 34.552
- type: ndcg_at_1
value: 27.668
- type: ndcg_at_10
value: 36.381
- type: ndcg_at_100
value: 43.062
- type: ndcg_at_1000
value: 45.656
- type: ndcg_at_3
value: 32.501999999999995
- type: ndcg_at_5
value: 34.105999999999995
- type: precision_at_1
value: 27.668
- type: precision_at_10
value: 6.798
- type: precision_at_100
value: 1.492
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 15.152
- type: precision_at_5
value: 10.791
- type: recall_at_1
value: 23.244
- type: recall_at_10
value: 45.979
- type: recall_at_100
value: 74.822
- type: recall_at_1000
value: 91.078
- type: recall_at_3
value: 34.925
- type: recall_at_5
value: 39.126
- type: map_at_1
value: 19.945
- type: map_at_10
value: 27.517999999999997
- type: map_at_100
value: 28.588
- type: map_at_1000
value: 28.682000000000002
- type: map_at_3
value: 25.345000000000002
- type: map_at_5
value: 26.555
- type: mrr_at_1
value: 21.996
- type: mrr_at_10
value: 29.845
- type: mrr_at_100
value: 30.775999999999996
- type: mrr_at_1000
value: 30.845
- type: mrr_at_3
value: 27.726
- type: mrr_at_5
value: 28.882
- type: ndcg_at_1
value: 21.996
- type: ndcg_at_10
value: 32.034
- type: ndcg_at_100
value: 37.185
- type: ndcg_at_1000
value: 39.645
- type: ndcg_at_3
value: 27.750999999999998
- type: ndcg_at_5
value: 29.805999999999997
- type: precision_at_1
value: 21.996
- type: precision_at_10
value: 5.065
- type: precision_at_100
value: 0.819
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 12.076
- type: precision_at_5
value: 8.392
- type: recall_at_1
value: 19.945
- type: recall_at_10
value: 43.62
- type: recall_at_100
value: 67.194
- type: recall_at_1000
value: 85.7
- type: recall_at_3
value: 32.15
- type: recall_at_5
value: 37.208999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.279
- type: map_at_10
value: 31.052999999999997
- type: map_at_100
value: 33.125
- type: map_at_1000
value: 33.306000000000004
- type: map_at_3
value: 26.208
- type: map_at_5
value: 28.857
- type: mrr_at_1
value: 42.671
- type: mrr_at_10
value: 54.557
- type: mrr_at_100
value: 55.142
- type: mrr_at_1000
value: 55.169000000000004
- type: mrr_at_3
value: 51.488
- type: mrr_at_5
value: 53.439
- type: ndcg_at_1
value: 42.671
- type: ndcg_at_10
value: 41.276
- type: ndcg_at_100
value: 48.376000000000005
- type: ndcg_at_1000
value: 51.318
- type: ndcg_at_3
value: 35.068
- type: ndcg_at_5
value: 37.242
- type: precision_at_1
value: 42.671
- type: precision_at_10
value: 12.638
- type: precision_at_100
value: 2.045
- type: precision_at_1000
value: 0.26
- type: precision_at_3
value: 26.08
- type: precision_at_5
value: 19.805
- type: recall_at_1
value: 18.279
- type: recall_at_10
value: 46.946
- type: recall_at_100
value: 70.97200000000001
- type: recall_at_1000
value: 87.107
- type: recall_at_3
value: 31.147999999999996
- type: recall_at_5
value: 38.099
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.573
- type: map_at_10
value: 19.747
- type: map_at_100
value: 28.205000000000002
- type: map_at_1000
value: 29.831000000000003
- type: map_at_3
value: 14.109
- type: map_at_5
value: 16.448999999999998
- type: mrr_at_1
value: 71
- type: mrr_at_10
value: 77.68599999999999
- type: mrr_at_100
value: 77.995
- type: mrr_at_1000
value: 78.00200000000001
- type: mrr_at_3
value: 76.292
- type: mrr_at_5
value: 77.029
- type: ndcg_at_1
value: 59.12500000000001
- type: ndcg_at_10
value: 43.9
- type: ndcg_at_100
value: 47.863
- type: ndcg_at_1000
value: 54.848
- type: ndcg_at_3
value: 49.803999999999995
- type: ndcg_at_5
value: 46.317
- type: precision_at_1
value: 71
- type: precision_at_10
value: 34.4
- type: precision_at_100
value: 11.063
- type: precision_at_1000
value: 1.989
- type: precision_at_3
value: 52.333
- type: precision_at_5
value: 43.7
- type: recall_at_1
value: 8.573
- type: recall_at_10
value: 25.615
- type: recall_at_100
value: 53.385000000000005
- type: recall_at_1000
value: 75.46000000000001
- type: recall_at_3
value: 15.429
- type: recall_at_5
value: 19.357
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.989999999999995
- type: f1
value: 42.776314451497555
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.13499999999999
- type: map_at_10
value: 82.825
- type: map_at_100
value: 83.096
- type: map_at_1000
value: 83.111
- type: map_at_3
value: 81.748
- type: map_at_5
value: 82.446
- type: mrr_at_1
value: 79.553
- type: mrr_at_10
value: 86.654
- type: mrr_at_100
value: 86.774
- type: mrr_at_1000
value: 86.778
- type: mrr_at_3
value: 85.981
- type: mrr_at_5
value: 86.462
- type: ndcg_at_1
value: 79.553
- type: ndcg_at_10
value: 86.345
- type: ndcg_at_100
value: 87.32
- type: ndcg_at_1000
value: 87.58200000000001
- type: ndcg_at_3
value: 84.719
- type: ndcg_at_5
value: 85.677
- type: precision_at_1
value: 79.553
- type: precision_at_10
value: 10.402000000000001
- type: precision_at_100
value: 1.1119999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.413
- type: precision_at_5
value: 20.138
- type: recall_at_1
value: 74.13499999999999
- type: recall_at_10
value: 93.215
- type: recall_at_100
value: 97.083
- type: recall_at_1000
value: 98.732
- type: recall_at_3
value: 88.79
- type: recall_at_5
value: 91.259
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.298000000000002
- type: map_at_10
value: 29.901
- type: map_at_100
value: 31.528
- type: map_at_1000
value: 31.713
- type: map_at_3
value: 25.740000000000002
- type: map_at_5
value: 28.227999999999998
- type: mrr_at_1
value: 36.728
- type: mrr_at_10
value: 45.401
- type: mrr_at_100
value: 46.27
- type: mrr_at_1000
value: 46.315
- type: mrr_at_3
value: 42.978
- type: mrr_at_5
value: 44.29
- type: ndcg_at_1
value: 36.728
- type: ndcg_at_10
value: 37.456
- type: ndcg_at_100
value: 43.832
- type: ndcg_at_1000
value: 47
- type: ndcg_at_3
value: 33.694
- type: ndcg_at_5
value: 35.085
- type: precision_at_1
value: 36.728
- type: precision_at_10
value: 10.386
- type: precision_at_100
value: 1.701
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 22.479
- type: precision_at_5
value: 16.605
- type: recall_at_1
value: 18.298000000000002
- type: recall_at_10
value: 44.369
- type: recall_at_100
value: 68.098
- type: recall_at_1000
value: 87.21900000000001
- type: recall_at_3
value: 30.215999999999998
- type: recall_at_5
value: 36.861
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.568
- type: map_at_10
value: 65.061
- type: map_at_100
value: 65.896
- type: map_at_1000
value: 65.95100000000001
- type: map_at_3
value: 61.831
- type: map_at_5
value: 63.849000000000004
- type: mrr_at_1
value: 79.136
- type: mrr_at_10
value: 84.58200000000001
- type: mrr_at_100
value: 84.765
- type: mrr_at_1000
value: 84.772
- type: mrr_at_3
value: 83.684
- type: mrr_at_5
value: 84.223
- type: ndcg_at_1
value: 79.136
- type: ndcg_at_10
value: 72.622
- type: ndcg_at_100
value: 75.539
- type: ndcg_at_1000
value: 76.613
- type: ndcg_at_3
value: 68.065
- type: ndcg_at_5
value: 70.58
- type: precision_at_1
value: 79.136
- type: precision_at_10
value: 15.215
- type: precision_at_100
value: 1.7500000000000002
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 44.011
- type: precision_at_5
value: 28.388999999999996
- type: recall_at_1
value: 39.568
- type: recall_at_10
value: 76.077
- type: recall_at_100
value: 87.481
- type: recall_at_1000
value: 94.56400000000001
- type: recall_at_3
value: 66.01599999999999
- type: recall_at_5
value: 70.97200000000001
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 85.312
- type: ap
value: 80.36296867333715
- type: f1
value: 85.26613311552218
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.363999999999997
- type: map_at_10
value: 35.711999999999996
- type: map_at_100
value: 36.876999999999995
- type: map_at_1000
value: 36.923
- type: map_at_3
value: 32.034
- type: map_at_5
value: 34.159
- type: mrr_at_1
value: 24.04
- type: mrr_at_10
value: 36.345
- type: mrr_at_100
value: 37.441
- type: mrr_at_1000
value: 37.480000000000004
- type: mrr_at_3
value: 32.713
- type: mrr_at_5
value: 34.824
- type: ndcg_at_1
value: 24.026
- type: ndcg_at_10
value: 42.531
- type: ndcg_at_100
value: 48.081
- type: ndcg_at_1000
value: 49.213
- type: ndcg_at_3
value: 35.044
- type: ndcg_at_5
value: 38.834
- type: precision_at_1
value: 24.026
- type: precision_at_10
value: 6.622999999999999
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.909
- type: precision_at_5
value: 10.871
- type: recall_at_1
value: 23.363999999999997
- type: recall_at_10
value: 63.426
- type: recall_at_100
value: 88.96300000000001
- type: recall_at_1000
value: 97.637
- type: recall_at_3
value: 43.095
- type: recall_at_5
value: 52.178000000000004
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.0095759233926
- type: f1
value: 92.78387794667408
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.0296397628819
- type: f1
value: 58.45699589820874
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.45662407531944
- type: f1
value: 71.42364781421813
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.07800941492937
- type: f1
value: 77.22799045640845
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.531234379250606
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.941490381193802
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.3115090856725
- type: mrr
value: 31.290667638675757
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.465
- type: map_at_10
value: 13.03
- type: map_at_100
value: 16.057
- type: map_at_1000
value: 17.49
- type: map_at_3
value: 9.553
- type: map_at_5
value: 11.204
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 53.269
- type: mrr_at_100
value: 53.72
- type: mrr_at_1000
value: 53.761
- type: mrr_at_3
value: 50.929
- type: mrr_at_5
value: 52.461
- type: ndcg_at_1
value: 42.26
- type: ndcg_at_10
value: 34.673
- type: ndcg_at_100
value: 30.759999999999998
- type: ndcg_at_1000
value: 39.728
- type: ndcg_at_3
value: 40.349000000000004
- type: ndcg_at_5
value: 37.915
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.789
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 2.07
- type: precision_at_3
value: 38.596000000000004
- type: precision_at_5
value: 33.251
- type: recall_at_1
value: 5.465
- type: recall_at_10
value: 17.148
- type: recall_at_100
value: 29.768
- type: recall_at_1000
value: 62.239
- type: recall_at_3
value: 10.577
- type: recall_at_5
value: 13.315
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.008
- type: map_at_10
value: 52.467
- type: map_at_100
value: 53.342999999999996
- type: map_at_1000
value: 53.366
- type: map_at_3
value: 48.412
- type: map_at_5
value: 50.875
- type: mrr_at_1
value: 41.541
- type: mrr_at_10
value: 54.967
- type: mrr_at_100
value: 55.611
- type: mrr_at_1000
value: 55.627
- type: mrr_at_3
value: 51.824999999999996
- type: mrr_at_5
value: 53.763000000000005
- type: ndcg_at_1
value: 41.541
- type: ndcg_at_10
value: 59.724999999999994
- type: ndcg_at_100
value: 63.38700000000001
- type: ndcg_at_1000
value: 63.883
- type: ndcg_at_3
value: 52.331
- type: ndcg_at_5
value: 56.327000000000005
- type: precision_at_1
value: 41.541
- type: precision_at_10
value: 9.447
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.262
- type: precision_at_5
value: 16.314999999999998
- type: recall_at_1
value: 37.008
- type: recall_at_10
value: 79.145
- type: recall_at_100
value: 94.986
- type: recall_at_1000
value: 98.607
- type: recall_at_3
value: 60.277
- type: recall_at_5
value: 69.407
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.402
- type: map_at_10
value: 84.181
- type: map_at_100
value: 84.796
- type: map_at_1000
value: 84.81400000000001
- type: map_at_3
value: 81.209
- type: map_at_5
value: 83.085
- type: mrr_at_1
value: 81.02000000000001
- type: mrr_at_10
value: 87.263
- type: mrr_at_100
value: 87.36
- type: mrr_at_1000
value: 87.36
- type: mrr_at_3
value: 86.235
- type: mrr_at_5
value: 86.945
- type: ndcg_at_1
value: 81.01
- type: ndcg_at_10
value: 87.99900000000001
- type: ndcg_at_100
value: 89.217
- type: ndcg_at_1000
value: 89.33
- type: ndcg_at_3
value: 85.053
- type: ndcg_at_5
value: 86.703
- type: precision_at_1
value: 81.01
- type: precision_at_10
value: 13.336
- type: precision_at_100
value: 1.52
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 37.14
- type: precision_at_5
value: 24.44
- type: recall_at_1
value: 70.402
- type: recall_at_10
value: 95.214
- type: recall_at_100
value: 99.438
- type: recall_at_1000
value: 99.928
- type: recall_at_3
value: 86.75699999999999
- type: recall_at_5
value: 91.44099999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.51721502758904
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.054808572333016
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.578
- type: map_at_10
value: 11.036999999999999
- type: map_at_100
value: 12.879999999999999
- type: map_at_1000
value: 13.150999999999998
- type: map_at_3
value: 8.133
- type: map_at_5
value: 9.559
- type: mrr_at_1
value: 22.6
- type: mrr_at_10
value: 32.68
- type: mrr_at_100
value: 33.789
- type: mrr_at_1000
value: 33.854
- type: mrr_at_3
value: 29.7
- type: mrr_at_5
value: 31.480000000000004
- type: ndcg_at_1
value: 22.6
- type: ndcg_at_10
value: 18.616
- type: ndcg_at_100
value: 25.883
- type: ndcg_at_1000
value: 30.944
- type: ndcg_at_3
value: 18.136
- type: ndcg_at_5
value: 15.625
- type: precision_at_1
value: 22.6
- type: precision_at_10
value: 9.48
- type: precision_at_100
value: 1.991
- type: precision_at_1000
value: 0.321
- type: precision_at_3
value: 16.8
- type: precision_at_5
value: 13.54
- type: recall_at_1
value: 4.578
- type: recall_at_10
value: 19.213
- type: recall_at_100
value: 40.397
- type: recall_at_1000
value: 65.2
- type: recall_at_3
value: 10.208
- type: recall_at_5
value: 13.718
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.44288351714071
- type: cos_sim_spearman
value: 79.37995604564952
- type: euclidean_pearson
value: 81.1078874670718
- type: euclidean_spearman
value: 79.37995905980499
- type: manhattan_pearson
value: 81.03697527288986
- type: manhattan_spearman
value: 79.33490235296236
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.95557650436523
- type: cos_sim_spearman
value: 78.5190672399868
- type: euclidean_pearson
value: 81.58064025904707
- type: euclidean_spearman
value: 78.5190672399868
- type: manhattan_pearson
value: 81.52857930619889
- type: manhattan_spearman
value: 78.50421361308034
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.79128416228737
- type: cos_sim_spearman
value: 86.05402451477147
- type: euclidean_pearson
value: 85.46280267054289
- type: euclidean_spearman
value: 86.05402451477147
- type: manhattan_pearson
value: 85.46278563858236
- type: manhattan_spearman
value: 86.08079590861004
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.20623089568763
- type: cos_sim_spearman
value: 81.53786907061009
- type: euclidean_pearson
value: 82.82272250091494
- type: euclidean_spearman
value: 81.53786907061009
- type: manhattan_pearson
value: 82.78850494027013
- type: manhattan_spearman
value: 81.5135618083407
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.46366618397936
- type: cos_sim_spearman
value: 86.96566013336908
- type: euclidean_pearson
value: 86.62651697548931
- type: euclidean_spearman
value: 86.96565526364454
- type: manhattan_pearson
value: 86.58812160258009
- type: manhattan_spearman
value: 86.9336484321288
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.51858358641559
- type: cos_sim_spearman
value: 84.7652527954999
- type: euclidean_pearson
value: 84.23914783766861
- type: euclidean_spearman
value: 84.7652527954999
- type: manhattan_pearson
value: 84.22749648503171
- type: manhattan_spearman
value: 84.74527996746386
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.28026563313065
- type: cos_sim_spearman
value: 87.46928143824915
- type: euclidean_pearson
value: 88.30558762000372
- type: euclidean_spearman
value: 87.46928143824915
- type: manhattan_pearson
value: 88.10513330809331
- type: manhattan_spearman
value: 87.21069787834173
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.376497134587375
- type: cos_sim_spearman
value: 65.0159550112516
- type: euclidean_pearson
value: 65.64572120879598
- type: euclidean_spearman
value: 65.0159550112516
- type: manhattan_pearson
value: 65.88143604989976
- type: manhattan_spearman
value: 65.17547297222434
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.22876368947644
- type: cos_sim_spearman
value: 85.46935577445318
- type: euclidean_pearson
value: 85.32830231392005
- type: euclidean_spearman
value: 85.46935577445318
- type: manhattan_pearson
value: 85.30353211758495
- type: manhattan_spearman
value: 85.42821085956945
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.60986667767133
- type: mrr
value: 94.29432314236236
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 54.528
- type: map_at_10
value: 65.187
- type: map_at_100
value: 65.62599999999999
- type: map_at_1000
value: 65.657
- type: map_at_3
value: 62.352
- type: map_at_5
value: 64.025
- type: mrr_at_1
value: 57.333
- type: mrr_at_10
value: 66.577
- type: mrr_at_100
value: 66.88
- type: mrr_at_1000
value: 66.908
- type: mrr_at_3
value: 64.556
- type: mrr_at_5
value: 65.739
- type: ndcg_at_1
value: 57.333
- type: ndcg_at_10
value: 70.275
- type: ndcg_at_100
value: 72.136
- type: ndcg_at_1000
value: 72.963
- type: ndcg_at_3
value: 65.414
- type: ndcg_at_5
value: 67.831
- type: precision_at_1
value: 57.333
- type: precision_at_10
value: 9.5
- type: precision_at_100
value: 1.057
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.778000000000002
- type: precision_at_5
value: 17.2
- type: recall_at_1
value: 54.528
- type: recall_at_10
value: 84.356
- type: recall_at_100
value: 92.833
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 71.283
- type: recall_at_5
value: 77.14999999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74158415841585
- type: cos_sim_ap
value: 92.90048959850317
- type: cos_sim_f1
value: 86.35650810245687
- type: cos_sim_precision
value: 90.4709748083242
- type: cos_sim_recall
value: 82.6
- type: dot_accuracy
value: 99.74158415841585
- type: dot_ap
value: 92.90048959850317
- type: dot_f1
value: 86.35650810245687
- type: dot_precision
value: 90.4709748083242
- type: dot_recall
value: 82.6
- type: euclidean_accuracy
value: 99.74158415841585
- type: euclidean_ap
value: 92.90048959850317
- type: euclidean_f1
value: 86.35650810245687
- type: euclidean_precision
value: 90.4709748083242
- type: euclidean_recall
value: 82.6
- type: manhattan_accuracy
value: 99.74158415841585
- type: manhattan_ap
value: 92.87344692947894
- type: manhattan_f1
value: 86.38497652582159
- type: manhattan_precision
value: 90.29443838604145
- type: manhattan_recall
value: 82.8
- type: max_accuracy
value: 99.74158415841585
- type: max_ap
value: 92.90048959850317
- type: max_f1
value: 86.38497652582159
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 63.191648770424216
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.02944668730218
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.466386167525265
- type: mrr
value: 51.19071492233257
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.198022505886435
- type: cos_sim_spearman
value: 30.40170257939193
- type: dot_pearson
value: 30.198015316402614
- type: dot_spearman
value: 30.40170257939193
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.242
- type: map_at_10
value: 2.17
- type: map_at_100
value: 12.221
- type: map_at_1000
value: 28.63
- type: map_at_3
value: 0.728
- type: map_at_5
value: 1.185
- type: mrr_at_1
value: 94
- type: mrr_at_10
value: 97
- type: mrr_at_100
value: 97
- type: mrr_at_1000
value: 97
- type: mrr_at_3
value: 97
- type: mrr_at_5
value: 97
- type: ndcg_at_1
value: 89
- type: ndcg_at_10
value: 82.30499999999999
- type: ndcg_at_100
value: 61.839999999999996
- type: ndcg_at_1000
value: 53.381
- type: ndcg_at_3
value: 88.877
- type: ndcg_at_5
value: 86.05199999999999
- type: precision_at_1
value: 94
- type: precision_at_10
value: 87
- type: precision_at_100
value: 63.38
- type: precision_at_1000
value: 23.498
- type: precision_at_3
value: 94
- type: precision_at_5
value: 92
- type: recall_at_1
value: 0.242
- type: recall_at_10
value: 2.302
- type: recall_at_100
value: 14.979000000000001
- type: recall_at_1000
value: 49.638
- type: recall_at_3
value: 0.753
- type: recall_at_5
value: 1.226
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.006
- type: map_at_10
value: 11.805
- type: map_at_100
value: 18.146
- type: map_at_1000
value: 19.788
- type: map_at_3
value: 5.914
- type: map_at_5
value: 8.801
- type: mrr_at_1
value: 40.816
- type: mrr_at_10
value: 56.36600000000001
- type: mrr_at_100
value: 56.721999999999994
- type: mrr_at_1000
value: 56.721999999999994
- type: mrr_at_3
value: 52.041000000000004
- type: mrr_at_5
value: 54.796
- type: ndcg_at_1
value: 37.755
- type: ndcg_at_10
value: 29.863
- type: ndcg_at_100
value: 39.571
- type: ndcg_at_1000
value: 51.385999999999996
- type: ndcg_at_3
value: 32.578
- type: ndcg_at_5
value: 32.351
- type: precision_at_1
value: 40.816
- type: precision_at_10
value: 26.531
- type: precision_at_100
value: 7.796
- type: precision_at_1000
value: 1.555
- type: precision_at_3
value: 32.653
- type: precision_at_5
value: 33.061
- type: recall_at_1
value: 3.006
- type: recall_at_10
value: 18.738
- type: recall_at_100
value: 48.058
- type: recall_at_1000
value: 83.41300000000001
- type: recall_at_3
value: 7.166
- type: recall_at_5
value: 12.102
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.4178
- type: ap
value: 14.648781342150446
- type: f1
value: 55.07299194946378
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.919637804187886
- type: f1
value: 61.24122013967399
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.207896583685695
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.23114978840078
- type: cos_sim_ap
value: 74.26624727825818
- type: cos_sim_f1
value: 68.72377190817083
- type: cos_sim_precision
value: 64.56400742115028
- type: cos_sim_recall
value: 73.45646437994723
- type: dot_accuracy
value: 86.23114978840078
- type: dot_ap
value: 74.26624032659652
- type: dot_f1
value: 68.72377190817083
- type: dot_precision
value: 64.56400742115028
- type: dot_recall
value: 73.45646437994723
- type: euclidean_accuracy
value: 86.23114978840078
- type: euclidean_ap
value: 74.26624714480556
- type: euclidean_f1
value: 68.72377190817083
- type: euclidean_precision
value: 64.56400742115028
- type: euclidean_recall
value: 73.45646437994723
- type: manhattan_accuracy
value: 86.16558383501221
- type: manhattan_ap
value: 74.2091943976357
- type: manhattan_f1
value: 68.64221520524654
- type: manhattan_precision
value: 63.59135913591359
- type: manhattan_recall
value: 74.5646437994723
- type: max_accuracy
value: 86.23114978840078
- type: max_ap
value: 74.26624727825818
- type: max_f1
value: 68.72377190817083
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.3681841114604
- type: cos_sim_ap
value: 86.65166387498546
- type: cos_sim_f1
value: 79.02581944698774
- type: cos_sim_precision
value: 75.35796605434099
- type: cos_sim_recall
value: 83.06898675700647
- type: dot_accuracy
value: 89.3681841114604
- type: dot_ap
value: 86.65166019802056
- type: dot_f1
value: 79.02581944698774
- type: dot_precision
value: 75.35796605434099
- type: dot_recall
value: 83.06898675700647
- type: euclidean_accuracy
value: 89.3681841114604
- type: euclidean_ap
value: 86.65166462876266
- type: euclidean_f1
value: 79.02581944698774
- type: euclidean_precision
value: 75.35796605434099
- type: euclidean_recall
value: 83.06898675700647
- type: manhattan_accuracy
value: 89.36624364497226
- type: manhattan_ap
value: 86.65076471274106
- type: manhattan_f1
value: 79.07408783532733
- type: manhattan_precision
value: 76.41102972856527
- type: manhattan_recall
value: 81.92947336002464
- type: max_accuracy
value: 89.3681841114604
- type: max_ap
value: 86.65166462876266
- type: max_f1
value: 79.07408783532733
---
# RinaChen/nomic-embed-text-v1.5-Q4_K_M-GGUF
This model was converted to GGUF format from [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo RinaChen/nomic-embed-text-v1.5-Q4_K_M-GGUF --hf-file nomic-embed-text-v1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo RinaChen/nomic-embed-text-v1.5-Q4_K_M-GGUF --hf-file nomic-embed-text-v1.5-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo RinaChen/nomic-embed-text-v1.5-Q4_K_M-GGUF --hf-file nomic-embed-text-v1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo RinaChen/nomic-embed-text-v1.5-Q4_K_M-GGUF --hf-file nomic-embed-text-v1.5-q4_k_m.gguf -c 2048
```
|
[
"BIOSSES",
"SCIFACT"
] |
William2357/bearthirty
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T02:26:56Z |
2024-08-23T02:32:28+00:00
| 30 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bearthirty
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
knowledgator/gliner-qwen-0.5B-v1.0
|
knowledgator
|
token-classification
|
[
"gliner",
"pytorch",
"NER",
"GLiNER",
"information extraction",
"encoder",
"entity recognition",
"token-classification",
"multilingual",
"dataset:urchade/pile-mistral-v0.1",
"dataset:knowledgator/GLINER-multi-task-synthetic-data",
"dataset:EmergentMethods/AskNews-NER-v0",
"license:apache-2.0",
"region:us"
] | 2024-09-01T09:29:26Z |
2024-09-06T14:56:17+00:00
| 30 | 1 |
---
datasets:
- urchade/pile-mistral-v0.1
- knowledgator/GLINER-multi-task-synthetic-data
- EmergentMethods/AskNews-NER-v0
language:
- multilingual
library_name: gliner
license: apache-2.0
pipeline_tag: token-classification
tags:
- NER
- GLiNER
- information extraction
- encoder
- entity recognition
---
# About
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
The initial versions of GLiNER relied on older encoder architectures like BERT and DeBERTA. These models, however, were trained on smaller datasets and lacked support for modern optimization techniques such as flash attention. Additionally, their context window was typically limited to 512 tokens, which is insufficient for many practical applications. Recognizing these limitations, we began exploring alternative backbones for GLiNER.
This latest model leverages the LLM2Vec approach, transforming the initial decoder model into a bidirectional encoder. We further enhanced the model by pre-training it on the masked token prediction task using the Wikipedia corpus. This approach introduces several advancements for GLiNER, including support for flash attention, an extended context window, and faster inference times. Additionally, by utilizing modern decoders trained on large, up-to-date datasets, the model exhibits improved generalization and performance.
Key Advantages Over Previous GLiNER Models:
* Enhanced performance and generalization capabilities
* Support for Flash Attention
* Extended context window (up to 32k tokens)
While these models are larger and require more computational resources compared to older encoders, they are still considered relatively small given current standards and provide significant benefits for a wide range of use cases.
### Installation & Usage
Install or update the gliner package:
```bash
pip install gliner -U
```
And LLM2Vec package:
```bash
pip install llm2vec
```
To use this particular Qwen-based model you need different `transformers` package version than llm2vec requires, so install it manually:
```bash
pip install transformers==4.44.1
```
Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
```python
from gliner import GLiNER
model = GLiNER.from_pretrained("knowledgator/gliner-qwen-0.5B-v1.0")
text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""
labels = ["person", "award", "date", "competitions", "teams"]
entities = model.predict_entities(text, labels, threshold=0.5)
for entity in entities:
print(entity["text"], "=>", entity["label"])
```
```
Cristiano Ronaldo dos Santos Aveiro => person
5 February 1985 => date
Al Nassr => teams
Portugal national team => teams
Ballon d'Or => award
UEFA Men's Player of the Year Awards => award
European Golden Shoes => award
UEFA Champions Leagues => competitions
UEFA European Championship => competitions
UEFA Nations League => competitions
Champions League => competitions
European Championship => competitions
```
If you want to use flash attention or increase sequence length, please, check the following code:
```python
model = GLiNER.from_pretrained("knowledgator/gliner-qwen-0.5B-v1.0",
_attn_implementation = 'flash_attention_2',
max_len = 2048).to('cuda:0')
```
If you have a large amount of entities and want to pre-embed them, please, refer to the following code snippet:
```python
labels = ["your entities"]
texts = ["your texts"]
entity_embeddings = model.encode_labels(labels, batch_size = 8)
outputs = model.batch_predict_with_embeds(texts, entity_embeddings, labels)
```
### Benchmarks
Below you can see the table with benchmarking results on various named entity recognition datasets:
Here’s the updated table with your new data:
| Dataset | Score |
|-------------------------|--------|
| ACE 2004 | 31.5% |
| ACE 2005 | 31.5% |
| AnatEM | 43.4% |
| Broad Tweet Corpus | 55.6% |
| CoNLL 2003 | 60.1% |
| FabNER | 23.9% |
| FindVehicle | 30.2% |
| GENIA_NER | 50.7% |
| HarveyNER | 16.9% |
| MultiNERD | 53.3% |
| Ontonotes | 28.1% |
| PolyglotNER | 39.2% |
| TweetNER7 | 35.3% |
| WikiANN en | 53.2% |
| WikiNeural | 65.0% |
| bc2gm | 56.3% |
| bc4chemd | 54.4% |
| bc5cdr | 71.0% |
| ncbi | 63.7% |
| **Average** | **45.4%** |
| | |
| CrossNER_AI | 54.0% |
| CrossNER_literature | 64.4% |
| CrossNER_music | 63.0% |
| CrossNER_politics | 69.3% |
| CrossNER_science | 64.2% |
| mit-movie | 52.7% |
| mit-restaurant | 37.6% |
| **Average (zero-shot benchmark)** | **57.9%** |
### Join Our Discord
Connect with our community on Discord for news, support, and discussion about our models. Join [Discord](https://discord.gg/dkyeAgs9DG).
|
[
"ANATEM",
"BC5CDR"
] |
erichennings/EH-sentiment-finetuned-Llama-3.2-1B-Instruct
|
erichennings
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:mteb/amazon_polarity",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-10-26T00:38:22Z |
2024-10-27T23:41:04+00:00
| 30 | 0 |
---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
datasets:
- mteb/amazon_polarity
library_name: transformers
license: llama3.2
---
# Model Card for EH-sentiment-finetuned-Llama-3.2-1B-Instruct/
This is a test project, fine tuning Llama3.1-1B-Instruct for sentiment classification, using a subset of an amazon reviews dataset
[mteb/amazon_polarity](https://huggingface.co/datasets/mteb/amazon_polarity) and ORPO fine tuning.
The finetuned model achieves moderate +10% improvement on sentiment classification
(as measured by SST2 - which asks the model to classify sentences in a single word,
either 'positive' or 'neagtive'), without general performance being impacted
(as measured by hellaswag, which asks the model to complete a sentence with a sensible
response, chosen from a list of choices).
| Metric Category | Metric | Base Model | Finetuned Model | Change |
|---------------------|--------------------|----------------|-----------------|--------|
| Sentiment | SST2/acc | 0.68 | 0.75 | +10% |
| | | | | |
| General Completions | hellaswag/acc | 0.447 | 0.459 | +3% |
| | hellaswag/acc_norm | 0.550 | 0.560 | +2% |
The training dataset was the first 10k samples from mteb/amazon_polarity, and the model was trained for
5 epochs. The dataset was nearly balanced across positive and negative sentiment -
~51% of examples were negative.
The finetuning training examples used an SST-like prompt format (see Prompt Formats, below). An attempt was
also made to train using exactly the SST Eval format. Oddly, using the SST Eval format resulted in the
SST accuracy going down (0.54 for 10k samples and 1 epoch, -20% compared to the base model.)
This was unexpected, and weird, and probably would bear further investigation.
The model was much worse at correctly identifying positive sentiment (57% accuracy) than it was at
identifying negative sentiment (93% accuracy) - see Confusion Matrix, below. This performance on
negative sentiment is good - State of the Art for SST2 overall is 97%
(achieved by [T5-11B](https://huggingface.co/google-t5/t5-11b)).
Since the training dataset was balanced across positive and negative examples, this mismatch seems likely
to have been present in the base model, although this was not confirmed. Next steps for improvement
should be to verify that the behavior is inherited, and if so probably train with a larger
set of positive statements.
## Confusion Matrix
<img src="confusion-matrix.png" width="500" height="500" />
## Prompt Formats
**SST Eval**: The SST Eval uses prompts like this:
> A complete waste of time. Typographical errors, poor grammar, and a totally pathetic plot add up to absolutely nothing.
> I'm embarrassed for this author and very disappointed I actually paid for this book.
>
> Question: Is this sentence positive or negative?
> Answer:
**SST-like**: Training examples were formulated using an SST-like prompt:
> Below is an instruction that describes a task. Write a response that appropriately completes the request.
>
> ###Instruction:
> Determine the sentiment of the input sentence. Please respond as positive or negative.
> ###Input:
> The best soundtrack ever to anything.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Fintuned model for sentiment classification.
- **Developed by:** Eric Hennings
- **Finetuned from model [optional]:** meta-llama/Llama-3.2-1B-Instruct
### Model Sources [optional]
|
[
"BEAR"
] |
OpenGVLab/Mini-InternVL2-4B-DA-DriveLM
|
OpenGVLab
|
image-text-to-text
|
[
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"internvl",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"arxiv:2410.16261",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"base_model:OpenGVLab/InternVL2-4B",
"base_model:merge:OpenGVLab/InternVL2-4B",
"license:mit",
"region:us"
] | 2024-12-07T15:27:36Z |
2024-12-09T13:45:32+00:00
| 30 | 3 |
---
base_model:
- OpenGVLab/InternVL2-4B
language:
- multilingual
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
tags:
- internvl
- custom_code
base_model_relation: merge
---
# Mini-InternVL2-DA-RS
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 1.0\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5\]](https://arxiv.org/abs/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)
[\[🗨️ InternVL Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#data-preparation)
## Introduction
We release the adaptation models for the specific domains: autonomous driving, medical images, and remote sensing.
These models are built upon Mini-InternVL and fine-tuned using a unified adaptation framework, achieving good performance on tasks in specific domains.
<table>
<tr>
<th>Model Name</th>
<th>HF Link</th>
<th>Note</th>
</tr>
<tr>
<td>Mini-InternVL2-DA-Drivelm</td>
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Drivelm">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Drivelm">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Drivelm">🤗4B</a></td>
<td> Adaptation for <a href="https://github.com/OpenDriveLab/DriveLM/tree/main/challenge"> CVPR 2024 Autonomous Driving Challenge </a></td>
</tr>
<tr>
<td>Mini-InternVL2-DA-BDD</td>
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-BDD">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-BDD">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-BDD">🤗4B</a></td>
<td> Fine-tuning with data constructed by <a href="https://tonyxuqaq.github.io/projects/DriveGPT4/"> DriveGPT4 </a></td>
</tr>
<tr>
<td>Mini-InternVL2-DA-RS</td>
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-RS">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-RS">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-RS">🤗4B</a></td>
<td> Adaptation for remote sensing domain </td>
</tr>
<tr>
<td>Mini-InternVL2-DA-Medical</td>
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Medical">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Medical">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Medical">🤗4B</a></td>
<td> Fine-tuning using our <a href="https://huggingface.co/datasets/OpenGVLab/InternVL-Domain-Adaptation-Data/blob/main/train_meta/internvl_1_2_finetune_medical.json">medical data</a>.</td>
</tr>
</table>
The script for evaluation is in the [document](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#id3).
## Training datasets
- General domain dataset:
ShareGPT4V, AllSeeingV2, LLaVA-Instruct-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, SynthDoG-EN
- Autonomous driving dataset:
[DriveLM](https://github.com/OpenDriveLab/DriveLM).
## Quick Start
We provide an example code to run `Mini-InternVL2-4B` using `transformers`.
> Please use transformers>=4.37.2 to ensure the model works normally.
```python
import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image_file, input_size=448, max_num=12):
image = Image.open(image_file).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
path = 'OpenGVLab/Mini-InternVL2-4B-DA-DriveLM'
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
use_flash_attn=True,
trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
# set the max number of tiles in `max_num`
pixel_values = load_image('path/to/image.jpg', max_num=12).to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=True)
# pure-text conversation (纯文本对话)
question = 'Hello, who are you?'
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Can you tell me a story?'
response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# single-image single-round conversation (单图单轮对话)
question = '<image>\nPlease describe the image shortly.'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}\nAssistant: {response}')
# single-image multi-round conversation (单图多轮对话)
question = '<image>\nPlease describe the image in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Please write a poem according to the image.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
question = '<image>\nDescribe the two images in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'What are the similarities and differences between these two images.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list,
history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'What are the similarities and differences between these two images.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list,
history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# batch inference, single image per sample (单图批处理)
pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
responses = model.batch_chat(tokenizer, pixel_values,
num_patches_list=num_patches_list,
questions=questions,
generation_config=generation_config)
for question, response in zip(questions, responses):
print(f'User: {question}\nAssistant: {response}')
```
## Citation
If you find this project useful in your research, please consider citing:
```BibTeX
@article{gao2024mini,
title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
journal={arXiv preprint arXiv:2410.16261},
year={2024}
}
@article{chen2024expanding,
title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
journal={arXiv preprint arXiv:2412.05271},
year={2024}
}
@article{chen2024far,
title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
journal={arXiv preprint arXiv:2404.16821},
year={2024}
}
@inproceedings{chen2024internvl,
title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={24185--24198},
year={2024}
}
```
|
[
"MEDICAL DATA"
] |
jncraton/multilingual-e5-small-ct2-int8
|
jncraton
|
sentence-similarity
|
[
"sentence-transformers",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-12-16T15:39:57Z |
2024-12-16T15:41:06+00:00
| 30 | 0 |
---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: intfloat/multilingual-e5-small
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 36.9996434842022
- type: f1
value: 67.95453679103099
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.64882226980728
- type: ap
value: 82.11942130026586
- type: f1
value: 69.87963421606715
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.8095952023988
- type: ap
value: 24.46869495579561
- type: f1
value: 63.00108480037597
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.186295503212
- type: ap
value: 15.496804690197042
- type: f1
value: 52.07153895475031
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.699325
- type: ap
value: 85.27039559917269
- type: f1
value: 88.65556295032513
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.69799999999999
- type: f1
value: 43.73187348654165
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.245999999999995
- type: f1
value: 39.3863530637684
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.394
- type: f1
value: 39.301223469483446
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.864
- type: f1
value: 37.97974261868003
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.682
- type: f1
value: 37.07399369768313
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.504
- type: f1
value: 36.62317273874278
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.061
- type: map_at_10
value: 31.703
- type: map_at_100
value: 32.967
- type: map_at_1000
value: 33.001000000000005
- type: map_at_3
value: 27.466
- type: map_at_5
value: 29.564
- type: mrr_at_1
value: 19.559
- type: mrr_at_10
value: 31.874999999999996
- type: mrr_at_100
value: 33.146
- type: mrr_at_1000
value: 33.18
- type: mrr_at_3
value: 27.667
- type: mrr_at_5
value: 29.74
- type: ndcg_at_1
value: 19.061
- type: ndcg_at_10
value: 39.062999999999995
- type: ndcg_at_100
value: 45.184000000000005
- type: ndcg_at_1000
value: 46.115
- type: ndcg_at_3
value: 30.203000000000003
- type: ndcg_at_5
value: 33.953
- type: precision_at_1
value: 19.061
- type: precision_at_10
value: 6.279999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 12.706999999999999
- type: precision_at_5
value: 9.431000000000001
- type: recall_at_1
value: 19.061
- type: recall_at_10
value: 62.802
- type: recall_at_100
value: 91.323
- type: recall_at_1000
value: 98.72
- type: recall_at_3
value: 38.122
- type: recall_at_5
value: 47.155
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.22266660528253
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 30.79980849482483
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.8790068352054
- type: mrr
value: 71.78791276436706
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.36328364043163
- type: cos_sim_spearman
value: 82.26211536195868
- type: euclidean_pearson
value: 80.3183865039173
- type: euclidean_spearman
value: 79.88495276296132
- type: manhattan_pearson
value: 80.14484480692127
- type: manhattan_spearman
value: 80.39279565980743
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.0375782881002
- type: f1
value: 97.86012526096033
- type: precision
value: 97.77139874739039
- type: recall
value: 98.0375782881002
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 93.35241030156286
- type: f1
value: 92.66050333846944
- type: precision
value: 92.3306919069631
- type: recall
value: 93.35241030156286
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 94.0699688257707
- type: f1
value: 93.50236693222492
- type: precision
value: 93.22791825424315
- type: recall
value: 94.0699688257707
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 89.25750394944708
- type: f1
value: 88.79234684921889
- type: precision
value: 88.57293312269616
- type: recall
value: 89.25750394944708
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 79.41558441558442
- type: f1
value: 79.25886487487219
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.747820820329736
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.045143830596146
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.252999999999997
- type: map_at_10
value: 31.655916666666666
- type: map_at_100
value: 32.680749999999996
- type: map_at_1000
value: 32.79483333333334
- type: map_at_3
value: 29.43691666666666
- type: map_at_5
value: 30.717416666666665
- type: mrr_at_1
value: 28.602750000000004
- type: mrr_at_10
value: 35.56875
- type: mrr_at_100
value: 36.3595
- type: mrr_at_1000
value: 36.427749999999996
- type: mrr_at_3
value: 33.586166666666664
- type: mrr_at_5
value: 34.73641666666666
- type: ndcg_at_1
value: 28.602750000000004
- type: ndcg_at_10
value: 36.06933333333334
- type: ndcg_at_100
value: 40.70141666666667
- type: ndcg_at_1000
value: 43.24341666666667
- type: ndcg_at_3
value: 32.307916666666664
- type: ndcg_at_5
value: 34.129999999999995
- type: precision_at_1
value: 28.602750000000004
- type: precision_at_10
value: 6.097666666666667
- type: precision_at_100
value: 0.9809166666666668
- type: precision_at_1000
value: 0.13766666666666663
- type: precision_at_3
value: 14.628166666666667
- type: precision_at_5
value: 10.266916666666667
- type: recall_at_1
value: 24.252999999999997
- type: recall_at_10
value: 45.31916666666667
- type: recall_at_100
value: 66.03575000000001
- type: recall_at_1000
value: 83.94708333333334
- type: recall_at_3
value: 34.71941666666666
- type: recall_at_5
value: 39.46358333333333
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.024000000000001
- type: map_at_10
value: 15.644
- type: map_at_100
value: 17.154
- type: map_at_1000
value: 17.345
- type: map_at_3
value: 13.028
- type: map_at_5
value: 14.251
- type: mrr_at_1
value: 19.674
- type: mrr_at_10
value: 29.826999999999998
- type: mrr_at_100
value: 30.935000000000002
- type: mrr_at_1000
value: 30.987
- type: mrr_at_3
value: 26.645000000000003
- type: mrr_at_5
value: 28.29
- type: ndcg_at_1
value: 19.674
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 29.207
- type: ndcg_at_1000
value: 32.912
- type: ndcg_at_3
value: 17.952
- type: ndcg_at_5
value: 19.363
- type: precision_at_1
value: 19.674
- type: precision_at_10
value: 7.212000000000001
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 13.507
- type: precision_at_5
value: 10.397
- type: recall_at_1
value: 9.024000000000001
- type: recall_at_10
value: 28.077999999999996
- type: recall_at_100
value: 51.403
- type: recall_at_1000
value: 72.406
- type: recall_at_3
value: 16.768
- type: recall_at_5
value: 20.737
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.012
- type: map_at_10
value: 17.138
- type: map_at_100
value: 24.146
- type: map_at_1000
value: 25.622
- type: map_at_3
value: 12.552
- type: map_at_5
value: 14.435
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 71.186
- type: mrr_at_100
value: 71.504
- type: mrr_at_1000
value: 71.514
- type: mrr_at_3
value: 69.333
- type: mrr_at_5
value: 70.408
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 37.76
- type: ndcg_at_100
value: 42.071
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 41.644
- type: ndcg_at_5
value: 39.812999999999995
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 30.15
- type: precision_at_100
value: 9.753
- type: precision_at_1000
value: 1.9189999999999998
- type: precision_at_3
value: 45.667
- type: precision_at_5
value: 39.15
- type: recall_at_1
value: 8.012
- type: recall_at_10
value: 22.599
- type: recall_at_100
value: 48.068
- type: recall_at_1000
value: 71.328
- type: recall_at_3
value: 14.043
- type: recall_at_5
value: 17.124
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.455
- type: f1
value: 37.59462649781862
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.092
- type: map_at_10
value: 69.586
- type: map_at_100
value: 69.968
- type: map_at_1000
value: 69.982
- type: map_at_3
value: 67.48100000000001
- type: map_at_5
value: 68.915
- type: mrr_at_1
value: 62.166
- type: mrr_at_10
value: 73.588
- type: mrr_at_100
value: 73.86399999999999
- type: mrr_at_1000
value: 73.868
- type: mrr_at_3
value: 71.6
- type: mrr_at_5
value: 72.99
- type: ndcg_at_1
value: 62.166
- type: ndcg_at_10
value: 75.27199999999999
- type: ndcg_at_100
value: 76.816
- type: ndcg_at_1000
value: 77.09700000000001
- type: ndcg_at_3
value: 71.36
- type: ndcg_at_5
value: 73.785
- type: precision_at_1
value: 62.166
- type: precision_at_10
value: 9.716
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 28.278
- type: precision_at_5
value: 18.343999999999998
- type: recall_at_1
value: 58.092
- type: recall_at_10
value: 88.73400000000001
- type: recall_at_100
value: 95.195
- type: recall_at_1000
value: 97.04599999999999
- type: recall_at_3
value: 78.45
- type: recall_at_5
value: 84.316
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.649
- type: map_at_10
value: 26.457000000000004
- type: map_at_100
value: 28.169
- type: map_at_1000
value: 28.352
- type: map_at_3
value: 23.305
- type: map_at_5
value: 25.169000000000004
- type: mrr_at_1
value: 32.407000000000004
- type: mrr_at_10
value: 40.922
- type: mrr_at_100
value: 41.931000000000004
- type: mrr_at_1000
value: 41.983
- type: mrr_at_3
value: 38.786
- type: mrr_at_5
value: 40.205999999999996
- type: ndcg_at_1
value: 32.407000000000004
- type: ndcg_at_10
value: 33.314
- type: ndcg_at_100
value: 40.312
- type: ndcg_at_1000
value: 43.685
- type: ndcg_at_3
value: 30.391000000000002
- type: ndcg_at_5
value: 31.525
- type: precision_at_1
value: 32.407000000000004
- type: precision_at_10
value: 8.966000000000001
- type: precision_at_100
value: 1.6019999999999999
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 20.165
- type: precision_at_5
value: 14.722
- type: recall_at_1
value: 16.649
- type: recall_at_10
value: 39.117000000000004
- type: recall_at_100
value: 65.726
- type: recall_at_1000
value: 85.784
- type: recall_at_3
value: 27.914
- type: recall_at_5
value: 33.289
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.253
- type: map_at_10
value: 56.16799999999999
- type: map_at_100
value: 57.06099999999999
- type: map_at_1000
value: 57.126
- type: map_at_3
value: 52.644999999999996
- type: map_at_5
value: 54.909
- type: mrr_at_1
value: 72.505
- type: mrr_at_10
value: 79.66
- type: mrr_at_100
value: 79.869
- type: mrr_at_1000
value: 79.88
- type: mrr_at_3
value: 78.411
- type: mrr_at_5
value: 79.19800000000001
- type: ndcg_at_1
value: 72.505
- type: ndcg_at_10
value: 65.094
- type: ndcg_at_100
value: 68.219
- type: ndcg_at_1000
value: 69.515
- type: ndcg_at_3
value: 59.99
- type: ndcg_at_5
value: 62.909000000000006
- type: precision_at_1
value: 72.505
- type: precision_at_10
value: 13.749
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.357
- type: precision_at_5
value: 25.313000000000002
- type: recall_at_1
value: 36.253
- type: recall_at_10
value: 68.744
- type: recall_at_100
value: 80.925
- type: recall_at_1000
value: 89.534
- type: recall_at_3
value: 57.535000000000004
- type: recall_at_5
value: 63.282000000000004
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 80.82239999999999
- type: ap
value: 75.65895781725314
- type: f1
value: 80.75880969095746
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 34.075
- type: map_at_100
value: 35.229
- type: map_at_1000
value: 35.276999999999994
- type: map_at_3
value: 30.245
- type: map_at_5
value: 32.42
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 34.638000000000005
- type: mrr_at_100
value: 35.744
- type: mrr_at_1000
value: 35.787
- type: mrr_at_3
value: 30.891000000000002
- type: mrr_at_5
value: 33.042
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 40.991
- type: ndcg_at_100
value: 46.563
- type: ndcg_at_1000
value: 47.743
- type: ndcg_at_3
value: 33.198
- type: ndcg_at_5
value: 37.069
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 14.216999999999999
- type: precision_at_5
value: 10.487
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 62.303
- type: recall_at_100
value: 88.124
- type: recall_at_1000
value: 97.08
- type: recall_at_3
value: 41.099999999999994
- type: recall_at_5
value: 50.381
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.06703146374831
- type: f1
value: 90.86867815863172
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.46970977740209
- type: f1
value: 86.36832872036588
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.26951300867245
- type: f1
value: 88.93561193959502
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 84.22799874725963
- type: f1
value: 84.30490069236556
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.02007888131948
- type: f1
value: 85.39376041027991
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 85.34900542495481
- type: f1
value: 85.39859673336713
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.078431372549
- type: f1
value: 53.45071102002276
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 65.85798816568047
- type: f1
value: 46.53112748993529
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.96864576384256
- type: f1
value: 45.966703022829506
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 61.31537738803633
- type: f1
value: 45.52601712835461
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 66.29616349946218
- type: f1
value: 47.24166485726613
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.75924680564896
- type: f1
value: 68.30833379585945
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (he)
type: mteb/amazon_massive_scenario
config: he
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.806321452589096
- type: f1
value: 63.273048243765054
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hi)
type: mteb/amazon_massive_scenario
config: hi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.68997982515133
- type: f1
value: 66.54703855381324
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hu)
type: mteb/amazon_massive_scenario
config: hu
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.46940147948891
- type: f1
value: 65.91017343463396
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hy)
type: mteb/amazon_massive_scenario
config: hy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.49899125756556
- type: f1
value: 57.90333469917769
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (id)
type: mteb/amazon_massive_scenario
config: id
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.9219905850706
- type: f1
value: 67.23169403762938
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (is)
type: mteb/amazon_massive_scenario
config: is
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.486213853396094
- type: f1
value: 54.85282355583758
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (it)
type: mteb/amazon_massive_scenario
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.04169468728985
- type: f1
value: 68.83833333320462
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ja)
type: mteb/amazon_massive_scenario
config: ja
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.88702084734365
- type: f1
value: 74.04474735232299
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (jv)
type: mteb/amazon_massive_scenario
config: jv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.63416274377943
- type: f1
value: 55.11332211687954
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ka)
type: mteb/amazon_massive_scenario
config: ka
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 52.23604572965702
- type: f1
value: 50.86529813991055
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (km)
type: mteb/amazon_massive_scenario
config: km
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 46.62407531943511
- type: f1
value: 43.63485467164535
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (kn)
type: mteb/amazon_massive_scenario
config: kn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.15601882985878
- type: f1
value: 57.522837510959924
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ko)
type: mteb/amazon_massive_scenario
config: ko
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.84532616005382
- type: f1
value: 69.60021127179697
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (lv)
type: mteb/amazon_massive_scenario
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.65770006724949
- type: f1
value: 55.84219135523227
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.53665097511768
- type: f1
value: 65.09087787792639
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.31405514458642
- type: f1
value: 58.06135303831491
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.88231338264964
- type: f1
value: 62.751099407787926
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.86012104909213
- type: f1
value: 56.29118323058282
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.37390719569602
- type: f1
value: 66.27922244885102
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.8675184936113
- type: f1
value: 70.22146529932019
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.2212508406187
- type: f1
value: 67.77454802056282
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.18090114324143
- type: f1
value: 68.03737625431621
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.65030262273034
- type: f1
value: 63.792945486912856
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.772749631087066
- type: f1
value: 63.4539101720024
- type: f1_weighted
value: 62.778603897469566
- type: main_score
value: 63.772749631087066
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.17821116341627
- type: f1
value: 59.3935969827171
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.86146603900471
- type: f1
value: 60.133692735032376
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.89441829186282
- type: f1
value: 70.03064076194089
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.15063887020847
- type: f1
value: 56.23326278499678
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.43846671149966
- type: f1
value: 57.70440450281974
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8507061197041
- type: f1
value: 59.22916396061171
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.65568258238063
- type: f1
value: 69.90736239440633
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8843308675185
- type: f1
value: 59.30332663713599
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.05312710154674
- type: f1
value: 67.44024062594775
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.111634162743776
- type: f1
value: 60.89083013084519
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.44115669132482
- type: f1
value: 67.92227541674552
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.4687289845326
- type: f1
value: 74.16376793486025
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.31876260928043
- type: f1
value: 68.5246745215607
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.90431696479766
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.259158476693774
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.28445330838555
- type: mrr
value: 31.15758529581164
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.353
- type: map_at_10
value: 11.565
- type: map_at_100
value: 14.097000000000001
- type: map_at_1000
value: 15.354999999999999
- type: map_at_3
value: 8.749
- type: map_at_5
value: 9.974
- type: mrr_at_1
value: 42.105
- type: mrr_at_10
value: 50.589
- type: mrr_at_100
value: 51.187000000000005
- type: mrr_at_1000
value: 51.233
- type: mrr_at_3
value: 48.246
- type: mrr_at_5
value: 49.546
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 31.009999999999998
- type: ndcg_at_100
value: 28.026
- type: ndcg_at_1000
value: 36.905
- type: ndcg_at_3
value: 35.983
- type: ndcg_at_5
value: 33.764
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 22.786
- type: precision_at_100
value: 6.916
- type: precision_at_1000
value: 1.981
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 28.731
- type: recall_at_1
value: 5.353
- type: recall_at_10
value: 15.039
- type: recall_at_100
value: 27.348
- type: recall_at_1000
value: 59.453
- type: recall_at_3
value: 9.792
- type: recall_at_5
value: 11.882
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.852
- type: map_at_10
value: 48.924
- type: map_at_100
value: 49.854
- type: map_at_1000
value: 49.886
- type: map_at_3
value: 44.9
- type: map_at_5
value: 47.387
- type: mrr_at_1
value: 38.035999999999994
- type: mrr_at_10
value: 51.644
- type: mrr_at_100
value: 52.339
- type: mrr_at_1000
value: 52.35999999999999
- type: mrr_at_3
value: 48.421
- type: mrr_at_5
value: 50.468999999999994
- type: ndcg_at_1
value: 38.007000000000005
- type: ndcg_at_10
value: 56.293000000000006
- type: ndcg_at_100
value: 60.167
- type: ndcg_at_1000
value: 60.916000000000004
- type: ndcg_at_3
value: 48.903999999999996
- type: ndcg_at_5
value: 52.978
- type: precision_at_1
value: 38.007000000000005
- type: precision_at_10
value: 9.041
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 22.084
- type: precision_at_5
value: 15.608
- type: recall_at_1
value: 33.852
- type: recall_at_10
value: 75.893
- type: recall_at_100
value: 92.589
- type: recall_at_1000
value: 98.153
- type: recall_at_3
value: 56.969
- type: recall_at_5
value: 66.283
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.174
- type: map_at_10
value: 82.891
- type: map_at_100
value: 83.545
- type: map_at_1000
value: 83.56700000000001
- type: map_at_3
value: 79.944
- type: map_at_5
value: 81.812
- type: mrr_at_1
value: 79.67999999999999
- type: mrr_at_10
value: 86.279
- type: mrr_at_100
value: 86.39
- type: mrr_at_1000
value: 86.392
- type: mrr_at_3
value: 85.21
- type: mrr_at_5
value: 85.92999999999999
- type: ndcg_at_1
value: 79.69000000000001
- type: ndcg_at_10
value: 86.929
- type: ndcg_at_100
value: 88.266
- type: ndcg_at_1000
value: 88.428
- type: ndcg_at_3
value: 83.899
- type: ndcg_at_5
value: 85.56700000000001
- type: precision_at_1
value: 79.69000000000001
- type: precision_at_10
value: 13.161000000000001
- type: precision_at_100
value: 1.513
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.603
- type: precision_at_5
value: 24.138
- type: recall_at_1
value: 69.174
- type: recall_at_10
value: 94.529
- type: recall_at_100
value: 99.15
- type: recall_at_1000
value: 99.925
- type: recall_at_3
value: 85.86200000000001
- type: recall_at_5
value: 90.501
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 39.13064340585255
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 58.97884249325877
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.4680000000000004
- type: map_at_10
value: 7.865
- type: map_at_100
value: 9.332
- type: map_at_1000
value: 9.587
- type: map_at_3
value: 5.800000000000001
- type: map_at_5
value: 6.8790000000000004
- type: mrr_at_1
value: 17.0
- type: mrr_at_10
value: 25.629
- type: mrr_at_100
value: 26.806
- type: mrr_at_1000
value: 26.889000000000003
- type: mrr_at_3
value: 22.8
- type: mrr_at_5
value: 24.26
- type: ndcg_at_1
value: 17.0
- type: ndcg_at_10
value: 13.895
- type: ndcg_at_100
value: 20.491999999999997
- type: ndcg_at_1000
value: 25.759999999999998
- type: ndcg_at_3
value: 13.347999999999999
- type: ndcg_at_5
value: 11.61
- type: precision_at_1
value: 17.0
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 1.669
- type: precision_at_1000
value: 0.294
- type: precision_at_3
value: 12.3
- type: precision_at_5
value: 10.02
- type: recall_at_1
value: 3.4680000000000004
- type: recall_at_10
value: 14.363000000000001
- type: recall_at_100
value: 33.875
- type: recall_at_1000
value: 59.711999999999996
- type: recall_at_3
value: 7.483
- type: recall_at_5
value: 10.173
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.04084311714061
- type: cos_sim_spearman
value: 77.51342467443078
- type: euclidean_pearson
value: 80.0321166028479
- type: euclidean_spearman
value: 77.29249114733226
- type: manhattan_pearson
value: 80.03105964262431
- type: manhattan_spearman
value: 77.22373689514794
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.1680158034387
- type: cos_sim_spearman
value: 76.55983344071117
- type: euclidean_pearson
value: 79.75266678300143
- type: euclidean_spearman
value: 75.34516823467025
- type: manhattan_pearson
value: 79.75959151517357
- type: manhattan_spearman
value: 75.42330344141912
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 76.48898993209346
- type: cos_sim_spearman
value: 76.96954120323366
- type: euclidean_pearson
value: 76.94139109279668
- type: euclidean_spearman
value: 76.85860283201711
- type: manhattan_pearson
value: 76.6944095091912
- type: manhattan_spearman
value: 76.61096912972553
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 77.85082366246944
- type: cos_sim_spearman
value: 75.52053350101731
- type: euclidean_pearson
value: 77.1165845070926
- type: euclidean_spearman
value: 75.31216065884388
- type: manhattan_pearson
value: 77.06193941833494
- type: manhattan_spearman
value: 75.31003701700112
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.36305246526497
- type: cos_sim_spearman
value: 87.11704613927415
- type: euclidean_pearson
value: 86.04199125810939
- type: euclidean_spearman
value: 86.51117572414263
- type: manhattan_pearson
value: 86.0805106816633
- type: manhattan_spearman
value: 86.52798366512229
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.18536255599724
- type: cos_sim_spearman
value: 83.63377151025418
- type: euclidean_pearson
value: 83.24657467993141
- type: euclidean_spearman
value: 84.02751481993825
- type: manhattan_pearson
value: 83.11941806582371
- type: manhattan_spearman
value: 83.84251281019304
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.95816528475514
- type: cos_sim_spearman
value: 78.86607380120462
- type: euclidean_pearson
value: 78.51268699230545
- type: euclidean_spearman
value: 79.11649316502229
- type: manhattan_pearson
value: 78.32367302808157
- type: manhattan_spearman
value: 78.90277699624637
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.89126914997624
- type: cos_sim_spearman
value: 73.0296921832678
- type: euclidean_pearson
value: 71.50385903677738
- type: euclidean_spearman
value: 73.13368899716289
- type: manhattan_pearson
value: 71.47421463379519
- type: manhattan_spearman
value: 73.03383242946575
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 59.22923684492637
- type: cos_sim_spearman
value: 57.41013211368396
- type: euclidean_pearson
value: 61.21107388080905
- type: euclidean_spearman
value: 60.07620768697254
- type: manhattan_pearson
value: 59.60157142786555
- type: manhattan_spearman
value: 59.14069604103739
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.24345978774299
- type: cos_sim_spearman
value: 77.24225743830719
- type: euclidean_pearson
value: 76.66226095469165
- type: euclidean_spearman
value: 77.60708820493146
- type: manhattan_pearson
value: 76.05303324760429
- type: manhattan_spearman
value: 76.96353149912348
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.50879160160852
- type: cos_sim_spearman
value: 86.43594662965224
- type: euclidean_pearson
value: 86.06846012826577
- type: euclidean_spearman
value: 86.02041395794136
- type: manhattan_pearson
value: 86.10916255616904
- type: manhattan_spearman
value: 86.07346068198953
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 58.39803698977196
- type: cos_sim_spearman
value: 55.96910950423142
- type: euclidean_pearson
value: 58.17941175613059
- type: euclidean_spearman
value: 55.03019330522745
- type: manhattan_pearson
value: 57.333358138183286
- type: manhattan_spearman
value: 54.04614023149965
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 70.98304089637197
- type: cos_sim_spearman
value: 72.44071656215888
- type: euclidean_pearson
value: 72.19224359033983
- type: euclidean_spearman
value: 73.89871188913025
- type: manhattan_pearson
value: 71.21098311547406
- type: manhattan_spearman
value: 72.93405764824821
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.99792397466308
- type: cos_sim_spearman
value: 84.83824377879495
- type: euclidean_pearson
value: 85.70043288694438
- type: euclidean_spearman
value: 84.70627558703686
- type: manhattan_pearson
value: 85.89570850150801
- type: manhattan_spearman
value: 84.95806105313007
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.21850322994712
- type: cos_sim_spearman
value: 72.28669398117248
- type: euclidean_pearson
value: 73.40082510412948
- type: euclidean_spearman
value: 73.0326539281865
- type: manhattan_pearson
value: 71.8659633964841
- type: manhattan_spearman
value: 71.57817425823303
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.80921368595645
- type: cos_sim_spearman
value: 77.33209091229315
- type: euclidean_pearson
value: 76.53159540154829
- type: euclidean_spearman
value: 78.17960842810093
- type: manhattan_pearson
value: 76.13530186637601
- type: manhattan_spearman
value: 78.00701437666875
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.74980608267349
- type: cos_sim_spearman
value: 75.37597374318821
- type: euclidean_pearson
value: 74.90506081911661
- type: euclidean_spearman
value: 75.30151613124521
- type: manhattan_pearson
value: 74.62642745918002
- type: manhattan_spearman
value: 75.18619716592303
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.632662289205584
- type: cos_sim_spearman
value: 60.938543391610914
- type: euclidean_pearson
value: 62.113200529767056
- type: euclidean_spearman
value: 61.410312633261164
- type: manhattan_pearson
value: 61.75494698945686
- type: manhattan_spearman
value: 60.92726195322362
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.283470551557244
- type: cos_sim_spearman
value: 53.44833015864201
- type: euclidean_pearson
value: 41.17892011120893
- type: euclidean_spearman
value: 53.81441383126767
- type: manhattan_pearson
value: 41.17482200420659
- type: manhattan_spearman
value: 53.82180269276363
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.5069165306236
- type: cos_sim_spearman
value: 66.87803259033826
- type: euclidean_pearson
value: 63.5428979418236
- type: euclidean_spearman
value: 66.9293576586897
- type: manhattan_pearson
value: 63.59789526178922
- type: manhattan_spearman
value: 66.86555009875066
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.23026196280264
- type: cos_sim_spearman
value: 35.79397812652861
- type: euclidean_pearson
value: 17.828102102767353
- type: euclidean_spearman
value: 35.721501145568894
- type: manhattan_pearson
value: 17.77134274219677
- type: manhattan_spearman
value: 35.98107902846267
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 56.51946541393812
- type: cos_sim_spearman
value: 63.714686006214485
- type: euclidean_pearson
value: 58.32104651305898
- type: euclidean_spearman
value: 62.237110895702216
- type: manhattan_pearson
value: 58.579416468759185
- type: manhattan_spearman
value: 62.459738981727
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.76009839569795
- type: cos_sim_spearman
value: 56.65188431953149
- type: euclidean_pearson
value: 50.997682160915595
- type: euclidean_spearman
value: 55.99910008818135
- type: manhattan_pearson
value: 50.76220659606342
- type: manhattan_spearman
value: 55.517347595391456
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.717524559088005
- type: cos_sim_spearman
value: 66.83570886252286
- type: euclidean_pearson
value: 58.41338625505467
- type: euclidean_spearman
value: 66.68991427704938
- type: manhattan_pearson
value: 58.78638572916807
- type: manhattan_spearman
value: 66.58684161046335
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.2962042954962
- type: cos_sim_spearman
value: 76.58255504852025
- type: euclidean_pearson
value: 75.70983192778257
- type: euclidean_spearman
value: 77.4547684870542
- type: manhattan_pearson
value: 75.75565853870485
- type: manhattan_spearman
value: 76.90208974949428
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.47396266924846
- type: cos_sim_spearman
value: 56.492267162048606
- type: euclidean_pearson
value: 55.998505203070195
- type: euclidean_spearman
value: 56.46447012960222
- type: manhattan_pearson
value: 54.873172394430995
- type: manhattan_spearman
value: 56.58111534551218
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.87177267688686
- type: cos_sim_spearman
value: 74.57160943395763
- type: euclidean_pearson
value: 70.88330406826788
- type: euclidean_spearman
value: 74.29767636038422
- type: manhattan_pearson
value: 71.38245248369536
- type: manhattan_spearman
value: 74.53102232732175
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.80225656959544
- type: cos_sim_spearman
value: 76.52646173725735
- type: euclidean_pearson
value: 73.95710720200799
- type: euclidean_spearman
value: 76.54040031984111
- type: manhattan_pearson
value: 73.89679971946774
- type: manhattan_spearman
value: 76.60886958161574
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.70844249898789
- type: cos_sim_spearman
value: 72.68571783670241
- type: euclidean_pearson
value: 72.38800772441031
- type: euclidean_spearman
value: 72.86804422703312
- type: manhattan_pearson
value: 71.29840508203515
- type: manhattan_spearman
value: 71.86264441749513
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.647478923935694
- type: cos_sim_spearman
value: 63.74453623540931
- type: euclidean_pearson
value: 59.60138032437505
- type: euclidean_spearman
value: 63.947930832166065
- type: manhattan_pearson
value: 58.59735509491861
- type: manhattan_spearman
value: 62.082503844627404
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.8722516867162
- type: cos_sim_spearman
value: 71.81208592523012
- type: euclidean_pearson
value: 67.95315252165956
- type: euclidean_spearman
value: 73.00749822046009
- type: manhattan_pearson
value: 68.07884688638924
- type: manhattan_spearman
value: 72.34210325803069
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.5405814240949
- type: cos_sim_spearman
value: 60.56838649023775
- type: euclidean_pearson
value: 53.011731611314104
- type: euclidean_spearman
value: 58.533194841668426
- type: manhattan_pearson
value: 53.623067729338494
- type: manhattan_spearman
value: 58.018756154446926
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 13.611046866216112
- type: cos_sim_spearman
value: 28.238192909158492
- type: euclidean_pearson
value: 22.16189199885129
- type: euclidean_spearman
value: 35.012895679076564
- type: manhattan_pearson
value: 21.969771178698387
- type: manhattan_spearman
value: 32.456985088607475
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 74.58077407011655
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 74.64613843596234
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 75.15335973101396
- type: manhattan_spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.0739825531578
- type: cos_sim_spearman
value: 84.01057479311115
- type: euclidean_pearson
value: 83.85453227433344
- type: euclidean_spearman
value: 84.01630226898655
- type: manhattan_pearson
value: 83.75323603028978
- type: manhattan_spearman
value: 83.89677983727685
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.12945623123957
- type: mrr
value: 93.87738713719106
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.983000000000004
- type: map_at_10
value: 62.946000000000005
- type: map_at_100
value: 63.514
- type: map_at_1000
value: 63.554
- type: map_at_3
value: 60.183
- type: map_at_5
value: 61.672000000000004
- type: mrr_at_1
value: 55.667
- type: mrr_at_10
value: 64.522
- type: mrr_at_100
value: 64.957
- type: mrr_at_1000
value: 64.995
- type: mrr_at_3
value: 62.388999999999996
- type: mrr_at_5
value: 63.639
- type: ndcg_at_1
value: 55.667
- type: ndcg_at_10
value: 67.704
- type: ndcg_at_100
value: 70.299
- type: ndcg_at_1000
value: 71.241
- type: ndcg_at_3
value: 62.866
- type: ndcg_at_5
value: 65.16999999999999
- type: precision_at_1
value: 55.667
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 16.133
- type: recall_at_1
value: 52.983000000000004
- type: recall_at_10
value: 80.656
- type: recall_at_100
value: 92.5
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 67.744
- type: recall_at_5
value: 73.433
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72772277227723
- type: cos_sim_ap
value: 92.17845897992215
- type: cos_sim_f1
value: 85.9746835443038
- type: cos_sim_precision
value: 87.07692307692308
- type: cos_sim_recall
value: 84.89999999999999
- type: dot_accuracy
value: 99.3039603960396
- type: dot_ap
value: 60.70244020124878
- type: dot_f1
value: 59.92742353551063
- type: dot_precision
value: 62.21743810548978
- type: dot_recall
value: 57.8
- type: euclidean_accuracy
value: 99.71683168316832
- type: euclidean_ap
value: 91.53997039964659
- type: euclidean_f1
value: 84.88372093023257
- type: euclidean_precision
value: 90.02242152466367
- type: euclidean_recall
value: 80.30000000000001
- type: manhattan_accuracy
value: 99.72376237623763
- type: manhattan_ap
value: 91.80756777790289
- type: manhattan_f1
value: 85.48468106479157
- type: manhattan_precision
value: 85.8728557013118
- type: manhattan_recall
value: 85.1
- type: max_accuracy
value: 99.72772277227723
- type: max_ap
value: 92.17845897992215
- type: max_f1
value: 85.9746835443038
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.52464042600003
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.071631948736
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.19552407604654
- type: mrr
value: 49.95269130379425
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.345293033095427
- type: cos_sim_spearman
value: 29.976931423258403
- type: dot_pearson
value: 27.047078008958408
- type: dot_spearman
value: 27.75894368380218
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.706
- type: map_at_100
value: 9.634
- type: map_at_1000
value: 23.665
- type: map_at_3
value: 0.5950000000000001
- type: map_at_5
value: 0.95
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 80.0
- type: ndcg_at_10
value: 72.573
- type: ndcg_at_100
value: 53.954
- type: ndcg_at_1000
value: 47.760999999999996
- type: ndcg_at_3
value: 76.173
- type: ndcg_at_5
value: 75.264
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 76.4
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.802
- type: precision_at_3
value: 81.333
- type: precision_at_5
value: 80.4
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 1.925
- type: recall_at_100
value: 12.762
- type: recall_at_1000
value: 44.946000000000005
- type: recall_at_3
value: 0.634
- type: recall_at_5
value: 1.051
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.0
- type: f1
value: 88.55666666666666
- type: precision
value: 87.46166666666667
- type: recall
value: 91.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.22543352601156
- type: f1
value: 51.03220478943021
- type: precision
value: 48.8150289017341
- type: recall
value: 57.22543352601156
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.58536585365854
- type: f1
value: 39.66870798578116
- type: precision
value: 37.416085946573745
- type: recall
value: 46.58536585365854
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 86.77999999999999
- type: precision
value: 85.45333333333332
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.58333333333331
- type: precision
value: 96.2
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.3
- type: precision
value: 89.31666666666668
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.67190476190476
- type: precision
value: 82.23333333333332
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.0
- type: f1
value: 42.23229092632078
- type: precision
value: 39.851634683724235
- type: recall
value: 50.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.3
- type: f1
value: 70.86190476190477
- type: precision
value: 68.68777777777777
- type: recall
value: 76.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.073170731707314
- type: f1
value: 50.658958927251604
- type: precision
value: 48.26480836236933
- type: recall
value: 57.073170731707314
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.2
- type: f1
value: 62.156507936507936
- type: precision
value: 59.84964285714286
- type: recall
value: 68.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.52126366950182
- type: f1
value: 72.8496210148701
- type: precision
value: 70.92171498003819
- type: recall
value: 77.52126366950182
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.78260869565217
- type: f1
value: 65.32422360248447
- type: precision
value: 63.063067367415194
- type: recall
value: 70.78260869565217
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.43478260869566
- type: f1
value: 73.02608695652172
- type: precision
value: 70.63768115942028
- type: recall
value: 78.43478260869566
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.9
- type: f1
value: 55.309753694581275
- type: precision
value: 53.130476190476195
- type: recall
value: 60.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.89999999999999
- type: f1
value: 67.92023809523809
- type: precision
value: 65.82595238095237
- type: recall
value: 72.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.80337756332931
- type: f1
value: 39.42174900558496
- type: precision
value: 36.97101116280851
- type: recall
value: 46.80337756332931
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.8
- type: f1
value: 86.79
- type: precision
value: 85.375
- type: recall
value: 89.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.199999999999996
- type: f1
value: 39.95484348984349
- type: precision
value: 37.561071428571424
- type: recall
value: 47.199999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.8
- type: f1
value: 84.68190476190475
- type: precision
value: 83.275
- type: recall
value: 87.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.76190476190476
- type: f1
value: 42.14965986394558
- type: precision
value: 39.96743626743626
- type: recall
value: 48.76190476190476
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.10000000000001
- type: f1
value: 59.58580086580086
- type: precision
value: 57.150238095238095
- type: recall
value: 66.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.3
- type: f1
value: 84.0
- type: precision
value: 82.48666666666666
- type: recall
value: 87.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 87.79523809523809
- type: precision
value: 86.6
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.0
- type: f1
value: 83.81
- type: precision
value: 82.36666666666666
- type: recall
value: 87.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.9
- type: f1
value: 57.76533189033189
- type: precision
value: 55.50595238095239
- type: recall
value: 63.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.1
- type: f1
value: 71.83690476190478
- type: precision
value: 70.04928571428573
- type: recall
value: 76.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.3
- type: f1
value: 59.32626984126984
- type: precision
value: 56.62535714285713
- type: recall
value: 66.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 89.76666666666667
- type: main_score
value: 89.76666666666667
- type: precision
value: 88.64999999999999
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.10000000000001
- type: precision
value: 90.16666666666666
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.71428571428571
- type: f1
value: 82.29142600436403
- type: precision
value: 80.8076626877166
- type: recall
value: 85.71428571428571
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.88888888888889
- type: f1
value: 85.7834757834758
- type: precision
value: 84.43732193732193
- type: recall
value: 88.88888888888889
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 85.67190476190476
- type: precision
value: 84.43333333333332
- type: recall
value: 88.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.72727272727273
- type: f1
value: 78.21969696969695
- type: precision
value: 76.18181818181819
- type: recall
value: 82.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 61.0062893081761
- type: f1
value: 55.13976240391334
- type: precision
value: 52.92112499659669
- type: recall
value: 61.0062893081761
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.86666666666666
- type: precision
value: 85.69166666666668
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.54085603112841
- type: f1
value: 68.56031128404669
- type: precision
value: 66.53047989623866
- type: recall
value: 73.54085603112841
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.58974358974359
- type: f1
value: 36.45299145299145
- type: precision
value: 33.81155881155882
- type: recall
value: 43.58974358974359
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.599999999999994
- type: f1
value: 53.264689754689755
- type: precision
value: 50.869166666666665
- type: recall
value: 59.599999999999994
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.2
- type: f1
value: 81.61666666666665
- type: precision
value: 80.02833333333335
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metrics:
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metrics:
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metrics:
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dataset:
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metrics:
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metrics:
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metrics:
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dataset:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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metrics:
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dataset:
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metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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dataset:
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config: dtp-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.1
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 86.9
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.2
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value: 90.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.15270935960591
- type: f1
value: 18.407224958949097
- type: precision
value: 16.982385430661292
- type: recall
value: 23.15270935960591
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.98591549295775
- type: f1
value: 49.94718309859154
- type: precision
value: 47.77864154624717
- type: recall
value: 55.98591549295775
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.07692307692307
- type: f1
value: 66.74358974358974
- type: precision
value: 64.06837606837607
- type: recall
value: 73.07692307692307
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.25
- type: precision
value: 92.43333333333332
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.78705636743215
- type: f1
value: 31.63899658680452
- type: precision
value: 29.72264397629742
- type: recall
value: 37.78705636743215
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.6
- type: f1
value: 16.91697302697303
- type: precision
value: 15.71225147075147
- type: recall
value: 21.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.01628664495115
- type: f1
value: 81.38514037536838
- type: precision
value: 79.83170466883823
- type: recall
value: 85.01628664495115
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.39999999999999
- type: f1
value: 79.96380952380952
- type: precision
value: 78.48333333333333
- type: recall
value: 83.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.2
- type: f1
value: 79.26190476190476
- type: precision
value: 77.58833333333334
- type: recall
value: 83.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.59055118110236
- type: f1
value: 71.66854143232096
- type: precision
value: 70.30183727034121
- type: recall
value: 75.59055118110236
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.5
- type: f1
value: 59.26095238095238
- type: precision
value: 56.81909090909092
- type: recall
value: 65.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.26315789473685
- type: f1
value: 47.986523325858506
- type: precision
value: 45.33950006595436
- type: recall
value: 55.26315789473685
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.89999999999999
- type: f1
value: 78.835
- type: precision
value: 77.04761904761905
- type: recall
value: 82.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.269230769230774
- type: f1
value: 36.20421245421245
- type: precision
value: 33.57371794871795
- type: recall
value: 43.269230769230774
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.70666666666666
- type: precision
value: 83.23166666666665
- type: recall
value: 88.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.4
- type: f1
value: 72.54666666666667
- type: precision
value: 70.54318181818181
- type: recall
value: 77.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.60000000000001
- type: f1
value: 74.1588888888889
- type: precision
value: 72.30250000000001
- type: recall
value: 78.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.40566037735849
- type: f1
value: 66.82587328813744
- type: precision
value: 64.75039308176099
- type: recall
value: 72.40566037735849
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.8
- type: f1
value: 68.56357142857144
- type: precision
value: 66.3178822055138
- type: recall
value: 73.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.78832116788321
- type: f1
value: 89.3552311435523
- type: precision
value: 88.20559610705597
- type: recall
value: 91.78832116788321
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.05085581085581
- type: precision
value: 66.955
- type: recall
value: 74.3
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.896
- type: map_at_10
value: 8.993
- type: map_at_100
value: 14.133999999999999
- type: map_at_1000
value: 15.668000000000001
- type: map_at_3
value: 5.862
- type: map_at_5
value: 7.17
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 42.931000000000004
- type: mrr_at_100
value: 44.81
- type: mrr_at_1000
value: 44.81
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 41.701
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 21.163
- type: ndcg_at_100
value: 33.306000000000004
- type: ndcg_at_1000
value: 45.275999999999996
- type: ndcg_at_3
value: 25.685999999999996
- type: ndcg_at_5
value: 23.732
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 17.755000000000003
- type: precision_at_100
value: 6.938999999999999
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.896
- type: recall_at_10
value: 13.333999999999998
- type: recall_at_100
value: 43.517
- type: recall_at_1000
value: 79.836
- type: recall_at_3
value: 6.306000000000001
- type: recall_at_5
value: 8.825
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.3874
- type: ap
value: 13.829909072469423
- type: f1
value: 53.54534203543492
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.62026032823995
- type: f1
value: 62.85251350485221
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 33.21527881409797
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.97943613280086
- type: cos_sim_ap
value: 70.75454316885921
- type: cos_sim_f1
value: 65.38274012676743
- type: cos_sim_precision
value: 60.761214318078835
- type: cos_sim_recall
value: 70.76517150395777
- type: dot_accuracy
value: 79.0546581629612
- type: dot_ap
value: 47.3197121792147
- type: dot_f1
value: 49.20106524633821
- type: dot_precision
value: 42.45499808502489
- type: dot_recall
value: 58.49604221635884
- type: euclidean_accuracy
value: 85.08076533349228
- type: euclidean_ap
value: 70.95016106374474
- type: euclidean_f1
value: 65.43987900176455
- type: euclidean_precision
value: 62.64478764478765
- type: euclidean_recall
value: 68.49604221635884
- type: manhattan_accuracy
value: 84.93771234428085
- type: manhattan_ap
value: 70.63668388755362
- type: manhattan_f1
value: 65.23895401262398
- type: manhattan_precision
value: 56.946084218811485
- type: manhattan_recall
value: 76.35883905013192
- type: max_accuracy
value: 85.08076533349228
- type: max_ap
value: 70.95016106374474
- type: max_f1
value: 65.43987900176455
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.69096130709822
- type: cos_sim_ap
value: 84.82526278228542
- type: cos_sim_f1
value: 77.65485060585536
- type: cos_sim_precision
value: 75.94582658619167
- type: cos_sim_recall
value: 79.44256236526024
- type: dot_accuracy
value: 80.97954748321496
- type: dot_ap
value: 64.81642914145866
- type: dot_f1
value: 60.631996987229975
- type: dot_precision
value: 54.5897293631712
- type: dot_recall
value: 68.17831844779796
- type: euclidean_accuracy
value: 88.6987231730508
- type: euclidean_ap
value: 84.80003825477253
- type: euclidean_f1
value: 77.67194179854496
- type: euclidean_precision
value: 75.7128235122094
- type: euclidean_recall
value: 79.73514012935017
- type: manhattan_accuracy
value: 88.62692591298949
- type: manhattan_ap
value: 84.80451408255276
- type: manhattan_f1
value: 77.69888949572183
- type: manhattan_precision
value: 73.70311528631622
- type: manhattan_recall
value: 82.15275639051433
- type: max_accuracy
value: 88.6987231730508
- type: max_ap
value: 84.82526278228542
- type: max_f1
value: 77.69888949572183
- task:
type: BitextMining
dataset:
name: MTEB BUCC.v2 (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: 1739dc11ffe9b7bfccd7f3d585aeb4c544fc6677
metrics:
- type: accuracy
value: 95.72566678212678
- type: f1
value: 94.42443135896548
- type: main_score
value: 94.42443135896548
- type: precision
value: 93.80868260016165
- type: recall
value: 95.72566678212678
- task:
type: Retrieval
dataset:
name: MTEB BelebeleRetrieval (rus_Cyrl-rus_Cyrl)
type: facebook/belebele
config: rus_Cyrl-rus_Cyrl
split: test
revision: 75b399394a9803252cfec289d103de462763db7c
metrics:
- type: main_score
value: 92.23599999999999
- type: map_at_1
value: 87.111
- type: map_at_10
value: 90.717
- type: map_at_100
value: 90.879
- type: map_at_1000
value: 90.881
- type: map_at_20
value: 90.849
- type: map_at_3
value: 90.074
- type: map_at_5
value: 90.535
- type: mrr_at_1
value: 87.1111111111111
- type: mrr_at_10
value: 90.7173721340388
- type: mrr_at_100
value: 90.87859682638407
- type: mrr_at_1000
value: 90.88093553612326
- type: mrr_at_20
value: 90.84863516113515
- type: mrr_at_3
value: 90.07407407407409
- type: mrr_at_5
value: 90.53518518518521
- type: nauc_map_at_1000_diff1
value: 92.37373187280554
- type: nauc_map_at_1000_max
value: 79.90465445423249
- type: nauc_map_at_1000_std
value: -0.6220290556185463
- type: nauc_map_at_100_diff1
value: 92.37386697345335
- type: nauc_map_at_100_max
value: 79.90991577223959
- type: nauc_map_at_100_std
value: -0.602247514642845
- type: nauc_map_at_10_diff1
value: 92.30907447072467
- type: nauc_map_at_10_max
value: 79.86831935337598
- type: nauc_map_at_10_std
value: -0.7455191860719699
- type: nauc_map_at_1_diff1
value: 93.29828518358822
- type: nauc_map_at_1_max
value: 78.69539619887887
- type: nauc_map_at_1_std
value: -4.097150817605763
- type: nauc_map_at_20_diff1
value: 92.38414149703077
- type: nauc_map_at_20_max
value: 79.94789814504661
- type: nauc_map_at_20_std
value: -0.3928031130400773
- type: nauc_map_at_3_diff1
value: 92.21688899306734
- type: nauc_map_at_3_max
value: 80.34586671780885
- type: nauc_map_at_3_std
value: 0.24088319695435909
- type: nauc_map_at_5_diff1
value: 92.27931726042982
- type: nauc_map_at_5_max
value: 79.99198834003367
- type: nauc_map_at_5_std
value: -0.6296366922840796
- type: nauc_mrr_at_1000_diff1
value: 92.37373187280554
- type: nauc_mrr_at_1000_max
value: 79.90465445423249
- type: nauc_mrr_at_1000_std
value: -0.6220290556185463
- type: nauc_mrr_at_100_diff1
value: 92.37386697345335
- type: nauc_mrr_at_100_max
value: 79.90991577223959
- type: nauc_mrr_at_100_std
value: -0.602247514642845
- type: nauc_mrr_at_10_diff1
value: 92.30907447072467
- type: nauc_mrr_at_10_max
value: 79.86831935337598
- type: nauc_mrr_at_10_std
value: -0.7455191860719699
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value: 93.29828518358822
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value: 78.69539619887887
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value: -4.097150817605763
- type: nauc_mrr_at_20_diff1
value: 92.38414149703077
- type: nauc_mrr_at_20_max
value: 79.94789814504661
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value: -0.3928031130400773
- type: nauc_mrr_at_3_diff1
value: 92.21688899306734
- type: nauc_mrr_at_3_max
value: 80.34586671780885
- type: nauc_mrr_at_3_std
value: 0.24088319695435909
- type: nauc_mrr_at_5_diff1
value: 92.27931726042982
- type: nauc_mrr_at_5_max
value: 79.99198834003367
- type: nauc_mrr_at_5_std
value: -0.6296366922840796
- type: nauc_ndcg_at_1000_diff1
value: 92.30526497646306
- type: nauc_ndcg_at_1000_max
value: 80.12734537480418
- type: nauc_ndcg_at_1000_std
value: 0.22849408935578744
- type: nauc_ndcg_at_100_diff1
value: 92.31347123202318
- type: nauc_ndcg_at_100_max
value: 80.29207038703142
- type: nauc_ndcg_at_100_std
value: 0.816825944406239
- type: nauc_ndcg_at_10_diff1
value: 92.05430189845808
- type: nauc_ndcg_at_10_max
value: 80.16515667442968
- type: nauc_ndcg_at_10_std
value: 0.7486447532544893
- type: nauc_ndcg_at_1_diff1
value: 93.29828518358822
- type: nauc_ndcg_at_1_max
value: 78.69539619887887
- type: nauc_ndcg_at_1_std
value: -4.097150817605763
- type: nauc_ndcg_at_20_diff1
value: 92.40147868825079
- type: nauc_ndcg_at_20_max
value: 80.5117307181802
- type: nauc_ndcg_at_20_std
value: 2.0431351539517033
- type: nauc_ndcg_at_3_diff1
value: 91.88894444422789
- type: nauc_ndcg_at_3_max
value: 81.09256084196045
- type: nauc_ndcg_at_3_std
value: 2.422705909643621
- type: nauc_ndcg_at_5_diff1
value: 91.99711052955728
- type: nauc_ndcg_at_5_max
value: 80.46996334573979
- type: nauc_ndcg_at_5_std
value: 0.9086986899040708
- type: nauc_precision_at_1000_diff1
value: .nan
- type: nauc_precision_at_1000_max
value: .nan
- type: nauc_precision_at_1000_std
value: .nan
- type: nauc_precision_at_100_diff1
value: 93.46405228758012
- type: nauc_precision_at_100_max
value: 100.0
- type: nauc_precision_at_100_std
value: 70.71661998132774
- type: nauc_precision_at_10_diff1
value: 90.13938908896874
- type: nauc_precision_at_10_max
value: 82.21121782046167
- type: nauc_precision_at_10_std
value: 13.075230092036083
- type: nauc_precision_at_1_diff1
value: 93.29828518358822
- type: nauc_precision_at_1_max
value: 78.69539619887887
- type: nauc_precision_at_1_std
value: -4.097150817605763
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- type: nauc_recall_at_5_max
value: 61.862709220131386
- type: nauc_recall_at_5_std
value: 20.18336254550361
- type: ndcg_at_1
value: 73.444
- type: ndcg_at_10
value: 82.748
- type: ndcg_at_100
value: 84.416
- type: ndcg_at_1000
value: 84.52300000000001
- type: ndcg_at_20
value: 83.646
- type: ndcg_at_3
value: 80.267
- type: ndcg_at_5
value: 81.922
- type: precision_at_1
value: 73.444
- type: precision_at_10
value: 9.167
- type: precision_at_100
value: 0.992
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.761
- type: precision_at_3
value: 28.37
- type: precision_at_5
value: 17.822
- type: recall_at_1
value: 73.444
- type: recall_at_10
value: 91.667
- type: recall_at_100
value: 99.222
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 95.222
- type: recall_at_3
value: 85.111
- type: recall_at_5
value: 89.11099999999999
- task:
type: BitextMining
dataset:
name: MTEB BibleNLPBitextMining (eng_Latn-rus_Cyrl)
type: davidstap/biblenlp-corpus-mmteb
config: eng_Latn-rus_Cyrl
split: train
revision: 264a18480c529d9e922483839b4b9758e690b762
metrics:
- type: accuracy
value: 96.875
- type: f1
value: 95.83333333333333
- type: main_score
value: 95.83333333333333
- type: precision
value: 95.3125
- type: recall
value: 96.875
- task:
type: BitextMining
dataset:
name: MTEB BibleNLPBitextMining (rus_Cyrl-eng_Latn)
type: davidstap/biblenlp-corpus-mmteb
config: rus_Cyrl-eng_Latn
split: train
revision: 264a18480c529d9e922483839b4b9758e690b762
metrics:
- type: accuracy
value: 88.671875
- type: f1
value: 85.3515625
- type: main_score
value: 85.3515625
- type: precision
value: 83.85416666666667
- type: recall
value: 88.671875
- task:
type: MultilabelClassification
dataset:
name: MTEB CEDRClassification (default)
type: ai-forever/cedr-classification
config: default
split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
metrics:
- type: accuracy
value: 40.06907545164719
- type: f1
value: 26.285000550712407
- type: lrap
value: 64.4280021253997
- type: main_score
value: 40.06907545164719
- task:
type: Classification
dataset:
name: MTEB CyrillicTurkicLangClassification (default)
type: tatiana-merz/cyrillic_turkic_langs
config: default
split: test
revision: e42d330f33d65b7b72dfd408883daf1661f06f18
metrics:
- type: accuracy
value: 43.3447265625
- type: f1
value: 40.08400146827895
- type: f1_weighted
value: 40.08499428040896
- type: main_score
value: 43.3447265625
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ace_Arab-rus_Cyrl)
type: mteb/flores
config: ace_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 6.225296442687747
- type: f1
value: 5.5190958860075
- type: main_score
value: 5.5190958860075
- type: precision
value: 5.3752643758000005
- type: recall
value: 6.225296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bam_Latn-rus_Cyrl)
type: mteb/flores
config: bam_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 68.37944664031622
- type: f1
value: 64.54819836666252
- type: main_score
value: 64.54819836666252
- type: precision
value: 63.07479233454916
- type: recall
value: 68.37944664031622
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dzo_Tibt-rus_Cyrl)
type: mteb/flores
config: dzo_Tibt-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 0.09881422924901186
- type: f1
value: 0.00019509225912934226
- type: main_score
value: 0.00019509225912934226
- type: precision
value: 9.76425190207627e-05
- type: recall
value: 0.09881422924901186
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hin_Deva-rus_Cyrl)
type: mteb/flores
config: hin_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.47299077733861
- type: main_score
value: 99.47299077733861
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (khm_Khmr-rus_Cyrl)
type: mteb/flores
config: khm_Khmr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 88.83399209486166
- type: f1
value: 87.71151056318254
- type: main_score
value: 87.71151056318254
- type: precision
value: 87.32012500709193
- type: recall
value: 88.83399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mag_Deva-rus_Cyrl)
type: mteb/flores
config: mag_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.7239789196311
- type: main_score
value: 97.7239789196311
- type: precision
value: 97.61904761904762
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pap_Latn-rus_Cyrl)
type: mteb/flores
config: pap_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.68187806922984
- type: main_score
value: 93.68187806922984
- type: precision
value: 93.58925452707051
- type: recall
value: 94.0711462450593
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sot_Latn-rus_Cyrl)
type: mteb/flores
config: sot_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 90.9090909090909
- type: f1
value: 89.23171936758892
- type: main_score
value: 89.23171936758892
- type: precision
value: 88.51790014083866
- type: recall
value: 90.9090909090909
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tur_Latn-rus_Cyrl)
type: mteb/flores
config: tur_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ace_Latn-rus_Cyrl)
type: mteb/flores
config: ace_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 66.10671936758892
- type: f1
value: 63.81888256297873
- type: main_score
value: 63.81888256297873
- type: precision
value: 63.01614067933451
- type: recall
value: 66.10671936758892
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ban_Latn-rus_Cyrl)
type: mteb/flores
config: ban_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 79.44664031620553
- type: f1
value: 77.6311962082713
- type: main_score
value: 77.6311962082713
- type: precision
value: 76.93977931929739
- type: recall
value: 79.44664031620553
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ell_Grek-rus_Cyrl)
type: mteb/flores
config: ell_Grek-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hne_Deva-rus_Cyrl)
type: mteb/flores
config: hne_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 96.25352907961603
- type: main_score
value: 96.25352907961603
- type: precision
value: 96.02155091285526
- type: recall
value: 96.83794466403161
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kik_Latn-rus_Cyrl)
type: mteb/flores
config: kik_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 76.28458498023716
- type: f1
value: 73.5596919895859
- type: main_score
value: 73.5596919895859
- type: precision
value: 72.40900759055246
- type: recall
value: 76.28458498023716
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mai_Deva-rus_Cyrl)
type: mteb/flores
config: mai_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.72727272727273
- type: f1
value: 97.37812911725956
- type: main_score
value: 97.37812911725956
- type: precision
value: 97.26002258610953
- type: recall
value: 97.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pbt_Arab-rus_Cyrl)
type: mteb/flores
config: pbt_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.34700387331966
- type: main_score
value: 93.34700387331966
- type: precision
value: 93.06920556920556
- type: recall
value: 94.0711462450593
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (spa_Latn-rus_Cyrl)
type: mteb/flores
config: spa_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (twi_Latn-rus_Cyrl)
type: mteb/flores
config: twi_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 77.77434363246721
- type: main_score
value: 77.77434363246721
- type: precision
value: 76.54444287596462
- type: recall
value: 80.73122529644269
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (acm_Arab-rus_Cyrl)
type: mteb/flores
config: acm_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.56521739130434
- type: f1
value: 92.92490118577075
- type: main_score
value: 92.92490118577075
- type: precision
value: 92.16897233201581
- type: recall
value: 94.56521739130434
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bel_Cyrl-rus_Cyrl)
type: mteb/flores
config: bel_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.98550724637681
- type: main_score
value: 98.98550724637681
- type: precision
value: 98.88833992094862
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (eng_Latn-rus_Cyrl)
type: mteb/flores
config: eng_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hrv_Latn-rus_Cyrl)
type: mteb/flores
config: hrv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 99.05138339920948
- type: main_score
value: 99.05138339920948
- type: precision
value: 99.00691699604744
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kin_Latn-rus_Cyrl)
type: mteb/flores
config: kin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 88.2411067193676
- type: f1
value: 86.5485246227658
- type: main_score
value: 86.5485246227658
- type: precision
value: 85.90652101521667
- type: recall
value: 88.2411067193676
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mal_Mlym-rus_Cyrl)
type: mteb/flores
config: mal_Mlym-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.07971014492753
- type: main_score
value: 98.07971014492753
- type: precision
value: 97.88372859025033
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pes_Arab-rus_Cyrl)
type: mteb/flores
config: pes_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.0566534914361
- type: main_score
value: 98.0566534914361
- type: precision
value: 97.82608695652173
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (srd_Latn-rus_Cyrl)
type: mteb/flores
config: srd_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 82.6086956521739
- type: f1
value: 80.9173470979821
- type: main_score
value: 80.9173470979821
- type: precision
value: 80.24468672882627
- type: recall
value: 82.6086956521739
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tzm_Tfng-rus_Cyrl)
type: mteb/flores
config: tzm_Tfng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 7.41106719367589
- type: f1
value: 6.363562740945329
- type: main_score
value: 6.363562740945329
- type: precision
value: 6.090373175353411
- type: recall
value: 7.41106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (acq_Arab-rus_Cyrl)
type: mteb/flores
config: acq_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.25691699604744
- type: f1
value: 93.81422924901187
- type: main_score
value: 93.81422924901187
- type: precision
value: 93.14064558629775
- type: recall
value: 95.25691699604744
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bem_Latn-rus_Cyrl)
type: mteb/flores
config: bem_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 68.08300395256917
- type: f1
value: 65.01368772860867
- type: main_score
value: 65.01368772860867
- type: precision
value: 63.91052337510628
- type: recall
value: 68.08300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (epo_Latn-rus_Cyrl)
type: mteb/flores
config: epo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.17193675889328
- type: main_score
value: 98.17193675889328
- type: precision
value: 98.08210564139418
- type: recall
value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hun_Latn-rus_Cyrl)
type: mteb/flores
config: hun_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.1106719367589
- type: main_score
value: 99.1106719367589
- type: precision
value: 99.01185770750988
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kir_Cyrl-rus_Cyrl)
type: mteb/flores
config: kir_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 97.07549806364035
- type: main_score
value: 97.07549806364035
- type: precision
value: 96.90958498023716
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mar_Deva-rus_Cyrl)
type: mteb/flores
config: mar_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.44400527009222
- type: main_score
value: 97.44400527009222
- type: precision
value: 97.28966685488425
- type: recall
value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (plt_Latn-rus_Cyrl)
type: mteb/flores
config: plt_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 79.9407114624506
- type: f1
value: 78.3154177760691
- type: main_score
value: 78.3154177760691
- type: precision
value: 77.69877344877344
- type: recall
value: 79.9407114624506
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (srp_Cyrl-rus_Cyrl)
type: mteb/flores
config: srp_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (uig_Arab-rus_Cyrl)
type: mteb/flores
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split: devtest
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metrics:
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ben_Beng-rus_Cyrl)
type: mteb/flores
config: ben_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (est_Latn-rus_Cyrl)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (hye_Armn-rus_Cyrl)
type: mteb/flores
config: hye_Armn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kmb_Latn-rus_Cyrl)
type: mteb/flores
config: kmb_Latn-rus_Cyrl
split: devtest
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metrics:
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dataset:
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metrics:
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type: BitextMining
dataset:
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type: mteb/flores
config: pol_Latn-rus_Cyrl
split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ssw_Latn-rus_Cyrl)
type: mteb/flores
config: ssw_Latn-rus_Cyrl
split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ukr_Cyrl-rus_Cyrl)
type: mteb/flores
config: ukr_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (afr_Latn-rus_Cyrl)
type: mteb/flores
config: afr_Latn-rus_Cyrl
split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (bho_Deva-rus_Cyrl)
type: mteb/flores
config: bho_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
name: MTEB FloresBitextMining (eus_Latn-rus_Cyrl)
type: mteb/flores
config: eus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kmr_Latn-rus_Cyrl)
type: mteb/flores
config: kmr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
name: MTEB FloresBitextMining (min_Latn-rus_Cyrl)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
name: MTEB FloresBitextMining (por_Latn-rus_Cyrl)
type: mteb/flores
config: por_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (sun_Latn-rus_Cyrl)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: bjn_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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type: mteb/flores
config: ilo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (knc_Arab-rus_Cyrl)
type: mteb/flores
config: knc_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 10.079051383399209
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (mkd_Cyrl-rus_Cyrl)
type: mteb/flores
config: mkd_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (prs_Arab-rus_Cyrl)
type: mteb/flores
config: prs_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (swe_Latn-rus_Cyrl)
type: mteb/flores
config: swe_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (urd_Arab-rus_Cyrl)
type: mteb/flores
config: urd_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (aka_Latn-rus_Cyrl)
type: mteb/flores
config: aka_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.22529644268775
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (bjn_Latn-rus_Cyrl)
type: mteb/flores
config: bjn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.27667984189723
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (fao_Latn-rus_Cyrl)
type: mteb/flores
config: fao_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.9288537549407
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ind_Latn-rus_Cyrl)
type: mteb/flores
config: ind_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (knc_Latn-rus_Cyrl)
type: mteb/flores
config: knc_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 33.49802371541502
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (mlt_Latn-rus_Cyrl)
type: mteb/flores
config: mlt_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.94845939633488
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value: 91.40316205533597
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (quy_Latn-rus_Cyrl)
type: mteb/flores
config: quy_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.133716022178575
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value: 40.612648221343875
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (swh_Latn-rus_Cyrl)
type: mteb/flores
config: swh_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.13438735177866
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (uzn_Latn-rus_Cyrl)
type: mteb/flores
config: uzn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.83794466403161
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (als_Latn-rus_Cyrl)
type: mteb/flores
config: als_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bod_Tibt-rus_Cyrl)
type: mteb/flores
config: bod_Tibt-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 0.8894275740459898
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value: 1.0869565217391304
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fij_Latn-rus_Cyrl)
type: mteb/flores
config: fij_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 59.32326368115546
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value: 63.24110671936759
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (isl_Latn-rus_Cyrl)
type: mteb/flores
config: isl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 89.03162055335969
- type: f1
value: 87.3102634715907
- type: main_score
value: 87.3102634715907
- type: precision
value: 86.65991814698712
- type: recall
value: 89.03162055335969
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kon_Latn-rus_Cyrl)
type: mteb/flores
config: kon_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 73.91304347826086
- type: f1
value: 71.518235523573
- type: main_score
value: 71.518235523573
- type: precision
value: 70.58714102449801
- type: recall
value: 73.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mni_Beng-rus_Cyrl)
type: mteb/flores
config: mni_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 29.545454545454547
- type: f1
value: 27.59513619889114
- type: main_score
value: 27.59513619889114
- type: precision
value: 26.983849851025344
- type: recall
value: 29.545454545454547
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ron_Latn-rus_Cyrl)
type: mteb/flores
config: ron_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (szl_Latn-rus_Cyrl)
type: mteb/flores
config: szl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.26482213438736
- type: f1
value: 85.18912031587512
- type: main_score
value: 85.18912031587512
- type: precision
value: 84.77199409959775
- type: recall
value: 86.26482213438736
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (vec_Latn-rus_Cyrl)
type: mteb/flores
config: vec_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 85.67193675889328
- type: f1
value: 84.62529734716581
- type: main_score
value: 84.62529734716581
- type: precision
value: 84.2611422440705
- type: recall
value: 85.67193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (amh_Ethi-rus_Cyrl)
type: mteb/flores
config: amh_Ethi-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.76284584980237
- type: f1
value: 93.91735076517685
- type: main_score
value: 93.91735076517685
- type: precision
value: 93.57553798858147
- type: recall
value: 94.76284584980237
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bos_Latn-rus_Cyrl)
type: mteb/flores
config: bos_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 99.05655938264634
- type: main_score
value: 99.05655938264634
- type: precision
value: 99.01185770750988
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fin_Latn-rus_Cyrl)
type: mteb/flores
config: fin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.43741765480895
- type: main_score
value: 97.43741765480895
- type: precision
value: 97.1590909090909
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ita_Latn-rus_Cyrl)
type: mteb/flores
config: ita_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kor_Hang-rus_Cyrl)
type: mteb/flores
config: kor_Hang-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.33201581027669
- type: f1
value: 96.49868247694334
- type: main_score
value: 96.49868247694334
- type: precision
value: 96.10507246376811
- type: recall
value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mos_Latn-rus_Cyrl)
type: mteb/flores
config: mos_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 34.683794466403164
- type: f1
value: 32.766819308009076
- type: main_score
value: 32.766819308009076
- type: precision
value: 32.1637493670237
- type: recall
value: 34.683794466403164
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (run_Latn-rus_Cyrl)
type: mteb/flores
config: run_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 83.399209486166
- type: f1
value: 81.10578750604326
- type: main_score
value: 81.10578750604326
- type: precision
value: 80.16763162673529
- type: recall
value: 83.399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tam_Taml-rus_Cyrl)
type: mteb/flores
config: tam_Taml-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.01548089591567
- type: main_score
value: 98.01548089591567
- type: precision
value: 97.84020327498588
- type: recall
value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (vie_Latn-rus_Cyrl)
type: mteb/flores
config: vie_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.1106719367589
- type: f1
value: 98.81422924901186
- type: main_score
value: 98.81422924901186
- type: precision
value: 98.66600790513834
- type: recall
value: 99.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (apc_Arab-rus_Cyrl)
type: mteb/flores
config: apc_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 93.87351778656127
- type: f1
value: 92.10803689064558
- type: main_score
value: 92.10803689064558
- type: precision
value: 91.30434782608695
- type: recall
value: 93.87351778656127
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bug_Latn-rus_Cyrl)
type: mteb/flores
config: bug_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 57.608695652173914
- type: f1
value: 54.95878654927162
- type: main_score
value: 54.95878654927162
- type: precision
value: 54.067987427805654
- type: recall
value: 57.608695652173914
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fon_Latn-rus_Cyrl)
type: mteb/flores
config: fon_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 61.95652173913043
- type: f1
value: 58.06537275812945
- type: main_score
value: 58.06537275812945
- type: precision
value: 56.554057596959204
- type: recall
value: 61.95652173913043
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (jav_Latn-rus_Cyrl)
type: mteb/flores
config: jav_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 93.47826086956522
- type: f1
value: 92.4784405318002
- type: main_score
value: 92.4784405318002
- type: precision
value: 92.09168143201127
- type: recall
value: 93.47826086956522
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lao_Laoo-rus_Cyrl)
type: mteb/flores
config: lao_Laoo-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 91.10671936758892
- type: f1
value: 89.76104922745239
- type: main_score
value: 89.76104922745239
- type: precision
value: 89.24754593232855
- type: recall
value: 91.10671936758892
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mri_Latn-rus_Cyrl)
type: mteb/flores
config: mri_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 71.14624505928853
- type: f1
value: 68.26947125119062
- type: main_score
value: 68.26947125119062
- type: precision
value: 67.15942311051006
- type: recall
value: 71.14624505928853
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ace_Arab)
type: mteb/flores
config: rus_Cyrl-ace_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 19.565217391304348
- type: f1
value: 16.321465000323805
- type: main_score
value: 16.321465000323805
- type: precision
value: 15.478527409347508
- type: recall
value: 19.565217391304348
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bam_Latn)
type: mteb/flores
config: rus_Cyrl-bam_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 73.41897233201581
- type: f1
value: 68.77366228182746
- type: main_score
value: 68.77366228182746
- type: precision
value: 66.96012924273795
- type: recall
value: 73.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dzo_Tibt)
type: mteb/flores
config: rus_Cyrl-dzo_Tibt
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 0.592885375494071
- type: f1
value: 0.02458062426370458
- type: main_score
value: 0.02458062426370458
- type: precision
value: 0.012824114724683876
- type: recall
value: 0.592885375494071
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hin_Deva)
type: mteb/flores
config: rus_Cyrl-hin_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.90118577075098
- type: f1
value: 99.86824769433464
- type: main_score
value: 99.86824769433464
- type: precision
value: 99.85177865612648
- type: recall
value: 99.90118577075098
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-khm_Khmr)
type: mteb/flores
config: rus_Cyrl-khm_Khmr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.13438735177866
- type: f1
value: 96.24505928853755
- type: main_score
value: 96.24505928853755
- type: precision
value: 95.81686429512516
- type: recall
value: 97.13438735177866
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mag_Deva)
type: mteb/flores
config: rus_Cyrl-mag_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.50592885375494
- type: f1
value: 99.35770750988142
- type: main_score
value: 99.35770750988142
- type: precision
value: 99.29183135704875
- type: recall
value: 99.50592885375494
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pap_Latn)
type: mteb/flores
config: rus_Cyrl-pap_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 96.93675889328063
- type: f1
value: 96.05072463768116
- type: main_score
value: 96.05072463768116
- type: precision
value: 95.66040843214758
- type: recall
value: 96.93675889328063
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sot_Latn)
type: mteb/flores
config: rus_Cyrl-sot_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 93.67588932806325
- type: f1
value: 91.7786561264822
- type: main_score
value: 91.7786561264822
- type: precision
value: 90.91238471673255
- type: recall
value: 93.67588932806325
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tur_Latn)
type: mteb/flores
config: rus_Cyrl-tur_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.68247694334651
- type: main_score
value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ace_Latn)
type: mteb/flores
config: rus_Cyrl-ace_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 74.1106719367589
- type: f1
value: 70.21737923911836
- type: main_score
value: 70.21737923911836
- type: precision
value: 68.7068791410511
- type: recall
value: 74.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ban_Latn)
type: mteb/flores
config: rus_Cyrl-ban_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 81.7193675889328
- type: f1
value: 78.76470334510617
- type: main_score
value: 78.76470334510617
- type: precision
value: 77.76208475761422
- type: recall
value: 81.7193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ell_Grek)
type: mteb/flores
config: rus_Cyrl-ell_Grek
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.3201581027668
- type: f1
value: 97.76021080368908
- type: main_score
value: 97.76021080368908
- type: precision
value: 97.48023715415019
- type: recall
value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hne_Deva)
type: mteb/flores
config: rus_Cyrl-hne_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.0566534914361
- type: main_score
value: 98.0566534914361
- type: precision
value: 97.82608695652173
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kik_Latn)
type: mteb/flores
config: rus_Cyrl-kik_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 76.42689244220864
- type: main_score
value: 76.42689244220864
- type: precision
value: 74.63877909530083
- type: recall
value: 80.73122529644269
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mai_Deva)
type: mteb/flores
config: rus_Cyrl-mai_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.56719367588933
- type: main_score
value: 98.56719367588933
- type: precision
value: 98.40250329380763
- type: recall
value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pbt_Arab)
type: mteb/flores
config: rus_Cyrl-pbt_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 96.73913043478261
- type: main_score
value: 96.73913043478261
- type: precision
value: 96.36034255599473
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-spa_Latn)
type: mteb/flores
config: rus_Cyrl-spa_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.20948616600789
- type: main_score
value: 99.20948616600789
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-twi_Latn)
type: mteb/flores
config: rus_Cyrl-twi_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 82.01581027667984
- type: f1
value: 78.064787822953
- type: main_score
value: 78.064787822953
- type: precision
value: 76.43272186750448
- type: recall
value: 82.01581027667984
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-acm_Arab)
type: mteb/flores
config: rus_Cyrl-acm_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.3201581027668
- type: f1
value: 97.76021080368908
- type: main_score
value: 97.76021080368908
- type: precision
value: 97.48023715415019
- type: recall
value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bel_Cyrl)
type: mteb/flores
config: rus_Cyrl-bel_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bul_Cyrl)
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split: devtest
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-fra_Latn)
type: mteb/flores
config: rus_Cyrl-fra_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.47299077733861
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value: 99.40711462450594
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value: 99.60474308300395
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-jpn_Jpan)
type: mteb/flores
config: rus_Cyrl-jpn_Jpan
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.76284584980237
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value: 96.44268774703558
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lij_Latn)
type: mteb/flores
config: rus_Cyrl-lij_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 86.29611330698287
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value: 90.21739130434783
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mya_Mymr)
type: mteb/flores
config: rus_Cyrl-mya_Mymr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sag_Latn)
type: mteb/flores
config: rus_Cyrl-sag_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 47.831968755881796
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value: 54.841897233201585
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-taq_Tfng)
type: mteb/flores
config: rus_Cyrl-taq_Tfng
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 15.316205533596838
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-wol_Latn)
type: mteb/flores
config: rus_Cyrl-wol_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 67.88537549407114
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-arb_Latn)
type: mteb/flores
config: rus_Cyrl-arb_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 27.766798418972332
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-cat_Latn)
type: mteb/flores
config: rus_Cyrl-cat_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.23320158102767
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-fur_Latn)
type: mteb/flores
config: rus_Cyrl-fur_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.61508901726293
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value: 88.63636363636364
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kab_Latn)
type: mteb/flores
config: rus_Cyrl-kab_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lim_Latn)
type: mteb/flores
config: rus_Cyrl-lim_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nld_Latn)
type: mteb/flores
config: rus_Cyrl-nld_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-san_Deva)
type: mteb/flores
config: rus_Cyrl-san_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.47826086956522
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tat_Cyrl)
type: mteb/flores
config: rus_Cyrl-tat_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-xho_Latn)
type: mteb/flores
config: rus_Cyrl-xho_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 90.22562582345192
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value: 93.08300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ars_Arab)
type: mteb/flores
config: rus_Cyrl-ars_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ceb_Latn)
type: mteb/flores
config: rus_Cyrl-ceb_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-fuv_Latn)
type: mteb/flores
config: rus_Cyrl-fuv_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kac_Latn)
type: mteb/flores
config: rus_Cyrl-kac_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 46.93675889328063
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lin_Latn)
type: mteb/flores
config: rus_Cyrl-lin_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.50197628458498
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value: 88.07147562582345
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value: 91.50197628458498
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nno_Latn)
type: mteb/flores
config: rus_Cyrl-nno_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.22134387351778
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value: 98.81422924901186
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sat_Olck)
type: mteb/flores
config: rus_Cyrl-sat_Olck
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 2.371541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tel_Telu)
type: mteb/flores
config: rus_Cyrl-tel_Telu
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ydd_Hebr)
type: mteb/flores
config: rus_Cyrl-ydd_Hebr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.13669301712781
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value: 89.42687747035573
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ary_Arab)
type: mteb/flores
config: rus_Cyrl-ary_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.82213438735178
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value: 85.76910408432148
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value: 89.82213438735178
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ces_Latn)
type: mteb/flores
config: rus_Cyrl-ces_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.9459815546772
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value: 98.81422924901186
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value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-gaz_Latn)
type: mteb/flores
config: rus_Cyrl-gaz_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 64.9209486166008
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value: 56.43402189597842
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value: 64.9209486166008
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kam_Latn)
type: mteb/flores
config: rus_Cyrl-kam_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 59.18972332015811
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value: 53.19031511966295
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value: 53.19031511966295
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value: 51.08128357343655
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value: 59.18972332015811
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lit_Latn)
type: mteb/flores
config: rus_Cyrl-lit_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.54150197628458
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value: 95.5368906455863
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value: 95.5368906455863
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value: 95.0592885375494
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value: 96.54150197628458
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nob_Latn)
type: mteb/flores
config: rus_Cyrl-nob_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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value: 97.51317523056655
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value: 97.2167325428195
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value: 98.12252964426878
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-scn_Latn)
type: mteb/flores
config: rus_Cyrl-scn_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.0909090909091
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value: 80.37000439174352
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value: 80.37000439174352
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value: 78.83994628559846
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value: 84.0909090909091
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tgk_Cyrl)
type: mteb/flores
config: rus_Cyrl-tgk_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.68774703557312
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value: 90.86344814605684
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value: 90.86344814605684
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value: 90.12516469038208
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value: 92.68774703557312
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-yor_Latn)
type: mteb/flores
config: rus_Cyrl-yor_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 72.13438735177866
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value: 66.78759646150951
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value: 64.85080192096002
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value: 72.13438735177866
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-arz_Arab)
type: mteb/flores
config: rus_Cyrl-arz_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.364953886693
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value: 97.03557312252964
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-cjk_Latn)
type: mteb/flores
config: rus_Cyrl-cjk_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 51.976284584980235
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value: 46.468762353149714
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value: 44.64073366247278
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value: 51.976284584980235
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-gla_Latn)
type: mteb/flores
config: rus_Cyrl-gla_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 79.74308300395256
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value: 75.55611165294958
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value: 75.55611165294958
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value: 73.95033408620365
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value: 79.74308300395256
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kan_Knda)
type: mteb/flores
config: rus_Cyrl-kan_Knda
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.96245059288538
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value: 98.96245059288538
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value: 98.84716732542819
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value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lmo_Latn)
type: mteb/flores
config: rus_Cyrl-lmo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.41106719367589
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value: 78.56413514022209
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value: 78.56413514022209
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value: 77.15313068573938
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value: 82.41106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-npi_Deva)
type: mteb/flores
config: rus_Cyrl-npi_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.3201581027668
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value: 98.3201581027668
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value: 98.12252964426878
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value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-shn_Mymr)
type: mteb/flores
config: rus_Cyrl-shn_Mymr
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metrics:
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value: 49.71860043649573
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value: 57.11462450592886
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tgl_Latn)
type: mteb/flores
config: rus_Cyrl-tgl_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.88735177865613
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value: 97.82608695652173
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-yue_Hant)
type: mteb/flores
config: rus_Cyrl-yue_Hant
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-asm_Beng)
type: mteb/flores
config: rus_Cyrl-asm_Beng
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ckb_Arab)
type: mteb/flores
config: rus_Cyrl-ckb_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.88537549407114
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-gle_Latn)
type: mteb/flores
config: rus_Cyrl-gle_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kas_Arab)
type: mteb/flores
config: rus_Cyrl-kas_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ltg_Latn)
type: mteb/flores
config: rus_Cyrl-ltg_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.399209486166
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nso_Latn)
type: mteb/flores
config: rus_Cyrl-nso_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.20553359683794
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sin_Sinh)
type: mteb/flores
config: rus_Cyrl-sin_Sinh
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tha_Thai)
type: mteb/flores
config: rus_Cyrl-tha_Thai
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zho_Hans)
type: mteb/flores
config: rus_Cyrl-zho_Hans
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ast_Latn)
type: mteb/flores
config: rus_Cyrl-ast_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-crh_Latn)
type: mteb/flores
config: rus_Cyrl-crh_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-glg_Latn)
type: mteb/flores
config: rus_Cyrl-glg_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kas_Deva)
type: mteb/flores
config: rus_Cyrl-kas_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ltz_Latn)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nus_Latn)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-slk_Latn)
type: mteb/flores
config: rus_Cyrl-slk_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tir_Ethi)
type: mteb/flores
config: rus_Cyrl-tir_Ethi
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zho_Hant)
type: mteb/flores
config: rus_Cyrl-zho_Hant
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.1106719367589
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-awa_Deva)
type: mteb/flores
config: rus_Cyrl-awa_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-cym_Latn)
type: mteb/flores
config: rus_Cyrl-cym_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-grn_Latn)
type: mteb/flores
config: rus_Cyrl-grn_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 77.96442687747036
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kat_Geor)
type: mteb/flores
config: rus_Cyrl-kat_Geor
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lua_Latn)
type: mteb/flores
config: rus_Cyrl-lua_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 65.30797101449275
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value: 72.03557312252964
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nya_Latn)
type: mteb/flores
config: rus_Cyrl-nya_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.29249011857708
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-slv_Latn)
type: mteb/flores
config: rus_Cyrl-slv_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tpi_Latn)
type: mteb/flores
config: rus_Cyrl-tpi_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 75.12913223140497
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value: 80.53359683794467
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zsm_Latn)
type: mteb/flores
config: rus_Cyrl-zsm_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
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value: 96.58620365142104
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value: 96.26152832674572
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ayr_Latn)
type: mteb/flores
config: rus_Cyrl-ayr_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 45.55335968379446
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value: 38.398064362362355
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value: 45.55335968379446
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dan_Latn)
type: mteb/flores
config: rus_Cyrl-dan_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.51778656126481
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value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-guj_Gujr)
type: mteb/flores
config: rus_Cyrl-guj_Gujr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.51778656126481
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value: 99.01185770750988
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kaz_Cyrl)
type: mteb/flores
config: rus_Cyrl-kaz_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.81422924901186
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value: 98.25428194993412
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value: 98.81422924901186
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lug_Latn)
type: mteb/flores
config: rus_Cyrl-lug_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.21343873517787
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value: 77.97485726833554
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value: 76.22376717485415
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value: 82.21343873517787
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-oci_Latn)
type: mteb/flores
config: rus_Cyrl-oci_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.87351778656127
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value: 91.5638528138528
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value: 93.87351778656127
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-smo_Latn)
type: mteb/flores
config: rus_Cyrl-smo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.88142292490119
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value: 81.24364765669114
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value: 79.69991416137661
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value: 84.88142292490119
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tsn_Latn)
type: mteb/flores
config: rus_Cyrl-tsn_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.05533596837944
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value: 83.90645586297761
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value: 83.90645586297761
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value: 82.56752305665349
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value: 87.05533596837944
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zul_Latn)
type: mteb/flores
config: rus_Cyrl-zul_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.15810276679841
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value: 93.77140974967062
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value: 93.77140974967062
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value: 93.16534914361002
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value: 95.15810276679841
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-azb_Arab)
type: mteb/flores
config: rus_Cyrl-azb_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.91699604743083
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value: 77.18050065876152
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value: 77.18050065876152
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value: 75.21519543258673
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value: 81.91699604743083
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-deu_Latn)
type: mteb/flores
config: rus_Cyrl-deu_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.50592885375494
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hat_Latn)
type: mteb/flores
config: rus_Cyrl-hat_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kbp_Latn)
type: mteb/flores
config: rus_Cyrl-kbp_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-luo_Latn)
type: mteb/flores
config: rus_Cyrl-luo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 60.47430830039525
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ory_Orya)
type: mteb/flores
config: rus_Cyrl-ory_Orya
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.1106719367589
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sna_Latn)
type: mteb/flores
config: rus_Cyrl-sna_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tso_Latn)
type: mteb/flores
config: rus_Cyrl-tso_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-azj_Latn)
type: mteb/flores
config: rus_Cyrl-azj_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dik_Latn)
type: mteb/flores
config: rus_Cyrl-dik_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hau_Latn)
type: mteb/flores
config: rus_Cyrl-hau_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kea_Latn)
type: mteb/flores
config: rus_Cyrl-kea_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lus_Latn)
type: mteb/flores
config: rus_Cyrl-lus_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pag_Latn)
type: mteb/flores
config: rus_Cyrl-pag_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 78.75494071146245
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-snd_Arab)
type: mteb/flores
config: rus_Cyrl-snd_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tuk_Latn)
type: mteb/flores
config: rus_Cyrl-tuk_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 68.08300395256917
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bak_Cyrl)
type: mteb/flores
config: rus_Cyrl-bak_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dyu_Latn)
type: mteb/flores
config: rus_Cyrl-dyu_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 37.45059288537549
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-heb_Hebr)
type: mteb/flores
config: rus_Cyrl-heb_Hebr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.99802371541502
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value: 97.23320158102767
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-khk_Cyrl)
type: mteb/flores
config: rus_Cyrl-khk_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.41897233201581
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lvs_Latn)
type: mteb/flores
config: rus_Cyrl-lvs_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.4308300395257
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pan_Guru)
type: mteb/flores
config: rus_Cyrl-pan_Guru
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-som_Latn)
type: mteb/flores
config: rus_Cyrl-som_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.74703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tum_Latn)
type: mteb/flores
config: rus_Cyrl-tum_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.43671183888576
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value: 87.15415019762845
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (taq_Latn-rus_Cyrl)
type: mteb/flores
config: taq_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 26.260342858265577
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value: 28.55731225296443
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (war_Latn-rus_Cyrl)
type: mteb/flores
config: war_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.70653606003322
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value: 94.86166007905138
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arb_Arab-rus_Cyrl)
type: mteb/flores
config: arb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.70520421607378
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value: 96.34387351778656
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bul_Cyrl-rus_Cyrl)
type: mteb/flores
config: bul_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.90118577075098
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value: 99.85177865612648
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value: 99.90118577075098
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fra_Latn-rus_Cyrl)
type: mteb/flores
config: fra_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.9459815546772
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value: 98.81422924901186
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value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (jpn_Jpan-rus_Cyrl)
type: mteb/flores
config: jpn_Jpan-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.3201581027668
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value: 97.76021080368905
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value: 97.48023715415019
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value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lij_Latn-rus_Cyrl)
type: mteb/flores
config: lij_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.49802371541502
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value: 81.64800059239636
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value: 81.64800059239636
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value: 80.9443055878478
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value: 83.49802371541502
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mya_Mymr-rus_Cyrl)
type: mteb/flores
config: mya_Mymr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 90.21739130434783
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value: 88.76776366313682
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value: 88.18370446119435
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value: 90.21739130434783
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sag_Latn-rus_Cyrl)
type: mteb/flores
config: sag_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 41.699604743083
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value: 38.822876239229274
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value: 41.699604743083
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (taq_Tfng-rus_Cyrl)
type: mteb/flores
config: taq_Tfng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 10.67193675889328
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value: 9.205744965817951
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value: 8.85195219073817
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value: 10.67193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (wol_Latn-rus_Cyrl)
type: mteb/flores
config: wol_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 63.537549407114625
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value: 60.65190727391827
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value: 59.61144833427442
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value: 63.537549407114625
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arb_Latn-rus_Cyrl)
type: mteb/flores
config: arb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 13.142292490118576
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value: 12.372910318176764
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value: 12.197580895919188
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value: 13.142292490118576
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (cat_Latn-rus_Cyrl)
type: mteb/flores
config: cat_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.80599472990777
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value: 98.72953133822698
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value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fur_Latn-rus_Cyrl)
type: mteb/flores
config: fur_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.02766798418972
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value: 79.36184294084613
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value: 79.36184294084613
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value: 78.69187826527705
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value: 81.02766798418972
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kab_Latn-rus_Cyrl)
type: mteb/flores
config: kab_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 34.387351778656125
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value: 32.02306921576947
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value: 32.02306921576947
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value: 31.246670347137467
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value: 34.387351778656125
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lim_Latn-rus_Cyrl)
type: mteb/flores
config: lim_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 78.26086956521739
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value: 75.90239449214359
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value: 75.90239449214359
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value: 75.02211430745493
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value: 78.26086956521739
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nld_Latn-rus_Cyrl)
type: mteb/flores
config: nld_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.9459815546772
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value: 98.9459815546772
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value: 98.81422924901186
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value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (san_Deva-rus_Cyrl)
type: mteb/flores
config: san_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.94466403162056
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value: 86.68928897189767
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value: 86.68928897189767
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value: 86.23822997079216
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value: 87.94466403162056
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tat_Cyrl-rus_Cyrl)
type: mteb/flores
config: tat_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.16847826086958
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value: 97.03557312252964
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (xho_Latn-rus_Cyrl)
type: mteb/flores
config: xho_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.95270479733395
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value: 86.95652173913044
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ars_Arab-rus_Cyrl)
type: mteb/flores
config: ars_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.13339920948617
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value: 96.6403162055336
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ceb_Latn-rus_Cyrl)
type: mteb/flores
config: ceb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.77662283368805
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value: 95.7509881422925
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (fuv_Latn-rus_Cyrl)
type: mteb/flores
config: fuv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kac_Latn-rus_Cyrl)
type: mteb/flores
config: kac_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 32.94673924616852
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value: 34.683794466403164
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (lin_Latn-rus_Cyrl)
type: mteb/flores
config: lin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.53038212173273
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value: 86.85770750988142
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (nno_Latn-rus_Cyrl)
type: mteb/flores
config: nno_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.51185770750989
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value: 97.4308300395257
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sat_Olck-rus_Cyrl)
type: mteb/flores
config: sat_Olck-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 1.0129746819135175
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value: 1.185770750988142
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tel_Telu-rus_Cyrl)
type: mteb/flores
config: tel_Telu-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ydd_Hebr-rus_Cyrl)
type: mteb/flores
config: ydd_Hebr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.25683020960382
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value: 75.8893280632411
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ary_Arab-rus_Cyrl)
type: mteb/flores
config: ary_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.65014273166447
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value: 86.56126482213439
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ces_Latn-rus_Cyrl)
type: mteb/flores
config: ces_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.60474308300395
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value: 99.4729907773386
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value: 99.40711462450594
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value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (gaz_Latn-rus_Cyrl)
type: mteb/flores
config: gaz_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 42.58893280632411
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value: 40.75832866805978
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value: 40.14285046917723
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value: 42.58893280632411
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kam_Latn-rus_Cyrl)
type: mteb/flores
config: kam_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 45.25691699604743
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value: 42.6975518029456
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value: 41.87472710984596
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value: 45.25691699604743
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lit_Latn-rus_Cyrl)
type: mteb/flores
config: lit_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
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value: 96.62384716732542
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value: 96.3175230566535
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nob_Latn-rus_Cyrl)
type: mteb/flores
config: nob_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.30368906455863
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value: 98.10606060606061
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value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (scn_Latn-rus_Cyrl)
type: mteb/flores
config: scn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 70.45454545454545
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value: 68.62561022640075
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value: 67.95229103411222
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value: 70.45454545454545
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tgk_Cyrl-rus_Cyrl)
type: mteb/flores
config: tgk_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.4901185770751
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value: 91.58514492753623
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value: 91.24759298672342
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value: 92.4901185770751
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (yor_Latn-rus_Cyrl)
type: mteb/flores
config: yor_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 67.98418972332016
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value: 64.72874247330768
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value: 63.450823399938685
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value: 67.98418972332016
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arz_Arab-rus_Cyrl)
type: mteb/flores
config: arz_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.56521739130434
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value: 93.07971014492755
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value: 92.42753623188406
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value: 94.56521739130434
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (cjk_Latn-rus_Cyrl)
type: mteb/flores
config: cjk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.63636363636363
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value: 36.25747140862938
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value: 35.49101355074723
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value: 38.63636363636363
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (gla_Latn-rus_Cyrl)
type: mteb/flores
config: gla_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 69.26877470355731
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value: 66.11797423328613
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value: 66.11797423328613
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value: 64.89369649409694
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value: 69.26877470355731
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kan_Knda-rus_Cyrl)
type: mteb/flores
config: kan_Knda-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.51505740636176
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value: 97.30731225296442
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lmo_Latn-rus_Cyrl)
type: mteb/flores
config: lmo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.3201581027668
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value: 71.06371608677273
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value: 71.06371608677273
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value: 70.26320288266223
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value: 73.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (npi_Deva-rus_Cyrl)
type: mteb/flores
config: npi_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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value: 97.36645107198466
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value: 97.36645107198466
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value: 97.1772068511199
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value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (shn_Mymr-rus_Cyrl)
type: mteb/flores
config: shn_Mymr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 39.426877470355734
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value: 37.16728785513024
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value: 37.16728785513024
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value: 36.56918548278505
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value: 39.426877470355734
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tgl_Latn-rus_Cyrl)
type: mteb/flores
config: tgl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.92490118577075
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value: 97.6378693769998
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value: 97.6378693769998
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value: 97.55371440154047
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value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (yue_Hant-rus_Cyrl)
type: mteb/flores
config: yue_Hant-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.92490118577075
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value: 97.3833051006964
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value: 97.3833051006964
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value: 97.1590909090909
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value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (asm_Beng-rus_Cyrl)
type: mteb/flores
config: asm_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.78656126482213
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value: 91.76917395296842
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value: 91.76917395296842
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value: 91.38292866553736
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value: 92.78656126482213
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ckb_Arab-rus_Cyrl)
type: mteb/flores
config: ckb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.8300395256917
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value: 79.17664345468799
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value: 79.17664345468799
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value: 78.5622171683459
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value: 80.8300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (gle_Latn-rus_Cyrl)
type: mteb/flores
config: gle_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.86956521739131
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value: 84.45408265372492
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value: 84.45408265372492
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value: 83.8774340026703
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value: 85.86956521739131
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kas_Arab-rus_Cyrl)
type: mteb/flores
config: kas_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 76.28458498023716
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value: 74.11216313578267
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value: 74.11216313578267
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value: 73.2491277759584
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value: 76.28458498023716
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ltg_Latn-rus_Cyrl)
type: mteb/flores
config: ltg_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 71.14624505928853
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value: 68.69245357723618
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value: 68.69245357723618
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value: 67.8135329666459
- type: recall
value: 71.14624505928853
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nso_Latn-rus_Cyrl)
type: mteb/flores
config: nso_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.64822134387352
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value: 85.98419219986725
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value: 85.98419219986725
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value: 85.32513873917036
- type: recall
value: 87.64822134387352
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sin_Sinh-rus_Cyrl)
type: mteb/flores
config: sin_Sinh-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.62845849802372
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value: 97.10144927536231
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value: 97.10144927536231
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value: 96.87986585219788
- type: recall
value: 97.62845849802372
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tha_Thai-rus_Cyrl)
type: mteb/flores
config: tha_Thai-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.28722002635045
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value: 98.28722002635045
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value: 98.07312252964427
- type: recall
value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zho_Hans-rus_Cyrl)
type: mteb/flores
config: zho_Hans-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.68247694334651
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value: 98.68247694334651
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value: 98.51778656126481
- type: recall
value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ast_Latn-rus_Cyrl)
type: mteb/flores
config: ast_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.65217391304348
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value: 94.90649683857505
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value: 94.90649683857505
- type: precision
value: 94.61352657004831
- type: recall
value: 95.65217391304348
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (crh_Latn-rus_Cyrl)
type: mteb/flores
config: crh_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.08300395256917
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value: 92.20988998886428
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value: 92.20988998886428
- type: precision
value: 91.85631013694254
- type: recall
value: 93.08300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (glg_Latn-rus_Cyrl)
type: mteb/flores
config: glg_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.55335968379447
- type: f1
value: 95.18006148440931
- type: main_score
value: 95.18006148440931
- type: precision
value: 95.06540560888386
- type: recall
value: 95.55335968379447
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kas_Deva-rus_Cyrl)
type: mteb/flores
config: kas_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 55.03952569169961
- type: f1
value: 52.19871938895554
- type: main_score
value: 52.19871938895554
- type: precision
value: 51.17660971469557
- type: recall
value: 55.03952569169961
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ltz_Latn-rus_Cyrl)
type: mteb/flores
config: ltz_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 87.64822134387352
- type: f1
value: 86.64179841897234
- type: main_score
value: 86.64179841897234
- type: precision
value: 86.30023235431587
- type: recall
value: 87.64822134387352
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nus_Latn-rus_Cyrl)
type: mteb/flores
config: nus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 27.4703557312253
- type: f1
value: 25.703014277858088
- type: main_score
value: 25.703014277858088
- type: precision
value: 25.194105476917315
- type: recall
value: 27.4703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (slk_Latn-rus_Cyrl)
type: mteb/flores
config: slk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.1106719367589
- type: main_score
value: 99.1106719367589
- type: precision
value: 99.02832674571805
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tir_Ethi-rus_Cyrl)
type: mteb/flores
config: tir_Ethi-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 78.66903754775608
- type: main_score
value: 78.66903754775608
- type: precision
value: 77.86431694163612
- type: recall
value: 80.73122529644269
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zho_Hant-rus_Cyrl)
type: mteb/flores
config: zho_Hant-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.22134387351778
- type: f1
value: 97.66798418972333
- type: main_score
value: 97.66798418972333
- type: precision
value: 97.40612648221344
- type: recall
value: 98.22134387351778
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (awa_Deva-rus_Cyrl)
type: mteb/flores
config: awa_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 96.94224857268335
- type: main_score
value: 96.94224857268335
- type: precision
value: 96.68560606060606
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (cym_Latn-rus_Cyrl)
type: mteb/flores
config: cym_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 92.68774703557312
- type: f1
value: 91.69854302097961
- type: main_score
value: 91.69854302097961
- type: precision
value: 91.31236846157795
- type: recall
value: 92.68774703557312
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (grn_Latn-rus_Cyrl)
type: mteb/flores
config: grn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 64.13043478260869
- type: f1
value: 61.850586118740004
- type: main_score
value: 61.850586118740004
- type: precision
value: 61.0049495186209
- type: recall
value: 64.13043478260869
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kat_Geor-rus_Cyrl)
type: mteb/flores
config: kat_Geor-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.59881422924902
- type: main_score
value: 97.59881422924902
- type: precision
value: 97.42534036012296
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lua_Latn-rus_Cyrl)
type: mteb/flores
config: lua_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 63.63636363636363
- type: f1
value: 60.9709122526128
- type: main_score
value: 60.9709122526128
- type: precision
value: 60.03915902282226
- type: recall
value: 63.63636363636363
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nya_Latn-rus_Cyrl)
type: mteb/flores
config: nya_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 89.2292490118577
- type: f1
value: 87.59723824473149
- type: main_score
value: 87.59723824473149
- type: precision
value: 86.90172707867349
- type: recall
value: 89.2292490118577
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (slv_Latn-rus_Cyrl)
type: mteb/flores
config: slv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.74835309617917
- type: main_score
value: 98.74835309617917
- type: precision
value: 98.63636363636364
- type: recall
value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tpi_Latn-rus_Cyrl)
type: mteb/flores
config: tpi_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 77.37154150197628
- type: f1
value: 75.44251611276084
- type: main_score
value: 75.44251611276084
- type: precision
value: 74.78103665109595
- type: recall
value: 77.37154150197628
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zsm_Latn-rus_Cyrl)
type: mteb/flores
config: zsm_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.96245059288538
- type: main_score
value: 98.96245059288538
- type: precision
value: 98.8471673254282
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ayr_Latn-rus_Cyrl)
type: mteb/flores
config: ayr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 27.766798418972332
- type: f1
value: 26.439103195281312
- type: main_score
value: 26.439103195281312
- type: precision
value: 26.052655604573964
- type: recall
value: 27.766798418972332
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dan_Latn-rus_Cyrl)
type: mteb/flores
config: dan_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034255
- type: main_score
value: 99.07773386034255
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (guj_Gujr-rus_Cyrl)
type: mteb/flores
config: guj_Gujr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.26449275362317
- type: main_score
value: 97.26449275362317
- type: precision
value: 97.02498588368154
- type: recall
value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kaz_Cyrl-rus_Cyrl)
type: mteb/flores
config: kaz_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 97.03557312252964
- type: main_score
value: 97.03557312252964
- type: precision
value: 96.85022158342316
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lug_Latn-rus_Cyrl)
type: mteb/flores
config: lug_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 68.57707509881423
- type: f1
value: 65.93361605820395
- type: main_score
value: 65.93361605820395
- type: precision
value: 64.90348248593789
- type: recall
value: 68.57707509881423
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (oci_Latn-rus_Cyrl)
type: mteb/flores
config: oci_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.26482213438736
- type: f1
value: 85.33176417155623
- type: main_score
value: 85.33176417155623
- type: precision
value: 85.00208833384637
- type: recall
value: 86.26482213438736
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (smo_Latn-rus_Cyrl)
type: mteb/flores
config: smo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 77.96442687747036
- type: f1
value: 75.70960450188885
- type: main_score
value: 75.70960450188885
- type: precision
value: 74.8312632736777
- type: recall
value: 77.96442687747036
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tsn_Latn-rus_Cyrl)
type: mteb/flores
config: tsn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 84.38735177865613
- type: f1
value: 82.13656376349225
- type: main_score
value: 82.13656376349225
- type: precision
value: 81.16794543904518
- type: recall
value: 84.38735177865613
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zul_Latn-rus_Cyrl)
type: mteb/flores
config: zul_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 90.21739130434783
- type: f1
value: 88.77570602050753
- type: main_score
value: 88.77570602050753
- type: precision
value: 88.15978104021582
- type: recall
value: 90.21739130434783
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (azb_Arab-rus_Cyrl)
type: mteb/flores
config: azb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 65.71146245059289
- type: f1
value: 64.18825390221271
- type: main_score
value: 64.18825390221271
- type: precision
value: 63.66811154793568
- type: recall
value: 65.71146245059289
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (deu_Latn-rus_Cyrl)
type: mteb/flores
config: deu_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hat_Latn-rus_Cyrl)
type: mteb/flores
config: hat_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.7588932806324
- type: f1
value: 85.86738623695146
- type: main_score
value: 85.86738623695146
- type: precision
value: 85.55235467420822
- type: recall
value: 86.7588932806324
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kbp_Latn-rus_Cyrl)
type: mteb/flores
config: kbp_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 34.88142292490119
- type: f1
value: 32.16511669463015
- type: main_score
value: 32.16511669463015
- type: precision
value: 31.432098549546318
- type: recall
value: 34.88142292490119
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (luo_Latn-rus_Cyrl)
type: mteb/flores
config: luo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 52.27272727272727
- type: f1
value: 49.60489626836975
- type: main_score
value: 49.60489626836975
- type: precision
value: 48.69639631803339
- type: recall
value: 52.27272727272727
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ory_Orya-rus_Cyrl)
type: mteb/flores
config: ory_Orya-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.27437417654808
- type: main_score
value: 97.27437417654808
- type: precision
value: 97.04968944099377
- type: recall
value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sna_Latn-rus_Cyrl)
type: mteb/flores
config: sna_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 85.37549407114624
- type: f1
value: 83.09911316305177
- type: main_score
value: 83.09911316305177
- type: precision
value: 82.1284950958864
- type: recall
value: 85.37549407114624
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tso_Latn-rus_Cyrl)
type: mteb/flores
config: tso_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 82.90513833992095
- type: f1
value: 80.28290385503824
- type: main_score
value: 80.28290385503824
- type: precision
value: 79.23672543237761
- type: recall
value: 82.90513833992095
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (azj_Latn-rus_Cyrl)
type: mteb/flores
config: azj_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.49200075287031
- type: main_score
value: 97.49200075287031
- type: precision
value: 97.266139657444
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dik_Latn-rus_Cyrl)
type: mteb/flores
config: dik_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 38.43873517786561
- type: f1
value: 35.78152442955223
- type: main_score
value: 35.78152442955223
- type: precision
value: 34.82424325078237
- type: recall
value: 38.43873517786561
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hau_Latn-rus_Cyrl)
type: mteb/flores
config: hau_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 81.42292490118577
- type: f1
value: 79.24612283124593
- type: main_score
value: 79.24612283124593
- type: precision
value: 78.34736070751448
- type: recall
value: 81.42292490118577
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kea_Latn-rus_Cyrl)
type: mteb/flores
config: kea_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 81.62055335968378
- type: f1
value: 80.47015182884748
- type: main_score
value: 80.47015182884748
- type: precision
value: 80.02671028885862
- type: recall
value: 81.62055335968378
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lus_Latn-rus_Cyrl)
type: mteb/flores
config: lus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 62.74703557312253
- type: f1
value: 60.53900079111122
- type: main_score
value: 60.53900079111122
- type: precision
value: 59.80024202850289
- type: recall
value: 62.74703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pag_Latn-rus_Cyrl)
type: mteb/flores
config: pag_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 74.01185770750988
- type: f1
value: 72.57280648279529
- type: main_score
value: 72.57280648279529
- type: precision
value: 71.99952968456789
- type: recall
value: 74.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (snd_Arab-rus_Cyrl)
type: mteb/flores
config: snd_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 91.30434782608695
- type: f1
value: 90.24653499445358
- type: main_score
value: 90.24653499445358
- type: precision
value: 89.83134068200232
- type: recall
value: 91.30434782608695
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tuk_Latn-rus_Cyrl)
type: mteb/flores
config: tuk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 47.62845849802372
- type: f1
value: 45.812928836644254
- type: main_score
value: 45.812928836644254
- type: precision
value: 45.23713833170355
- type: recall
value: 47.62845849802372
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bak_Cyrl-rus_Cyrl)
type: mteb/flores
config: bak_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.8498023715415
- type: f1
value: 95.18904459615922
- type: main_score
value: 95.18904459615922
- type: precision
value: 94.92812441182006
- type: recall
value: 95.8498023715415
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dyu_Latn-rus_Cyrl)
type: mteb/flores
config: dyu_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 29.64426877470356
- type: f1
value: 27.287335193938166
- type: main_score
value: 27.287335193938166
- type: precision
value: 26.583996026587492
- type: recall
value: 29.64426877470356
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (heb_Hebr-rus_Cyrl)
type: mteb/flores
config: heb_Hebr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768116
- type: main_score
value: 98.55072463768116
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (khk_Cyrl-rus_Cyrl)
type: mteb/flores
config: khk_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.15810276679841
- type: f1
value: 94.44009547764487
- type: main_score
value: 94.44009547764487
- type: precision
value: 94.16579797014579
- type: recall
value: 95.15810276679841
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lvs_Latn-rus_Cyrl)
type: mteb/flores
config: lvs_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.51467241585817
- type: main_score
value: 97.51467241585817
- type: precision
value: 97.36166007905138
- type: recall
value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pan_Guru-rus_Cyrl)
type: mteb/flores
config: pan_Guru-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.42918313570486
- type: main_score
value: 97.42918313570486
- type: precision
value: 97.22261434217955
- type: recall
value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (som_Latn-rus_Cyrl)
type: mteb/flores
config: som_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 75.69169960474308
- type: f1
value: 73.7211667065916
- type: main_score
value: 73.7211667065916
- type: precision
value: 72.95842401892384
- type: recall
value: 75.69169960474308
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tum_Latn-rus_Cyrl)
type: mteb/flores
config: tum_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 85.67193675889328
- type: f1
value: 82.9296066252588
- type: main_score
value: 82.9296066252588
- type: precision
value: 81.77330225447936
- type: recall
value: 85.67193675889328
- task:
type: Classification
dataset:
name: MTEB GeoreviewClassification (default)
type: ai-forever/georeview-classification
config: default
split: test
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
metrics:
- type: accuracy
value: 44.6630859375
- type: f1
value: 42.607425073610536
- type: f1_weighted
value: 42.60639474586065
- type: main_score
value: 44.6630859375
- task:
type: Clustering
dataset:
name: MTEB GeoreviewClusteringP2P (default)
type: ai-forever/georeview-clustering-p2p
config: default
split: test
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
metrics:
- type: main_score
value: 58.15951247070825
- type: v_measure
value: 58.15951247070825
- type: v_measure_std
value: 0.6739615788288809
- task:
type: Classification
dataset:
name: MTEB HeadlineClassification (default)
type: ai-forever/headline-classification
config: default
split: test
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
metrics:
- type: accuracy
value: 73.935546875
- type: f1
value: 73.8654872186846
- type: f1_weighted
value: 73.86733122685095
- type: main_score
value: 73.935546875
- task:
type: Classification
dataset:
name: MTEB InappropriatenessClassification (default)
type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
metrics:
- type: accuracy
value: 59.16015624999999
- type: ap
value: 55.52276605836938
- type: ap_weighted
value: 55.52276605836938
- type: f1
value: 58.614248199637956
- type: f1_weighted
value: 58.614248199637956
- type: main_score
value: 59.16015624999999
- task:
type: Classification
dataset:
name: MTEB KinopoiskClassification (default)
type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
metrics:
- type: accuracy
value: 49.959999999999994
- type: f1
value: 48.4900332316098
- type: f1_weighted
value: 48.4900332316098
- type: main_score
value: 49.959999999999994
- task:
type: Classification
dataset:
name: MTEB LanguageClassification (default)
type: papluca/language-identification
config: default
split: test
revision: aa56583bf2bc52b0565770607d6fc3faebecf9e2
metrics:
- type: accuracy
value: 71.005859375
- type: f1
value: 69.63481100303348
- type: f1_weighted
value: 69.64640413409529
- type: main_score
value: 71.005859375
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (ru)
type: reciTAL/mlsum
config: ru
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
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value: 42.11280087032343
- type: v_measure
value: 42.11280087032343
- type: v_measure_std
value: 6.7619971723605135
- type: main_score
value: 43.00112546945811
- type: v_measure
value: 43.00112546945811
- type: v_measure_std
value: 1.4740560414835675
- type: main_score
value: 39.81446080575161
- type: v_measure
value: 39.81446080575161
- type: v_measure_std
value: 7.125661320308298
- type: main_score
value: 39.29659668980239
- type: v_measure
value: 39.29659668980239
- type: v_measure_std
value: 2.6570502923023094
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ru)
type: Shitao/MLDR
config: ru
split: dev
revision: d67138e705d963e346253a80e59676ddb418810a
metrics:
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value: 38.671
- type: map_at_1
value: 30.0
- type: map_at_10
value: 36.123
- type: map_at_100
value: 36.754999999999995
- type: map_at_1000
value: 36.806
- type: map_at_20
value: 36.464
- type: map_at_3
value: 35.25
- type: map_at_5
value: 35.8
- type: mrr_at_1
value: 30.0
- type: mrr_at_10
value: 36.122817460317464
- type: mrr_at_100
value: 36.75467016625293
- type: mrr_at_1000
value: 36.80612724920882
- type: mrr_at_20
value: 36.46359681984682
- type: mrr_at_3
value: 35.25
- type: mrr_at_5
value: 35.800000000000004
- type: nauc_map_at_1000_diff1
value: 55.61987610843598
- type: nauc_map_at_1000_max
value: 52.506795017152186
- type: nauc_map_at_1000_std
value: 2.95487192066911
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value: 55.598419532054734
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value: 52.48192017040307
- type: nauc_map_at_100_std
value: 2.930120252521189
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value: 56.02309155375198
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value: 52.739573233234424
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value: 2.4073432421641545
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value: 52.57059856776112
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value: 50.55668152952304
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value: 1.6572084853398048
- type: nauc_map_at_20_diff1
value: 55.75769029917031
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value: 52.53663737242853
- type: nauc_map_at_20_std
value: 2.8489192879814
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value: 56.90294128342709
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value: 53.10608389782041
- type: nauc_map_at_3_std
value: 1.4909731657889491
- type: nauc_map_at_5_diff1
value: 56.1258315436073
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value: 52.398078357541564
- type: nauc_map_at_5_std
value: 1.8256862015101467
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value: 55.61987610843598
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value: 52.506795017152186
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value: 2.95487192066911
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value: 55.598419532054734
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value: 52.48192017040307
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value: 2.930120252521189
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value: 56.02309155375198
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value: 52.739573233234424
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value: 2.4073432421641545
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value: 52.57059856776112
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value: 50.55668152952304
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value: 1.6572084853398048
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value: 55.75769029917031
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value: 52.53663737242853
- type: nauc_mrr_at_20_std
value: 2.8489192879814
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value: 56.90294128342709
- type: nauc_mrr_at_3_max
value: 53.10608389782041
- type: nauc_mrr_at_3_std
value: 1.4909731657889491
- type: nauc_mrr_at_5_diff1
value: 56.1258315436073
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value: 52.398078357541564
- type: nauc_mrr_at_5_std
value: 1.8256862015101467
- type: nauc_ndcg_at_1000_diff1
value: 55.30733548408918
- type: nauc_ndcg_at_1000_max
value: 53.51143366189318
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value: 7.133789405525702
- type: nauc_ndcg_at_100_diff1
value: 54.32209039488095
- type: nauc_ndcg_at_100_max
value: 52.67499334461009
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value: 6.878823275077807
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value: 56.266780806997716
- type: nauc_ndcg_at_10_max
value: 53.52837255793743
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value: 3.756832592964262
- type: nauc_ndcg_at_1_diff1
value: 52.57059856776112
- type: nauc_ndcg_at_1_max
value: 50.55668152952304
- type: nauc_ndcg_at_1_std
value: 1.6572084853398048
- type: nauc_ndcg_at_20_diff1
value: 55.39255420432796
- type: nauc_ndcg_at_20_max
value: 52.946114684072235
- type: nauc_ndcg_at_20_std
value: 5.414933414031693
- type: nauc_ndcg_at_3_diff1
value: 57.92826624996289
- type: nauc_ndcg_at_3_max
value: 53.89907760306972
- type: nauc_ndcg_at_3_std
value: 1.6661401245309218
- type: nauc_ndcg_at_5_diff1
value: 56.47508936029308
- type: nauc_ndcg_at_5_max
value: 52.66800998045517
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value: 2.4127296184140423
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value: 57.25924020238401
- type: nauc_precision_at_1000_max
value: 65.1132590931922
- type: nauc_precision_at_1000_std
value: 40.60788709618145
- type: nauc_precision_at_100_diff1
value: 46.49620002554606
- type: nauc_precision_at_100_max
value: 53.02960148167071
- type: nauc_precision_at_100_std
value: 28.206028867032863
- type: nauc_precision_at_10_diff1
value: 56.562744749606765
- type: nauc_precision_at_10_max
value: 56.00594967783547
- type: nauc_precision_at_10_std
value: 8.368379831645163
- type: nauc_precision_at_1_diff1
value: 52.57059856776112
- type: nauc_precision_at_1_max
value: 50.55668152952304
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value: 1.6572084853398048
- type: nauc_precision_at_20_diff1
value: 53.25915754614111
- type: nauc_precision_at_20_max
value: 54.03255118937036
- type: nauc_precision_at_20_std
value: 15.161611674272718
- type: nauc_precision_at_3_diff1
value: 60.726785748943854
- type: nauc_precision_at_3_max
value: 56.139896875869354
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value: 2.2306901035769893
- type: nauc_precision_at_5_diff1
value: 57.1201127525187
- type: nauc_precision_at_5_max
value: 53.28665761862506
- type: nauc_precision_at_5_std
value: 4.358720050112237
- type: nauc_recall_at_1000_diff1
value: 57.259240202383964
- type: nauc_recall_at_1000_max
value: 65.11325909319218
- type: nauc_recall_at_1000_std
value: 40.60788709618142
- type: nauc_recall_at_100_diff1
value: 46.49620002554603
- type: nauc_recall_at_100_max
value: 53.02960148167071
- type: nauc_recall_at_100_std
value: 28.206028867032835
- type: nauc_recall_at_10_diff1
value: 56.562744749606765
- type: nauc_recall_at_10_max
value: 56.00594967783549
- type: nauc_recall_at_10_std
value: 8.368379831645147
- type: nauc_recall_at_1_diff1
value: 52.57059856776112
- type: nauc_recall_at_1_max
value: 50.55668152952304
- type: nauc_recall_at_1_std
value: 1.6572084853398048
- type: nauc_recall_at_20_diff1
value: 53.259157546141154
- type: nauc_recall_at_20_max
value: 54.03255118937038
- type: nauc_recall_at_20_std
value: 15.16161167427274
- type: nauc_recall_at_3_diff1
value: 60.72678574894387
- type: nauc_recall_at_3_max
value: 56.13989687586933
- type: nauc_recall_at_3_std
value: 2.2306901035770066
- type: nauc_recall_at_5_diff1
value: 57.12011275251864
- type: nauc_recall_at_5_max
value: 53.28665761862502
- type: nauc_recall_at_5_std
value: 4.3587200501122245
- type: ndcg_at_1
value: 30.0
- type: ndcg_at_10
value: 38.671
- type: ndcg_at_100
value: 42.173
- type: ndcg_at_1000
value: 44.016
- type: ndcg_at_20
value: 39.845000000000006
- type: ndcg_at_3
value: 36.863
- type: ndcg_at_5
value: 37.874
- type: precision_at_1
value: 30.0
- type: precision_at_10
value: 4.65
- type: precision_at_100
value: 0.64
- type: precision_at_1000
value: 0.08
- type: precision_at_20
value: 2.55
- type: precision_at_3
value: 13.833
- type: precision_at_5
value: 8.799999999999999
- type: recall_at_1
value: 30.0
- type: recall_at_10
value: 46.5
- type: recall_at_100
value: 64.0
- type: recall_at_1000
value: 79.5
- type: recall_at_20
value: 51.0
- type: recall_at_3
value: 41.5
- type: recall_at_5
value: 44.0
- task:
type: Classification
dataset:
name: MTEB MultilingualSentimentClassification (rus)
type: mteb/multilingual-sentiment-classification
config: rus
split: test
revision: 2b9b4d10fc589af67794141fe8cbd3739de1eb33
metrics:
- type: accuracy
value: 79.52710495963092
- type: ap
value: 84.5713457178972
- type: ap_weighted
value: 84.5713457178972
- type: f1
value: 77.88661181524105
- type: f1_weighted
value: 79.87563079922718
- type: main_score
value: 79.52710495963092
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (arb_Arab-rus_Cyrl)
type: mteb/NTREX
config: arb_Arab-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 86.47971957936905
- type: f1
value: 82.79864240805654
- type: main_score
value: 82.79864240805654
- type: precision
value: 81.21485800128767
- type: recall
value: 86.47971957936905
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (bel_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: bel_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 94.84226339509264
- type: f1
value: 93.56399067465667
- type: main_score
value: 93.56399067465667
- type: precision
value: 93.01619095309631
- type: recall
value: 94.84226339509264
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (ben_Beng-rus_Cyrl)
type: mteb/NTREX
config: ben_Beng-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.18828242363544
- type: f1
value: 90.42393889620612
- type: main_score
value: 90.42393889620612
- type: precision
value: 89.67904925153297
- type: recall
value: 92.18828242363544
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (bos_Latn-rus_Cyrl)
type: mteb/NTREX
config: bos_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 94.69203805708563
- type: f1
value: 93.37172425304624
- type: main_score
value: 93.37172425304624
- type: precision
value: 92.79204521067315
- type: recall
value: 94.69203805708563
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (bul_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: bul_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 96.99549323985978
- type: f1
value: 96.13086296110833
- type: main_score
value: 96.13086296110833
- type: precision
value: 95.72441996327827
- type: recall
value: 96.99549323985978
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (ces_Latn-rus_Cyrl)
type: mteb/NTREX
config: ces_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.94391587381071
- type: f1
value: 94.90680465142157
- type: main_score
value: 94.90680465142157
- type: precision
value: 94.44541812719079
- type: recall
value: 95.94391587381071
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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metrics:
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value: 91.04323151393756
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value: 90.14688699716241
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value: 92.98948422633951
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-pol_Latn)
type: mteb/NTREX
config: rus_Cyrl-pol_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 94.34151226840261
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-por_Latn)
type: mteb/NTREX
config: rus_Cyrl-por_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 86.17926890335504
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-slk_Latn)
type: mteb/NTREX
config: rus_Cyrl-slk_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 92.7391086629945
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-slv_Latn)
type: mteb/NTREX
config: rus_Cyrl-slv_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 92.8893340010015
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-spa_Latn)
type: mteb/NTREX
config: rus_Cyrl-spa_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 94.9799699549324
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value: 96.49474211316975
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-srp_Cyrl)
type: mteb/NTREX
config: rus_Cyrl-srp_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 81.07160741111667
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-srp_Latn)
type: mteb/NTREX
config: rus_Cyrl-srp_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 93.73302377809138
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value: 93.06960440660991
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value: 95.14271407110667
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-swa_Latn)
type: mteb/NTREX
config: rus_Cyrl-swa_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 94.79218828242364
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value: 93.25988983475212
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value: 92.53463528626273
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value: 94.79218828242364
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-swe_Latn)
type: mteb/NTREX
config: rus_Cyrl-swe_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 95.04256384576865
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value: 92.91437155733601
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value: 95.04256384576865
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-tam_Taml)
type: mteb/NTREX
config: rus_Cyrl-tam_Taml
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 93.28993490235354
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value: 90.87738750983617
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value: 93.28993490235354
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-tur_Latn)
type: mteb/NTREX
config: rus_Cyrl-tur_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 93.74061091637456
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value: 91.15923885828742
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value: 93.74061091637456
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-ukr_Cyrl)
type: mteb/NTREX
config: rus_Cyrl-ukr_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 95.99399098647972
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value: 94.89567684860624
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value: 94.89567684860624
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value: 94.37072275079286
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value: 95.99399098647972
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-vie_Latn)
type: mteb/NTREX
config: rus_Cyrl-vie_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 91.4371557336004
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value: 88.98681355366382
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value: 88.98681355366382
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value: 87.89183775663496
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value: 91.4371557336004
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-zho_Hant)
type: mteb/NTREX
config: rus_Cyrl-zho_Hant
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 92.7891837756635
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value: 89.86980470706058
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value: 92.7891837756635
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-zul_Latn)
type: mteb/NTREX
config: rus_Cyrl-zul_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 87.43114672008012
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value: 84.04618833011422
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value: 82.52259341393041
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value: 87.43114672008012
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (slk_Latn-rus_Cyrl)
type: mteb/NTREX
config: slk_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 93.67384409948257
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value: 95.34301452178268
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (slv_Latn-rus_Cyrl)
type: mteb/NTREX
config: slv_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 92.23835753630446
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value: 89.74231188051918
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value: 92.23835753630446
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (spa_Latn-rus_Cyrl)
type: mteb/NTREX
config: spa_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 96.54481722583876
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (srp_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: srp_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 83.62543815723585
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value: 83.62543815723585
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (srp_Latn-rus_Cyrl)
type: mteb/NTREX
config: srp_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 94.44166249374061
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value: 93.00733206591994
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value: 92.37203026762366
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value: 94.44166249374061
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (swa_Latn-rus_Cyrl)
type: mteb/NTREX
config: swa_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 90.23535302954431
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value: 87.89596482636041
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value: 86.87060227370694
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value: 90.23535302954431
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (swe_Latn-rus_Cyrl)
type: mteb/NTREX
config: swe_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 95.44316474712068
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value: 94.1896177599733
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value: 94.1896177599733
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value: 93.61542313470206
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value: 95.44316474712068
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (tam_Taml-rus_Cyrl)
type: mteb/NTREX
config: tam_Taml-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 89.68452679018529
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value: 87.37341160650037
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value: 87.37341160650037
- type: precision
value: 86.38389402285247
- type: recall
value: 89.68452679018529
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (tur_Latn-rus_Cyrl)
type: mteb/NTREX
config: tur_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 93.89083625438157
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value: 92.33892505424804
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value: 92.33892505424804
- type: precision
value: 91.63125640842216
- type: recall
value: 93.89083625438157
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (ukr_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: ukr_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 96.14421632448673
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value: 95.11028447433054
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value: 95.11028447433054
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value: 94.62944416624937
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value: 96.14421632448673
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (vie_Latn-rus_Cyrl)
type: mteb/NTREX
config: vie_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 93.79068602904357
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value: 92.14989150392256
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value: 92.14989150392256
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value: 91.39292271740945
- type: recall
value: 93.79068602904357
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (zho_Hant-rus_Cyrl)
type: mteb/NTREX
config: zho_Hant-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 89.13370055082625
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value: 86.51514618639217
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value: 86.51514618639217
- type: precision
value: 85.383920035898
- type: recall
value: 89.13370055082625
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (zul_Latn-rus_Cyrl)
type: mteb/NTREX
config: zul_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 81.17175763645467
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value: 77.72331766047338
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value: 77.72331766047338
- type: precision
value: 76.24629555848075
- type: recall
value: 81.17175763645467
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (ru)
type: GEM/opusparcus
config: ru
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
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value: 73.09136420525657
- type: cosine_accuracy_threshold
value: 87.70400881767273
- type: cosine_ap
value: 86.51938550599533
- type: cosine_f1
value: 80.84358523725834
- type: cosine_f1_threshold
value: 86.90648078918457
- type: cosine_precision
value: 73.24840764331209
- type: cosine_recall
value: 90.19607843137256
- type: dot_accuracy
value: 73.09136420525657
- type: dot_accuracy_threshold
value: 87.7040147781372
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value: 86.51934769946833
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value: 80.84358523725834
- type: dot_f1_threshold
value: 86.90648078918457
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value: 73.24840764331209
- type: dot_recall
value: 90.19607843137256
- type: euclidean_accuracy
value: 73.09136420525657
- type: euclidean_accuracy_threshold
value: 49.590304493904114
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value: 86.51934769946833
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- type: euclidean_f1_threshold
value: 51.173269748687744
- type: euclidean_precision
value: 73.24840764331209
- type: euclidean_recall
value: 90.19607843137256
- type: main_score
value: 86.51976811057995
- type: manhattan_accuracy
value: 73.40425531914893
- type: manhattan_accuracy_threshold
value: 757.8278541564941
- type: manhattan_ap
value: 86.51976811057995
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value: 80.92898615453328
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value: 778.3821105957031
- type: manhattan_precision
value: 74.32321575061526
- type: manhattan_recall
value: 88.8235294117647
- type: max_ap
value: 86.51976811057995
- type: max_f1
value: 80.92898615453328
- type: max_precision
value: 74.32321575061526
- type: max_recall
value: 90.19607843137256
- type: similarity_accuracy
value: 73.09136420525657
- type: similarity_accuracy_threshold
value: 87.70400881767273
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value: 86.51938550599533
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value: 80.84358523725834
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value: 86.90648078918457
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value: 73.24840764331209
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value: 90.19607843137256
- task:
type: Retrieval
dataset:
name: MTEB PublicHealthQA (russian)
type: xhluca/publichealth-qa
config: russian
split: test
revision: main
metrics:
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value: 79.303
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value: 61.538000000000004
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value: 74.449
- type: map_at_100
value: 74.687
- type: map_at_1000
value: 74.687
- type: map_at_20
value: 74.589
- type: map_at_3
value: 73.333
- type: map_at_5
value: 74.256
- type: mrr_at_1
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- type: mrr_at_3
value: 73.33333333333333
- type: mrr_at_5
value: 74.25641025641025
- type: nauc_map_at_1000_diff1
value: 61.375798048778506
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value: 32.38542979413408
- type: nauc_map_at_20_diff1
value: 61.16611123434347
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value: 41.94787773313971
- type: nauc_map_at_3_diff1
value: 61.40157474408937
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value: 42.63540269440141
- type: nauc_map_at_5_diff1
value: 61.07631147583098
- type: nauc_map_at_5_max
value: 52.02626939341523
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value: 42.511607332150334
- type: nauc_mrr_at_1000_diff1
value: 61.375798048778506
- type: nauc_mrr_at_1000_max
value: 51.37093181241067
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value: 41.735794471409015
- type: nauc_mrr_at_100_diff1
value: 61.375798048778506
- type: nauc_mrr_at_100_max
value: 51.37093181241067
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value: 41.735794471409015
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value: 61.12796039757213
- type: nauc_mrr_at_10_max
value: 51.843445267118014
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value: 42.243121474939365
- type: nauc_mrr_at_1_diff1
value: 66.39100974909151
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value: 44.77165601342703
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value: 32.38542979413408
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value: 51.52605092407306
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value: 41.94787773313971
- type: nauc_mrr_at_3_diff1
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- type: nauc_ndcg_at_1000_std
value: -4.78971011604655
- type: nauc_ndcg_at_100_diff1
value: 41.16918850185783
- type: nauc_ndcg_at_100_max
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- type: nauc_ndcg_at_100_std
value: -4.092442251697269
- type: nauc_ndcg_at_10_diff1
value: 40.300065598615205
- type: nauc_ndcg_at_10_max
value: 34.87866296788365
- type: nauc_ndcg_at_10_std
value: -5.866529277842453
- type: nauc_ndcg_at_1_diff1
value: 51.74612915209495
- type: nauc_ndcg_at_1_max
value: 37.71907067970078
- type: nauc_ndcg_at_1_std
value: -9.064124266098696
- type: nauc_ndcg_at_20_diff1
value: 40.493949850214584
- type: nauc_ndcg_at_20_max
value: 35.69331503650286
- type: nauc_ndcg_at_20_std
value: -4.995310342975443
- type: nauc_ndcg_at_3_diff1
value: 41.269443212112364
- type: nauc_ndcg_at_3_max
value: 32.572844460953334
- type: nauc_ndcg_at_3_std
value: -9.063015396458791
- type: nauc_ndcg_at_5_diff1
value: 41.37039652522888
- type: nauc_ndcg_at_5_max
value: 34.67416011393571
- type: nauc_ndcg_at_5_std
value: -7.106845569862319
- type: nauc_precision_at_1000_diff1
value: -9.571769961090155
- type: nauc_precision_at_1000_max
value: 5.574782583417188
- type: nauc_precision_at_1000_std
value: 7.28333847923847
- type: nauc_precision_at_100_diff1
value: -7.7405012003383735
- type: nauc_precision_at_100_max
value: 9.67745355070353
- type: nauc_precision_at_100_std
value: 9.327890294080992
- type: nauc_precision_at_10_diff1
value: -1.006879647532931
- type: nauc_precision_at_10_max
value: 15.899825481231064
- type: nauc_precision_at_10_std
value: 4.2284084852153105
- type: nauc_precision_at_1_diff1
value: 51.74612915209495
- type: nauc_precision_at_1_max
value: 37.71907067970078
- type: nauc_precision_at_1_std
value: -9.064124266098696
- type: nauc_precision_at_20_diff1
value: -4.982301544401409
- type: nauc_precision_at_20_max
value: 13.241674471380568
- type: nauc_precision_at_20_std
value: 7.052280133821539
- type: nauc_precision_at_3_diff1
value: 15.442614376387374
- type: nauc_precision_at_3_max
value: 25.12695418083
- type: nauc_precision_at_3_std
value: -3.1150066697920638
- type: nauc_precision_at_5_diff1
value: 8.381026072692444
- type: nauc_precision_at_5_max
value: 22.839056540604822
- type: nauc_precision_at_5_std
value: 1.5126905486524331
- type: nauc_recall_at_1000_diff1
value: -0.8869709920433502
- type: nauc_recall_at_1000_max
value: 45.092324433377264
- type: nauc_recall_at_1000_std
value: 62.21264093315108
- type: nauc_recall_at_100_diff1
value: 16.036715011075714
- type: nauc_recall_at_100_max
value: 39.79963411771158
- type: nauc_recall_at_100_std
value: 28.41850069503361
- type: nauc_recall_at_10_diff1
value: 25.189622794479998
- type: nauc_recall_at_10_max
value: 30.82355277039427
- type: nauc_recall_at_10_std
value: 0.0964544736531047
- type: nauc_recall_at_1_diff1
value: 44.36306892906905
- type: nauc_recall_at_1_max
value: 25.61348630699028
- type: nauc_recall_at_1_std
value: -8.713074613333902
- type: nauc_recall_at_20_diff1
value: 20.43424504746087
- type: nauc_recall_at_20_max
value: 33.96010554649377
- type: nauc_recall_at_20_std
value: 6.900984030301936
- type: nauc_recall_at_3_diff1
value: 33.86531858793492
- type: nauc_recall_at_3_max
value: 27.725692256711188
- type: nauc_recall_at_3_std
value: -8.533124289305709
- type: nauc_recall_at_5_diff1
value: 32.006964557701686
- type: nauc_recall_at_5_max
value: 31.493370659289806
- type: nauc_recall_at_5_std
value: -4.8639793547793255
- type: ndcg_at_1
value: 60.461
- type: ndcg_at_10
value: 68.529
- type: ndcg_at_100
value: 71.664
- type: ndcg_at_1000
value: 72.396
- type: ndcg_at_20
value: 70.344
- type: ndcg_at_3
value: 61.550000000000004
- type: ndcg_at_5
value: 64.948
- type: precision_at_1
value: 60.461
- type: precision_at_10
value: 13.28
- type: precision_at_100
value: 1.555
- type: precision_at_1000
value: 0.164
- type: precision_at_20
value: 7.216
- type: precision_at_3
value: 33.077
- type: precision_at_5
value: 23.014000000000003
- type: recall_at_1
value: 42.529
- type: recall_at_10
value: 81.169
- type: recall_at_100
value: 93.154
- type: recall_at_1000
value: 98.18299999999999
- type: recall_at_20
value: 87.132
- type: recall_at_3
value: 63.905
- type: recall_at_5
value: 71.967
- task:
type: Classification
dataset:
name: MTEB RuReviewsClassification (default)
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
- type: accuracy
value: 61.17675781250001
- type: f1
value: 60.354535346041374
- type: f1_weighted
value: 60.35437313166116
- type: main_score
value: 61.17675781250001
- task:
type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS (default)
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
- type: cosine_pearson
value: 78.1301041727274
- type: cosine_spearman
value: 78.08238025421747
- type: euclidean_pearson
value: 77.35224254583635
- type: euclidean_spearman
value: 78.08235336582496
- type: main_score
value: 78.08238025421747
- type: manhattan_pearson
value: 77.24138550052075
- type: manhattan_spearman
value: 77.98199107904142
- type: pearson
value: 78.1301041727274
- type: spearman
value: 78.08238025421747
- task:
type: Classification
dataset:
name: MTEB RuSciBenchGRNTIClassification (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: accuracy
value: 54.990234375
- type: f1
value: 53.537019057131374
- type: f1_weighted
value: 53.552745354520766
- type: main_score
value: 54.990234375
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: main_score
value: 50.775228895355106
- type: v_measure
value: 50.775228895355106
- type: v_measure_std
value: 0.9533571150165796
- task:
type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: accuracy
value: 41.71875
- type: f1
value: 39.289100975858304
- type: f1_weighted
value: 39.29257829217775
- type: main_score
value: 41.71875
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchOECDClusteringP2P (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: main_score
value: 45.10904808834516
- type: v_measure
value: 45.10904808834516
- type: v_measure_std
value: 1.0572643410157534
- task:
type: Classification
dataset:
name: MTEB SIB200Classification (rus_Cyrl)
type: mteb/sib200
config: rus_Cyrl
split: test
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
metrics:
- type: accuracy
value: 66.36363636363637
- type: f1
value: 64.6940336621617
- type: f1_weighted
value: 66.43317771876966
- type: main_score
value: 66.36363636363637
- task:
type: Clustering
dataset:
name: MTEB SIB200ClusteringS2S (rus_Cyrl)
type: mteb/sib200
config: rus_Cyrl
split: test
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
metrics:
- type: main_score
value: 33.99178497314711
- type: v_measure
value: 33.99178497314711
- type: v_measure_std
value: 4.036337464043786
- task:
type: STS
dataset:
name: MTEB STS22.v2 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (ru)
type: mteb/stsb_multi_mt
config: ru
split: dev
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 78.43928769569945
- type: cosine_spearman
value: 78.23961768018884
- type: euclidean_pearson
value: 77.4718694027985
- type: euclidean_spearman
value: 78.23887044760475
- type: main_score
value: 78.23961768018884
- type: manhattan_pearson
value: 77.34517128089547
- type: manhattan_spearman
value: 78.1146477340426
- type: pearson
value: 78.43928769569945
- type: spearman
value: 78.23961768018884
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification (default)
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 22.8125
- type: f1
value: 17.31969589593409
- type: lrap
value: 33.82412380642287
- type: main_score
value: 22.8125
- task:
type: PairClassification
dataset:
name: MTEB TERRa (default)
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
- type: cosine_accuracy
value: 57.32899022801303
- type: cosine_accuracy_threshold
value: 85.32201051712036
- type: cosine_ap
value: 55.14264553720072
- type: cosine_f1
value: 66.83544303797468
- type: cosine_f1_threshold
value: 85.32201051712036
- type: cosine_precision
value: 54.54545454545454
- type: cosine_recall
value: 86.27450980392157
- type: dot_accuracy
value: 57.32899022801303
- type: dot_accuracy_threshold
value: 85.32201051712036
- type: dot_ap
value: 55.14264553720072
- type: dot_f1
value: 66.83544303797468
- type: dot_f1_threshold
value: 85.32201051712036
- type: dot_precision
value: 54.54545454545454
- type: dot_recall
value: 86.27450980392157
- type: euclidean_accuracy
value: 57.32899022801303
- type: euclidean_accuracy_threshold
value: 54.18117046356201
- type: euclidean_ap
value: 55.14264553720072
- type: euclidean_f1
value: 66.83544303797468
- type: euclidean_f1_threshold
value: 54.18117046356201
- type: euclidean_precision
value: 54.54545454545454
- type: euclidean_recall
value: 86.27450980392157
- type: main_score
value: 55.14264553720072
- type: manhattan_accuracy
value: 57.32899022801303
- type: manhattan_accuracy_threshold
value: 828.8480758666992
- type: manhattan_ap
value: 55.077974053622555
- type: manhattan_f1
value: 66.82352941176471
- type: manhattan_f1_threshold
value: 885.6784820556641
- type: manhattan_precision
value: 52.20588235294118
- type: manhattan_recall
value: 92.81045751633987
- type: max_ap
value: 55.14264553720072
- type: max_f1
value: 66.83544303797468
- type: max_precision
value: 54.54545454545454
- type: max_recall
value: 92.81045751633987
- type: similarity_accuracy
value: 57.32899022801303
- type: similarity_accuracy_threshold
value: 85.32201051712036
- type: similarity_ap
value: 55.14264553720072
- type: similarity_f1
value: 66.83544303797468
- type: similarity_f1_threshold
value: 85.32201051712036
- type: similarity_precision
value: 54.54545454545454
- type: similarity_recall
value: 86.27450980392157
- task:
type: PairClassification
dataset:
name: MTEB XNLI (ru)
type: mteb/xnli
config: ru
split: test
revision: 09698e0180d87dc247ca447d3a1248b931ac0cdb
metrics:
- type: cosine_accuracy
value: 67.6923076923077
- type: cosine_accuracy_threshold
value: 87.6681923866272
- type: cosine_ap
value: 73.18693800863593
- type: cosine_f1
value: 70.40641099026904
- type: cosine_f1_threshold
value: 85.09706258773804
- type: cosine_precision
value: 57.74647887323944
- type: cosine_recall
value: 90.17595307917888
- type: dot_accuracy
value: 67.6923076923077
- type: dot_accuracy_threshold
value: 87.66818642616272
- type: dot_ap
value: 73.18693800863593
- type: dot_f1
value: 70.40641099026904
- type: dot_f1_threshold
value: 85.09706258773804
- type: dot_precision
value: 57.74647887323944
- type: dot_recall
value: 90.17595307917888
- type: euclidean_accuracy
value: 67.6923076923077
- type: euclidean_accuracy_threshold
value: 49.662476778030396
- type: euclidean_ap
value: 73.18693800863593
- type: euclidean_f1
value: 70.40641099026904
- type: euclidean_f1_threshold
value: 54.59475517272949
- type: euclidean_precision
value: 57.74647887323944
- type: euclidean_recall
value: 90.17595307917888
- type: main_score
value: 73.18693800863593
- type: manhattan_accuracy
value: 67.54578754578755
- type: manhattan_accuracy_threshold
value: 777.1001815795898
- type: manhattan_ap
value: 72.98861474758783
- type: manhattan_f1
value: 70.6842435655995
- type: manhattan_f1_threshold
value: 810.3782653808594
- type: manhattan_precision
value: 61.80021953896817
- type: manhattan_recall
value: 82.55131964809385
- type: max_ap
value: 73.18693800863593
- type: max_f1
value: 70.6842435655995
- type: max_precision
value: 61.80021953896817
- type: max_recall
value: 90.17595307917888
- type: similarity_accuracy
value: 67.6923076923077
- type: similarity_accuracy_threshold
value: 87.6681923866272
- type: similarity_ap
value: 73.18693800863593
- type: similarity_f1
value: 70.40641099026904
- type: similarity_f1_threshold
value: 85.09706258773804
- type: similarity_precision
value: 57.74647887323944
- type: similarity_recall
value: 90.17595307917888
- task:
type: PairClassification
dataset:
name: MTEB XNLIV2 (russian)
type: mteb/xnli2.0-multi-pair
config: russian
split: test
revision: 5b7d477a8c62cdd18e2fed7e015497c20b4371ad
metrics:
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value: 68.35164835164835
- type: cosine_accuracy_threshold
value: 88.48621845245361
- type: cosine_ap
value: 73.10205506215699
- type: cosine_f1
value: 71.28712871287128
- type: cosine_f1_threshold
value: 87.00399398803711
- type: cosine_precision
value: 61.67023554603854
- type: cosine_recall
value: 84.4574780058651
- type: dot_accuracy
value: 68.35164835164835
- type: dot_accuracy_threshold
value: 88.48622441291809
- type: dot_ap
value: 73.10191110714706
- type: dot_f1
value: 71.28712871287128
- type: dot_f1_threshold
value: 87.00399398803711
- type: dot_precision
value: 61.67023554603854
- type: dot_recall
value: 84.4574780058651
- type: euclidean_accuracy
value: 68.35164835164835
- type: euclidean_accuracy_threshold
value: 47.98704385757446
- type: euclidean_ap
value: 73.10205506215699
- type: euclidean_f1
value: 71.28712871287128
- type: euclidean_f1_threshold
value: 50.982362031936646
- type: euclidean_precision
value: 61.67023554603854
- type: euclidean_recall
value: 84.4574780058651
- type: main_score
value: 73.10205506215699
- type: manhattan_accuracy
value: 67.91208791208791
- type: manhattan_accuracy_threshold
value: 746.1360931396484
- type: manhattan_ap
value: 72.8954736175069
- type: manhattan_f1
value: 71.1297071129707
- type: manhattan_f1_threshold
value: 808.0789566040039
- type: manhattan_precision
value: 60.04036326942482
- type: manhattan_recall
value: 87.2434017595308
- type: max_ap
value: 73.10205506215699
- type: max_f1
value: 71.28712871287128
- type: max_precision
value: 61.67023554603854
- type: max_recall
value: 87.2434017595308
- type: similarity_accuracy
value: 68.35164835164835
- type: similarity_accuracy_threshold
value: 88.48621845245361
- type: similarity_ap
value: 73.10205506215699
- type: similarity_f1
value: 71.28712871287128
- type: similarity_f1_threshold
value: 87.00399398803711
- type: similarity_precision
value: 61.67023554603854
- type: similarity_recall
value: 84.4574780058651
- task:
type: Retrieval
dataset:
name: MTEB XQuADRetrieval (ru)
type: google/xquad
config: ru
split: validation
revision: 51adfef1c1287aab1d2d91b5bead9bcfb9c68583
metrics:
- type: main_score
value: 95.705
- type: map_at_1
value: 90.802
- type: map_at_10
value: 94.427
- type: map_at_100
value: 94.451
- type: map_at_1000
value: 94.451
- type: map_at_20
value: 94.446
- type: map_at_3
value: 94.121
- type: map_at_5
value: 94.34
- type: mrr_at_1
value: 90.80168776371308
- type: mrr_at_10
value: 94.42659567343111
- type: mrr_at_100
value: 94.45099347521871
- type: mrr_at_1000
value: 94.45099347521871
- type: mrr_at_20
value: 94.44574530017569
- type: mrr_at_3
value: 94.12095639943743
- type: mrr_at_5
value: 94.34036568213786
- type: nauc_map_at_1000_diff1
value: 87.40573202946949
- type: nauc_map_at_1000_max
value: 65.56220344468791
- type: nauc_map_at_1000_std
value: 8.865583291735863
- type: nauc_map_at_100_diff1
value: 87.40573202946949
- type: nauc_map_at_100_max
value: 65.56220344468791
- type: nauc_map_at_100_std
value: 8.865583291735863
- type: nauc_map_at_10_diff1
value: 87.43657080570291
- type: nauc_map_at_10_max
value: 65.71295628534446
- type: nauc_map_at_10_std
value: 9.055399339099655
- type: nauc_map_at_1_diff1
value: 88.08395824560428
- type: nauc_map_at_1_max
value: 62.92813192908893
- type: nauc_map_at_1_std
value: 6.738987385482432
- type: nauc_map_at_20_diff1
value: 87.40979818966589
- type: nauc_map_at_20_max
value: 65.59474346926105
- type: nauc_map_at_20_std
value: 8.944420599300914
- type: nauc_map_at_3_diff1
value: 86.97771892161035
- type: nauc_map_at_3_max
value: 66.14330030122467
- type: nauc_map_at_3_std
value: 8.62516327793521
- type: nauc_map_at_5_diff1
value: 87.30273362211798
- type: nauc_map_at_5_max
value: 66.1522476584607
- type: nauc_map_at_5_std
value: 9.780940862679724
- type: nauc_mrr_at_1000_diff1
value: 87.40573202946949
- type: nauc_mrr_at_1000_max
value: 65.56220344468791
- type: nauc_mrr_at_1000_std
value: 8.865583291735863
- type: nauc_mrr_at_100_diff1
value: 87.40573202946949
- type: nauc_mrr_at_100_max
value: 65.56220344468791
- type: nauc_mrr_at_100_std
value: 8.865583291735863
- type: nauc_mrr_at_10_diff1
value: 87.43657080570291
- type: nauc_mrr_at_10_max
value: 65.71295628534446
- type: nauc_mrr_at_10_std
value: 9.055399339099655
- type: nauc_mrr_at_1_diff1
value: 88.08395824560428
- type: nauc_mrr_at_1_max
value: 62.92813192908893
- type: nauc_mrr_at_1_std
value: 6.738987385482432
- type: nauc_mrr_at_20_diff1
value: 87.40979818966589
- type: nauc_mrr_at_20_max
value: 65.59474346926105
- type: nauc_mrr_at_20_std
value: 8.944420599300914
- type: nauc_mrr_at_3_diff1
value: 86.97771892161035
- type: nauc_mrr_at_3_max
value: 66.14330030122467
- type: nauc_mrr_at_3_std
value: 8.62516327793521
- type: nauc_mrr_at_5_diff1
value: 87.30273362211798
- type: nauc_mrr_at_5_max
value: 66.1522476584607
- type: nauc_mrr_at_5_std
value: 9.780940862679724
- type: nauc_ndcg_at_1000_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_1000_max
value: 66.00874244792789
- type: nauc_ndcg_at_1000_std
value: 9.479929342875067
- type: nauc_ndcg_at_100_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_100_max
value: 66.00874244792789
- type: nauc_ndcg_at_100_std
value: 9.479929342875067
- type: nauc_ndcg_at_10_diff1
value: 87.54508467181488
- type: nauc_ndcg_at_10_max
value: 66.88756470312894
- type: nauc_ndcg_at_10_std
value: 10.812624405397022
- type: nauc_ndcg_at_1_diff1
value: 88.08395824560428
- type: nauc_ndcg_at_1_max
value: 62.92813192908893
- type: nauc_ndcg_at_1_std
value: 6.738987385482432
- type: nauc_ndcg_at_20_diff1
value: 87.42097894104597
- type: nauc_ndcg_at_20_max
value: 66.37031898778943
- type: nauc_ndcg_at_20_std
value: 10.34862538094813
- type: nauc_ndcg_at_3_diff1
value: 86.50039907157999
- type: nauc_ndcg_at_3_max
value: 67.97798288917929
- type: nauc_ndcg_at_3_std
value: 10.162410286746852
- type: nauc_ndcg_at_5_diff1
value: 87.13322094568531
- type: nauc_ndcg_at_5_max
value: 68.08576118683821
- type: nauc_ndcg_at_5_std
value: 12.639637379592855
- type: nauc_precision_at_1000_diff1
value: 100.0
- type: nauc_precision_at_1000_max
value: 100.0
- type: nauc_precision_at_1000_std
value: 100.0
- type: nauc_precision_at_100_diff1
value: 100.0
- type: nauc_precision_at_100_max
value: 100.0
- type: nauc_precision_at_100_std
value: 100.0
- type: nauc_precision_at_10_diff1
value: 93.46711505595813
- type: nauc_precision_at_10_max
value: 100.0
- type: nauc_precision_at_10_std
value: 65.42573557179935
- type: nauc_precision_at_1_diff1
value: 88.08395824560428
- type: nauc_precision_at_1_max
value: 62.92813192908893
- type: nauc_precision_at_1_std
value: 6.738987385482432
- type: nauc_precision_at_20_diff1
value: 91.28948674127133
- type: nauc_precision_at_20_max
value: 100.0
- type: nauc_precision_at_20_std
value: 90.74278258632364
- type: nauc_precision_at_3_diff1
value: 82.64606115071832
- type: nauc_precision_at_3_max
value: 83.26201582412921
- type: nauc_precision_at_3_std
value: 23.334013491433762
- type: nauc_precision_at_5_diff1
value: 85.0867539350284
- type: nauc_precision_at_5_max
value: 96.57011448655484
- type: nauc_precision_at_5_std
value: 56.46869543426768
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: .nan
- type: nauc_recall_at_100_max
value: .nan
- type: nauc_recall_at_100_std
value: .nan
- type: nauc_recall_at_10_diff1
value: 93.46711505595623
- type: nauc_recall_at_10_max
value: 100.0
- type: nauc_recall_at_10_std
value: 65.42573557180279
- type: nauc_recall_at_1_diff1
value: 88.08395824560428
- type: nauc_recall_at_1_max
value: 62.92813192908893
- type: nauc_recall_at_1_std
value: 6.738987385482432
- type: nauc_recall_at_20_diff1
value: 91.28948674127474
- type: nauc_recall_at_20_max
value: 100.0
- type: nauc_recall_at_20_std
value: 90.74278258632704
- type: nauc_recall_at_3_diff1
value: 82.64606115071967
- type: nauc_recall_at_3_max
value: 83.26201582413023
- type: nauc_recall_at_3_std
value: 23.334013491434007
- type: nauc_recall_at_5_diff1
value: 85.08675393502854
- type: nauc_recall_at_5_max
value: 96.57011448655487
- type: nauc_recall_at_5_std
value: 56.46869543426658
- type: ndcg_at_1
value: 90.802
- type: ndcg_at_10
value: 95.705
- type: ndcg_at_100
value: 95.816
- type: ndcg_at_1000
value: 95.816
- type: ndcg_at_20
value: 95.771
- type: ndcg_at_3
value: 95.11699999999999
- type: ndcg_at_5
value: 95.506
- type: precision_at_1
value: 90.802
- type: precision_at_10
value: 9.949
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.987
- type: precision_at_3
value: 32.658
- type: precision_at_5
value: 19.781000000000002
- type: recall_at_1
value: 90.802
- type: recall_at_10
value: 99.494
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.747
- type: recall_at_3
value: 97.975
- type: recall_at_5
value: 98.90299999999999
---
## Multilingual-E5-small
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 12 layers and the embedding size is 384.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-small')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-small')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
|
[
"BIOSSES",
"SCIFACT"
] |
Daxtra/jina-embeddings-v3
|
Daxtra
|
feature-extraction
|
[
"transformers",
"pytorch",
"onnx",
"safetensors",
"feature-extraction",
"sentence-similarity",
"mteb",
"sentence-transformers",
"custom_code",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2409.10173",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2025-01-28T13:03:36Z |
2025-01-28T14:37:28+00:00
| 30 | 1 |
---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- false
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
library_name: transformers
license: cc-by-nc-4.0
tags:
- feature-extraction
- sentence-similarity
- mteb
- sentence-transformers
inference: false
model-index:
- name: jina-embeddings-v3
results:
- task:
type: STS
dataset:
name: MTEB AFQMC (default)
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cosine_pearson
value: 41.74237700998808
- type: cosine_spearman
value: 43.4726782647566
- type: euclidean_pearson
value: 42.244585459479964
- type: euclidean_spearman
value: 43.525070045169606
- type: main_score
value: 43.4726782647566
- type: manhattan_pearson
value: 42.04616728224863
- type: manhattan_spearman
value: 43.308828270754645
- type: pearson
value: 41.74237700998808
- type: spearman
value: 43.4726782647566
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL (default)
type: clarin-knext/arguana-pl
config: default
split: test
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
metrics:
- type: main_score
value: 50.117999999999995
- type: map_at_1
value: 24.253
- type: map_at_10
value: 40.725
- type: map_at_100
value: 41.699999999999996
- type: map_at_1000
value: 41.707
- type: map_at_20
value: 41.467999999999996
- type: map_at_3
value: 35.467
- type: map_at_5
value: 38.291
- type: mrr_at_1
value: 24.751066856330013
- type: mrr_at_10
value: 40.91063808169072
- type: mrr_at_100
value: 41.885497923928675
- type: mrr_at_1000
value: 41.89301098419842
- type: mrr_at_20
value: 41.653552355442514
- type: mrr_at_3
value: 35.656709340919775
- type: mrr_at_5
value: 38.466097676623946
- type: nauc_map_at_1000_diff1
value: 7.503000359807567
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- task:
type: MultilabelClassification
dataset:
name: MTEB CEDRClassification (default)
type: ai-forever/cedr-classification
config: default
split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
metrics:
- type: accuracy
value: 47.30605738575983
- type: f1
value: 41.26091043925065
- type: lrap
value: 72.89452709883206
- type: main_score
value: 47.30605738575983
- task:
type: Reranking
dataset:
name: MTEB MIRACLReranking (ru)
type: miracl/mmteb-miracl-reranking
config: ru
split: dev
revision: 6d1962c527217f8927fca80f890f14f36b2802af
metrics:
- type: MAP@1(MIRACL)
value: 20.721999999999998
- type: MAP@10(MIRACL)
value: 33.900999999999996
- type: MAP@100(MIRACL)
value: 36.813
- type: MAP@1000(MIRACL)
value: 36.813
- type: MAP@20(MIRACL)
value: 35.684
- type: MAP@3(MIRACL)
value: 28.141
- type: MAP@5(MIRACL)
value: 31.075000000000003
- type: NDCG@1(MIRACL)
value: 32.799
- type: NDCG@10(MIRACL)
value: 42.065000000000005
- type: NDCG@100(MIRACL)
value: 49.730999999999995
- type: NDCG@1000(MIRACL)
value: 49.730999999999995
- type: NDCG@20(MIRACL)
value: 46.0
- type: NDCG@3(MIRACL)
value: 34.481
- type: NDCG@5(MIRACL)
value: 37.452999999999996
- type: P@1(MIRACL)
value: 32.799
- type: P@10(MIRACL)
value: 11.668000000000001
- type: P@100(MIRACL)
value: 1.9529999999999998
- type: P@1000(MIRACL)
value: 0.19499999999999998
- type: P@20(MIRACL)
value: 7.51
- type: P@3(MIRACL)
value: 20.823
- type: P@5(MIRACL)
value: 16.728
- type: Recall@1(MIRACL)
value: 20.721999999999998
- type: Recall@10(MIRACL)
value: 54.762
- type: Recall@100(MIRACL)
value: 79.952
- type: Recall@1000(MIRACL)
value: 79.952
- type: Recall@20(MIRACL)
value: 66.26100000000001
- type: Recall@3(MIRACL)
value: 34.410000000000004
- type: Recall@5(MIRACL)
value: 42.659000000000006
- type: main_score
value: 42.065000000000005
- type: nAUC_MAP@1000_diff1(MIRACL)
value: 14.33534992502818
- type: nAUC_MAP@1000_max(MIRACL)
value: 12.367998764646115
- type: nAUC_MAP@1000_std(MIRACL)
value: 4.569686002935006
- type: nAUC_MAP@100_diff1(MIRACL)
value: 14.33534992502818
- type: nAUC_MAP@100_max(MIRACL)
value: 12.367998764646115
- type: nAUC_MAP@100_std(MIRACL)
value: 4.569686002935006
- type: nAUC_MAP@10_diff1(MIRACL)
value: 16.920323975680027
- type: nAUC_MAP@10_max(MIRACL)
value: 9.327171297204082
- type: nAUC_MAP@10_std(MIRACL)
value: 3.2039133783079015
- type: nAUC_MAP@1_diff1(MIRACL)
value: 28.698973487482206
- type: nAUC_MAP@1_max(MIRACL)
value: 2.9217687660885034
- type: nAUC_MAP@1_std(MIRACL)
value: -1.1247408800976524
- type: nAUC_MAP@20_diff1(MIRACL)
value: 15.359083081640476
- type: nAUC_MAP@20_max(MIRACL)
value: 11.310494233946345
- type: nAUC_MAP@20_std(MIRACL)
value: 4.4171898386022885
- type: nAUC_MAP@3_diff1(MIRACL)
value: 22.27430591851617
- type: nAUC_MAP@3_max(MIRACL)
value: 6.407438291284658
- type: nAUC_MAP@3_std(MIRACL)
value: 0.9799184530397409
- type: nAUC_MAP@5_diff1(MIRACL)
value: 19.20571689941054
- type: nAUC_MAP@5_max(MIRACL)
value: 7.987468654026893
- type: nAUC_MAP@5_std(MIRACL)
value: 1.8324246565938962
- type: nAUC_NDCG@1000_diff1(MIRACL)
value: 3.7537669018914768
- type: nAUC_NDCG@1000_max(MIRACL)
value: 20.7944707840533
- type: nAUC_NDCG@1000_std(MIRACL)
value: 8.444837055303063
- type: nAUC_NDCG@100_diff1(MIRACL)
value: 3.7537669018914768
- type: nAUC_NDCG@100_max(MIRACL)
value: 20.7944707840533
- type: nAUC_NDCG@100_std(MIRACL)
value: 8.444837055303063
- type: nAUC_NDCG@10_diff1(MIRACL)
value: 10.829575656103888
- type: nAUC_NDCG@10_max(MIRACL)
value: 13.0445496498929
- type: nAUC_NDCG@10_std(MIRACL)
value: 6.050412212625362
- type: nAUC_NDCG@1_diff1(MIRACL)
value: 19.1388712233292
- type: nAUC_NDCG@1_max(MIRACL)
value: 10.871900994781642
- type: nAUC_NDCG@1_std(MIRACL)
value: 3.218568248751811
- type: nAUC_NDCG@20_diff1(MIRACL)
value: 7.093172181746442
- type: nAUC_NDCG@20_max(MIRACL)
value: 16.955238078958836
- type: nAUC_NDCG@20_std(MIRACL)
value: 8.325656379573035
- type: nAUC_NDCG@3_diff1(MIRACL)
value: 17.134437303330802
- type: nAUC_NDCG@3_max(MIRACL)
value: 10.235328822955793
- type: nAUC_NDCG@3_std(MIRACL)
value: 3.2341358691084814
- type: nAUC_NDCG@5_diff1(MIRACL)
value: 14.733664618337636
- type: nAUC_NDCG@5_max(MIRACL)
value: 11.181897412035282
- type: nAUC_NDCG@5_std(MIRACL)
value: 3.642277088791985
- type: nAUC_P@1000_diff1(MIRACL)
value: -26.330038284867573
- type: nAUC_P@1000_max(MIRACL)
value: 28.450694137240458
- type: nAUC_P@1000_std(MIRACL)
value: 9.892993775474912
- type: nAUC_P@100_diff1(MIRACL)
value: -26.330038284867552
- type: nAUC_P@100_max(MIRACL)
value: 28.45069413724051
- type: nAUC_P@100_std(MIRACL)
value: 9.892993775474928
- type: nAUC_P@10_diff1(MIRACL)
value: -17.436937353231112
- type: nAUC_P@10_max(MIRACL)
value: 24.327018012947857
- type: nAUC_P@10_std(MIRACL)
value: 11.78803527706634
- type: nAUC_P@1_diff1(MIRACL)
value: 19.1388712233292
- type: nAUC_P@1_max(MIRACL)
value: 10.871900994781642
- type: nAUC_P@1_std(MIRACL)
value: 3.218568248751811
- type: nAUC_P@20_diff1(MIRACL)
value: -22.947528755272426
- type: nAUC_P@20_max(MIRACL)
value: 27.773093471902538
- type: nAUC_P@20_std(MIRACL)
value: 14.898619107087221
- type: nAUC_P@3_diff1(MIRACL)
value: 1.4100426412400944
- type: nAUC_P@3_max(MIRACL)
value: 17.397472872058845
- type: nAUC_P@3_std(MIRACL)
value: 8.240008229861875
- type: nAUC_P@5_diff1(MIRACL)
value: -7.971349332207021
- type: nAUC_P@5_max(MIRACL)
value: 22.198441167940963
- type: nAUC_P@5_std(MIRACL)
value: 9.00265164460082
- type: nAUC_Recall@1000_diff1(MIRACL)
value: -38.69835271863148
- type: nAUC_Recall@1000_max(MIRACL)
value: 50.9545152809108
- type: nAUC_Recall@1000_std(MIRACL)
value: 20.44270887092116
- type: nAUC_Recall@100_diff1(MIRACL)
value: -38.69835271863148
- type: nAUC_Recall@100_max(MIRACL)
value: 50.9545152809108
- type: nAUC_Recall@100_std(MIRACL)
value: 20.44270887092116
- type: nAUC_Recall@10_diff1(MIRACL)
value: -0.08109036309433801
- type: nAUC_Recall@10_max(MIRACL)
value: 12.696619907773568
- type: nAUC_Recall@10_std(MIRACL)
value: 8.791982704261589
- type: nAUC_Recall@1_diff1(MIRACL)
value: 28.698973487482206
- type: nAUC_Recall@1_max(MIRACL)
value: 2.9217687660885034
- type: nAUC_Recall@1_std(MIRACL)
value: -1.1247408800976524
- type: nAUC_Recall@20_diff1(MIRACL)
value: -13.312171017942623
- type: nAUC_Recall@20_max(MIRACL)
value: 24.19847346821666
- type: nAUC_Recall@20_std(MIRACL)
value: 15.8157702609797
- type: nAUC_Recall@3_diff1(MIRACL)
value: 16.909128321353343
- type: nAUC_Recall@3_max(MIRACL)
value: 6.552122731902991
- type: nAUC_Recall@3_std(MIRACL)
value: 1.9963898223457228
- type: nAUC_Recall@5_diff1(MIRACL)
value: 9.990292655247721
- type: nAUC_Recall@5_max(MIRACL)
value: 9.361722273507574
- type: nAUC_Recall@5_std(MIRACL)
value: 3.270918827854495
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification (default)
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 30.634765625
- type: f1
value: 32.647559808678665
- type: lrap
value: 45.94319661458259
- type: main_score
value: 30.634765625
- task:
type: STS
dataset:
name: MTEB ATEC (default)
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cosine_pearson
value: 47.541497334563296
- type: cosine_spearman
value: 49.06268944206629
- type: euclidean_pearson
value: 51.838926748581635
- type: euclidean_spearman
value: 48.930697157135356
- type: main_score
value: 49.06268944206629
- type: manhattan_pearson
value: 51.835306769406365
- type: manhattan_spearman
value: 48.86135493444834
- type: pearson
value: 47.541497334563296
- type: spearman
value: 49.06268944206629
- task:
type: Classification
dataset:
name: MTEB AllegroReviews (default)
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
metrics:
- type: accuracy
value: 49.51292246520874
- type: f1
value: 44.14350234332397
- type: f1_weighted
value: 51.65508998354552
- type: main_score
value: 49.51292246520874
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P (default)
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: main_score
value: 63.883383458621665
- type: v_measure
value: 63.883383458621665
- type: v_measure_std
value: 2.693666879958465
- type: main_score
value: 46.85924588755251
- type: v_measure
value: 46.85924588755251
- type: v_measure_std
value: 2.1918258880872377
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 43.65721212452554
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking (default)
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
metrics:
- type: map
value: 66.39013753839347
- type: mrr
value: 67.68045617786551
- type: main_score
value: 66.39013753839347
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval (default)
type: lyon-nlp/alloprof
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: main_score
value: 54.284
- type: map_at_1
value: 37.047000000000004
- type: map_at_10
value: 48.53
- type: map_at_100
value: 49.357
- type: map_at_1000
value: 49.39
- type: map_at_20
value: 49.064
- type: map_at_3
value: 45.675
- type: map_at_5
value: 47.441
- type: mrr_at_1
value: 37.04663212435233
- type: mrr_at_10
value: 48.5300326232969
- type: mrr_at_100
value: 49.35708199037581
- type: mrr_at_1000
value: 49.39005824603193
- type: mrr_at_20
value: 49.06417416464799
- type: mrr_at_3
value: 45.67501439263105
- type: mrr_at_5
value: 47.44099021301103
- type: nauc_map_at_1000_diff1
value: 43.32474221868009
- type: nauc_map_at_1000_max
value: 39.407334029058575
- type: nauc_map_at_1000_std
value: -2.3728154448932606
- type: nauc_map_at_100_diff1
value: 43.32336300929909
- type: nauc_map_at_100_max
value: 39.432174777554835
- type: nauc_map_at_100_std
value: -2.356396922384349
- type: nauc_map_at_10_diff1
value: 43.1606520154482
- type: nauc_map_at_10_max
value: 39.33734650558226
- type: nauc_map_at_10_std
value: -2.5156222475075256
- type: nauc_map_at_1_diff1
value: 46.2178975214499
- type: nauc_map_at_1_max
value: 36.26173199049361
- type: nauc_map_at_1_std
value: -3.0897555582816443
- type: nauc_map_at_20_diff1
value: 43.272980702916456
- type: nauc_map_at_20_max
value: 39.4896977052276
- type: nauc_map_at_20_std
value: -2.3305501742917043
- type: nauc_map_at_3_diff1
value: 43.49525042967079
- type: nauc_map_at_3_max
value: 38.66352501824728
- type: nauc_map_at_3_std
value: -3.202794391620473
- type: nauc_map_at_5_diff1
value: 43.2266692546611
- type: nauc_map_at_5_max
value: 38.77368661115743
- type: nauc_map_at_5_std
value: -3.0897532130127954
- type: nauc_mrr_at_1000_diff1
value: 43.32474221868009
- type: nauc_mrr_at_1000_max
value: 39.407334029058575
- type: nauc_mrr_at_1000_std
value: -2.3728154448932606
- type: nauc_mrr_at_100_diff1
value: 43.32336300929909
- type: nauc_mrr_at_100_max
value: 39.432174777554835
- type: nauc_mrr_at_100_std
value: -2.356396922384349
- type: nauc_mrr_at_10_diff1
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- type: nauc_mrr_at_10_max
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- type: nauc_mrr_at_10_std
value: -2.5156222475075256
- type: nauc_mrr_at_1_diff1
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- type: nauc_mrr_at_1_max
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- type: nauc_mrr_at_1_std
value: -3.0897555582816443
- type: nauc_mrr_at_20_diff1
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- type: nauc_mrr_at_20_max
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- type: nauc_mrr_at_20_std
value: -2.3305501742917043
- type: nauc_mrr_at_3_diff1
value: 43.49525042967079
- type: nauc_mrr_at_3_max
value: 38.66352501824728
- type: nauc_mrr_at_3_std
value: -3.202794391620473
- type: nauc_mrr_at_5_diff1
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- type: nauc_mrr_at_5_max
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- type: nauc_mrr_at_5_std
value: -3.0897532130127954
- type: nauc_ndcg_at_1000_diff1
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- type: nauc_ndcg_at_1000_max
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- type: nauc_ndcg_at_1000_std
value: -1.3150412981845496
- type: nauc_ndcg_at_100_diff1
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- type: nauc_ndcg_at_100_max
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- type: nauc_ndcg_at_100_std
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- type: nauc_ndcg_at_10_diff1
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
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- type: nauc_ndcg_at_1_diff1
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- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_std
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- type: nauc_ndcg_at_20_diff1
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- type: nauc_ndcg_at_20_std
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- type: nauc_ndcg_at_3_diff1
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- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_3_std
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- type: nauc_ndcg_at_5_diff1
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- type: nauc_ndcg_at_5_std
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- type: nauc_precision_at_1000_diff1
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- type: nauc_precision_at_1000_std
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- type: nauc_precision_at_100_diff1
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- type: nauc_precision_at_10_diff1
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_5_std
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- type: nauc_recall_at_1000_diff1
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- type: nauc_recall_at_1000_max
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- type: nauc_recall_at_1000_std
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- type: nauc_recall_at_100_diff1
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- type: nauc_recall_at_100_max
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- type: nauc_recall_at_100_std
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- type: nauc_recall_at_10_diff1
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- type: nauc_recall_at_10_max
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- type: nauc_recall_at_1_diff1
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- type: nauc_recall_at_1_max
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- type: nauc_recall_at_20_diff1
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- type: nauc_recall_at_20_max
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- type: nauc_recall_at_20_std
value: 11.460219754285431
- type: nauc_recall_at_3_diff1
value: 40.90538379461527
- type: nauc_recall_at_3_max
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- type: nauc_recall_at_3_std
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- type: nauc_recall_at_5_diff1
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- type: nauc_recall_at_5_max
value: 43.28317158174053
- type: nauc_recall_at_5_std
value: -2.3469094487738054
- type: ndcg_at_1
value: 37.047000000000004
- type: ndcg_at_10
value: 54.284
- type: ndcg_at_100
value: 58.34
- type: ndcg_at_1000
value: 59.303
- type: ndcg_at_20
value: 56.235
- type: ndcg_at_3
value: 48.503
- type: ndcg_at_5
value: 51.686
- type: precision_at_1
value: 37.047000000000004
- type: precision_at_10
value: 7.237
- type: precision_at_100
value: 0.914
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 4.005
- type: precision_at_3
value: 18.898
- type: precision_at_5
value: 12.884
- type: recall_at_1
value: 37.047000000000004
- type: recall_at_10
value: 72.366
- type: recall_at_100
value: 91.408
- type: recall_at_1000
value: 99.136
- type: recall_at_20
value: 80.095
- type: recall_at_3
value: 56.693000000000005
- type: recall_at_5
value: 64.42099999999999
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 89.49253731343283
- type: ap
value: 61.88098616359918
- type: ap_weighted
value: 61.88098616359918
- type: f1
value: 84.76516623679144
- type: f1_weighted
value: 89.92745276292968
- type: main_score
value: 89.49253731343283
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 89.61456102783727
- type: ap
value: 93.11816566733742
- type: ap_weighted
value: 93.11816566733742
- type: f1
value: 88.27635757733722
- type: f1_weighted
value: 89.82581568285453
- type: main_score
value: 89.61456102783727
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.3825
- type: ap
value: 93.393033869502
- type: ap_weighted
value: 93.393033869502
- type: f1
value: 95.38109007966307
- type: f1_weighted
value: 95.38109007966305
- type: main_score
value: 95.3825
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.768
- type: f1
value: 48.95084821944411
- type: f1_weighted
value: 48.9508482194441
- type: main_score
value: 49.768
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.071999999999996
- type: f1
value: 47.24171107487612
- type: f1_weighted
value: 47.24171107487612
- type: main_score
value: 48.071999999999996
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.102000000000004
- type: f1
value: 47.27193805278696
- type: f1_weighted
value: 47.27193805278696
- type: main_score
value: 48.102000000000004
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.30800000000001
- type: f1
value: 46.41683358017851
- type: f1_weighted
value: 46.41683358017851
- type: main_score
value: 47.30800000000001
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.944
- type: f1
value: 44.223824487744395
- type: f1_weighted
value: 44.22382448774439
- type: main_score
value: 44.944
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 29.232000000000003
- type: map_at_10
value: 45.117000000000004
- type: map_at_100
value: 45.977000000000004
- type: map_at_1000
value: 45.98
- type: map_at_20
value: 45.815
- type: map_at_3
value: 39.912
- type: map_at_5
value: 42.693
- type: mrr_at_1
value: 29.659000000000002
- type: mrr_at_10
value: 45.253
- type: mrr_at_100
value: 46.125
- type: mrr_at_1000
value: 46.129
- type: mrr_at_20
value: 45.964
- type: mrr_at_3
value: 40.043
- type: mrr_at_5
value: 42.870000000000005
- type: ndcg_at_1
value: 29.232000000000003
- type: ndcg_at_10
value: 54.327999999999996
- type: ndcg_at_100
value: 57.86
- type: ndcg_at_1000
value: 57.935
- type: ndcg_at_20
value: 56.794
- type: ndcg_at_3
value: 43.516
- type: ndcg_at_5
value: 48.512
- type: precision_at_1
value: 29.232000000000003
- type: precision_at_10
value: 8.393
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.676
- type: precision_at_3
value: 17.994
- type: precision_at_5
value: 13.215
- type: recall_at_1
value: 29.232000000000003
- type: recall_at_10
value: 83.926
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 93.528
- type: recall_at_3
value: 53.983000000000004
- type: recall_at_5
value: 66.074
- type: main_score
value: 54.327999999999996
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 46.6636824632419
- type: v_measure
value: 46.6636824632419
- type: v_measure_std
value: 13.817129140714963
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 39.271141892800024
- type: v_measure
value: 39.271141892800024
- type: v_measure_std
value: 14.276782483454827
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 65.04363277324629
- type: mrr
value: 78.2372598162072
- type: main_score
value: 65.04363277324629
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.83
- type: main_score
value: 30.83
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 88.80382082011027
- type: cosine_spearman
value: 88.68876782169106
- type: euclidean_pearson
value: 87.00802890147176
- type: euclidean_spearman
value: 87.43211268192712
- type: main_score
value: 88.68876782169106
- type: manhattan_pearson
value: 87.14062537179474
- type: manhattan_spearman
value: 87.59115245033443
- type: pearson
value: 88.80382082011027
- type: spearman
value: 88.68876782169106
- task:
type: STS
dataset:
name: MTEB BQ (default)
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
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value: 61.588006604878196
- type: cosine_spearman
value: 63.20615427154465
- type: euclidean_pearson
value: 61.818547092516496
- type: euclidean_spearman
value: 63.21558009151778
- type: main_score
value: 63.20615427154465
- type: manhattan_pearson
value: 61.665588158487616
- type: manhattan_spearman
value: 63.051544488238584
- type: pearson
value: 61.588006604878196
- type: spearman
value: 63.20615427154465
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval (default)
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
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value: 64.414
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value: 14.865
- type: map_at_10
value: 21.605
- type: map_at_100
value: 22.762
- type: map_at_1000
value: 22.854
- type: map_at_20
value: 22.259999999999998
- type: map_at_3
value: 20.119999999999997
- type: map_at_5
value: 20.931
- type: mrr_at_1
value: 14.864864864864865
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_20
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value: 20.12012012012012
- type: mrr_at_5
value: 20.930930930930934
- type: nauc_map_at_1000_diff1
value: 17.486265968689338
- type: nauc_map_at_1000_max
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value: 9.831687441977147
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
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- type: nauc_recall_at_1000_diff1
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value: 39.332224386769546
- type: nauc_recall_at_100_diff1
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value: 26.97003448435901
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value: 12.102191084210837
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value: 0.7358784689405188
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- type: nauc_recall_at_20_diff1
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value: 19.751237460499222
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value: 17.413756049409503
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value: 6.793486779050083
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value: 23.3170870244215
- type: nauc_recall_at_5_std
value: 3.459135827966664
- type: ndcg_at_1
value: 14.865
- type: ndcg_at_10
value: 24.764
- type: ndcg_at_100
value: 30.861
- type: ndcg_at_1000
value: 33.628
- type: ndcg_at_20
value: 27.078000000000003
- type: ndcg_at_3
value: 21.675
- type: ndcg_at_5
value: 23.148
- type: precision_at_1
value: 14.865
- type: precision_at_10
value: 3.4680000000000004
- type: precision_at_100
value: 0.644
- type: precision_at_1000
value: 0.087
- type: precision_at_20
value: 2.185
- type: precision_at_3
value: 8.709
- type: precision_at_5
value: 5.946
- type: recall_at_1
value: 14.865
- type: recall_at_10
value: 34.685
- type: recall_at_100
value: 64.414
- type: recall_at_1000
value: 86.937
- type: recall_at_20
value: 43.694
- type: recall_at_3
value: 26.125999999999998
- type: recall_at_5
value: 29.73
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.08116883116882
- type: f1
value: 84.05587055990273
- type: f1_weighted
value: 84.05587055990274
- type: main_score
value: 84.08116883116882
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: main_score
value: 38.1941007822277
- type: v_measure
value: 38.1941007822277
- type: v_measure_std
value: 0.7502113547288178
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S (default)
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: main_score
value: 34.42075599178318
- type: v_measure
value: 34.42075599178318
- type: v_measure_std
value: 0.600256720497283
- task:
type: Clustering
dataset:
name: MTEB BlurbsClusteringP2P (default)
type: slvnwhrl/blurbs-clustering-p2p
config: default
split: test
revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
metrics:
- type: main_score
value: 41.634627363047265
- type: v_measure
value: 41.634627363047265
- type: v_measure_std
value: 9.726923191225307
- task:
type: Clustering
dataset:
name: MTEB BlurbsClusteringS2S (default)
type: slvnwhrl/blurbs-clustering-s2s
config: default
split: test
revision: 22793b6a6465bf00120ad525e38c51210858132c
metrics:
- type: main_score
value: 20.996468295584197
- type: v_measure
value: 20.996468295584197
- type: v_measure_std
value: 9.225766688272197
- task:
type: Classification
dataset:
name: MTEB CBD (default)
type: PL-MTEB/cbd
config: default
split: test
revision: 36ddb419bcffe6a5374c3891957912892916f28d
metrics:
- type: accuracy
value: 69.99
- type: ap
value: 22.57826353116948
- type: ap_weighted
value: 22.57826353116948
- type: f1
value: 59.04574955548393
- type: f1_weighted
value: 74.36235022309789
- type: main_score
value: 69.99
- task:
type: PairClassification
dataset:
name: MTEB CDSC-E (default)
type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
metrics:
- type: cosine_accuracy
value: 88.7
- type: cosine_accuracy_threshold
value: 97.37848043441772
- type: cosine_ap
value: 73.0405088928302
- type: cosine_f1
value: 63.52201257861635
- type: cosine_f1_threshold
value: 96.98888063430786
- type: cosine_precision
value: 78.90625
- type: cosine_recall
value: 53.1578947368421
- type: dot_accuracy
value: 84.89999999999999
- type: dot_accuracy_threshold
value: 43603.09753417969
- type: dot_ap
value: 56.98157569085279
- type: dot_f1
value: 57.606490872210955
- type: dot_f1_threshold
value: 40406.23779296875
- type: dot_precision
value: 46.864686468646866
- type: dot_recall
value: 74.73684210526315
- type: euclidean_accuracy
value: 88.5
- type: euclidean_accuracy_threshold
value: 498.0483055114746
- type: euclidean_ap
value: 72.97328234816734
- type: euclidean_f1
value: 63.722397476340696
- type: euclidean_f1_threshold
value: 508.6186408996582
- type: euclidean_precision
value: 79.52755905511812
- type: euclidean_recall
value: 53.1578947368421
- type: main_score
value: 73.0405088928302
- type: manhattan_accuracy
value: 88.6
- type: manhattan_accuracy_threshold
value: 12233.079528808594
- type: manhattan_ap
value: 72.92148503992615
- type: manhattan_f1
value: 63.69426751592356
- type: manhattan_f1_threshold
value: 12392.754364013672
- type: manhattan_precision
value: 80.64516129032258
- type: manhattan_recall
value: 52.63157894736842
- type: max_accuracy
value: 88.7
- type: max_ap
value: 73.0405088928302
- type: max_f1
value: 63.722397476340696
- type: max_precision
value: 80.64516129032258
- type: max_recall
value: 74.73684210526315
- type: similarity_accuracy
value: 88.7
- type: similarity_accuracy_threshold
value: 97.37848043441772
- type: similarity_ap
value: 73.0405088928302
- type: similarity_f1
value: 63.52201257861635
- type: similarity_f1_threshold
value: 96.98888063430786
- type: similarity_precision
value: 78.90625
- type: similarity_recall
value: 53.1578947368421
- task:
type: STS
dataset:
name: MTEB CDSC-R (default)
type: PL-MTEB/cdscr-sts
config: default
split: test
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
metrics:
- type: cosine_pearson
value: 92.97492495289738
- type: cosine_spearman
value: 92.63248098608472
- type: euclidean_pearson
value: 92.04712487782031
- type: euclidean_spearman
value: 92.19679486755008
- type: main_score
value: 92.63248098608472
- type: manhattan_pearson
value: 92.0101187740438
- type: manhattan_spearman
value: 92.20926859332754
- type: pearson
value: 92.97492495289738
- type: spearman
value: 92.63248098608472
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P (default)
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: main_score
value: 39.96377851800628
- type: v_measure
value: 39.96377851800628
- type: v_measure_std
value: 0.9793033243093288
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S (default)
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: main_score
value: 38.788850224595784
- type: v_measure
value: 38.788850224595784
- type: v_measure_std
value: 1.0712604145916924
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 77.95952507806115
- type: mrr
value: 80.8643253968254
- type: main_score
value: 77.95952507806115
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 78.21522500165045
- type: mrr
value: 81.28194444444443
- type: main_score
value: 78.21522500165045
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.377
- type: map_at_10
value: 46.371
- type: map_at_100
value: 47.829
- type: map_at_1000
value: 47.94
- type: map_at_20
value: 47.205000000000005
- type: map_at_3
value: 42.782
- type: map_at_5
value: 44.86
- type: mrr_at_1
value: 41.345
- type: mrr_at_10
value: 52.187
- type: mrr_at_100
value: 52.893
- type: mrr_at_1000
value: 52.929
- type: mrr_at_20
value: 52.637
- type: mrr_at_3
value: 49.714000000000006
- type: mrr_at_5
value: 51.373000000000005
- type: ndcg_at_1
value: 41.345
- type: ndcg_at_10
value: 52.946000000000005
- type: ndcg_at_100
value: 57.92699999999999
- type: ndcg_at_1000
value: 59.609
- type: ndcg_at_20
value: 54.900999999999996
- type: ndcg_at_3
value: 48.357
- type: ndcg_at_5
value: 50.739000000000004
- type: precision_at_1
value: 41.345
- type: precision_at_10
value: 10.186
- type: precision_at_100
value: 1.554
- type: precision_at_1000
value: 0.2
- type: precision_at_20
value: 5.959
- type: precision_at_3
value: 23.796
- type: precision_at_5
value: 17.024
- type: recall_at_1
value: 33.377
- type: recall_at_10
value: 65.067
- type: recall_at_100
value: 86.04899999999999
- type: recall_at_1000
value: 96.54899999999999
- type: recall_at_20
value: 72.071
- type: recall_at_3
value: 51.349999999999994
- type: recall_at_5
value: 58.41
- type: main_score
value: 52.946000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval (default)
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 31.097
- type: map_at_10
value: 42.183
- type: map_at_100
value: 43.580999999999996
- type: map_at_1000
value: 43.718
- type: map_at_20
value: 42.921
- type: map_at_3
value: 38.963
- type: map_at_5
value: 40.815
- type: mrr_at_1
value: 39.745000000000005
- type: mrr_at_10
value: 48.736000000000004
- type: mrr_at_100
value: 49.405
- type: mrr_at_1000
value: 49.452
- type: mrr_at_20
value: 49.118
- type: mrr_at_3
value: 46.497
- type: mrr_at_5
value: 47.827999999999996
- type: ndcg_at_1
value: 39.745000000000005
- type: ndcg_at_10
value: 48.248000000000005
- type: ndcg_at_100
value: 52.956
- type: ndcg_at_1000
value: 54.99699999999999
- type: ndcg_at_20
value: 50.01
- type: ndcg_at_3
value: 43.946000000000005
- type: ndcg_at_5
value: 46.038000000000004
- type: precision_at_1
value: 39.745000000000005
- type: precision_at_10
value: 9.229
- type: precision_at_100
value: 1.5070000000000001
- type: precision_at_1000
value: 0.199
- type: precision_at_20
value: 5.489999999999999
- type: precision_at_3
value: 21.38
- type: precision_at_5
value: 15.274
- type: recall_at_1
value: 31.097
- type: recall_at_10
value: 58.617
- type: recall_at_100
value: 78.55199999999999
- type: recall_at_1000
value: 91.13900000000001
- type: recall_at_20
value: 64.92
- type: recall_at_3
value: 45.672000000000004
- type: recall_at_5
value: 51.669
- type: main_score
value: 48.248000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval (default)
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 39.745000000000005
- type: map_at_10
value: 52.063
- type: map_at_100
value: 53.077
- type: map_at_1000
value: 53.13
- type: map_at_20
value: 52.66
- type: map_at_3
value: 48.662
- type: map_at_5
value: 50.507000000000005
- type: mrr_at_1
value: 45.391999999999996
- type: mrr_at_10
value: 55.528
- type: mrr_at_100
value: 56.16100000000001
- type: mrr_at_1000
value: 56.192
- type: mrr_at_20
value: 55.923
- type: mrr_at_3
value: 52.93600000000001
- type: mrr_at_5
value: 54.435
- type: ndcg_at_1
value: 45.391999999999996
- type: ndcg_at_10
value: 58.019
- type: ndcg_at_100
value: 61.936
- type: ndcg_at_1000
value: 63.015
- type: ndcg_at_20
value: 59.691
- type: ndcg_at_3
value: 52.294
- type: ndcg_at_5
value: 55.017
- type: precision_at_1
value: 45.391999999999996
- type: precision_at_10
value: 9.386
- type: precision_at_100
value: 1.232
- type: precision_at_1000
value: 0.136
- type: precision_at_20
value: 5.223
- type: precision_at_3
value: 23.177
- type: precision_at_5
value: 15.9
- type: recall_at_1
value: 39.745000000000005
- type: recall_at_10
value: 72.08099999999999
- type: recall_at_100
value: 88.85300000000001
- type: recall_at_1000
value: 96.569
- type: recall_at_20
value: 78.203
- type: recall_at_3
value: 56.957
- type: recall_at_5
value: 63.63100000000001
- type: main_score
value: 58.019
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval (default)
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.651999999999997
- type: map_at_10
value: 35.799
- type: map_at_100
value: 36.846000000000004
- type: map_at_1000
value: 36.931000000000004
- type: map_at_20
value: 36.341
- type: map_at_3
value: 32.999
- type: map_at_5
value: 34.597
- type: mrr_at_1
value: 28.814
- type: mrr_at_10
value: 37.869
- type: mrr_at_100
value: 38.728
- type: mrr_at_1000
value: 38.795
- type: mrr_at_20
value: 38.317
- type: mrr_at_3
value: 35.235
- type: mrr_at_5
value: 36.738
- type: ndcg_at_1
value: 28.814
- type: ndcg_at_10
value: 41.028
- type: ndcg_at_100
value: 46.162
- type: ndcg_at_1000
value: 48.15
- type: ndcg_at_20
value: 42.824
- type: ndcg_at_3
value: 35.621
- type: ndcg_at_5
value: 38.277
- type: precision_at_1
value: 28.814
- type: precision_at_10
value: 6.361999999999999
- type: precision_at_100
value: 0.9450000000000001
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 3.6159999999999997
- type: precision_at_3
value: 15.140999999999998
- type: precision_at_5
value: 10.712000000000002
- type: recall_at_1
value: 26.651999999999997
- type: recall_at_10
value: 55.038
- type: recall_at_100
value: 78.806
- type: recall_at_1000
value: 93.485
- type: recall_at_20
value: 61.742
- type: recall_at_3
value: 40.682
- type: recall_at_5
value: 46.855000000000004
- type: main_score
value: 41.028
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval (default)
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 17.627000000000002
- type: map_at_10
value: 26.436999999999998
- type: map_at_100
value: 27.85
- type: map_at_1000
value: 27.955999999999996
- type: map_at_20
value: 27.233
- type: map_at_3
value: 23.777
- type: map_at_5
value: 25.122
- type: mrr_at_1
value: 22.387999999999998
- type: mrr_at_10
value: 31.589
- type: mrr_at_100
value: 32.641999999999996
- type: mrr_at_1000
value: 32.696999999999996
- type: mrr_at_20
value: 32.201
- type: mrr_at_3
value: 28.98
- type: mrr_at_5
value: 30.342000000000002
- type: ndcg_at_1
value: 22.387999999999998
- type: ndcg_at_10
value: 32.129999999999995
- type: ndcg_at_100
value: 38.562999999999995
- type: ndcg_at_1000
value: 40.903
- type: ndcg_at_20
value: 34.652
- type: ndcg_at_3
value: 27.26
- type: ndcg_at_5
value: 29.235
- type: precision_at_1
value: 22.387999999999998
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 1.068
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_20
value: 3.6999999999999997
- type: precision_at_3
value: 13.267000000000001
- type: precision_at_5
value: 9.403
- type: recall_at_1
value: 17.627000000000002
- type: recall_at_10
value: 44.71
- type: recall_at_100
value: 72.426
- type: recall_at_1000
value: 88.64699999999999
- type: recall_at_20
value: 53.65
- type: recall_at_3
value: 30.989
- type: recall_at_5
value: 36.237
- type: main_score
value: 32.129999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval (default)
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 30.891000000000002
- type: map_at_10
value: 41.519
- type: map_at_100
value: 42.896
- type: map_at_1000
value: 42.992999999999995
- type: map_at_20
value: 42.287
- type: map_at_3
value: 37.822
- type: map_at_5
value: 39.976
- type: mrr_at_1
value: 37.921
- type: mrr_at_10
value: 47.260999999999996
- type: mrr_at_100
value: 48.044
- type: mrr_at_1000
value: 48.08
- type: mrr_at_20
value: 47.699999999999996
- type: mrr_at_3
value: 44.513999999999996
- type: mrr_at_5
value: 46.064
- type: ndcg_at_1
value: 37.921
- type: ndcg_at_10
value: 47.806
- type: ndcg_at_100
value: 53.274
- type: ndcg_at_1000
value: 55.021
- type: ndcg_at_20
value: 49.973
- type: ndcg_at_3
value: 42.046
- type: ndcg_at_5
value: 44.835
- type: precision_at_1
value: 37.921
- type: precision_at_10
value: 8.767999999999999
- type: precision_at_100
value: 1.353
- type: precision_at_1000
value: 0.168
- type: precision_at_20
value: 5.135
- type: precision_at_3
value: 20.051
- type: precision_at_5
value: 14.398
- type: recall_at_1
value: 30.891000000000002
- type: recall_at_10
value: 60.897999999999996
- type: recall_at_100
value: 83.541
- type: recall_at_1000
value: 94.825
- type: recall_at_20
value: 68.356
- type: recall_at_3
value: 44.65
- type: recall_at_5
value: 51.919000000000004
- type: main_score
value: 47.806
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval (default)
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 27.654
- type: map_at_10
value: 38.025999999999996
- type: map_at_100
value: 39.425
- type: map_at_1000
value: 39.528
- type: map_at_20
value: 38.838
- type: map_at_3
value: 34.745
- type: map_at_5
value: 36.537
- type: mrr_at_1
value: 34.018
- type: mrr_at_10
value: 43.314
- type: mrr_at_100
value: 44.283
- type: mrr_at_1000
value: 44.327
- type: mrr_at_20
value: 43.929
- type: mrr_at_3
value: 40.868
- type: mrr_at_5
value: 42.317
- type: ndcg_at_1
value: 34.018
- type: ndcg_at_10
value: 43.887
- type: ndcg_at_100
value: 49.791000000000004
- type: ndcg_at_1000
value: 51.834
- type: ndcg_at_20
value: 46.376
- type: ndcg_at_3
value: 38.769999999999996
- type: ndcg_at_5
value: 41.144
- type: precision_at_1
value: 34.018
- type: precision_at_10
value: 8.001999999999999
- type: precision_at_100
value: 1.2630000000000001
- type: precision_at_1000
value: 0.16
- type: precision_at_20
value: 4.737
- type: precision_at_3
value: 18.417
- type: precision_at_5
value: 13.150999999999998
- type: recall_at_1
value: 27.654
- type: recall_at_10
value: 56.111
- type: recall_at_100
value: 81.136
- type: recall_at_1000
value: 94.788
- type: recall_at_20
value: 65.068
- type: recall_at_3
value: 41.713
- type: recall_at_5
value: 48.106
- type: main_score
value: 43.887
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval (default)
type: CQADupstackRetrieval_is_a_combined_dataset
config: default
split: test
revision: CQADupstackRetrieval_is_a_combined_dataset
metrics:
- type: main_score
value: 42.58858333333333
- type: ndcg_at_10
value: 42.58858333333333
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval (default)
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 24.501
- type: map_at_10
value: 32.814
- type: map_at_100
value: 33.754
- type: map_at_1000
value: 33.859
- type: map_at_20
value: 33.324
- type: map_at_3
value: 30.758000000000003
- type: map_at_5
value: 31.936999999999998
- type: mrr_at_1
value: 27.761000000000003
- type: mrr_at_10
value: 35.662
- type: mrr_at_100
value: 36.443999999999996
- type: mrr_at_1000
value: 36.516999999999996
- type: mrr_at_20
value: 36.085
- type: mrr_at_3
value: 33.742
- type: mrr_at_5
value: 34.931
- type: ndcg_at_1
value: 27.761000000000003
- type: ndcg_at_10
value: 37.208000000000006
- type: ndcg_at_100
value: 41.839
- type: ndcg_at_1000
value: 44.421
- type: ndcg_at_20
value: 38.917
- type: ndcg_at_3
value: 33.544000000000004
- type: ndcg_at_5
value: 35.374
- type: precision_at_1
value: 27.761000000000003
- type: precision_at_10
value: 5.92
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.12
- type: precision_at_20
value: 3.4130000000000003
- type: precision_at_3
value: 15.031
- type: precision_at_5
value: 10.306999999999999
- type: recall_at_1
value: 24.501
- type: recall_at_10
value: 47.579
- type: recall_at_100
value: 69.045
- type: recall_at_1000
value: 88.032
- type: recall_at_20
value: 54.125
- type: recall_at_3
value: 37.202
- type: recall_at_5
value: 41.927
- type: main_score
value: 37.208000000000006
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval (default)
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.29
- type: map_at_10
value: 26.183
- type: map_at_100
value: 27.351999999999997
- type: map_at_1000
value: 27.483999999999998
- type: map_at_20
value: 26.798
- type: map_at_3
value: 23.629
- type: map_at_5
value: 24.937
- type: mrr_at_1
value: 22.299
- type: mrr_at_10
value: 30.189
- type: mrr_at_100
value: 31.098
- type: mrr_at_1000
value: 31.177
- type: mrr_at_20
value: 30.697000000000003
- type: mrr_at_3
value: 27.862
- type: mrr_at_5
value: 29.066
- type: ndcg_at_1
value: 22.299
- type: ndcg_at_10
value: 31.202
- type: ndcg_at_100
value: 36.617
- type: ndcg_at_1000
value: 39.544000000000004
- type: ndcg_at_20
value: 33.177
- type: ndcg_at_3
value: 26.639000000000003
- type: ndcg_at_5
value: 28.526
- type: precision_at_1
value: 22.299
- type: precision_at_10
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- type: precision_at_100
value: 1.0070000000000001
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_20
value: 3.505
- type: precision_at_3
value: 12.698
- type: precision_at_5
value: 9.174
- type: recall_at_1
value: 18.29
- type: recall_at_10
value: 42.254999999999995
- type: recall_at_100
value: 66.60000000000001
- type: recall_at_1000
value: 87.31400000000001
- type: recall_at_20
value: 49.572
- type: recall_at_3
value: 29.342000000000002
- type: recall_at_5
value: 34.221000000000004
- type: main_score
value: 31.202
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval (default)
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
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value: 27.722
- type: map_at_10
value: 37.698
- type: map_at_100
value: 38.899
- type: map_at_1000
value: 38.998
- type: map_at_20
value: 38.381
- type: map_at_3
value: 34.244
- type: map_at_5
value: 36.295
- type: mrr_at_1
value: 32.183
- type: mrr_at_10
value: 41.429
- type: mrr_at_100
value: 42.308
- type: mrr_at_1000
value: 42.358000000000004
- type: mrr_at_20
value: 41.957
- type: mrr_at_3
value: 38.401999999999994
- type: mrr_at_5
value: 40.294999999999995
- type: ndcg_at_1
value: 32.183
- type: ndcg_at_10
value: 43.519000000000005
- type: ndcg_at_100
value: 48.786
- type: ndcg_at_1000
value: 50.861999999999995
- type: ndcg_at_20
value: 45.654
- type: ndcg_at_3
value: 37.521
- type: ndcg_at_5
value: 40.615
- type: precision_at_1
value: 32.183
- type: precision_at_10
value: 7.603
- type: precision_at_100
value: 1.135
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_20
value: 4.408
- type: precision_at_3
value: 17.071
- type: precision_at_5
value: 12.668
- type: recall_at_1
value: 27.722
- type: recall_at_10
value: 57.230000000000004
- type: recall_at_100
value: 79.97999999999999
- type: recall_at_1000
value: 94.217
- type: recall_at_20
value: 64.864
- type: recall_at_3
value: 41.215
- type: recall_at_5
value: 48.774
- type: main_score
value: 43.519000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval (default)
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
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value: 25.852999999999998
- type: map_at_10
value: 35.394999999999996
- type: map_at_100
value: 37.291999999999994
- type: map_at_1000
value: 37.495
- type: map_at_20
value: 36.372
- type: map_at_3
value: 32.336
- type: map_at_5
value: 34.159
- type: mrr_at_1
value: 31.818
- type: mrr_at_10
value: 40.677
- type: mrr_at_100
value: 41.728
- type: mrr_at_1000
value: 41.778
- type: mrr_at_20
value: 41.301
- type: mrr_at_3
value: 38.208
- type: mrr_at_5
value: 39.592
- type: ndcg_at_1
value: 31.818
- type: ndcg_at_10
value: 41.559000000000005
- type: ndcg_at_100
value: 48.012
- type: ndcg_at_1000
value: 50.234
- type: ndcg_at_20
value: 44.15
- type: ndcg_at_3
value: 36.918
- type: ndcg_at_5
value: 39.227000000000004
- type: precision_at_1
value: 31.818
- type: precision_at_10
value: 8.043
- type: precision_at_100
value: 1.625
- type: precision_at_1000
value: 0.245
- type: precision_at_20
value: 5.2170000000000005
- type: precision_at_3
value: 17.655
- type: precision_at_5
value: 12.845999999999998
- type: recall_at_1
value: 25.852999999999998
- type: recall_at_10
value: 53.093
- type: recall_at_100
value: 81.05799999999999
- type: recall_at_1000
value: 94.657
- type: recall_at_20
value: 62.748000000000005
- type: recall_at_3
value: 39.300000000000004
- type: recall_at_5
value: 45.754
- type: main_score
value: 41.559000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval (default)
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
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value: 19.23
- type: map_at_10
value: 28.128999999999998
- type: map_at_100
value: 29.195
- type: map_at_1000
value: 29.310000000000002
- type: map_at_20
value: 28.713
- type: map_at_3
value: 25.191000000000003
- type: map_at_5
value: 26.69
- type: mrr_at_1
value: 21.257
- type: mrr_at_10
value: 30.253999999999998
- type: mrr_at_100
value: 31.195
- type: mrr_at_1000
value: 31.270999999999997
- type: mrr_at_20
value: 30.747999999999998
- type: mrr_at_3
value: 27.633999999999997
- type: mrr_at_5
value: 28.937
- type: ndcg_at_1
value: 21.257
- type: ndcg_at_10
value: 33.511
- type: ndcg_at_100
value: 38.733000000000004
- type: ndcg_at_1000
value: 41.489
- type: ndcg_at_20
value: 35.476
- type: ndcg_at_3
value: 27.845
- type: ndcg_at_5
value: 30.264999999999997
- type: precision_at_1
value: 21.257
- type: precision_at_10
value: 5.619
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.124
- type: precision_at_20
value: 3.29
- type: precision_at_3
value: 12.508
- type: precision_at_5
value: 8.946
- type: recall_at_1
value: 19.23
- type: recall_at_10
value: 48.185
- type: recall_at_100
value: 71.932
- type: recall_at_1000
value: 92.587
- type: recall_at_20
value: 55.533
- type: recall_at_3
value: 32.865
- type: recall_at_5
value: 38.577
- type: main_score
value: 33.511
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER (default)
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
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value: 19.594
- type: map_at_10
value: 32.519
- type: map_at_100
value: 34.1
- type: map_at_1000
value: 34.263
- type: map_at_20
value: 33.353
- type: map_at_3
value: 27.898
- type: map_at_5
value: 30.524
- type: mrr_at_1
value: 46.515
- type: mrr_at_10
value: 56.958
- type: mrr_at_100
value: 57.54899999999999
- type: mrr_at_1000
value: 57.574999999999996
- type: mrr_at_20
value: 57.315000000000005
- type: mrr_at_3
value: 54.852999999999994
- type: mrr_at_5
value: 56.153
- type: ndcg_at_1
value: 46.515
- type: ndcg_at_10
value: 42.363
- type: ndcg_at_100
value: 48.233
- type: ndcg_at_1000
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- type: ndcg_at_20
value: 44.533
- type: ndcg_at_3
value: 37.297000000000004
- type: ndcg_at_5
value: 38.911
- type: precision_at_1
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- type: precision_at_10
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- type: precision_at_100
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- type: precision_at_1000
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- type: precision_at_20
value: 7.212000000000001
- type: precision_at_3
value: 27.752
- type: precision_at_5
value: 20.391000000000002
- type: recall_at_1
value: 19.594
- type: recall_at_10
value: 46.539
- type: recall_at_100
value: 66.782
- type: recall_at_1000
value: 82.049
- type: recall_at_20
value: 52.611
- type: recall_at_3
value: 32.528
- type: recall_at_5
value: 38.933
- type: main_score
value: 42.363
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval (default)
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
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- type: map_at_100
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- type: map_at_1000
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- type: map_at_5
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value: 8.15
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- type: recall_at_1
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- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
value: 94.477
- type: recall_at_20
value: 53.37
- type: recall_at_3
value: 31.141000000000002
- type: recall_at_5
value: 36.721
- task:
type: PairClassification
dataset:
name: MTEB Cmnli (default)
type: C-MTEB/CMNLI
config: default
split: validation
revision: None
metrics:
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value: 71.25676488274203
- type: cos_sim_accuracy_threshold
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- type: cos_sim_ap
value: 79.10444825556077
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- type: dot_accuracy
value: 68.11785929043896
- type: dot_accuracy_threshold
value: 34783.23974609375
- type: dot_ap
value: 75.80201827987712
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value: 72.31670990679349
- type: dot_f1_threshold
value: 31978.036499023438
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value: 61.386623164763456
- type: dot_recall
value: 87.98223053542202
- type: euclidean_accuracy
value: 71.41310883944678
- type: euclidean_accuracy_threshold
value: 1374.9353408813477
- type: euclidean_ap
value: 79.23359768836457
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- type: euclidean_precision
value: 64.97816593886463
- type: euclidean_recall
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- type: manhattan_accuracy
value: 71.32892363199038
- type: manhattan_accuracy_threshold
value: 33340.49072265625
- type: manhattan_ap
value: 79.11973684118587
- type: manhattan_f1
value: 74.29401993355481
- type: manhattan_f1_threshold
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- type: manhattan_precision
value: 66.81605975723622
- type: manhattan_recall
value: 83.65676876315175
- type: max_accuracy
value: 71.41310883944678
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type: mteb/emotion
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value: -13.598996360976903
- type: nauc_recall_at_20_diff1
value: 78.78206037685645
- type: nauc_recall_at_20_max
value: 82.52264166455791
- type: nauc_recall_at_20_std
value: -5.958065992168697
- type: nauc_recall_at_3_diff1
value: 90.12709256456463
- type: nauc_recall_at_3_max
value: 90.7267880583832
- type: nauc_recall_at_3_std
value: -11.047599315631881
- type: nauc_recall_at_5_diff1
value: 89.90668735665676
- type: nauc_recall_at_5_max
value: 93.51571626543753
- type: nauc_recall_at_5_std
value: 22.632403279126112
- type: ndcg_at_1
value: 90.789
- type: ndcg_at_10
value: 95.46
- type: ndcg_at_100
value: 95.652
- type: ndcg_at_1000
value: 95.659
- type: ndcg_at_20
value: 95.575
- type: ndcg_at_3
value: 94.82000000000001
- type: ndcg_at_5
value: 95.26400000000001
- type: precision_at_1
value: 90.789
- type: precision_at_10
value: 9.908999999999999
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.977
- type: precision_at_3
value: 32.471
- type: precision_at_5
value: 19.701
- type: recall_at_1
value: 90.789
- type: recall_at_10
value: 99.093
- type: recall_at_100
value: 99.955
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.546
- type: recall_at_3
value: 97.414
- type: recall_at_5
value: 98.503
- task:
type: STS
dataset:
name: MTEB GermanSTSBenchmark (default)
type: jinaai/german-STSbenchmark
config: default
split: test
revision: e36907544d44c3a247898ed81540310442329e20
metrics:
- type: cosine_pearson
value: 86.55319003300265
- type: cosine_spearman
value: 87.50267373081324
- type: euclidean_pearson
value: 87.41630636501863
- type: euclidean_spearman
value: 88.02170803409365
- type: main_score
value: 87.50267373081324
- type: manhattan_pearson
value: 87.33703179056744
- type: manhattan_spearman
value: 87.99192826922514
- type: pearson
value: 86.55319003300265
- type: spearman
value: 87.50267373081324
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S (default)
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: main_score
value: 27.477557517301303
- type: v_measure
value: 27.477557517301303
- type: v_measure_std
value: 3.3525736581861336
- task:
type: Classification
dataset:
name: MTEB HeadlineClassification (default)
type: ai-forever/headline-classification
config: default
split: test
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
metrics:
- type: accuracy
value: 75.0830078125
- type: f1
value: 75.08863209267814
- type: f1_weighted
value: 75.08895979060917
- type: main_score
value: 75.0830078125
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA (default)
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 38.143
- type: map_at_10
value: 55.916999999999994
- type: map_at_100
value: 56.706
- type: map_at_1000
value: 56.77100000000001
- type: map_at_20
value: 56.367
- type: map_at_3
value: 53.111
- type: map_at_5
value: 54.839000000000006
- type: mrr_at_1
value: 76.286
- type: mrr_at_10
value: 81.879
- type: mrr_at_100
value: 82.09100000000001
- type: mrr_at_1000
value: 82.101
- type: mrr_at_20
value: 82.01
- type: mrr_at_3
value: 80.972
- type: mrr_at_5
value: 81.537
- type: ndcg_at_1
value: 76.286
- type: ndcg_at_10
value: 64.673
- type: ndcg_at_100
value: 67.527
- type: ndcg_at_1000
value: 68.857
- type: ndcg_at_20
value: 65.822
- type: ndcg_at_3
value: 60.616
- type: ndcg_at_5
value: 62.827999999999996
- type: precision_at_1
value: 76.286
- type: precision_at_10
value: 13.196
- type: precision_at_100
value: 1.544
- type: precision_at_1000
value: 0.172
- type: precision_at_20
value: 6.968000000000001
- type: precision_at_3
value: 37.992
- type: precision_at_5
value: 24.54
- type: recall_at_1
value: 38.143
- type: recall_at_10
value: 65.982
- type: recall_at_100
value: 77.225
- type: recall_at_1000
value: 86.077
- type: recall_at_20
value: 69.68299999999999
- type: recall_at_3
value: 56.989000000000004
- type: recall_at_5
value: 61.35
- type: main_score
value: 64.673
- task:
type: Classification
dataset:
name: MTEB IFlyTek (default)
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 41.67756829549827
- type: f1
value: 33.929325579581636
- type: f1_weighted
value: 43.03952025643197
- type: main_score
value: 41.67756829549827
- task:
type: Classification
dataset:
name: MTEB ImdbClassification (default)
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.90440000000001
- type: ap
value: 88.78663714603425
- type: ap_weighted
value: 88.78663714603425
- type: f1
value: 91.89564361975891
- type: f1_weighted
value: 91.89564361975891
- type: main_score
value: 91.90440000000001
- task:
type: Classification
dataset:
name: MTEB InappropriatenessClassification (default)
type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
metrics:
- type: accuracy
value: 61.0498046875
- type: ap
value: 57.04240566648215
- type: ap_weighted
value: 57.04240566648215
- type: f1
value: 60.867630038606954
- type: f1_weighted
value: 60.867630038606954
- type: main_score
value: 61.0498046875
- task:
type: Classification
dataset:
name: MTEB JDReview (default)
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 83.50844277673546
- type: ap
value: 48.46732380712268
- type: ap_weighted
value: 48.46732380712268
- type: f1
value: 77.43967451387445
- type: f1_weighted
value: 84.78462929014114
- type: main_score
value: 83.50844277673546
- task:
type: Classification
dataset:
name: MTEB KinopoiskClassification (default)
type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
metrics:
- type: accuracy
value: 62.393333333333324
- type: f1
value: 61.35940129568015
- type: f1_weighted
value: 61.35940129568015
- type: main_score
value: 62.393333333333324
- task:
type: STS
dataset:
name: MTEB LCQMC (default)
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cosine_pearson
value: 67.74375505907872
- type: cosine_spearman
value: 75.94582231399434
- type: euclidean_pearson
value: 74.52501692443582
- type: euclidean_spearman
value: 75.88428434746646
- type: main_score
value: 75.94582231399434
- type: manhattan_pearson
value: 74.55015441749529
- type: manhattan_spearman
value: 75.83288262176175
- type: pearson
value: 67.74375505907872
- type: spearman
value: 75.94582231399434
- task:
type: Retrieval
dataset:
name: MTEB LEMBNarrativeQARetrieval (default)
type: dwzhu/LongEmbed
config: default
split: test
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
metrics:
- type: map_at_1
value: 23.093
- type: map_at_10
value: 30.227999999999998
- type: map_at_100
value: 31.423000000000002
- type: map_at_1000
value: 31.533
- type: map_at_20
value: 30.835
- type: map_at_3
value: 27.983999999999998
- type: map_at_5
value: 29.253
- type: mrr_at_1
value: 23.093
- type: mrr_at_10
value: 30.227999999999998
- type: mrr_at_100
value: 31.423000000000002
- type: mrr_at_1000
value: 31.533
- type: mrr_at_20
value: 30.835
- type: mrr_at_3
value: 27.983999999999998
- type: mrr_at_5
value: 29.253
- type: ndcg_at_1
value: 23.093
- type: ndcg_at_10
value: 34.297
- type: ndcg_at_100
value: 41.049
- type: ndcg_at_1000
value: 43.566
- type: ndcg_at_20
value: 36.52
- type: ndcg_at_3
value: 29.629
- type: ndcg_at_5
value: 31.926
- type: precision_at_1
value: 23.093
- type: precision_at_10
value: 4.735
- type: precision_at_100
value: 0.8109999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 2.8080000000000003
- type: precision_at_3
value: 11.468
- type: precision_at_5
value: 8.001
- type: recall_at_1
value: 23.093
- type: recall_at_10
value: 47.354
- type: recall_at_100
value: 81.147
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 56.16799999999999
- type: recall_at_3
value: 34.405
- type: recall_at_5
value: 40.004
- type: main_score
value: 34.297
- type: map_at_1
value: 24.361
- type: map_at_10
value: 33.641
- type: map_at_100
value: 35.104
- type: map_at_1000
value: 35.127
- type: map_at_20
value: 34.388999999999996
- type: map_at_3
value: 30.255
- type: map_at_5
value: 32.079
- type: mrr_at_1
value: 24.361
- type: mrr_at_10
value: 33.641
- type: mrr_at_100
value: 35.104
- type: mrr_at_1000
value: 35.127
- type: mrr_at_20
value: 34.388999999999996
- type: mrr_at_3
value: 30.255
- type: mrr_at_5
value: 32.079
- type: ndcg_at_1
value: 24.361
- type: ndcg_at_10
value: 39.337
- type: ndcg_at_100
value: 47.384
- type: ndcg_at_1000
value: 47.75
- type: ndcg_at_20
value: 42.077999999999996
- type: ndcg_at_3
value: 32.235
- type: ndcg_at_5
value: 35.524
- type: precision_at_1
value: 24.361
- type: precision_at_10
value: 5.783
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 3.435
- type: precision_at_3
value: 12.661
- type: precision_at_5
value: 9.193999999999999
- type: recall_at_1
value: 24.361
- type: recall_at_10
value: 57.826
- type: recall_at_100
value: 97.51100000000001
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 68.697
- type: recall_at_3
value: 37.983
- type: recall_at_5
value: 45.972
- type: main_score
value: 39.337
- type: map_at_1
value: 53.667
- type: map_at_10
value: 61.719
- type: map_at_100
value: 62.471
- type: map_at_1000
value: 62.492000000000004
- type: map_at_20
value: 62.153000000000006
- type: map_at_3
value: 59.167
- type: map_at_5
value: 60.95
- type: mrr_at_1
value: 53.667
- type: mrr_at_10
value: 61.719
- type: mrr_at_100
value: 62.471
- type: mrr_at_1000
value: 62.492000000000004
- type: mrr_at_20
value: 62.153000000000006
- type: mrr_at_3
value: 59.167
- type: mrr_at_5
value: 60.95
- type: ndcg_at_1
value: 53.667
- type: ndcg_at_10
value: 66.018
- type: ndcg_at_100
value: 69.726
- type: ndcg_at_1000
value: 70.143
- type: ndcg_at_20
value: 67.61399999999999
- type: ndcg_at_3
value: 60.924
- type: ndcg_at_5
value: 64.10900000000001
- type: precision_at_1
value: 53.667
- type: precision_at_10
value: 7.9670000000000005
- type: precision_at_100
value: 0.97
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.3
- type: precision_at_3
value: 22.0
- type: precision_at_5
value: 14.732999999999999
- type: recall_at_1
value: 53.667
- type: recall_at_10
value: 79.667
- type: recall_at_100
value: 97.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 86.0
- type: recall_at_3
value: 66.0
- type: recall_at_5
value: 73.667
- type: main_score
value: 66.018
- task:
type: Retrieval
dataset:
name: MTEB LEMBNeedleRetrieval (default)
type: dwzhu/LongEmbed
config: default
split: test_256
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
metrics:
- type: map_at_1
value: 64.0
- type: map_at_10
value: 77.083
- type: map_at_100
value: 77.265
- type: map_at_1000
value: 77.265
- type: map_at_20
value: 77.265
- type: map_at_3
value: 76.333
- type: map_at_5
value: 76.833
- type: mrr_at_1
value: 64.0
- type: mrr_at_10
value: 77.083
- type: mrr_at_100
value: 77.265
- type: mrr_at_1000
value: 77.265
- type: mrr_at_20
value: 77.265
- type: mrr_at_3
value: 76.333
- type: mrr_at_5
value: 76.833
- type: ndcg_at_1
value: 64.0
- type: ndcg_at_10
value: 82.325
- type: ndcg_at_100
value: 82.883
- type: ndcg_at_1000
value: 82.883
- type: ndcg_at_20
value: 82.883
- type: ndcg_at_3
value: 80.833
- type: ndcg_at_5
value: 81.694
- type: precision_at_1
value: 64.0
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 5.0
- type: precision_at_3
value: 31.333
- type: precision_at_5
value: 19.2
- type: recall_at_1
value: 64.0
- type: recall_at_10
value: 98.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 100.0
- type: recall_at_3
value: 94.0
- type: recall_at_5
value: 96.0
- type: main_score
value: 64.0
- type: map_at_1
value: 100.0
- type: map_at_10
value: 100.0
- type: map_at_100
value: 100.0
- type: map_at_1000
value: 100.0
- type: map_at_20
value: 100.0
- type: map_at_3
value: 100.0
- type: map_at_5
value: 100.0
- type: mrr_at_1
value: 100.0
- type: mrr_at_10
value: 100.0
- type: mrr_at_100
value: 100.0
- type: mrr_at_1000
value: 100.0
- type: mrr_at_20
value: 100.0
- type: mrr_at_3
value: 100.0
- type: mrr_at_5
value: 100.0
- type: ndcg_at_1
value: 100.0
- type: ndcg_at_10
value: 100.0
- type: ndcg_at_100
value: 100.0
- type: ndcg_at_1000
value: 100.0
- type: ndcg_at_20
value: 100.0
- type: ndcg_at_3
value: 100.0
- type: ndcg_at_5
value: 100.0
- type: precision_at_1
value: 100.0
- type: precision_at_10
value: 10.0
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 5.0
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 20.0
- type: recall_at_1
value: 100.0
- type: recall_at_10
value: 100.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 100.0
- type: recall_at_3
value: 100.0
- type: recall_at_5
value: 100.0
- type: main_score
value: 100.0
- task:
type: Retrieval
dataset:
name: MTEB LEMBSummScreenFDRetrieval (default)
type: dwzhu/LongEmbed
config: default
split: validation
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
metrics:
- type: map_at_1
value: 84.821
- type: map_at_10
value: 90.11200000000001
- type: map_at_100
value: 90.158
- type: map_at_1000
value: 90.158
- type: map_at_20
value: 90.137
- type: map_at_3
value: 89.385
- type: map_at_5
value: 89.876
- type: mrr_at_1
value: 84.821
- type: mrr_at_10
value: 90.11200000000001
- type: mrr_at_100
value: 90.158
- type: mrr_at_1000
value: 90.158
- type: mrr_at_20
value: 90.137
- type: mrr_at_3
value: 89.385
- type: mrr_at_5
value: 89.876
- type: ndcg_at_1
value: 84.821
- type: ndcg_at_10
value: 92.334
- type: ndcg_at_100
value: 92.535
- type: ndcg_at_1000
value: 92.535
- type: ndcg_at_20
value: 92.414
- type: ndcg_at_3
value: 90.887
- type: ndcg_at_5
value: 91.758
- type: precision_at_1
value: 84.821
- type: precision_at_10
value: 9.911
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.97
- type: precision_at_3
value: 31.746000000000002
- type: precision_at_5
value: 19.464000000000002
- type: recall_at_1
value: 84.821
- type: recall_at_10
value: 99.107
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.405
- type: recall_at_3
value: 95.238
- type: recall_at_5
value: 97.321
- type: main_score
value: 92.334
- task:
type: Retrieval
dataset:
name: MTEB MLQARetrieval (deu-deu)
type: facebook/mlqa
config: deu-deu
split: test
revision: 397ed406c1a7902140303e7faf60fff35b58d285
metrics:
- type: main_score
value: 67.548
- type: map_at_1
value: 56.559000000000005
- type: map_at_10
value: 63.867
- type: map_at_100
value: 64.429
- type: map_at_1000
value: 64.457
- type: map_at_20
value: 64.215
- type: map_at_3
value: 62.109
- type: map_at_5
value: 63.101
- type: mrr_at_1
value: 56.56990915134057
- type: mrr_at_10
value: 63.86820789324668
- type: mrr_at_100
value: 64.42973602152581
- type: mrr_at_1000
value: 64.45818598090155
- type: mrr_at_20
value: 64.2163052263868
- type: mrr_at_3
value: 62.10946155550634
- type: mrr_at_5
value: 63.10104143585199
- type: nauc_map_at_1000_diff1
value: 73.78440163370111
- type: nauc_map_at_1000_max
value: 66.37875518052162
- type: nauc_map_at_1000_std
value: -17.063915098135396
- type: nauc_map_at_100_diff1
value: 73.77180802985815
- type: nauc_map_at_100_max
value: 66.38365998362033
- type: nauc_map_at_100_std
value: -17.053345109661972
- type: nauc_map_at_10_diff1
value: 73.70041876696037
- type: nauc_map_at_10_max
value: 66.33213342705997
- type: nauc_map_at_10_std
value: -17.40657791273925
- type: nauc_map_at_1_diff1
value: 76.8784374396948
- type: nauc_map_at_1_max
value: 64.07170606935357
- type: nauc_map_at_1_std
value: -18.464213686790654
- type: nauc_map_at_20_diff1
value: 73.72371377231813
- type: nauc_map_at_20_max
value: 66.42108121059451
- type: nauc_map_at_20_std
value: -17.05384923889036
- type: nauc_map_at_3_diff1
value: 74.08287018839246
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value: 54.308
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value: 75.622
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value: 89.744
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value: 97.539
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value: 80.085
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value: 66.09
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value: 70.446
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type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (de)
type: reciTAL/mlsum
config: de
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
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value: 41.267647761702854
- type: v_measure
value: 41.267647761702854
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value: 10.93390895077248
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value: 40.07927325071353
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value: 40.07927325071353
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value: 9.296680835266145
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type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (fr)
type: reciTAL/mlsum
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
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value: 44.68714862333979
- type: v_measure
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- type: v_measure_std
value: 1.811036989797814
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value: 44.88484854069901
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value: 44.88484854069901
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type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (ru)
type: reciTAL/mlsum
config: ru
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
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value: 41.92518785753813
- type: v_measure
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value: 43.97657450929179
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type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (es)
type: reciTAL/mlsum
config: es
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
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type: Reranking
dataset:
name: MTEB MMarcoReranking (default)
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
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- type: mrr
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value: 21.050447576170395
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type: Retrieval
dataset:
name: MTEB MMarcoRetrieval (default)
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
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value: 69.18299999999999
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value: 9.636
- type: precision_at_100
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- type: precision_at_1000
value: 0.105
- type: precision_at_20
value: 4.958
- type: precision_at_3
value: 28.515
- type: precision_at_5
value: 18.201
- type: recall_at_1
value: 66.872
- type: recall_at_10
value: 90.688
- type: recall_at_100
value: 96.99
- type: recall_at_1000
value: 98.958
- type: recall_at_20
value: 93.21199999999999
- type: recall_at_3
value: 80.84599999999999
- type: recall_at_5
value: 85.732
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO (default)
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 21.861
- type: map_at_10
value: 34.008
- type: map_at_100
value: 35.174
- type: map_at_1000
value: 35.224
- type: map_at_20
value: 34.705999999999996
- type: map_at_3
value: 30.209000000000003
- type: map_at_5
value: 32.351
- type: mrr_at_1
value: 22.493
- type: mrr_at_10
value: 34.583999999999996
- type: mrr_at_100
value: 35.691
- type: mrr_at_1000
value: 35.736000000000004
- type: mrr_at_20
value: 35.257
- type: mrr_at_3
value: 30.85
- type: mrr_at_5
value: 32.962
- type: ndcg_at_1
value: 22.493
- type: ndcg_at_10
value: 40.815
- type: ndcg_at_100
value: 46.483999999999995
- type: ndcg_at_1000
value: 47.73
- type: ndcg_at_20
value: 43.302
- type: ndcg_at_3
value: 33.056000000000004
- type: ndcg_at_5
value: 36.879
- type: precision_at_1
value: 22.493
- type: precision_at_10
value: 6.465999999999999
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_20
value: 3.752
- type: precision_at_3
value: 14.069
- type: precision_at_5
value: 10.384
- type: recall_at_1
value: 21.861
- type: recall_at_10
value: 61.781
- type: recall_at_100
value: 88.095
- type: recall_at_1000
value: 97.625
- type: recall_at_20
value: 71.44500000000001
- type: recall_at_3
value: 40.653
- type: recall_at_5
value: 49.841
- type: main_score
value: 40.815
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 97.4874601003192
- type: f1
value: 97.19067544931094
- type: f1_weighted
value: 97.49331776181019
- type: main_score
value: 97.4874601003192
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.89489997182305
- type: f1
value: 96.51138586512977
- type: f1_weighted
value: 96.89723065967186
- type: main_score
value: 96.89489997182305
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 97.17144763175452
- type: f1
value: 96.81785681878274
- type: f1_weighted
value: 97.1778974586874
- type: main_score
value: 97.17144763175452
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.30128405887879
- type: f1
value: 95.94555923088487
- type: f1_weighted
value: 96.30399416794926
- type: main_score
value: 96.30128405887879
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 84.53488372093022
- type: f1
value: 61.77995074251401
- type: f1_weighted
value: 86.8005170485101
- type: main_score
value: 84.53488372093022
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 80.79459002535924
- type: f1
value: 56.08938302001448
- type: f1_weighted
value: 83.66582131948252
- type: main_score
value: 80.79459002535924
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 84.7765176784523
- type: f1
value: 61.39860057885528
- type: f1_weighted
value: 86.94881745670745
- type: main_score
value: 84.7765176784523
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 82.2079549013467
- type: f1
value: 59.90260478749016
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value: 84.36861708593257
- type: main_score
value: 82.2079549013467
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (eng)
type: mteb/masakhanews
config: eng
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
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value: 74.98945147679325
- type: f1
value: 74.3157483560261
- type: f1_weighted
value: 75.01179008904884
- type: main_score
value: 74.98945147679325
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: mteb/masakhanews
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
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value: 74.02843601895735
- type: f1
value: 70.40326349620732
- type: f1_weighted
value: 74.6596277063484
- type: main_score
value: 74.02843601895735
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (amh)
type: masakhane/masakhanews
config: amh
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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value: 69.45780291725053
- type: v_measure
value: 69.45780291725053
- type: v_measure_std
value: 36.54340055904091
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value: 60.95132147787602
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value: 60.95132147787602
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value: 37.330148394033365
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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value: 64.88996119332239
- type: v_measure
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value: 60.974810831426595
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value: 60.974810831426595
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value: 24.934675467507827
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type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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value: 42.362383958691666
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value: 44.479206673553335
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value: 32.58254804499339
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type: Clustering
dataset:
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type: masakhane/masakhanews
config: hau
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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value: 26.4742082741682
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value: 40.8
- type: recall_at_100
value: 70.338
- type: recall_at_1000
value: 96.82300000000001
- type: recall_at_20
value: 49.34
- type: recall_at_3
value: 27.186
- type: recall_at_5
value: 33.251
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: main_score
value: 26.909
- type: map_at_1
value: 14.701
- type: map_at_10
value: 22.613
- type: map_at_100
value: 23.729
- type: map_at_1000
value: 23.837
- type: map_at_20
value: 23.262
- type: map_at_3
value: 20.236
- type: map_at_5
value: 21.673000000000002
- type: mrr_at_1
value: 14.7010647010647
- type: mrr_at_10
value: 22.613165113165113
- type: mrr_at_100
value: 23.72877605989423
- type: mrr_at_1000
value: 23.837150802746805
- type: mrr_at_20
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- type: mrr_at_3
value: 20.2361452361452
- type: mrr_at_5
value: 21.673491673491625
- type: nauc_map_at_1000_diff1
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- type: nauc_map_at_1000_std
value: 20.559244258100275
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- type: nauc_map_at_100_std
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- type: nauc_map_at_10_std
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- type: nauc_map_at_1_std
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- type: nauc_map_at_20_diff1
value: 17.157460199542136
- type: nauc_map_at_20_max
value: 47.4776610667456
- type: nauc_map_at_20_std
value: 20.499186342964478
- type: nauc_map_at_3_diff1
value: 19.393119961356277
- type: nauc_map_at_3_max
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- type: nauc_map_at_3_std
value: 19.293122796321292
- type: nauc_map_at_5_diff1
value: 17.76275044752008
- type: nauc_map_at_5_max
value: 48.01292548040298
- type: nauc_map_at_5_std
value: 19.928449977400504
- type: nauc_mrr_at_1000_diff1
value: 17.08927788889635
- type: nauc_mrr_at_1000_max
value: 47.240929150603336
- type: nauc_mrr_at_1000_std
value: 20.559244258100275
- type: nauc_mrr_at_100_diff1
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- type: nauc_mrr_at_100_max
value: 47.207381115550696
- type: nauc_mrr_at_100_std
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- type: nauc_mrr_at_10_std
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- type: nauc_mrr_at_1_max
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- type: nauc_mrr_at_1_std
value: 18.304203168281834
- type: nauc_mrr_at_20_diff1
value: 17.157460199542136
- type: nauc_mrr_at_20_max
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- type: nauc_mrr_at_20_std
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- type: nauc_ndcg_at_1000_diff1
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_1_std
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- type: nauc_ndcg_at_20_diff1
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- type: nauc_ndcg_at_20_max
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- type: nauc_ndcg_at_20_std
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- type: nauc_ndcg_at_3_diff1
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- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_5_max
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- type: nauc_ndcg_at_5_std
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- type: nauc_precision_at_1000_diff1
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- type: nauc_precision_at_1000_std
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- type: nauc_precision_at_100_max
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- type: nauc_precision_at_100_std
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- type: nauc_precision_at_10_diff1
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_10_std
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- type: nauc_precision_at_1_max
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- type: nauc_precision_at_1_std
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- type: nauc_precision_at_20_diff1
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_3_max
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_5_diff1
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- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
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- type: nauc_recall_at_1000_diff1
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- type: nauc_recall_at_1000_max
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- type: nauc_recall_at_1000_std
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- type: nauc_recall_at_100_diff1
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- type: nauc_recall_at_100_max
value: 31.340480360546863
- type: nauc_recall_at_100_std
value: 33.68979357205046
- type: nauc_recall_at_10_diff1
value: 5.96500199792656
- type: nauc_recall_at_10_max
value: 46.1251529616225
- type: nauc_recall_at_10_std
value: 25.409433135253543
- type: nauc_recall_at_1_diff1
value: 27.297525357600776
- type: nauc_recall_at_1_max
value: 47.18835074959486
- type: nauc_recall_at_1_std
value: 18.304203168281834
- type: nauc_recall_at_20_diff1
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- type: nauc_recall_at_20_max
value: 42.36095587714498
- type: nauc_recall_at_20_std
value: 27.36790051279787
- type: nauc_recall_at_3_diff1
value: 13.165017224718916
- type: nauc_recall_at_3_max
value: 50.589318254845054
- type: nauc_recall_at_3_std
value: 20.852009214609435
- type: nauc_recall_at_5_diff1
value: 7.840087177549891
- type: nauc_recall_at_5_max
value: 46.99388755575112
- type: nauc_recall_at_5_std
value: 23.048702393099845
- type: ndcg_at_1
value: 14.701
- type: ndcg_at_10
value: 26.909
- type: ndcg_at_100
value: 32.727000000000004
- type: ndcg_at_1000
value: 36.086
- type: ndcg_at_20
value: 29.236
- type: ndcg_at_3
value: 22.004
- type: ndcg_at_5
value: 24.615000000000002
- type: precision_at_1
value: 14.701
- type: precision_at_10
value: 4.062
- type: precision_at_100
value: 0.688
- type: precision_at_1000
value: 0.096
- type: precision_at_20
value: 2.488
- type: precision_at_3
value: 9.036
- type: precision_at_5
value: 6.699
- type: recall_at_1
value: 14.701
- type: recall_at_10
value: 40.622
- type: recall_at_100
value: 68.796
- type: recall_at_1000
value: 96.314
- type: recall_at_20
value: 49.754
- type: recall_at_3
value: 27.108999999999998
- type: recall_at_5
value: 33.497
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment (default)
type: C-MTEB/MultilingualSentiment-classification
config: default
split: test
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 73.20999999999998
- type: f1
value: 73.18755986777474
- type: f1_weighted
value: 73.18755986777475
- type: main_score
value: 73.20999999999998
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus (default)
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 4.822
- type: map_at_10
value: 13.144
- type: map_at_100
value: 17.254
- type: map_at_1000
value: 18.931
- type: map_at_20
value: 14.834
- type: map_at_3
value: 8.975
- type: map_at_5
value: 10.922
- type: mrr_at_1
value: 47.059
- type: mrr_at_10
value: 55.806999999999995
- type: mrr_at_100
value: 56.286
- type: mrr_at_1000
value: 56.327000000000005
- type: mrr_at_20
value: 56.00000000000001
- type: mrr_at_3
value: 54.17999999999999
- type: mrr_at_5
value: 55.155
- type: ndcg_at_1
value: 44.427
- type: ndcg_at_10
value: 36.623
- type: ndcg_at_100
value: 33.664
- type: ndcg_at_1000
value: 42.538
- type: ndcg_at_20
value: 34.066
- type: ndcg_at_3
value: 41.118
- type: ndcg_at_5
value: 39.455
- type: precision_at_1
value: 46.44
- type: precision_at_10
value: 28.607
- type: precision_at_100
value: 9.189
- type: precision_at_1000
value: 2.261
- type: precision_at_20
value: 21.238
- type: precision_at_3
value: 39.628
- type: precision_at_5
value: 35.604
- type: recall_at_1
value: 4.822
- type: recall_at_10
value: 17.488999999999997
- type: recall_at_100
value: 35.052
- type: recall_at_1000
value: 66.67999999999999
- type: recall_at_20
value: 21.343999999999998
- type: recall_at_3
value: 10.259
- type: recall_at_5
value: 13.406
- type: main_score
value: 36.623
- task:
type: Retrieval
dataset:
name: MTEB NQ (default)
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 41.411
- type: map_at_10
value: 57.179
- type: map_at_100
value: 57.945
- type: map_at_1000
value: 57.967999999999996
- type: map_at_20
value: 57.687
- type: map_at_3
value: 53.46300000000001
- type: map_at_5
value: 55.696999999999996
- type: mrr_at_1
value: 46.233999999999995
- type: mrr_at_10
value: 59.831999999999994
- type: mrr_at_100
value: 60.33500000000001
- type: mrr_at_1000
value: 60.348
- type: mrr_at_20
value: 60.167
- type: mrr_at_3
value: 56.972
- type: mrr_at_5
value: 58.74
- type: ndcg_at_1
value: 46.205
- type: ndcg_at_10
value: 64.23100000000001
- type: ndcg_at_100
value: 67.242
- type: ndcg_at_1000
value: 67.72500000000001
- type: ndcg_at_20
value: 65.77300000000001
- type: ndcg_at_3
value: 57.516
- type: ndcg_at_5
value: 61.11600000000001
- type: precision_at_1
value: 46.205
- type: precision_at_10
value: 9.873
- type: precision_at_100
value: 1.158
- type: precision_at_1000
value: 0.12
- type: precision_at_20
value: 5.319
- type: precision_at_3
value: 25.424999999999997
- type: precision_at_5
value: 17.375
- type: recall_at_1
value: 41.411
- type: recall_at_10
value: 82.761
- type: recall_at_100
value: 95.52199999999999
- type: recall_at_1000
value: 99.02499999999999
- type: recall_at_20
value: 88.34
- type: recall_at_3
value: 65.73
- type: recall_at_5
value: 73.894
- type: main_score
value: 64.23100000000001
- task:
type: PairClassification
dataset:
name: MTEB Ocnli (default)
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
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value: 62.3714131023281
- type: cosine_accuracy_threshold
value: 79.70921993255615
- type: cosine_ap
value: 66.41380155495659
- type: cosine_f1
value: 68.89547185780786
- type: cosine_f1_threshold
value: 72.91591167449951
- type: cosine_precision
value: 57.485875706214685
- type: cosine_recall
value: 85.95564941921859
- type: dot_accuracy
value: 60.47644829453167
- type: dot_accuracy_threshold
value: 36627.362060546875
- type: dot_ap
value: 63.696303449293204
- type: dot_f1
value: 68.3986041101202
- type: dot_f1_threshold
value: 30452.72216796875
- type: dot_precision
value: 54.04411764705882
- type: dot_recall
value: 93.13621964097149
- type: euclidean_accuracy
value: 63.02111532214402
- type: euclidean_accuracy_threshold
value: 1392.76762008667
- type: euclidean_ap
value: 66.65907089443218
- type: euclidean_f1
value: 69.05036524413688
- type: euclidean_f1_threshold
value: 1711.5310668945312
- type: euclidean_precision
value: 54.29262394195889
- type: euclidean_recall
value: 94.82576557550159
- type: main_score
value: 63.02111532214402
- type: manhattan_accuracy
value: 62.75040606388739
- type: manhattan_accuracy_threshold
value: 32475.347900390625
- type: manhattan_ap
value: 66.50943585125434
- type: manhattan_f1
value: 69.08382066276802
- type: manhattan_f1_threshold
value: 41238.470458984375
- type: manhattan_precision
value: 54.75896168108776
- type: manhattan_recall
value: 93.55860612460401
- type: max_accuracy
value: 63.02111532214402
- type: max_ap
value: 66.65907089443218
- type: max_f1
value: 69.08382066276802
- type: max_precision
value: 57.485875706214685
- type: max_recall
value: 94.82576557550159
- type: similarity_accuracy
value: 62.3714131023281
- type: similarity_accuracy_threshold
value: 79.70921993255615
- type: similarity_ap
value: 66.41380155495659
- type: similarity_f1
value: 68.89547185780786
- type: similarity_f1_threshold
value: 72.91591167449951
- type: similarity_precision
value: 57.485875706214685
- type: similarity_recall
value: 85.95564941921859
- task:
type: Classification
dataset:
name: MTEB OnlineShopping (default)
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 91.88000000000001
- type: ap
value: 89.52463684448476
- type: ap_weighted
value: 89.52463684448476
- type: f1
value: 91.86313022306673
- type: f1_weighted
value: 91.87806318146912
- type: main_score
value: 91.88000000000001
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (en)
type: GEM/opusparcus
config: en
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 92.65578635014838
- type: cosine_accuracy_threshold
value: 74.02530312538147
- type: cosine_ap
value: 98.3834226153613
- type: cosine_f1
value: 94.92567913890312
- type: cosine_f1_threshold
value: 74.02530312538147
- type: cosine_precision
value: 95.562435500516
- type: cosine_recall
value: 94.29735234215886
- type: dot_accuracy
value: 91.54302670623146
- type: dot_accuracy_threshold
value: 34452.29187011719
- type: dot_ap
value: 98.1237257754439
- type: dot_f1
value: 94.22400803616273
- type: dot_f1_threshold
value: 33670.41931152344
- type: dot_precision
value: 92.9633300297324
- type: dot_recall
value: 95.5193482688391
- type: euclidean_accuracy
value: 92.28486646884274
- type: euclidean_accuracy_threshold
value: 1602.8022766113281
- type: euclidean_ap
value: 98.3099021504706
- type: euclidean_f1
value: 94.75277497477296
- type: euclidean_f1_threshold
value: 1604.7462463378906
- type: euclidean_precision
value: 93.89999999999999
- type: euclidean_recall
value: 95.62118126272912
- type: main_score
value: 98.3834226153613
- type: manhattan_accuracy
value: 92.2106824925816
- type: manhattan_accuracy_threshold
value: 38872.90954589844
- type: manhattan_ap
value: 98.28694101230218
- type: manhattan_f1
value: 94.67815509376584
- type: manhattan_f1_threshold
value: 38872.90954589844
- type: manhattan_precision
value: 94.24823410696267
- type: manhattan_recall
value: 95.11201629327903
- type: max_accuracy
value: 92.65578635014838
- type: max_ap
value: 98.3834226153613
- type: max_f1
value: 94.92567913890312
- type: max_precision
value: 95.562435500516
- type: max_recall
value: 95.62118126272912
- type: similarity_accuracy
value: 92.65578635014838
- type: similarity_accuracy_threshold
value: 74.02530312538147
- type: similarity_ap
value: 98.3834226153613
- type: similarity_f1
value: 94.92567913890312
- type: similarity_f1_threshold
value: 74.02530312538147
- type: similarity_precision
value: 95.562435500516
- type: similarity_recall
value: 94.29735234215886
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (de)
type: GEM/opusparcus
config: de
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
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value: 87.72178850248403
- type: cosine_accuracy_threshold
value: 73.33863377571106
- type: cosine_ap
value: 96.98901408834976
- type: cosine_f1
value: 91.89944134078212
- type: cosine_f1_threshold
value: 71.45810127258301
- type: cosine_precision
value: 89.64577656675749
- type: cosine_recall
value: 94.26934097421203
- type: dot_accuracy
value: 86.30234208658624
- type: dot_accuracy_threshold
value: 32027.130126953125
- type: dot_ap
value: 96.12260574893256
- type: dot_f1
value: 91.31602506714414
- type: dot_f1_threshold
value: 30804.376220703125
- type: dot_precision
value: 85.93091828138164
- type: dot_recall
value: 97.42120343839542
- type: euclidean_accuracy
value: 87.9347054648687
- type: euclidean_accuracy_threshold
value: 1609.6670150756836
- type: euclidean_ap
value: 97.00238860358252
- type: euclidean_f1
value: 92.1089063221043
- type: euclidean_f1_threshold
value: 1641.8487548828125
- type: euclidean_precision
value: 89.10714285714286
- type: euclidean_recall
value: 95.31996179560649
- type: main_score
value: 97.00238860358252
- type: manhattan_accuracy
value: 87.72178850248403
- type: manhattan_accuracy_threshold
value: 40137.060546875
- type: manhattan_ap
value: 96.98653728159941
- type: manhattan_f1
value: 92.03865623561896
- type: manhattan_f1_threshold
value: 40137.060546875
- type: manhattan_precision
value: 88.80994671403198
- type: manhattan_recall
value: 95.51098376313276
- type: max_accuracy
value: 87.9347054648687
- type: max_ap
value: 97.00238860358252
- type: max_f1
value: 92.1089063221043
- type: max_precision
value: 89.64577656675749
- type: max_recall
value: 97.42120343839542
- type: similarity_accuracy
value: 87.72178850248403
- type: similarity_accuracy_threshold
value: 73.33863377571106
- type: similarity_ap
value: 96.98901408834976
- type: similarity_f1
value: 91.89944134078212
- type: similarity_f1_threshold
value: 71.45810127258301
- type: similarity_precision
value: 89.64577656675749
- type: similarity_recall
value: 94.26934097421203
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 80.92643051771117
- type: cosine_accuracy_threshold
value: 76.68856382369995
- type: cosine_ap
value: 93.74622381534307
- type: cosine_f1
value: 87.12328767123287
- type: cosine_f1_threshold
value: 71.64022922515869
- type: cosine_precision
value: 80.64243448858834
- type: cosine_recall
value: 94.73684210526315
- type: dot_accuracy
value: 80.858310626703
- type: dot_accuracy_threshold
value: 34028.3935546875
- type: dot_ap
value: 91.18448457633308
- type: dot_f1
value: 86.82606657290202
- type: dot_f1_threshold
value: 34028.3935546875
- type: dot_precision
value: 82.2380106571936
- type: dot_recall
value: 91.9563058589871
- type: euclidean_accuracy
value: 80.858310626703
- type: euclidean_accuracy_threshold
value: 1595.7651138305664
- type: euclidean_ap
value: 93.8182717829648
- type: euclidean_f1
value: 87.04044117647058
- type: euclidean_f1_threshold
value: 1609.2475891113281
- type: euclidean_precision
value: 81.00940975192472
- type: euclidean_recall
value: 94.04170804369414
- type: main_score
value: 93.8182717829648
- type: manhattan_accuracy
value: 80.99455040871935
- type: manhattan_accuracy_threshold
value: 38092.132568359375
- type: manhattan_ap
value: 93.77563401151711
- type: manhattan_f1
value: 86.91983122362869
- type: manhattan_f1_threshold
value: 38092.132568359375
- type: manhattan_precision
value: 82.32682060390763
- type: manhattan_recall
value: 92.05561072492551
- type: max_accuracy
value: 80.99455040871935
- type: max_ap
value: 93.8182717829648
- type: max_f1
value: 87.12328767123287
- type: max_precision
value: 82.32682060390763
- type: max_recall
value: 94.73684210526315
- type: similarity_accuracy
value: 80.92643051771117
- type: similarity_accuracy_threshold
value: 76.68856382369995
- type: similarity_ap
value: 93.74622381534307
- type: similarity_f1
value: 87.12328767123287
- type: similarity_f1_threshold
value: 71.64022922515869
- type: similarity_precision
value: 80.64243448858834
- type: similarity_recall
value: 94.73684210526315
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (ru)
type: GEM/opusparcus
config: ru
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 76.83823529411765
- type: cosine_accuracy_threshold
value: 72.70769476890564
- type: cosine_ap
value: 89.56692049908222
- type: cosine_f1
value: 83.99832003359934
- type: cosine_f1_threshold
value: 70.9052324295044
- type: cosine_precision
value: 76.16146230007617
- type: cosine_recall
value: 93.63295880149812
- type: dot_accuracy
value: 76.28676470588235
- type: dot_accuracy_threshold
value: 33740.68908691406
- type: dot_ap
value: 87.77185177141567
- type: dot_f1
value: 83.62251375370292
- type: dot_f1_threshold
value: 32726.611328125
- type: dot_precision
value: 76.29343629343629
- type: dot_recall
value: 92.50936329588015
- type: euclidean_accuracy
value: 77.32843137254902
- type: euclidean_accuracy_threshold
value: 1566.510009765625
- type: euclidean_ap
value: 89.60605626791111
- type: euclidean_f1
value: 84.06546080964686
- type: euclidean_f1_threshold
value: 1576.4202117919922
- type: euclidean_precision
value: 77.83094098883574
- type: euclidean_recall
value: 91.38576779026218
- type: main_score
value: 89.60605626791111
- type: manhattan_accuracy
value: 76.89950980392157
- type: manhattan_accuracy_threshold
value: 38202.215576171875
- type: manhattan_ap
value: 89.55766894104868
- type: manhattan_f1
value: 83.80462724935732
- type: manhattan_f1_threshold
value: 38934.375
- type: manhattan_precision
value: 77.25118483412322
- type: manhattan_recall
value: 91.57303370786516
- type: max_accuracy
value: 77.32843137254902
- type: max_ap
value: 89.60605626791111
- type: max_f1
value: 84.06546080964686
- type: max_precision
value: 77.83094098883574
- type: max_recall
value: 93.63295880149812
- type: similarity_accuracy
value: 76.83823529411765
- type: similarity_accuracy_threshold
value: 72.70769476890564
- type: similarity_ap
value: 89.56692049908222
- type: similarity_f1
value: 83.99832003359934
- type: similarity_f1_threshold
value: 70.9052324295044
- type: similarity_precision
value: 76.16146230007617
- type: similarity_recall
value: 93.63295880149812
- task:
type: Classification
dataset:
name: MTEB PAC (default)
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
metrics:
- type: accuracy
value: 68.39559803069794
- type: ap
value: 77.68074206719457
- type: ap_weighted
value: 77.68074206719457
- type: f1
value: 66.23485605467732
- type: f1_weighted
value: 69.03201442129347
- type: main_score
value: 68.39559803069794
- task:
type: STS
dataset:
name: MTEB PAWSX (default)
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cosine_pearson
value: 13.161523266433587
- type: cosine_spearman
value: 15.557333873773386
- type: euclidean_pearson
value: 17.147508431907525
- type: euclidean_spearman
value: 15.664112857732146
- type: main_score
value: 15.557333873773386
- type: manhattan_pearson
value: 17.130875906264386
- type: manhattan_spearman
value: 15.624397342229637
- type: pearson
value: 13.161523266433587
- type: spearman
value: 15.557333873773386
- task:
type: PairClassification
dataset:
name: MTEB PSC (default)
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
metrics:
- type: cosine_accuracy
value: 97.86641929499072
- type: cosine_accuracy_threshold
value: 79.0391206741333
- type: cosine_ap
value: 99.19403807771533
- type: cosine_f1
value: 96.45608628659475
- type: cosine_f1_threshold
value: 79.0391206741333
- type: cosine_precision
value: 97.50778816199377
- type: cosine_recall
value: 95.42682926829268
- type: dot_accuracy
value: 98.14471243042672
- type: dot_accuracy_threshold
value: 29808.1787109375
- type: dot_ap
value: 99.331999859971
- type: dot_f1
value: 97.01492537313433
- type: dot_f1_threshold
value: 29808.1787109375
- type: dot_precision
value: 95.02923976608187
- type: dot_recall
value: 99.08536585365853
- type: euclidean_accuracy
value: 97.49536178107606
- type: euclidean_accuracy_threshold
value: 1276.227855682373
- type: euclidean_ap
value: 98.91056467717377
- type: euclidean_f1
value: 95.83975346687212
- type: euclidean_f1_threshold
value: 1276.227855682373
- type: euclidean_precision
value: 96.88473520249221
- type: euclidean_recall
value: 94.8170731707317
- type: main_score
value: 99.331999859971
- type: manhattan_accuracy
value: 97.49536178107606
- type: manhattan_accuracy_threshold
value: 31097.674560546875
- type: manhattan_ap
value: 98.95694691792707
- type: manhattan_f1
value: 95.83975346687212
- type: manhattan_f1_threshold
value: 31097.674560546875
- type: manhattan_precision
value: 96.88473520249221
- type: manhattan_recall
value: 94.8170731707317
- type: max_accuracy
value: 98.14471243042672
- type: max_ap
value: 99.331999859971
- type: max_f1
value: 97.01492537313433
- type: max_precision
value: 97.50778816199377
- type: max_recall
value: 99.08536585365853
- type: similarity_accuracy
value: 97.86641929499072
- type: similarity_accuracy_threshold
value: 79.0391206741333
- type: similarity_ap
value: 99.19403807771533
- type: similarity_f1
value: 96.45608628659475
- type: similarity_f1_threshold
value: 79.0391206741333
- type: similarity_precision
value: 97.50778816199377
- type: similarity_recall
value: 95.42682926829268
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (en)
type: google-research-datasets/paws-x
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 61.8
- type: cosine_accuracy_threshold
value: 99.5664119720459
- type: cosine_ap
value: 60.679317786040585
- type: cosine_f1
value: 63.17354143441101
- type: cosine_f1_threshold
value: 97.22164869308472
- type: cosine_precision
value: 47.6457399103139
- type: cosine_recall
value: 93.71554575523705
- type: dot_accuracy
value: 55.7
- type: dot_accuracy_threshold
value: 48353.62548828125
- type: dot_ap
value: 48.53805970536875
- type: dot_f1
value: 62.42214532871972
- type: dot_f1_threshold
value: 38215.53955078125
- type: dot_precision
value: 45.48663640948058
- type: dot_recall
value: 99.44873208379272
- type: euclidean_accuracy
value: 61.75000000000001
- type: euclidean_accuracy_threshold
value: 189.0761137008667
- type: euclidean_ap
value: 60.55517418691518
- type: euclidean_f1
value: 63.07977736549165
- type: euclidean_f1_threshold
value: 504.3168067932129
- type: euclidean_precision
value: 47.53914988814318
- type: euclidean_recall
value: 93.71554575523705
- type: main_score
value: 60.679317786040585
- type: manhattan_accuracy
value: 61.9
- type: manhattan_accuracy_threshold
value: 4695.778274536133
- type: manhattan_ap
value: 60.48686620413608
- type: manhattan_f1
value: 62.92880855772778
- type: manhattan_f1_threshold
value: 12542.36831665039
- type: manhattan_precision
value: 47.28381374722838
- type: manhattan_recall
value: 94.04630650496141
- type: max_accuracy
value: 61.9
- type: max_ap
value: 60.679317786040585
- type: max_f1
value: 63.17354143441101
- type: max_precision
value: 47.6457399103139
- type: max_recall
value: 99.44873208379272
- type: similarity_accuracy
value: 61.8
- type: similarity_accuracy_threshold
value: 99.5664119720459
- type: similarity_ap
value: 60.679317786040585
- type: similarity_f1
value: 63.17354143441101
- type: similarity_f1_threshold
value: 97.22164869308472
- type: similarity_precision
value: 47.6457399103139
- type: similarity_recall
value: 93.71554575523705
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (de)
type: google-research-datasets/paws-x
config: de
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 60.25
- type: cosine_accuracy_threshold
value: 99.54338073730469
- type: cosine_ap
value: 56.7863613689054
- type: cosine_f1
value: 62.23499820337766
- type: cosine_f1_threshold
value: 89.95014429092407
- type: cosine_precision
value: 45.86864406779661
- type: cosine_recall
value: 96.75977653631284
- type: dot_accuracy
value: 56.8
- type: dot_accuracy_threshold
value: 47349.78332519531
- type: dot_ap
value: 49.7857806061729
- type: dot_f1
value: 62.31225986727209
- type: dot_f1_threshold
value: 30143.206787109375
- type: dot_precision
value: 45.32520325203252
- type: dot_recall
value: 99.66480446927373
- type: euclidean_accuracy
value: 60.3
- type: euclidean_accuracy_threshold
value: 219.78106498718262
- type: euclidean_ap
value: 56.731544327179606
- type: euclidean_f1
value: 62.19895287958115
- type: euclidean_f1_threshold
value: 1792.1623229980469
- type: euclidean_precision
value: 45.22842639593909
- type: euclidean_recall
value: 99.55307262569832
- type: main_score
value: 56.7863613689054
- type: manhattan_accuracy
value: 60.150000000000006
- type: manhattan_accuracy_threshold
value: 5104.503631591797
- type: manhattan_ap
value: 56.70304479768734
- type: manhattan_f1
value: 62.22067039106145
- type: manhattan_f1_threshold
value: 42839.471435546875
- type: manhattan_precision
value: 45.2513966480447
- type: manhattan_recall
value: 99.55307262569832
- type: max_accuracy
value: 60.3
- type: max_ap
value: 56.7863613689054
- type: max_f1
value: 62.31225986727209
- type: max_precision
value: 45.86864406779661
- type: max_recall
value: 99.66480446927373
- type: similarity_accuracy
value: 60.25
- type: similarity_accuracy_threshold
value: 99.54338073730469
- type: similarity_ap
value: 56.7863613689054
- type: similarity_f1
value: 62.23499820337766
- type: similarity_f1_threshold
value: 89.95014429092407
- type: similarity_precision
value: 45.86864406779661
- type: similarity_recall
value: 96.75977653631284
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (es)
type: google-research-datasets/paws-x
config: es
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 59.699999999999996
- type: cosine_accuracy_threshold
value: 99.55930709838867
- type: cosine_ap
value: 57.31662248806265
- type: cosine_f1
value: 62.444061962134256
- type: cosine_f1_threshold
value: 74.75898265838623
- type: cosine_precision
value: 45.3953953953954
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 55.900000000000006
- type: dot_accuracy_threshold
value: 47512.90283203125
- type: dot_ap
value: 49.39339147787568
- type: dot_f1
value: 62.487082328625554
- type: dot_f1_threshold
value: 34989.03503417969
- type: dot_precision
value: 45.44088176352705
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 59.599999999999994
- type: euclidean_accuracy_threshold
value: 200.82547664642334
- type: euclidean_ap
value: 57.19737488445163
- type: euclidean_f1
value: 62.444061962134256
- type: euclidean_f1_threshold
value: 1538.8837814331055
- type: euclidean_precision
value: 45.3953953953954
- type: euclidean_recall
value: 100.0
- type: main_score
value: 57.31662248806265
- type: manhattan_accuracy
value: 59.550000000000004
- type: manhattan_accuracy_threshold
value: 5016.501617431641
- type: manhattan_ap
value: 57.089959907945065
- type: manhattan_f1
value: 62.444061962134256
- type: manhattan_f1_threshold
value: 37523.53515625
- type: manhattan_precision
value: 45.3953953953954
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 59.699999999999996
- type: max_ap
value: 57.31662248806265
- type: max_f1
value: 62.487082328625554
- type: max_precision
value: 45.44088176352705
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 59.699999999999996
- type: similarity_accuracy_threshold
value: 99.55930709838867
- type: similarity_ap
value: 57.31662248806265
- type: similarity_f1
value: 62.444061962134256
- type: similarity_f1_threshold
value: 74.75898265838623
- type: similarity_precision
value: 45.3953953953954
- type: similarity_recall
value: 100.0
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (fr)
type: google-research-datasets/paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 61.150000000000006
- type: cosine_accuracy_threshold
value: 99.36153888702393
- type: cosine_ap
value: 59.43845317938599
- type: cosine_f1
value: 62.51298026998961
- type: cosine_f1_threshold
value: 76.77866220474243
- type: cosine_precision
value: 45.468277945619334
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 55.75
- type: dot_accuracy_threshold
value: 48931.55212402344
- type: dot_ap
value: 50.15949290538757
- type: dot_f1
value: 62.53462603878117
- type: dot_f1_threshold
value: 34415.7958984375
- type: dot_precision
value: 45.4911838790932
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 61.050000000000004
- type: euclidean_accuracy_threshold
value: 240.8097267150879
- type: euclidean_ap
value: 59.367971294226216
- type: euclidean_f1
value: 62.51298026998961
- type: euclidean_f1_threshold
value: 1444.132423400879
- type: euclidean_precision
value: 45.468277945619334
- type: euclidean_recall
value: 100.0
- type: main_score
value: 59.43845317938599
- type: manhattan_accuracy
value: 60.95
- type: manhattan_accuracy_threshold
value: 5701.206207275391
- type: manhattan_ap
value: 59.30094096378774
- type: manhattan_f1
value: 62.53462603878117
- type: manhattan_f1_threshold
value: 33445.672607421875
- type: manhattan_precision
value: 45.4911838790932
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 61.150000000000006
- type: max_ap
value: 59.43845317938599
- type: max_f1
value: 62.53462603878117
- type: max_precision
value: 45.4911838790932
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 61.150000000000006
- type: similarity_accuracy_threshold
value: 99.36153888702393
- type: similarity_ap
value: 59.43845317938599
- type: similarity_f1
value: 62.51298026998961
- type: similarity_f1_threshold
value: 76.77866220474243
- type: similarity_precision
value: 45.468277945619334
- type: similarity_recall
value: 100.0
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (zh)
type: google-research-datasets/paws-x
config: zh
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 58.85
- type: cosine_accuracy_threshold
value: 99.73838329315186
- type: cosine_ap
value: 54.66913160570546
- type: cosine_f1
value: 62.32136632973162
- type: cosine_f1_threshold
value: 76.4499306678772
- type: cosine_precision
value: 45.265822784810126
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 56.25
- type: dot_accuracy_threshold
value: 47351.9287109375
- type: dot_ap
value: 48.5266232989438
- type: dot_f1
value: 62.277951933124356
- type: dot_f1_threshold
value: 31325.28076171875
- type: dot_precision
value: 45.220030349013655
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 58.9
- type: euclidean_accuracy_threshold
value: 144.24468278884888
- type: euclidean_ap
value: 54.66981490353506
- type: euclidean_f1
value: 62.32136632973162
- type: euclidean_f1_threshold
value: 1484.908676147461
- type: euclidean_precision
value: 45.265822784810126
- type: euclidean_recall
value: 100.0
- type: main_score
value: 54.66981490353506
- type: manhattan_accuracy
value: 58.9
- type: manhattan_accuracy_threshold
value: 3586.785125732422
- type: manhattan_ap
value: 54.668355260247736
- type: manhattan_f1
value: 62.32136632973162
- type: manhattan_f1_threshold
value: 36031.22863769531
- type: manhattan_precision
value: 45.265822784810126
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 58.9
- type: max_ap
value: 54.66981490353506
- type: max_f1
value: 62.32136632973162
- type: max_precision
value: 45.265822784810126
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 58.85
- type: similarity_accuracy_threshold
value: 99.73838329315186
- type: similarity_ap
value: 54.66913160570546
- type: similarity_f1
value: 62.32136632973162
- type: similarity_f1_threshold
value: 76.4499306678772
- type: similarity_precision
value: 45.265822784810126
- type: similarity_recall
value: 100.0
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN (default)
type: PL-MTEB/polemo2_in
config: default
split: test
revision: d90724373c70959f17d2331ad51fb60c71176b03
metrics:
- type: accuracy
value: 83.75346260387812
- type: f1
value: 81.98304891214909
- type: f1_weighted
value: 84.29623200830078
- type: main_score
value: 83.75346260387812
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT (default)
type: PL-MTEB/polemo2_out
config: default
split: test
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
metrics:
- type: accuracy
value: 66.53846153846153
- type: f1
value: 52.71826064368638
- type: f1_weighted
value: 69.10010124630334
- type: main_score
value: 66.53846153846153
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cosine_accuracy
value: 81.8
- type: cosine_accuracy_threshold
value: 90.47793745994568
- type: cosine_ap
value: 91.42490266080884
- type: cosine_f1
value: 85.4632587859425
- type: cosine_f1_threshold
value: 90.47793745994568
- type: cosine_precision
value: 82.56172839506173
- type: cosine_recall
value: 88.57615894039735
- type: dot_accuracy
value: 74.6
- type: dot_accuracy_threshold
value: 42102.23693847656
- type: dot_ap
value: 86.20060009096979
- type: dot_f1
value: 80.02842928216063
- type: dot_f1_threshold
value: 38970.16906738281
- type: dot_precision
value: 70.1120797011208
- type: dot_recall
value: 93.21192052980133
- type: euclidean_accuracy
value: 81.5
- type: euclidean_accuracy_threshold
value: 880.433464050293
- type: euclidean_ap
value: 91.33143477982087
- type: euclidean_f1
value: 85.44600938967135
- type: euclidean_f1_threshold
value: 964.0384674072266
- type: euclidean_precision
value: 81.00890207715133
- type: euclidean_recall
value: 90.39735099337747
- type: main_score
value: 91.42490266080884
- type: manhattan_accuracy
value: 81.3
- type: manhattan_accuracy_threshold
value: 22100.830078125
- type: manhattan_ap
value: 91.25996158651282
- type: manhattan_f1
value: 85.38102643856921
- type: manhattan_f1_threshold
value: 24043.515014648438
- type: manhattan_precision
value: 80.49853372434018
- type: manhattan_recall
value: 90.89403973509934
- type: max_accuracy
value: 81.8
- type: max_ap
value: 91.42490266080884
- type: max_f1
value: 85.4632587859425
- type: max_precision
value: 82.56172839506173
- type: max_recall
value: 93.21192052980133
- type: similarity_accuracy
value: 81.8
- type: similarity_accuracy_threshold
value: 90.47793745994568
- type: similarity_ap
value: 91.42490266080884
- type: similarity_f1
value: 85.4632587859425
- type: similarity_f1_threshold
value: 90.47793745994568
- type: similarity_precision
value: 82.56172839506173
- type: similarity_recall
value: 88.57615894039735
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval (default)
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.419
- type: map_at_10
value: 85.542
- type: map_at_100
value: 86.161
- type: map_at_1000
value: 86.175
- type: map_at_20
value: 85.949
- type: map_at_3
value: 82.623
- type: map_at_5
value: 84.5
- type: mrr_at_1
value: 82.27
- type: mrr_at_10
value: 88.21900000000001
- type: mrr_at_100
value: 88.313
- type: mrr_at_1000
value: 88.31400000000001
- type: mrr_at_20
value: 88.286
- type: mrr_at_3
value: 87.325
- type: mrr_at_5
value: 87.97500000000001
- type: ndcg_at_1
value: 82.3
- type: ndcg_at_10
value: 89.088
- type: ndcg_at_100
value: 90.217
- type: ndcg_at_1000
value: 90.29700000000001
- type: ndcg_at_20
value: 89.697
- type: ndcg_at_3
value: 86.435
- type: ndcg_at_5
value: 87.966
- type: precision_at_1
value: 82.3
- type: precision_at_10
value: 13.527000000000001
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.165000000000001
- type: precision_at_3
value: 37.92
- type: precision_at_5
value: 24.914
- type: recall_at_1
value: 71.419
- type: recall_at_10
value: 95.831
- type: recall_at_100
value: 99.64
- type: recall_at_1000
value: 99.988
- type: recall_at_20
value: 97.76599999999999
- type: recall_at_3
value: 88.081
- type: recall_at_5
value: 92.50500000000001
- type: main_score
value: 89.088
- task:
type: STS
dataset:
name: MTEB RUParaPhraserSTS (default)
type: merionum/ru_paraphraser
config: default
split: test
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
metrics:
- type: cosine_pearson
value: 67.91177744712421
- type: cosine_spearman
value: 76.77113726753656
- type: euclidean_pearson
value: 73.81454206068638
- type: euclidean_spearman
value: 76.92529493599028
- type: main_score
value: 76.77113726753656
- type: manhattan_pearson
value: 73.81690454439168
- type: manhattan_spearman
value: 76.87333776705002
- type: pearson
value: 67.91177744712421
- type: spearman
value: 76.77113726753656
- task:
type: Clustering
dataset:
name: MTEB RedditClustering (default)
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: main_score
value: 55.39924225216962
- type: v_measure
value: 55.39924225216962
- type: v_measure_std
value: 4.723802279292467
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P (default)
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: main_score
value: 62.87465161304012
- type: v_measure
value: 62.87465161304012
- type: v_measure_std
value: 12.082670914488473
- task:
type: Retrieval
dataset:
name: MTEB RiaNewsRetrieval (default)
type: ai-forever/ria-news-retrieval
config: default
split: test
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
metrics:
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value: 79.209
- type: map_at_1
value: 67.33
- type: map_at_10
value: 75.633
- type: map_at_100
value: 75.897
- type: map_at_1000
value: 75.907
- type: map_at_20
value: 75.804
- type: map_at_3
value: 74.2
- type: map_at_5
value: 75.13300000000001
- type: mrr_at_1
value: 67.31
- type: mrr_at_10
value: 75.62709126984095
- type: mrr_at_100
value: 75.89105697041113
- type: mrr_at_1000
value: 75.90115653883124
- type: mrr_at_20
value: 75.79802332308172
- type: mrr_at_3
value: 74.19499999999961
- type: mrr_at_5
value: 75.12849999999939
- type: nauc_map_at_1000_diff1
value: 74.30304869630591
- type: nauc_map_at_1000_max
value: 36.477146725784046
- type: nauc_map_at_1000_std
value: -20.862772498461723
- type: nauc_map_at_100_diff1
value: 74.29833058090355
- type: nauc_map_at_100_max
value: 36.483678619667884
- type: nauc_map_at_100_std
value: -20.856274849980135
- type: nauc_map_at_10_diff1
value: 74.20729220697967
- type: nauc_map_at_10_max
value: 36.56543146170092
- type: nauc_map_at_10_std
value: -20.991081015484728
- type: nauc_map_at_1_diff1
value: 77.38899022125185
- type: nauc_map_at_1_max
value: 32.45918619669731
- type: nauc_map_at_1_std
value: -22.149586336167324
- type: nauc_map_at_20_diff1
value: 74.2447573558587
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value: 36.50383130240387
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value: -20.87013743041831
- type: nauc_map_at_3_diff1
value: 74.3054577294586
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value: 36.55670532498779
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value: -21.488786900676942
- type: nauc_mrr_at_1000_diff1
value: 74.31619177956684
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value: -20.75341959250079
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value: -21.964402859613937
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value: 74.25784396230718
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value: 36.561412224507336
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value: -20.767665000065723
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- type: nauc_mrr_at_3_std
value: -21.81259529019546
- type: nauc_mrr_at_5_diff1
value: 74.22404613312771
- type: nauc_mrr_at_5_max
value: 36.60743768455219
- type: nauc_mrr_at_5_std
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- type: nauc_ndcg_at_1000_diff1
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- type: nauc_ndcg_at_1000_std
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- type: nauc_ndcg_at_100_diff1
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- type: nauc_ndcg_at_100_std
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- type: nauc_ndcg_at_10_diff1
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
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- type: nauc_ndcg_at_1_diff1
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- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_std
value: -22.149586336167324
- type: nauc_ndcg_at_20_diff1
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- type: nauc_ndcg_at_3_diff1
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value: 38.008109328364
- type: nauc_ndcg_at_3_std
value: -21.68785687594153
- type: nauc_ndcg_at_5_diff1
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- type: nauc_ndcg_at_5_std
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- type: nauc_precision_at_1000_diff1
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- type: nauc_precision_at_1000_max
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- type: nauc_precision_at_100_diff1
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- type: nauc_precision_at_100_max
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- type: nauc_precision_at_100_std
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- type: nauc_precision_at_10_diff1
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_10_std
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- type: nauc_precision_at_20_diff1
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_3_max
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_5_diff1
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- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
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- type: nauc_recall_at_1000_diff1
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- type: nauc_recall_at_1000_max
value: 72.20747948302191
- type: nauc_recall_at_1000_std
value: 45.588371800962655
- type: nauc_recall_at_100_diff1
value: 62.872868445623894
- type: nauc_recall_at_100_max
value: 61.33108214045813
- type: nauc_recall_at_100_std
value: 20.67481963545666
- type: nauc_recall_at_10_diff1
value: 64.11222984256698
- type: nauc_recall_at_10_max
value: 50.32369774603755
- type: nauc_recall_at_10_std
value: -7.999454463433321
- type: nauc_recall_at_1_diff1
value: 77.38899022125185
- type: nauc_recall_at_1_max
value: 32.45918619669731
- type: nauc_recall_at_1_std
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- type: nauc_recall_at_20_diff1
value: 62.3022812728695
- type: nauc_recall_at_20_max
value: 52.02090746208397
- type: nauc_recall_at_20_std
value: 0.7629898806369458
- type: nauc_recall_at_3_diff1
value: 68.82856645994157
- type: nauc_recall_at_3_max
value: 43.94171571306612
- type: nauc_recall_at_3_std
value: -20.78595255410157
- type: nauc_recall_at_5_diff1
value: 66.62157622497897
- type: nauc_recall_at_5_max
value: 46.693981736038246
- type: nauc_recall_at_5_std
value: -17.412423571162954
- type: ndcg_at_1
value: 67.33
- type: ndcg_at_10
value: 79.209
- type: ndcg_at_100
value: 80.463
- type: ndcg_at_1000
value: 80.74799999999999
- type: ndcg_at_20
value: 79.81899999999999
- type: ndcg_at_3
value: 76.335
- type: ndcg_at_5
value: 78.011
- type: precision_at_1
value: 67.33
- type: precision_at_10
value: 9.020999999999999
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 4.63
- type: precision_at_3
value: 27.493000000000002
- type: precision_at_5
value: 17.308
- type: recall_at_1
value: 67.33
- type: recall_at_10
value: 90.21000000000001
- type: recall_at_100
value: 96.00999999999999
- type: recall_at_1000
value: 98.29
- type: recall_at_20
value: 92.60000000000001
- type: recall_at_3
value: 82.48
- type: recall_at_5
value: 86.53999999999999
- task:
type: Reranking
dataset:
name: MTEB RuBQReranking (default)
type: ai-forever/rubq-reranking
config: default
split: test
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
metrics:
- type: main_score
value: 65.57453932493252
- type: map
value: 65.57453932493252
- type: mrr
value: 70.51408205663526
- type: nAUC_map_diff1
value: 26.69583260609023
- type: nAUC_map_max
value: 12.928262749610663
- type: nAUC_map_std
value: 11.702468857903128
- type: nAUC_mrr_diff1
value: 28.5206955462174
- type: nAUC_mrr_max
value: 14.207162454694227
- type: nAUC_mrr_std
value: 10.725721001555296
- task:
type: Retrieval
dataset:
name: MTEB RuBQRetrieval (default)
type: ai-forever/rubq-retrieval
config: default
split: test
revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
metrics:
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value: 72.306
- type: map_at_1
value: 44.187
- type: map_at_10
value: 64.836
- type: map_at_100
value: 65.771
- type: map_at_1000
value: 65.8
- type: map_at_20
value: 65.497
- type: map_at_3
value: 59.692
- type: map_at_5
value: 63.105
- type: mrr_at_1
value: 62.23404255319149
- type: mrr_at_10
value: 73.40810161732159
- type: mrr_at_100
value: 73.67949305473395
- type: mrr_at_1000
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- type: mrr_at_20
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- type: mrr_at_3
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- type: mrr_at_5
value: 72.8447596532704
- type: nauc_map_at_1000_diff1
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- type: nauc_recall_at_1000_max
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- type: nauc_recall_at_1000_std
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- type: nauc_recall_at_100_max
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value: 0.6137471711639239
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- type: nauc_recall_at_1_max
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- type: nauc_recall_at_1_std
value: -8.06951079061697
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- type: nauc_recall_at_20_max
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value: 9.290819557000022
- type: nauc_recall_at_3_diff1
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- type: nauc_recall_at_3_max
value: 28.54289782140701
- type: nauc_recall_at_3_std
value: -5.1395675072005576
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- type: nauc_recall_at_5_max
value: 33.24658754857624
- type: nauc_recall_at_5_std
value: -4.211226036746632
- type: ndcg_at_1
value: 62.175000000000004
- type: ndcg_at_10
value: 72.306
- type: ndcg_at_100
value: 75.074
- type: ndcg_at_1000
value: 75.581
- type: ndcg_at_20
value: 73.875
- type: ndcg_at_3
value: 65.641
- type: ndcg_at_5
value: 69.48299999999999
- type: precision_at_1
value: 62.175000000000004
- type: precision_at_10
value: 13.907
- type: precision_at_100
value: 1.591
- type: precision_at_1000
value: 0.166
- type: precision_at_20
value: 7.446999999999999
- type: precision_at_3
value: 35.619
- type: precision_at_5
value: 24.917
- type: recall_at_1
value: 44.187
- type: recall_at_10
value: 85.10600000000001
- type: recall_at_100
value: 95.488
- type: recall_at_1000
value: 98.831
- type: recall_at_20
value: 90.22200000000001
- type: recall_at_3
value: 68.789
- type: recall_at_5
value: 77.85499999999999
- task:
type: Classification
dataset:
name: MTEB RuReviewsClassification (default)
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
- type: accuracy
value: 67.5830078125
- type: f1
value: 67.56931936632446
- type: f1_weighted
value: 67.57137733752779
- type: main_score
value: 67.5830078125
- task:
type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS (default)
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
- type: cosine_pearson
value: 85.90493484626788
- type: cosine_spearman
value: 86.21965691667411
- type: euclidean_pearson
value: 86.07499842984909
- type: euclidean_spearman
value: 86.55506818735688
- type: main_score
value: 86.21965691667411
- type: manhattan_pearson
value: 85.95976420231729
- type: manhattan_spearman
value: 86.48604243661234
- type: pearson
value: 85.90493484626788
- type: spearman
value: 86.21965691667411
- task:
type: Classification
dataset:
name: MTEB RuSciBenchGRNTIClassification (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: accuracy
value: 59.1943359375
- type: f1
value: 58.894480861440414
- type: f1_weighted
value: 58.903615560240866
- type: main_score
value: 59.1943359375
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: main_score
value: 57.99209448663228
- type: v_measure
value: 57.99209448663228
- type: v_measure_std
value: 1.0381163861993816
- task:
type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: accuracy
value: 45.556640625
- type: f1
value: 45.159163104085906
- type: f1_weighted
value: 45.16098316398626
- type: main_score
value: 45.556640625
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchOECDClusteringP2P (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: main_score
value: 50.787548070488974
- type: v_measure
value: 50.787548070488974
- type: v_measure_std
value: 0.8569958168946827
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS (default)
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.843
- type: map_at_10
value: 11.752
- type: map_at_100
value: 13.919
- type: map_at_1000
value: 14.198
- type: map_at_20
value: 12.898000000000001
- type: map_at_3
value: 8.603
- type: map_at_5
value: 10.069
- type: mrr_at_1
value: 23.799999999999997
- type: mrr_at_10
value: 34.449999999999996
- type: mrr_at_100
value: 35.64
- type: mrr_at_1000
value: 35.691
- type: mrr_at_20
value: 35.213
- type: mrr_at_3
value: 31.383
- type: mrr_at_5
value: 33.062999999999995
- type: ndcg_at_1
value: 23.799999999999997
- type: ndcg_at_10
value: 19.811
- type: ndcg_at_100
value: 28.108
- type: ndcg_at_1000
value: 33.1
- type: ndcg_at_20
value: 22.980999999999998
- type: ndcg_at_3
value: 19.153000000000002
- type: ndcg_at_5
value: 16.408
- type: precision_at_1
value: 23.799999999999997
- type: precision_at_10
value: 10.16
- type: precision_at_100
value: 2.1999999999999997
- type: precision_at_1000
value: 0.34099999999999997
- type: precision_at_20
value: 6.915
- type: precision_at_3
value: 17.8
- type: precision_at_5
value: 14.14
- type: recall_at_1
value: 4.843
- type: recall_at_10
value: 20.595
- type: recall_at_100
value: 44.66
- type: recall_at_1000
value: 69.152
- type: recall_at_20
value: 28.04
- type: recall_at_3
value: 10.833
- type: recall_at_5
value: 14.346999999999998
- type: main_score
value: 19.811
- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL (default)
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
metrics:
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value: 80.90093762739502
- type: cosine_accuracy_threshold
value: 94.40930485725403
- type: cosine_ap
value: 71.15400909912427
- type: cosine_f1
value: 66.8213457076566
- type: cosine_f1_threshold
value: 91.53673648834229
- type: cosine_precision
value: 62.4922504649721
- type: cosine_recall
value: 71.7948717948718
- type: dot_accuracy
value: 78.41418671015083
- type: dot_accuracy_threshold
value: 42924.45068359375
- type: dot_ap
value: 63.34003025365763
- type: dot_f1
value: 62.518258837277244
- type: dot_f1_threshold
value: 40900.738525390625
- type: dot_precision
value: 52.99653293709758
- type: dot_recall
value: 76.21082621082621
- type: euclidean_accuracy
value: 80.67672238075826
- type: euclidean_accuracy_threshold
value: 696.0524559020996
- type: euclidean_ap
value: 70.88762835990224
- type: euclidean_f1
value: 66.711051930759
- type: euclidean_f1_threshold
value: 878.5581588745117
- type: euclidean_precision
value: 62.625
- type: euclidean_recall
value: 71.36752136752136
- type: main_score
value: 71.15400909912427
- type: manhattan_accuracy
value: 80.65633917651854
- type: manhattan_accuracy_threshold
value: 17277.72674560547
- type: manhattan_ap
value: 70.67105336611716
- type: manhattan_f1
value: 66.51346027577151
- type: manhattan_f1_threshold
value: 21687.957763671875
- type: manhattan_precision
value: 61.69305724725944
- type: manhattan_recall
value: 72.15099715099716
- type: max_accuracy
value: 80.90093762739502
- type: max_ap
value: 71.15400909912427
- type: max_f1
value: 66.8213457076566
- type: max_precision
value: 62.625
- type: max_recall
value: 76.21082621082621
- type: similarity_accuracy
value: 80.90093762739502
- type: similarity_accuracy_threshold
value: 94.40930485725403
- type: similarity_ap
value: 71.15400909912427
- type: similarity_f1
value: 66.8213457076566
- type: similarity_f1_threshold
value: 91.53673648834229
- type: similarity_precision
value: 62.4922504649721
- type: similarity_recall
value: 71.7948717948718
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 92.3339946866199
- type: cosine_spearman
value: 89.61697355115497
- type: euclidean_pearson
value: 90.3264916449669
- type: euclidean_spearman
value: 89.36270451308866
- type: main_score
value: 89.61697355115497
- type: manhattan_pearson
value: 90.18909339052534
- type: manhattan_spearman
value: 89.28337093097377
- type: pearson
value: 92.3339946866199
- type: spearman
value: 89.61697355115497
- task:
type: STS
dataset:
name: MTEB SICK-R-PL (default)
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
metrics:
- type: cosine_pearson
value: 85.27883048457821
- type: cosine_spearman
value: 80.53204892678619
- type: euclidean_pearson
value: 82.78520705216168
- type: euclidean_spearman
value: 80.27848359873212
- type: main_score
value: 80.53204892678619
- type: manhattan_pearson
value: 82.63270640583454
- type: manhattan_spearman
value: 80.21507977473146
- type: pearson
value: 85.27883048457821
- type: spearman
value: 80.53204892678619
- task:
type: STS
dataset:
name: MTEB SICKFr (default)
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cosine_pearson
value: 88.77029361817212
- type: cosine_spearman
value: 83.9453600346894
- type: euclidean_pearson
value: 85.85331086208573
- type: euclidean_spearman
value: 83.70852031985308
- type: main_score
value: 83.9453600346894
- type: manhattan_pearson
value: 85.66222265885914
- type: manhattan_spearman
value: 83.60833111525962
- type: pearson
value: 88.77029361817212
- type: spearman
value: 83.9453600346894
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 88.76435859522375
- type: cosine_spearman
value: 82.43768167804375
- type: euclidean_pearson
value: 87.43566183874832
- type: euclidean_spearman
value: 82.82166873757507
- type: main_score
value: 82.43768167804375
- type: manhattan_pearson
value: 87.39450871380951
- type: manhattan_spearman
value: 82.89253043430163
- type: pearson
value: 88.76435859522375
- type: spearman
value: 82.43768167804375
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 88.86627241652141
- type: cosine_spearman
value: 89.49011599120688
- type: euclidean_pearson
value: 89.3314120073772
- type: euclidean_spearman
value: 89.8226502776963
- type: main_score
value: 89.49011599120688
- type: manhattan_pearson
value: 89.2252179076963
- type: manhattan_spearman
value: 89.74573844021225
- type: pearson
value: 88.86627241652141
- type: spearman
value: 89.49011599120688
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 87.22891405215968
- type: cosine_spearman
value: 84.9467188157614
- type: euclidean_pearson
value: 87.20330004726237
- type: euclidean_spearman
value: 85.34806059461808
- type: main_score
value: 84.9467188157614
- type: manhattan_pearson
value: 87.15224666107623
- type: manhattan_spearman
value: 85.34596898699708
- type: pearson
value: 87.22891405215968
- type: spearman
value: 84.9467188157614
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 88.14066430111033
- type: cosine_spearman
value: 89.31337445552545
- type: euclidean_pearson
value: 89.08039335366983
- type: euclidean_spearman
value: 89.6658762856415
- type: main_score
value: 89.31337445552545
- type: manhattan_pearson
value: 89.08057438154486
- type: manhattan_spearman
value: 89.68673984203022
- type: pearson
value: 88.14066430111033
- type: spearman
value: 89.31337445552545
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 85.14908856657084
- type: cosine_spearman
value: 86.84648320786727
- type: euclidean_pearson
value: 86.11454713131947
- type: euclidean_spearman
value: 86.77738862047961
- type: main_score
value: 86.84648320786727
- type: manhattan_pearson
value: 86.07804821916372
- type: manhattan_spearman
value: 86.78676064310474
- type: pearson
value: 85.14908856657084
- type: spearman
value: 86.84648320786727
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 89.61633502468356
- type: cosine_spearman
value: 89.99772663224805
- type: euclidean_pearson
value: 90.14056501501044
- type: euclidean_spearman
value: 90.04496896837503
- type: main_score
value: 89.99772663224805
- type: manhattan_pearson
value: 90.08964860311801
- type: manhattan_spearman
value: 90.00091712362196
- type: pearson
value: 89.61633502468356
- type: spearman
value: 89.99772663224805
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 86.44548026840202
- type: cosine_spearman
value: 87.26263108768539
- type: euclidean_pearson
value: 86.42844593583838
- type: euclidean_spearman
value: 86.89388428664364
- type: main_score
value: 87.26263108768539
- type: manhattan_pearson
value: 86.47186940800881
- type: manhattan_spearman
value: 87.02163091089946
- type: pearson
value: 86.44548026840202
- type: spearman
value: 87.26263108768539
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 87.89345132532758
- type: cosine_spearman
value: 87.96246221327699
- type: euclidean_pearson
value: 88.49013032701419
- type: euclidean_spearman
value: 87.81981265317344
- type: main_score
value: 87.96246221327699
- type: manhattan_pearson
value: 88.31360914178538
- type: manhattan_spearman
value: 87.62734530005075
- type: pearson
value: 87.89345132532758
- type: spearman
value: 87.96246221327699
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 88.4084678497171
- type: cosine_spearman
value: 88.77640638748285
- type: euclidean_pearson
value: 89.60124312475843
- type: euclidean_spearman
value: 88.4321442688528
- type: main_score
value: 88.77640638748285
- type: manhattan_pearson
value: 89.62375118021299
- type: manhattan_spearman
value: 88.46998118661577
- type: pearson
value: 88.4084678497171
- type: spearman
value: 88.77640638748285
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 87.30688801326498
- type: cosine_spearman
value: 87.55684697258378
- type: euclidean_pearson
value: 87.89672951056794
- type: euclidean_spearman
value: 87.28050429201674
- type: main_score
value: 87.55684697258378
- type: manhattan_pearson
value: 87.74292745320572
- type: manhattan_spearman
value: 87.16383993876582
- type: pearson
value: 87.30688801326498
- type: spearman
value: 87.55684697258378
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 73.46180375170147
- type: cosine_spearman
value: 73.39559590127081
- type: euclidean_pearson
value: 73.72613901293681
- type: euclidean_spearman
value: 71.85465165176795
- type: main_score
value: 73.39559590127081
- type: manhattan_pearson
value: 73.07859140869076
- type: manhattan_spearman
value: 71.22047343718893
- type: pearson
value: 73.46180375170147
- type: spearman
value: 73.39559590127081
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 62.47531620842637
- type: cosine_spearman
value: 66.22504667157702
- type: euclidean_pearson
value: 66.76201254783692
- type: euclidean_spearman
value: 66.86115760269463
- type: main_score
value: 66.22504667157702
- type: manhattan_pearson
value: 66.73847836793489
- type: manhattan_spearman
value: 66.7677116377695
- type: pearson
value: 62.47531620842637
- type: spearman
value: 66.22504667157702
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 69.89707002436481
- type: cosine_spearman
value: 72.2054865735116
- type: euclidean_pearson
value: 71.81856615570756
- type: euclidean_spearman
value: 72.72593304629407
- type: main_score
value: 72.2054865735116
- type: manhattan_pearson
value: 72.00362684700072
- type: manhattan_spearman
value: 72.62783534769964
- type: pearson
value: 69.89707002436481
- type: spearman
value: 72.2054865735116
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 81.59623734395916
- type: cosine_spearman
value: 83.28946105111358
- type: euclidean_pearson
value: 79.377330171466
- type: euclidean_spearman
value: 81.81029781662205
- type: main_score
value: 83.28946105111358
- type: manhattan_pearson
value: 78.96970881689698
- type: manhattan_spearman
value: 81.91773236079703
- type: pearson
value: 81.59623734395916
- type: spearman
value: 83.28946105111358
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 55.03825643126142
- type: cosine_spearman
value: 58.25792501780429
- type: euclidean_pearson
value: 50.38007603973409
- type: euclidean_spearman
value: 59.39961789383097
- type: main_score
value: 58.25792501780429
- type: manhattan_pearson
value: 50.518568927999155
- type: manhattan_spearman
value: 59.84185466003894
- type: pearson
value: 55.03825643126142
- type: spearman
value: 58.25792501780429
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 77.77233721490776
- type: cosine_spearman
value: 76.17596588017625
- type: euclidean_pearson
value: 74.47600468156611
- type: euclidean_spearman
value: 72.61278728057012
- type: main_score
value: 76.17596588017625
- type: manhattan_pearson
value: 74.48118910099699
- type: manhattan_spearman
value: 73.33167419101696
- type: pearson
value: 77.77233721490776
- type: spearman
value: 76.17596588017625
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 42.87453608131507
- type: cosine_spearman
value: 45.137849894401185
- type: euclidean_pearson
value: 31.66964197694796
- type: euclidean_spearman
value: 44.1014900837869
- type: main_score
value: 45.137849894401185
- type: manhattan_pearson
value: 31.007199259384745
- type: manhattan_spearman
value: 43.48181523288926
- type: pearson
value: 42.87453608131507
- type: spearman
value: 45.137849894401185
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 66.87400150638176
- type: cosine_spearman
value: 67.27861354834066
- type: euclidean_pearson
value: 66.81789582140216
- type: euclidean_spearman
value: 66.44220479858708
- type: main_score
value: 67.27861354834066
- type: manhattan_pearson
value: 66.92509859033235
- type: manhattan_spearman
value: 66.46841124185076
- type: pearson
value: 66.87400150638176
- type: spearman
value: 67.27861354834066
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 61.819804551576084
- type: cosine_spearman
value: 65.0864146772135
- type: euclidean_pearson
value: 62.518151090361876
- type: euclidean_spearman
value: 65.13608138548017
- type: main_score
value: 65.0864146772135
- type: manhattan_pearson
value: 62.51413246915267
- type: manhattan_spearman
value: 65.19077543064323
- type: pearson
value: 61.819804551576084
- type: spearman
value: 65.0864146772135
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 54.85728696035389
- type: cosine_spearman
value: 61.60906359227576
- type: euclidean_pearson
value: 52.57582587901851
- type: euclidean_spearman
value: 61.41823097598308
- type: main_score
value: 61.60906359227576
- type: manhattan_pearson
value: 52.500978361080506
- type: manhattan_spearman
value: 61.30365596659758
- type: pearson
value: 54.85728696035389
- type: spearman
value: 61.60906359227576
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 67.68016005631422
- type: cosine_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 66.19871164667245
- type: euclidean_spearman
value: 73.24670207647144
- type: main_score
value: 84.51542547285167
- type: manhattan_pearson
value: 67.0443525268974
- type: manhattan_spearman
value: 73.24670207647144
- type: pearson
value: 67.68016005631422
- type: spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 47.49467414030747
- type: cosine_spearman
value: 56.81512095681289
- type: euclidean_pearson
value: 48.42860221765214
- type: euclidean_spearman
value: 58.63197306329092
- type: main_score
value: 56.81512095681289
- type: manhattan_pearson
value: 48.39594959260441
- type: manhattan_spearman
value: 58.63197306329092
- type: pearson
value: 47.49467414030747
- type: spearman
value: 56.81512095681289
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 76.8364678896155
- type: cosine_spearman
value: 78.45516413087114
- type: euclidean_pearson
value: 78.62779318576634
- type: euclidean_spearman
value: 78.88760695649488
- type: main_score
value: 78.45516413087114
- type: manhattan_pearson
value: 78.62131335760031
- type: manhattan_spearman
value: 78.81861844200388
- type: pearson
value: 76.8364678896155
- type: spearman
value: 78.45516413087114
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 65.16640313911604
- type: cosine_spearman
value: 60.887608967403914
- type: euclidean_pearson
value: 67.49902244990913
- type: euclidean_spearman
value: 59.2458787136538
- type: main_score
value: 60.887608967403914
- type: manhattan_pearson
value: 67.34313506388378
- type: manhattan_spearman
value: 59.05283429200166
- type: pearson
value: 65.16640313911604
- type: spearman
value: 60.887608967403914
- task:
type: STS
dataset:
name: MTEB STSB (default)
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cosine_pearson
value: 81.5092853013241
- type: cosine_spearman
value: 83.54005474244292
- type: euclidean_pearson
value: 83.7246578378554
- type: euclidean_spearman
value: 84.46767551087716
- type: main_score
value: 83.54005474244292
- type: manhattan_pearson
value: 83.65922665594636
- type: manhattan_spearman
value: 84.42431449101848
- type: pearson
value: 81.5092853013241
- type: spearman
value: 83.54005474244292
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 87.70246866744966
- type: cosine_spearman
value: 89.44070045346106
- type: euclidean_pearson
value: 89.56956519641007
- type: euclidean_spearman
value: 89.95830112784283
- type: main_score
value: 89.44070045346106
- type: manhattan_pearson
value: 89.48264471425145
- type: manhattan_spearman
value: 89.87900732483114
- type: pearson
value: 87.70246866744966
- type: spearman
value: 89.44070045346106
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (de)
type: mteb/stsb_multi_mt
config: de
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 86.83701990805217
- type: cosine_spearman
value: 87.80280785492258
- type: euclidean_pearson
value: 87.77325330043514
- type: euclidean_spearman
value: 88.3564607283144
- type: main_score
value: 87.80280785492258
- type: manhattan_pearson
value: 87.6745449945946
- type: manhattan_spearman
value: 88.30660465978795
- type: pearson
value: 86.83701990805217
- type: spearman
value: 87.80280785492258
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (zh)
type: mteb/stsb_multi_mt
config: zh
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 84.27751020600267
- type: cosine_spearman
value: 85.63500407412486
- type: euclidean_pearson
value: 85.21829891649696
- type: euclidean_spearman
value: 85.9384575715382
- type: main_score
value: 85.63500407412486
- type: manhattan_pearson
value: 85.10797194089801
- type: manhattan_spearman
value: 85.8770162042784
- type: pearson
value: 84.27751020600267
- type: spearman
value: 85.63500407412486
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: mteb/stsb_multi_mt
config: fr
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 86.56833656723254
- type: cosine_spearman
value: 87.4393978501382
- type: euclidean_pearson
value: 87.45171512751267
- type: euclidean_spearman
value: 88.13106516566947
- type: main_score
value: 87.4393978501382
- type: manhattan_pearson
value: 87.33010961793333
- type: manhattan_spearman
value: 88.06707425102182
- type: pearson
value: 86.56833656723254
- type: spearman
value: 87.4393978501382
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (pl)
type: mteb/stsb_multi_mt
config: pl
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 85.45065540325523
- type: cosine_spearman
value: 85.47881076789359
- type: euclidean_pearson
value: 85.1999493863155
- type: euclidean_spearman
value: 85.7874947669187
- type: main_score
value: 85.47881076789359
- type: manhattan_pearson
value: 85.06075305990376
- type: manhattan_spearman
value: 85.71563015639558
- type: pearson
value: 85.45065540325523
- type: spearman
value: 85.47881076789359
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (es)
type: mteb/stsb_multi_mt
config: es
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 87.11952824079832
- type: cosine_spearman
value: 87.9643473573153
- type: euclidean_pearson
value: 88.11750364639971
- type: euclidean_spearman
value: 88.63695109016498
- type: main_score
value: 87.9643473573153
- type: manhattan_pearson
value: 88.00294453126699
- type: manhattan_spearman
value: 88.53750241758391
- type: pearson
value: 87.11952824079832
- type: spearman
value: 87.9643473573153
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (ru)
type: mteb/stsb_multi_mt
config: ru
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 85.99804354414991
- type: cosine_spearman
value: 86.30252111551002
- type: euclidean_pearson
value: 86.1880652037762
- type: euclidean_spearman
value: 86.69556223944502
- type: main_score
value: 86.30252111551002
- type: manhattan_pearson
value: 86.0736400320898
- type: manhattan_spearman
value: 86.61747927593393
- type: pearson
value: 85.99804354414991
- type: spearman
value: 86.30252111551002
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (en)
type: mteb/stsb_multi_mt
config: en
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 87.70246861738103
- type: cosine_spearman
value: 89.44070045346106
- type: euclidean_pearson
value: 89.56956518833663
- type: euclidean_spearman
value: 89.95830112784283
- type: main_score
value: 89.44070045346106
- type: manhattan_pearson
value: 89.48264470792915
- type: manhattan_spearman
value: 89.87900732483114
- type: pearson
value: 87.70246861738103
- type: spearman
value: 89.44070045346106
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.88064122814694
- type: mrr
value: 95.84832651009123
- type: main_score
value: 84.88064122814694
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 57.289
- type: map_at_10
value: 67.88499999999999
- type: map_at_100
value: 68.477
- type: map_at_1000
value: 68.50500000000001
- type: map_at_20
value: 68.33500000000001
- type: map_at_3
value: 65.08
- type: map_at_5
value: 67.001
- type: mrr_at_1
value: 59.667
- type: mrr_at_10
value: 68.626
- type: mrr_at_100
value: 69.082
- type: mrr_at_1000
value: 69.108
- type: mrr_at_20
value: 68.958
- type: mrr_at_3
value: 66.667
- type: mrr_at_5
value: 67.983
- type: ndcg_at_1
value: 59.667
- type: ndcg_at_10
value: 72.309
- type: ndcg_at_100
value: 74.58399999999999
- type: ndcg_at_1000
value: 75.25500000000001
- type: ndcg_at_20
value: 73.656
- type: ndcg_at_3
value: 67.791
- type: ndcg_at_5
value: 70.45
- type: precision_at_1
value: 59.667
- type: precision_at_10
value: 9.567
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.083
- type: precision_at_3
value: 26.333000000000002
- type: precision_at_5
value: 17.666999999999998
- type: recall_at_1
value: 57.289
- type: recall_at_10
value: 84.756
- type: recall_at_100
value: 94.5
- type: recall_at_1000
value: 99.667
- type: recall_at_20
value: 89.7
- type: recall_at_3
value: 73.22800000000001
- type: recall_at_5
value: 79.444
- type: main_score
value: 72.309
- task:
type: Clustering
dataset:
name: MTEB SpanishNewsClusteringP2P (default)
type: jinaai/spanish_news_clustering
config: default
split: test
revision: bf8ca8ddc5b7da4f7004720ddf99bbe0483480e6
metrics:
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type: Retrieval
dataset:
name: MTEB SpanishPassageRetrievalS2S (default)
type: jinaai/spanish_passage_retrieval
config: default
split: test
revision: 9cddf2ce5209ade52c2115ccfa00eb22c6d3a837
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- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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- type: euclidean_recall
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- type: main_score
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- type: manhattan_accuracy
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value: 92.5
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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dataset:
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type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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dataset:
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type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
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dataset:
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type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
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type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
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type: C-MTEB/T2Reranking
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type: C-MTEB/T2Retrieval
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- task:
type: PairClassification
dataset:
name: MTEB TERRa (default)
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
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- type: cosine_recall
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- type: euclidean_accuracy
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- type: euclidean_recall
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- type: main_score
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type: Classification
dataset:
name: MTEB TNews (default)
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
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value: 45.967999999999996
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type: Retrieval
dataset:
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type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
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- type: map_at_1000
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value: 0.679
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- type: mrr_at_1000
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- type: mrr_at_20
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- type: precision_at_1
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- type: precision_at_1000
value: 23.244
- type: precision_at_20
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- type: precision_at_3
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- type: precision_at_5
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- type: recall_at_1000
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- type: recall_at_3
value: 0.696
- type: recall_at_5
value: 1.1400000000000001
- type: main_score
value: 77.71900000000001
- task:
type: Clustering
dataset:
name: MTEB TenKGnadClusteringP2P (default)
type: slvnwhrl/tenkgnad-clustering-p2p
config: default
split: test
revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
metrics:
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dataset:
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type: slvnwhrl/tenkgnad-clustering-s2s
config: default
split: test
revision: 6cddbe003f12b9b140aec477b583ac4191f01786
metrics:
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type: Clustering
dataset:
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type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
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dataset:
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type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
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dataset:
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type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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value: 28.219
- type: precision_at_1
value: 46.939
- type: precision_at_10
value: 22.245
- type: precision_at_100
value: 7.531000000000001
- type: precision_at_1000
value: 1.5350000000000001
- type: precision_at_20
value: 17.041
- type: precision_at_3
value: 30.612000000000002
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 3.3550000000000004
- type: recall_at_10
value: 16.41
- type: recall_at_100
value: 47.272
- type: recall_at_1000
value: 83.584
- type: recall_at_20
value: 24.091
- type: recall_at_3
value: 6.8180000000000005
- type: recall_at_5
value: 9.677
- type: main_score
value: 26.298
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 91.2890625
- type: ap
value: 33.95547153875715
- type: ap_weighted
value: 33.95547153875715
- type: f1
value: 75.10768597556462
- type: f1_weighted
value: 92.00161208992606
- type: main_score
value: 91.2890625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 71.3978494623656
- type: f1
value: 71.7194818511814
- type: f1_weighted
value: 71.13860187349744
- type: main_score
value: 71.3978494623656
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 52.4921688720602
- type: v_measure
value: 52.4921688720602
- type: v_measure_std
value: 0.992768152658908
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 85.11652858079513
- type: cosine_accuracy_threshold
value: 87.90839910507202
- type: cosine_ap
value: 70.90459908851724
- type: cosine_f1
value: 65.66581227877457
- type: cosine_f1_threshold
value: 85.13308763504028
- type: cosine_precision
value: 61.094708153531684
- type: cosine_recall
value: 70.97625329815304
- type: dot_accuracy
value: 83.41181379269239
- type: dot_accuracy_threshold
value: 43110.113525390625
- type: dot_ap
value: 65.64869491143095
- type: dot_f1
value: 62.05308447460914
- type: dot_f1_threshold
value: 41412.542724609375
- type: dot_precision
value: 57.38623626989464
- type: dot_recall
value: 67.54617414248021
- type: euclidean_accuracy
value: 85.15229182809799
- type: euclidean_accuracy_threshold
value: 1043.08500289917
- type: euclidean_ap
value: 70.71204383269375
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value: 65.20304568527919
- type: euclidean_f1_threshold
value: 1179.2595863342285
- type: euclidean_precision
value: 62.81173594132029
- type: euclidean_recall
value: 67.78364116094987
- type: main_score
value: 70.90459908851724
- type: manhattan_accuracy
value: 85.1820945341837
- type: manhattan_accuracy_threshold
value: 26115.0390625
- type: manhattan_ap
value: 70.66113937117431
- type: manhattan_f1
value: 65.33383628819313
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value: 29105.181884765625
- type: manhattan_precision
value: 62.40691808791736
- type: manhattan_recall
value: 68.54881266490766
- type: max_accuracy
value: 85.1820945341837
- type: max_ap
value: 70.90459908851724
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value: 65.66581227877457
- type: max_precision
value: 62.81173594132029
- type: max_recall
value: 70.97625329815304
- type: similarity_accuracy
value: 85.11652858079513
- type: similarity_accuracy_threshold
value: 87.90839910507202
- type: similarity_ap
value: 70.90459908851724
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value: 65.66581227877457
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value: 85.13308763504028
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value: 61.094708153531684
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value: 70.97625329815304
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type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 88.10299996119068
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value: 84.13755787769226
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value: 76.0967548076923
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value: 82.8936219215393
- type: cosine_precision
value: 74.28864769727193
- type: cosine_recall
value: 77.99507237449954
- type: dot_accuracy
value: 86.64182869561843
- type: dot_accuracy_threshold
value: 38794.677734375
- type: dot_ap
value: 80.20301567411457
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value: 73.50650291634967
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- type: dot_precision
value: 69.41498460485802
- type: dot_recall
value: 78.11056359716662
- type: euclidean_accuracy
value: 87.9361198432103
- type: euclidean_accuracy_threshold
value: 1184.421157836914
- type: euclidean_ap
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- type: euclidean_f1
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- type: euclidean_precision
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- type: euclidean_recall
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- type: main_score
value: 84.13755787769226
- type: manhattan_accuracy
value: 87.96134590755618
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value: 29077.291870117188
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value: 83.79487172269923
- type: manhattan_f1
value: 75.82421603424935
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value: 72.24740255212329
- type: manhattan_recall
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- type: max_accuracy
value: 88.10299996119068
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value: 84.13755787769226
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value: 76.0967548076923
- type: max_precision
value: 74.28864769727193
- type: max_recall
value: 79.77363720357253
- type: similarity_accuracy
value: 88.10299996119068
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value: 74.28864769727193
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value: 77.99507237449954
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval (default)
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
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- type: map_at_1000
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- type: map_at_20
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- type: map_at_3
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- type: map_at_5
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- type: nauc_map_at_1000_diff1
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- type: nauc_recall_at_5_std
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- type: ndcg_at_1
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- type: ndcg_at_10
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- type: ndcg_at_100
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value: 69.085
- type: precision_at_1
value: 55.7
- type: precision_at_10
value: 8.4
- type: precision_at_100
value: 0.959
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 4.425
- type: precision_at_3
value: 24.8
- type: precision_at_5
value: 15.98
- type: recall_at_1
value: 55.7
- type: recall_at_10
value: 84.0
- type: recall_at_100
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- type: recall_at_1000
value: 98.2
- type: recall_at_20
value: 88.5
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value: 74.4
- type: recall_at_5
value: 79.9
- task:
type: Classification
dataset:
name: MTEB Waimai (default)
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
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value: 86.58999999999999
- type: ap
value: 70.02619249927523
- type: ap_weighted
value: 70.02619249927523
- type: f1
value: 84.97572770889423
- type: f1_weighted
value: 86.6865713531272
- type: main_score
value: 86.58999999999999
- task:
type: Retrieval
dataset:
name: MTEB XMarket (en)
type: jinaai/xmarket_ml
config: en
split: test
revision: dfe57acff5b62c23732a7b7d3e3fb84ff501708b
metrics:
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value: 7.2620000000000005
- type: map_at_10
value: 17.98
- type: map_at_100
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- type: map_at_1000
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- type: map_at_20
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- type: map_at_3
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- type: map_at_5
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value: 90.7384575424963
- type: nauc_recall_at_100_diff1
value: 41.47842281590715
- type: nauc_recall_at_100_max
value: 67.47271545727422
- type: nauc_recall_at_100_std
value: 14.555561992253999
- type: nauc_recall_at_10_diff1
value: 33.05308907973924
- type: nauc_recall_at_10_max
value: 45.49878918493155
- type: nauc_recall_at_10_std
value: -11.560069806810926
- type: nauc_recall_at_1_diff1
value: 61.89699552471587
- type: nauc_recall_at_1_max
value: 22.387748207421946
- type: nauc_recall_at_1_std
value: -17.139518194308437
- type: nauc_recall_at_20_diff1
value: 31.305721376453754
- type: nauc_recall_at_20_max
value: 51.24817763724019
- type: nauc_recall_at_20_std
value: -5.0809908162023145
- type: nauc_recall_at_3_diff1
value: 49.27109038342917
- type: nauc_recall_at_3_max
value: 37.69188317998447
- type: nauc_recall_at_3_std
value: -17.119900758664336
- type: nauc_recall_at_5_diff1
value: 42.74501803377967
- type: nauc_recall_at_5_max
value: 46.877008503354844
- type: nauc_recall_at_5_std
value: -15.704892082115975
- type: ndcg_at_1
value: 71.829
- type: ndcg_at_10
value: 77.581
- type: ndcg_at_100
value: 80.75
- type: ndcg_at_1000
value: 81.026
- type: ndcg_at_20
value: 79.092
- type: ndcg_at_3
value: 72.81
- type: ndcg_at_5
value: 74.22999999999999
- type: precision_at_1
value: 71.829
- type: precision_at_10
value: 17.717
- type: precision_at_100
value: 2.031
- type: precision_at_1000
value: 0.207
- type: precision_at_20
value: 9.399000000000001
- type: precision_at_3
value: 44.458999999999996
- type: precision_at_5
value: 31.535000000000004
- type: recall_at_1
value: 46.444
- type: recall_at_10
value: 86.275
- type: recall_at_100
value: 98.017
- type: recall_at_1000
value: 99.8
- type: recall_at_20
value: 90.935
- type: recall_at_3
value: 70.167
- type: recall_at_5
value: 78.2
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="center">
<b>jina-embeddings-v3: Multilingual Embeddings With Task LoRA</b>
</p>
## Quick Start
[Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3-vm) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings)
## Intended Usage & Model Info
`jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications.
Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation),
this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**.
Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.
### Key Features:
- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.
- **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options:
- `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks
- `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks
- `separation`: Used for embeddings in clustering and re-ranking applications
- `classification`: Used for embeddings in classification tasks
- `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application.
### Supported Languages:
While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages:
**Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek,
Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian,
Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
> **⚠️ Important Notice:**
> We fixed a bug in the `encode` function [#60](https://huggingface.co/jinaai/jina-embeddings-v3/discussions/60) where **Matryoshka embedding truncation** occurred *after normalization*, leading to non-normalized truncated embeddings. This issue has been resolved in the latest code revision.
>
> If you have encoded data using the previous version and wish to maintain consistency, please use the specific code revision when loading the model: `AutoModel.from_pretrained('jinaai/jina-embeddings-v3', code_revision='da863dd04a4e5dce6814c6625adfba87b83838aa', ...)`
## Usage
**<details><summary>Apply mean pooling when integrating the model.</summary>**
<p>
### Why Use Mean Pooling?
Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level.
This approach has been shown to produce high-quality sentence embeddings.
We provide an `encode` function that handles this for you automatically.
However, if you're working with the model directly, outside of the `encode` function,
you'll need to apply mean pooling manually. Here's how you can do it:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
sentences = ["How is the weather today?", "What is the current weather like today?"]
tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
task = 'retrieval.query'
task_id = model._adaptation_map[task]
adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32)
with torch.no_grad():
model_output = model(**encoded_input, adapter_mask=adapter_mask)
embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/).
Alternatively, you can use `jina-embeddings-v3` directly via Transformers package:
```bash
!pip install transformers torch einops
!pip install 'numpy<2'
```
If you run it on a GPU that support [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100),
```bash
!pip install flash-attn --no-build-isolation
```
```python
from transformers import AutoModel
# Initialize the model
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
texts = [
"Follow the white rabbit.", # English
"Sigue al conejo blanco.", # Spanish
"Suis le lapin blanc.", # French
"跟着白兔走。", # Chinese
"اتبع الأرنب الأبيض.", # Arabic
"Folge dem weißen Kaninchen.", # German
]
# When calling the `encode` function, you can choose a `task` based on the use case:
# 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching'
# Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used.
embeddings = model.encode(texts, task="text-matching")
# Compute similarities
print(embeddings[0] @ embeddings[1].T)
```
By default, the model supports a maximum sequence length of 8192 tokens.
However, if you want to truncate your input texts to a shorter length, you can pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(["Very long ... document"], max_length=2048)
```
In case you want to use **Matryoshka embeddings** and switch to a different dimension,
you can adjust it by passing the `truncate_dim` parameter to the `encode` function:
```python
embeddings = model.encode(['Sample text'], truncate_dim=256)
```
The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`:
```bash
!pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
task = "retrieval.query"
embeddings = model.encode(
["What is the weather like in Berlin today?"],
task=task,
prompt_name=task,
)
```
You can fine-tune `jina-embeddings-v3` using [SentenceTransformerTrainer](https://sbert.net/docs/package_reference/sentence_transformer/trainer.html).
To fine-tune for a specific task, you should set the task before passing the model to the ST Trainer, either during initialization:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'default_task': 'classification'})
```
Or afterwards:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
model[0].default_task = 'classification'
```
This way you can fine-tune the LoRA adapter for the chosen task.
However, If you want to fine-tune the entire model, make sure the main parameters are set as trainable when loading the model:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'lora_main_params_trainable': True})
```
This will allow fine-tuning the whole model instead of just the LoRA adapters.
**<details><summary>ONNX Inference.</summary>**
<p>
You can use ONNX for efficient inference with `jina-embeddings-v3`:
```python
import onnxruntime
import numpy as np
from transformers import AutoTokenizer, PretrainedConfig
# Mean pool function
def mean_pooling(model_output: np.ndarray, attention_mask: np.ndarray):
token_embeddings = model_output
input_mask_expanded = np.expand_dims(attention_mask, axis=-1)
input_mask_expanded = np.broadcast_to(input_mask_expanded, token_embeddings.shape)
sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
sum_mask = np.clip(np.sum(input_mask_expanded, axis=1), a_min=1e-9, a_max=None)
return sum_embeddings / sum_mask
# Load tokenizer and model config
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')
config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')
# Tokenize input
input_text = tokenizer('sample text', return_tensors='np')
# ONNX session
model_path = 'jina-embeddings-v3/onnx/model.onnx'
session = onnxruntime.InferenceSession(model_path)
# Prepare inputs for ONNX model
task_type = 'text-matching'
task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)
inputs = {
'input_ids': input_text['input_ids'],
'attention_mask': input_text['attention_mask'],
'task_id': task_id
}
# Run model
outputs = session.run(None, inputs)[0]
# Apply mean pooling and normalization to the model outputs
embeddings = mean_pooling(outputs, input_text["attention_mask"])
embeddings = embeddings / np.linalg.norm(embeddings, ord=2, axis=1, keepdims=True)
```
</p>
</details>
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## License
`jina-embeddings-v3` is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to [contact us](https://jina.ai/contact-sales/).
## Citation
If you find `jina-embeddings-v3` useful in your research, please cite the following paper:
```bibtex
@misc{sturua2024jinaembeddingsv3multilingualembeddingstask,
title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA},
author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao},
year={2024},
eprint={2409.10173},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.10173},
}
```
|
[
"BIOSSES",
"SCIFACT"
] |
RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel
|
RomainDarous
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:4460010",
"loss:CoSENTLoss",
"dataset:RomainDarous/corrupted_os_by_language",
"arxiv:1908.10084",
"base_model:RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel",
"base_model:finetune:RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2025-02-14T17:26:45Z |
2025-02-14T17:27:22+00:00
| 30 | 0 |
---
base_model: RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel
datasets:
- RomainDarous/corrupted_os_by_language
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4460010
- loss:CoSENTLoss
widget:
- source_sentence: Malformed target specific variable definition
sentences:
- Hedefe özgü değişken tanımı bozuk
- Kan alle data in die gids lees
- "слава Украине! героям слава!\uFEFF"
- source_sentence: Can't write an inode bitmap
sentences:
- Skontrolujte stav aktualizácií alebo to skúste znova neskôr.
- Malsukcesis skribi i nodan bitmapon
- Zastępuje wersję GL obsługiwaną przez sterownik
- source_sentence: Optimize soft proofing color transformations
sentences:
- 'arkadaslar biz artik her an kirmizi kart yiyecek,bencil,pas yapamayan,isabetsiz
orta yapani istemiyoruz. sozde efsaneniz bu sezon Besiktasa en cok zarar verenlerden
biriydi. kendini dusunmeden once Besiktasi dusunecek adam lazim bize. o yuzden
#GoHomeQuaresma'
- Yav bizim dedikodusunu yaptığımız insanın bile bi vizyonu var. Senin hakkında
neden oturup konuşalım?
- Ik ben een transgender.
- source_sentence: 'Pass 1: Checking @is, @bs, and sizes'
sentences:
- Bu adam cidden kurabiye gibi ben bunu çayın yanında yerim
- sagnat. errada. invisible. justificació. idioma
- Wilt u echt de primaire sleutel verplaatsen? (j N)
- source_sentence: Search for matching log entries
sentences:
- quem te lembra? caralho tô assustada aqui kkkkk
- sendotasunik gabeko\ egoera bistaratuko den ala ez adierazten du
- En aquest cas, hem d'incloure les imatges del contenidor )sr iov per a càrregues
de treball de telco (per exemple, com a referència, es podrien obtenir des de
valors de helm chart)
model-index:
- name: SentenceTransformer based on RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts eval
type: sts-eval
metrics:
- type: pearson_cosine
value: 0.9792227196278926
name: Pearson Cosine
- type: spearman_cosine
value: 0.8655734210695927
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9793897891905906
name: Pearson Cosine
- type: spearman_cosine
value: 0.8656311088147751
name: Spearman Cosine
- type: pearson_cosine
value: 0.9793897891905906
name: Pearson Cosine
- type: spearman_cosine
value: 0.8656311088147751
name: Spearman Cosine
---
# SentenceTransformer based on RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel](https://huggingface.co/RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel) on the [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel](https://huggingface.co/RomainDarous/large_directOneEpoch_additivePooling_noisedInit_mistranslationModel) <!-- at revision 69c26472f98f5f9e712638d6d7cc2be1c561e169 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): MultiHeadGeneralizedPooling(
(P): ModuleList(
(0-7): 8 x Linear(in_features=768, out_features=96, bias=True)
)
(W1): ModuleList(
(0-7): 8 x Linear(in_features=96, out_features=384, bias=True)
)
(W2): ModuleList(
(0-7): 8 x Linear(in_features=384, out_features=96, bias=True)
)
)
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel")
# Run inference
sentences = [
'Search for matching log entries',
'quem te lembra? caralho tô assustada aqui kkkkk',
'sendotasunik gabeko\\ egoera bistaratuko den ala ez adierazten du',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-eval`, `sts-test` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-eval | sts-test |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.9792 | 0.9794 |
| **spearman_cosine** | **0.8656** | **0.8656** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### corrupted_open_os_by_language
* Dataset: [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) at [9d25780](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language/tree/9d25780e2032b1e8f06af6a4ff55124d7a930c3c)
* Size: 4,460,010 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.33 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.47 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------|
| <code>Check spelling. Print the document. Show completion window. General. Show help</code> | <code>Kontrolli õigekirja. присоединяюсь. </code> | <code>0</code> |
| <code>EXIF not supported for this file format.</code> | <code>Šiam failo formatui EXIF nepalaikomas.</code> | <code>1</code> |
| <code>This package includes the documentation for texlive everyhook</code> | <code>Paket ini menyertakan dokumentasi untuk texlive everyhook</code> | <code>1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### corrupted_open_os_by_language
* Dataset: [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) at [9d25780](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language/tree/9d25780e2032b1e8f06af6a4ff55124d7a930c3c)
* Size: 4,460,010 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 17.71 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 26.95 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Could not identify the current seat.</code> | <code> 天天花着男人的钱还这这创造新词汇男权你可真牛批,你也就这一出了一问男权，就说是我是吧,到现在我也没听到你给我们讲的男权,你也就是在网上喷喷，现实走道都不敢探头自卑，你现实要把你女权的劲拿出来总低啥头,您老应该去国家教育局把男权加上是吧,你们女权天天说自己生活不好没地位，给你们地位了你们能干啥？用你们的女权打到全世界男性是吧,能相出男权这一词您老也是人才呀，是不是庆幸自己是个女的,活在自己想想的世界里不觉得孤单吗，假象有男权是吧,自己假象和男权还说自己不是田园女权,田园女权能连自己都骂说自己妈是驴爸是大鼎的也是奇葩呀,那我们国家大肆宣扬过你们这么田园女权吗，国家要的是女性人群自主自理，你们可好看看你们女权干的啥事,给你们女权地位高了，看看你们女权干的事n绿地集团高管怎么都不说呀，人家可是有钱有地位,也不是我们说三从四德洗衣做饭你们女权会吗？,那我问问你们女权干过啥惊天大事,还甩锅给孔子，还封建社会，那我问问你们女权在福利面前为啥说自己是女性呀不是社会主义社会吗不应该男女平等吗,天天自己也不知道是不是抱个手机天天欧巴欧巴，你家那位要是不陪你看一会就会问你是不是不爱我了是吧大姐,您老也就赚这白菜钱操心国家事,中国五千年的历史被您老一句否决，还嘲讽人家日本女性，好意思说自己不是女权，三从四德流传这么久到您这变成日本文化了,我就想问问男权您老是怎么想的,那你问孔子老人家呗为什么女人要三从四德,我说的是女权你干嘛自己对号入座,连中华人民传承的东西都不认跟我这谈男权,还男权您老给我举个例子呗，让我们男权听听都是h啥,这些不都是你们女权的标准吗？,还男权，您老醒醒吧这里是现实，不是你的公主世界，总觉得自己多么多么重要，地球没你是不能转了还是人类要灭亡呀,我真的想问一句你给我找一条男权的新闻,咋了我们男人不能提女权呗你老授权了呗,那我们谈论田园女权你老对号入座干嘛,天天过节要礼物，还嫌弃自己男朋友没有钱，我寻思你找个有钱人包养你呗，对了有钱人怎么可能看上你这种女权的呢,还要孩子跟女方姓我也没看见你没跟你妈姓呀,年年过节男人给你们送礼物你们女人给男人送过礼物吗？，一问我不是陪着他吗我对他说我爱你了这不是最好的礼物吗？，男人只要不送礼物就是不爱你们了呗,人家国际女权讲的男人能做的我们女人也能做，田园女权男人能做的我们女人为啥要做,还男权我笑了，以前结婚几头牛换个衣服原装的，现在几十万彩...</code> | <code>0</code> |
| <code>Undoing Date and Time Adjustment</code> | <code>正在取消日期和时间调整</code> | <code>1</code> |
| <code>Dependency package for gsl_2_6 gnu hpc</code> | <code>Pacotes de desenvolvimento do KDE</code> | <code>1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | corrupted open os by language loss | sts-eval_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:-----:|:-------------:|:----------------------------------:|:------------------------:|:------------------------:|
| 1.0 | 55751 | 0.2647 | 0.2770 | 0.8656 | - |
| -1 | -1 | - | - | - | 0.8656 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.3.0
- Datasets: 2.16.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
[
"CAS"
] |
pythonist/distilbert-base-uncased-finetuned-PubmedQA
|
pythonist
|
question-answering
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-10-17T04:37:10Z |
2022-12-04T03:51:05+00:00
| 29 | 0 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-PubmedQA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-PubmedQA
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 21 | 4.6513 |
| No log | 2.0 | 42 | 4.1809 |
| No log | 3.0 | 63 | 4.1888 |
| No log | 4.0 | 84 | 4.0779 |
| No log | 5.0 | 105 | 4.1221 |
| No log | 6.0 | 126 | 4.1381 |
| No log | 7.0 | 147 | 4.0619 |
| No log | 8.0 | 168 | 4.1242 |
| No log | 9.0 | 189 | 4.1044 |
| No log | 10.0 | 210 | 4.1699 |
| No log | 11.0 | 231 | 4.1761 |
| No log | 12.0 | 252 | 4.3132 |
| No log | 13.0 | 273 | 4.2233 |
| No log | 14.0 | 294 | 4.3036 |
| No log | 15.0 | 315 | 4.2894 |
| No log | 16.0 | 336 | 4.3075 |
| No log | 17.0 | 357 | 4.3120 |
| No log | 18.0 | 378 | 4.2841 |
| No log | 19.0 | 399 | 4.3161 |
| No log | 20.0 | 420 | 4.2957 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
[
"PUBMEDQA"
] |
GBaker/bioclinicalbert-base-medqa-usmle-nocontext
|
GBaker
|
multiple-choice
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-02-19T23:12:53Z |
2023-02-20T00:03:50+00:00
| 29 | 0 |
---
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bioclinicalbert-base-medqa-usmle-nocontext
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bioclinicalbert-base-medqa-usmle-nocontext
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4564
- Accuracy: 0.3009
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.98 | 39 | 1.3836 | 0.2757 |
| No log | 1.98 | 78 | 1.3801 | 0.2828 |
| No log | 2.98 | 117 | 1.3816 | 0.3024 |
| No log | 3.98 | 156 | 1.4107 | 0.3111 |
| No log | 4.98 | 195 | 1.4412 | 0.3032 |
| No log | 5.98 | 234 | 1.4564 | 0.3009 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
[
"MEDQA"
] |
Dogebooch/BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi
|
Dogebooch
|
token-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-05-16T02:42:46Z |
2023-05-16T12:49:11+00:00
| 29 | 0 |
---
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: ncbi_disease
type: ncbi_disease
config: ncbi_disease
split: test
args: ncbi_disease
metrics:
- type: precision
value: 0.8604187437686939
name: Precision
- type: recall
value: 0.8989583333333333
name: Recall
- type: f1
value: 0.879266428935303
name: F1
- type: accuracy
value: 0.9870188186308527
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi
This model is a fine-tuned version of [pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb](https://huggingface.co/pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0814
- Precision: 0.8604
- Recall: 0.8990
- F1: 0.8793
- Accuracy: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 340 | 0.0481 | 0.8308 | 0.8438 | 0.8372 | 0.9840 |
| 0.0715 | 2.0 | 680 | 0.0497 | 0.8337 | 0.8771 | 0.8548 | 0.9857 |
| 0.0152 | 3.0 | 1020 | 0.0588 | 0.8596 | 0.8802 | 0.8698 | 0.9858 |
| 0.0152 | 4.0 | 1360 | 0.0589 | 0.8589 | 0.8875 | 0.8730 | 0.9873 |
| 0.0059 | 5.0 | 1700 | 0.0693 | 0.8412 | 0.8938 | 0.8667 | 0.9852 |
| 0.003 | 6.0 | 2040 | 0.0770 | 0.8701 | 0.9 | 0.8848 | 0.9863 |
| 0.003 | 7.0 | 2380 | 0.0787 | 0.861 | 0.8969 | 0.8786 | 0.9863 |
| 0.0014 | 8.0 | 2720 | 0.0760 | 0.8655 | 0.8979 | 0.8814 | 0.9872 |
| 0.0007 | 9.0 | 3060 | 0.0817 | 0.8589 | 0.8938 | 0.8760 | 0.9865 |
| 0.0007 | 10.0 | 3400 | 0.0814 | 0.8604 | 0.8990 | 0.8793 | 0.9870 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"MEDNLI",
"NCBI DISEASE",
"SCITAIL"
] |
michaelfeil/ct2fast-e5-small
|
michaelfeil
|
sentence-similarity
|
[
"sentence-transformers",
"bert",
"ctranslate2",
"int8",
"float16",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-18T11:41:56Z |
2023-10-13T13:36:53+00:00
| 29 | 1 |
---
language:
- en
license: mit
tags:
- ctranslate2
- int8
- float16
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-small
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.22388059701493
- type: ap
value: 40.27466219523129
- type: f1
value: 70.60533006025108
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 87.525775
- type: ap
value: 83.51063993897611
- type: f1
value: 87.49342736805572
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.611999999999995
- type: f1
value: 42.05088045932892
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.826
- type: map_at_10
value: 38.269
- type: map_at_100
value: 39.322
- type: map_at_1000
value: 39.344
- type: map_at_3
value: 33.428000000000004
- type: map_at_5
value: 36.063
- type: mrr_at_1
value: 24.253
- type: mrr_at_10
value: 38.425
- type: mrr_at_100
value: 39.478
- type: mrr_at_1000
value: 39.5
- type: mrr_at_3
value: 33.606
- type: mrr_at_5
value: 36.195
- type: ndcg_at_1
value: 23.826
- type: ndcg_at_10
value: 46.693
- type: ndcg_at_100
value: 51.469
- type: ndcg_at_1000
value: 52.002
- type: ndcg_at_3
value: 36.603
- type: ndcg_at_5
value: 41.365
- type: precision_at_1
value: 23.826
- type: precision_at_10
value: 7.383000000000001
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 15.268
- type: precision_at_5
value: 11.479000000000001
- type: recall_at_1
value: 23.826
- type: recall_at_10
value: 73.82600000000001
- type: recall_at_100
value: 95.306
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 45.804
- type: recall_at_5
value: 57.397
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.13995374767436
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 37.13950072624313
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.35843292105327
- type: mrr
value: 73.72312359846987
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.55140418324174
- type: cos_sim_spearman
value: 84.21637675860022
- type: euclidean_pearson
value: 81.26069614610006
- type: euclidean_spearman
value: 83.25069210421785
- type: manhattan_pearson
value: 80.17441422581014
- type: manhattan_spearman
value: 81.87596198487877
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.87337662337661
- type: f1
value: 81.76647866926402
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.80600542614507
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.86321613256603
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.054
- type: map_at_10
value: 40.699999999999996
- type: map_at_100
value: 41.818
- type: map_at_1000
value: 41.959999999999994
- type: map_at_3
value: 37.742
- type: map_at_5
value: 39.427
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 46.150000000000006
- type: mrr_at_100
value: 46.865
- type: mrr_at_1000
value: 46.925
- type: mrr_at_3
value: 43.705
- type: mrr_at_5
value: 45.214999999999996
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 45.778
- type: ndcg_at_100
value: 50.38
- type: ndcg_at_1000
value: 52.922999999999995
- type: ndcg_at_3
value: 41.597
- type: ndcg_at_5
value: 43.631
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 8.269
- type: precision_at_100
value: 1.278
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.266
- type: precision_at_5
value: 13.705
- type: recall_at_1
value: 32.054
- type: recall_at_10
value: 54.947
- type: recall_at_100
value: 74.79599999999999
- type: recall_at_1000
value: 91.40899999999999
- type: recall_at_3
value: 42.431000000000004
- type: recall_at_5
value: 48.519
- type: map_at_1
value: 29.035
- type: map_at_10
value: 38.007000000000005
- type: map_at_100
value: 39.125
- type: map_at_1000
value: 39.251999999999995
- type: map_at_3
value: 35.77
- type: map_at_5
value: 37.057
- type: mrr_at_1
value: 36.497
- type: mrr_at_10
value: 44.077
- type: mrr_at_100
value: 44.743
- type: mrr_at_1000
value: 44.79
- type: mrr_at_3
value: 42.123
- type: mrr_at_5
value: 43.308
- type: ndcg_at_1
value: 36.497
- type: ndcg_at_10
value: 42.986000000000004
- type: ndcg_at_100
value: 47.323
- type: ndcg_at_1000
value: 49.624
- type: ndcg_at_3
value: 39.805
- type: ndcg_at_5
value: 41.286
- type: precision_at_1
value: 36.497
- type: precision_at_10
value: 7.8340000000000005
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.248
- type: recall_at_1
value: 29.035
- type: recall_at_10
value: 51.06
- type: recall_at_100
value: 69.64099999999999
- type: recall_at_1000
value: 84.49
- type: recall_at_3
value: 41.333999999999996
- type: recall_at_5
value: 45.663
- type: map_at_1
value: 37.239
- type: map_at_10
value: 47.873
- type: map_at_100
value: 48.842999999999996
- type: map_at_1000
value: 48.913000000000004
- type: map_at_3
value: 45.050000000000004
- type: map_at_5
value: 46.498
- type: mrr_at_1
value: 42.508
- type: mrr_at_10
value: 51.44
- type: mrr_at_100
value: 52.087
- type: mrr_at_1000
value: 52.129999999999995
- type: mrr_at_3
value: 49.164
- type: mrr_at_5
value: 50.343
- type: ndcg_at_1
value: 42.508
- type: ndcg_at_10
value: 53.31399999999999
- type: ndcg_at_100
value: 57.245000000000005
- type: ndcg_at_1000
value: 58.794000000000004
- type: ndcg_at_3
value: 48.295
- type: ndcg_at_5
value: 50.415
- type: precision_at_1
value: 42.508
- type: precision_at_10
value: 8.458
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 21.191
- type: precision_at_5
value: 14.307
- type: recall_at_1
value: 37.239
- type: recall_at_10
value: 65.99000000000001
- type: recall_at_100
value: 82.99499999999999
- type: recall_at_1000
value: 94.128
- type: recall_at_3
value: 52.382
- type: recall_at_5
value: 57.648999999999994
- type: map_at_1
value: 23.039
- type: map_at_10
value: 29.694
- type: map_at_100
value: 30.587999999999997
- type: map_at_1000
value: 30.692999999999998
- type: map_at_3
value: 27.708
- type: map_at_5
value: 28.774
- type: mrr_at_1
value: 24.633
- type: mrr_at_10
value: 31.478
- type: mrr_at_100
value: 32.299
- type: mrr_at_1000
value: 32.381
- type: mrr_at_3
value: 29.435
- type: mrr_at_5
value: 30.446
- type: ndcg_at_1
value: 24.633
- type: ndcg_at_10
value: 33.697
- type: ndcg_at_100
value: 38.080000000000005
- type: ndcg_at_1000
value: 40.812
- type: ndcg_at_3
value: 29.654000000000003
- type: ndcg_at_5
value: 31.474000000000004
- type: precision_at_1
value: 24.633
- type: precision_at_10
value: 5.0729999999999995
- type: precision_at_100
value: 0.753
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.279
- type: precision_at_5
value: 8.452
- type: recall_at_1
value: 23.039
- type: recall_at_10
value: 44.275999999999996
- type: recall_at_100
value: 64.4
- type: recall_at_1000
value: 85.135
- type: recall_at_3
value: 33.394
- type: recall_at_5
value: 37.687
- type: map_at_1
value: 13.594999999999999
- type: map_at_10
value: 19.933999999999997
- type: map_at_100
value: 20.966
- type: map_at_1000
value: 21.087
- type: map_at_3
value: 17.749000000000002
- type: map_at_5
value: 19.156000000000002
- type: mrr_at_1
value: 17.662
- type: mrr_at_10
value: 24.407
- type: mrr_at_100
value: 25.385
- type: mrr_at_1000
value: 25.465
- type: mrr_at_3
value: 22.056
- type: mrr_at_5
value: 23.630000000000003
- type: ndcg_at_1
value: 17.662
- type: ndcg_at_10
value: 24.391
- type: ndcg_at_100
value: 29.681
- type: ndcg_at_1000
value: 32.923
- type: ndcg_at_3
value: 20.271
- type: ndcg_at_5
value: 22.621
- type: precision_at_1
value: 17.662
- type: precision_at_10
value: 4.44
- type: precision_at_100
value: 0.8200000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 9.577
- type: precision_at_5
value: 7.313
- type: recall_at_1
value: 13.594999999999999
- type: recall_at_10
value: 33.976
- type: recall_at_100
value: 57.43000000000001
- type: recall_at_1000
value: 80.958
- type: recall_at_3
value: 22.897000000000002
- type: recall_at_5
value: 28.714000000000002
- type: map_at_1
value: 26.683
- type: map_at_10
value: 35.068
- type: map_at_100
value: 36.311
- type: map_at_1000
value: 36.436
- type: map_at_3
value: 32.371
- type: map_at_5
value: 33.761
- type: mrr_at_1
value: 32.435
- type: mrr_at_10
value: 40.721000000000004
- type: mrr_at_100
value: 41.535
- type: mrr_at_1000
value: 41.593
- type: mrr_at_3
value: 38.401999999999994
- type: mrr_at_5
value: 39.567
- type: ndcg_at_1
value: 32.435
- type: ndcg_at_10
value: 40.538000000000004
- type: ndcg_at_100
value: 45.963
- type: ndcg_at_1000
value: 48.400999999999996
- type: ndcg_at_3
value: 36.048
- type: ndcg_at_5
value: 37.899
- type: precision_at_1
value: 32.435
- type: precision_at_10
value: 7.1129999999999995
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 16.683
- type: precision_at_5
value: 11.684
- type: recall_at_1
value: 26.683
- type: recall_at_10
value: 51.517
- type: recall_at_100
value: 74.553
- type: recall_at_1000
value: 90.649
- type: recall_at_3
value: 38.495000000000005
- type: recall_at_5
value: 43.495
- type: map_at_1
value: 24.186
- type: map_at_10
value: 31.972
- type: map_at_100
value: 33.117000000000004
- type: map_at_1000
value: 33.243
- type: map_at_3
value: 29.423
- type: map_at_5
value: 30.847
- type: mrr_at_1
value: 29.794999999999998
- type: mrr_at_10
value: 36.767
- type: mrr_at_100
value: 37.645
- type: mrr_at_1000
value: 37.716
- type: mrr_at_3
value: 34.513
- type: mrr_at_5
value: 35.791000000000004
- type: ndcg_at_1
value: 29.794999999999998
- type: ndcg_at_10
value: 36.786
- type: ndcg_at_100
value: 41.94
- type: ndcg_at_1000
value: 44.830999999999996
- type: ndcg_at_3
value: 32.504
- type: ndcg_at_5
value: 34.404
- type: precision_at_1
value: 29.794999999999998
- type: precision_at_10
value: 6.518
- type: precision_at_100
value: 1.0659999999999998
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 15.296999999999999
- type: precision_at_5
value: 10.731
- type: recall_at_1
value: 24.186
- type: recall_at_10
value: 46.617
- type: recall_at_100
value: 68.75
- type: recall_at_1000
value: 88.864
- type: recall_at_3
value: 34.199
- type: recall_at_5
value: 39.462
- type: map_at_1
value: 24.22083333333333
- type: map_at_10
value: 31.606666666666662
- type: map_at_100
value: 32.6195
- type: map_at_1000
value: 32.739999999999995
- type: map_at_3
value: 29.37825
- type: map_at_5
value: 30.596083333333336
- type: mrr_at_1
value: 28.607916666666668
- type: mrr_at_10
value: 35.54591666666666
- type: mrr_at_100
value: 36.33683333333333
- type: mrr_at_1000
value: 36.40624999999999
- type: mrr_at_3
value: 33.526250000000005
- type: mrr_at_5
value: 34.6605
- type: ndcg_at_1
value: 28.607916666666668
- type: ndcg_at_10
value: 36.07966666666667
- type: ndcg_at_100
value: 40.73308333333333
- type: ndcg_at_1000
value: 43.40666666666666
- type: ndcg_at_3
value: 32.23525
- type: ndcg_at_5
value: 33.97083333333333
- type: precision_at_1
value: 28.607916666666668
- type: precision_at_10
value: 6.120333333333335
- type: precision_at_100
value: 0.9921666666666668
- type: precision_at_1000
value: 0.14091666666666666
- type: precision_at_3
value: 14.54975
- type: precision_at_5
value: 10.153166666666667
- type: recall_at_1
value: 24.22083333333333
- type: recall_at_10
value: 45.49183333333334
- type: recall_at_100
value: 66.28133333333332
- type: recall_at_1000
value: 85.16541666666667
- type: recall_at_3
value: 34.6485
- type: recall_at_5
value: 39.229749999999996
- type: map_at_1
value: 21.842
- type: map_at_10
value: 27.573999999999998
- type: map_at_100
value: 28.410999999999998
- type: map_at_1000
value: 28.502
- type: map_at_3
value: 25.921
- type: map_at_5
value: 26.888
- type: mrr_at_1
value: 24.08
- type: mrr_at_10
value: 29.915999999999997
- type: mrr_at_100
value: 30.669
- type: mrr_at_1000
value: 30.746000000000002
- type: mrr_at_3
value: 28.349000000000004
- type: mrr_at_5
value: 29.246
- type: ndcg_at_1
value: 24.08
- type: ndcg_at_10
value: 30.898999999999997
- type: ndcg_at_100
value: 35.272999999999996
- type: ndcg_at_1000
value: 37.679
- type: ndcg_at_3
value: 27.881
- type: ndcg_at_5
value: 29.432000000000002
- type: precision_at_1
value: 24.08
- type: precision_at_10
value: 4.678
- type: precision_at_100
value: 0.744
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 11.860999999999999
- type: precision_at_5
value: 8.16
- type: recall_at_1
value: 21.842
- type: recall_at_10
value: 38.66
- type: recall_at_100
value: 59.169000000000004
- type: recall_at_1000
value: 76.887
- type: recall_at_3
value: 30.532999999999998
- type: recall_at_5
value: 34.354
- type: map_at_1
value: 17.145
- type: map_at_10
value: 22.729
- type: map_at_100
value: 23.574
- type: map_at_1000
value: 23.695
- type: map_at_3
value: 21.044
- type: map_at_5
value: 21.981
- type: mrr_at_1
value: 20.888
- type: mrr_at_10
value: 26.529000000000003
- type: mrr_at_100
value: 27.308
- type: mrr_at_1000
value: 27.389000000000003
- type: mrr_at_3
value: 24.868000000000002
- type: mrr_at_5
value: 25.825
- type: ndcg_at_1
value: 20.888
- type: ndcg_at_10
value: 26.457000000000004
- type: ndcg_at_100
value: 30.764000000000003
- type: ndcg_at_1000
value: 33.825
- type: ndcg_at_3
value: 23.483999999999998
- type: ndcg_at_5
value: 24.836
- type: precision_at_1
value: 20.888
- type: precision_at_10
value: 4.58
- type: precision_at_100
value: 0.784
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 10.874
- type: precision_at_5
value: 7.639
- type: recall_at_1
value: 17.145
- type: recall_at_10
value: 33.938
- type: recall_at_100
value: 53.672
- type: recall_at_1000
value: 76.023
- type: recall_at_3
value: 25.363000000000003
- type: recall_at_5
value: 29.023
- type: map_at_1
value: 24.275
- type: map_at_10
value: 30.438
- type: map_at_100
value: 31.489
- type: map_at_1000
value: 31.601000000000003
- type: map_at_3
value: 28.647
- type: map_at_5
value: 29.660999999999998
- type: mrr_at_1
value: 28.077999999999996
- type: mrr_at_10
value: 34.098
- type: mrr_at_100
value: 35.025
- type: mrr_at_1000
value: 35.109
- type: mrr_at_3
value: 32.4
- type: mrr_at_5
value: 33.379999999999995
- type: ndcg_at_1
value: 28.077999999999996
- type: ndcg_at_10
value: 34.271
- type: ndcg_at_100
value: 39.352
- type: ndcg_at_1000
value: 42.199
- type: ndcg_at_3
value: 30.978
- type: ndcg_at_5
value: 32.498
- type: precision_at_1
value: 28.077999999999996
- type: precision_at_10
value: 5.345
- type: precision_at_100
value: 0.897
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 13.526
- type: precision_at_5
value: 9.16
- type: recall_at_1
value: 24.275
- type: recall_at_10
value: 42.362
- type: recall_at_100
value: 64.461
- type: recall_at_1000
value: 84.981
- type: recall_at_3
value: 33.249
- type: recall_at_5
value: 37.214999999999996
- type: map_at_1
value: 22.358
- type: map_at_10
value: 30.062
- type: map_at_100
value: 31.189
- type: map_at_1000
value: 31.386999999999997
- type: map_at_3
value: 27.672
- type: map_at_5
value: 28.76
- type: mrr_at_1
value: 26.877000000000002
- type: mrr_at_10
value: 33.948
- type: mrr_at_100
value: 34.746
- type: mrr_at_1000
value: 34.816
- type: mrr_at_3
value: 31.884
- type: mrr_at_5
value: 33.001000000000005
- type: ndcg_at_1
value: 26.877000000000002
- type: ndcg_at_10
value: 34.977000000000004
- type: ndcg_at_100
value: 39.753
- type: ndcg_at_1000
value: 42.866
- type: ndcg_at_3
value: 30.956
- type: ndcg_at_5
value: 32.381
- type: precision_at_1
value: 26.877000000000002
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 1.287
- type: precision_at_1000
value: 0.215
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.119
- type: recall_at_1
value: 22.358
- type: recall_at_10
value: 44.183
- type: recall_at_100
value: 67.14
- type: recall_at_1000
value: 87.53999999999999
- type: recall_at_3
value: 32.79
- type: recall_at_5
value: 36.829
- type: map_at_1
value: 19.198999999999998
- type: map_at_10
value: 25.229000000000003
- type: map_at_100
value: 26.003
- type: map_at_1000
value: 26.111
- type: map_at_3
value: 23.442
- type: map_at_5
value: 24.343
- type: mrr_at_1
value: 21.072
- type: mrr_at_10
value: 27.02
- type: mrr_at_100
value: 27.735
- type: mrr_at_1000
value: 27.815
- type: mrr_at_3
value: 25.416
- type: mrr_at_5
value: 26.173999999999996
- type: ndcg_at_1
value: 21.072
- type: ndcg_at_10
value: 28.862
- type: ndcg_at_100
value: 33.043
- type: ndcg_at_1000
value: 36.003
- type: ndcg_at_3
value: 25.35
- type: ndcg_at_5
value: 26.773000000000003
- type: precision_at_1
value: 21.072
- type: precision_at_10
value: 4.436
- type: precision_at_100
value: 0.713
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 10.659
- type: precision_at_5
value: 7.32
- type: recall_at_1
value: 19.198999999999998
- type: recall_at_10
value: 38.376
- type: recall_at_100
value: 58.36900000000001
- type: recall_at_1000
value: 80.92099999999999
- type: recall_at_3
value: 28.715000000000003
- type: recall_at_5
value: 32.147
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.9319999999999995
- type: map_at_10
value: 10.483
- type: map_at_100
value: 11.97
- type: map_at_1000
value: 12.171999999999999
- type: map_at_3
value: 8.477
- type: map_at_5
value: 9.495000000000001
- type: mrr_at_1
value: 13.094
- type: mrr_at_10
value: 21.282
- type: mrr_at_100
value: 22.556
- type: mrr_at_1000
value: 22.628999999999998
- type: mrr_at_3
value: 18.218999999999998
- type: mrr_at_5
value: 19.900000000000002
- type: ndcg_at_1
value: 13.094
- type: ndcg_at_10
value: 15.811
- type: ndcg_at_100
value: 23.035
- type: ndcg_at_1000
value: 27.089999999999996
- type: ndcg_at_3
value: 11.905000000000001
- type: ndcg_at_5
value: 13.377
- type: precision_at_1
value: 13.094
- type: precision_at_10
value: 5.225
- type: precision_at_100
value: 1.2970000000000002
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 8.86
- type: precision_at_5
value: 7.309
- type: recall_at_1
value: 5.9319999999999995
- type: recall_at_10
value: 20.305
- type: recall_at_100
value: 46.314
- type: recall_at_1000
value: 69.612
- type: recall_at_3
value: 11.21
- type: recall_at_5
value: 14.773
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.674
- type: map_at_10
value: 17.822
- type: map_at_100
value: 24.794
- type: map_at_1000
value: 26.214
- type: map_at_3
value: 12.690999999999999
- type: map_at_5
value: 15.033
- type: mrr_at_1
value: 61.75000000000001
- type: mrr_at_10
value: 71.58
- type: mrr_at_100
value: 71.923
- type: mrr_at_1000
value: 71.932
- type: mrr_at_3
value: 70.125
- type: mrr_at_5
value: 71.038
- type: ndcg_at_1
value: 51
- type: ndcg_at_10
value: 38.637
- type: ndcg_at_100
value: 42.398
- type: ndcg_at_1000
value: 48.962
- type: ndcg_at_3
value: 43.29
- type: ndcg_at_5
value: 40.763
- type: precision_at_1
value: 61.75000000000001
- type: precision_at_10
value: 30.125
- type: precision_at_100
value: 9.53
- type: precision_at_1000
value: 1.9619999999999997
- type: precision_at_3
value: 45.583
- type: precision_at_5
value: 38.95
- type: recall_at_1
value: 8.674
- type: recall_at_10
value: 23.122
- type: recall_at_100
value: 47.46
- type: recall_at_1000
value: 67.662
- type: recall_at_3
value: 13.946
- type: recall_at_5
value: 17.768
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.86000000000001
- type: f1
value: 41.343580452760776
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.609
- type: map_at_10
value: 47.552
- type: map_at_100
value: 48.283
- type: map_at_1000
value: 48.321
- type: map_at_3
value: 44.869
- type: map_at_5
value: 46.509
- type: mrr_at_1
value: 39.214
- type: mrr_at_10
value: 50.434999999999995
- type: mrr_at_100
value: 51.122
- type: mrr_at_1000
value: 51.151
- type: mrr_at_3
value: 47.735
- type: mrr_at_5
value: 49.394
- type: ndcg_at_1
value: 39.214
- type: ndcg_at_10
value: 53.52400000000001
- type: ndcg_at_100
value: 56.997
- type: ndcg_at_1000
value: 57.975
- type: ndcg_at_3
value: 48.173
- type: ndcg_at_5
value: 51.05800000000001
- type: precision_at_1
value: 39.214
- type: precision_at_10
value: 7.573
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 19.782
- type: precision_at_5
value: 13.453000000000001
- type: recall_at_1
value: 36.609
- type: recall_at_10
value: 69.247
- type: recall_at_100
value: 84.99600000000001
- type: recall_at_1000
value: 92.40899999999999
- type: recall_at_3
value: 54.856
- type: recall_at_5
value: 61.797000000000004
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.466
- type: map_at_10
value: 27.060000000000002
- type: map_at_100
value: 28.511999999999997
- type: map_at_1000
value: 28.693
- type: map_at_3
value: 22.777
- type: map_at_5
value: 25.086000000000002
- type: mrr_at_1
value: 32.716
- type: mrr_at_10
value: 41.593999999999994
- type: mrr_at_100
value: 42.370000000000005
- type: mrr_at_1000
value: 42.419000000000004
- type: mrr_at_3
value: 38.143
- type: mrr_at_5
value: 40.288000000000004
- type: ndcg_at_1
value: 32.716
- type: ndcg_at_10
value: 34.795
- type: ndcg_at_100
value: 40.58
- type: ndcg_at_1000
value: 43.993
- type: ndcg_at_3
value: 29.573
- type: ndcg_at_5
value: 31.583
- type: precision_at_1
value: 32.716
- type: precision_at_10
value: 9.937999999999999
- type: precision_at_100
value: 1.585
- type: precision_at_1000
value: 0.22
- type: precision_at_3
value: 19.496
- type: precision_at_5
value: 15.247
- type: recall_at_1
value: 16.466
- type: recall_at_10
value: 42.886
- type: recall_at_100
value: 64.724
- type: recall_at_1000
value: 85.347
- type: recall_at_3
value: 26.765
- type: recall_at_5
value: 33.603
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.025
- type: map_at_10
value: 47.343
- type: map_at_100
value: 48.207
- type: map_at_1000
value: 48.281
- type: map_at_3
value: 44.519
- type: map_at_5
value: 46.217000000000006
- type: mrr_at_1
value: 66.05
- type: mrr_at_10
value: 72.94699999999999
- type: mrr_at_100
value: 73.289
- type: mrr_at_1000
value: 73.30499999999999
- type: mrr_at_3
value: 71.686
- type: mrr_at_5
value: 72.491
- type: ndcg_at_1
value: 66.05
- type: ndcg_at_10
value: 56.338
- type: ndcg_at_100
value: 59.599999999999994
- type: ndcg_at_1000
value: 61.138000000000005
- type: ndcg_at_3
value: 52.034000000000006
- type: ndcg_at_5
value: 54.352000000000004
- type: precision_at_1
value: 66.05
- type: precision_at_10
value: 11.693000000000001
- type: precision_at_100
value: 1.425
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 32.613
- type: precision_at_5
value: 21.401999999999997
- type: recall_at_1
value: 33.025
- type: recall_at_10
value: 58.467
- type: recall_at_100
value: 71.242
- type: recall_at_1000
value: 81.452
- type: recall_at_3
value: 48.92
- type: recall_at_5
value: 53.504
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 75.5492
- type: ap
value: 69.42911637216271
- type: f1
value: 75.39113704261024
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.173
- type: map_at_10
value: 35.453
- type: map_at_100
value: 36.573
- type: map_at_1000
value: 36.620999999999995
- type: map_at_3
value: 31.655
- type: map_at_5
value: 33.823
- type: mrr_at_1
value: 23.868000000000002
- type: mrr_at_10
value: 36.085
- type: mrr_at_100
value: 37.15
- type: mrr_at_1000
value: 37.193
- type: mrr_at_3
value: 32.376
- type: mrr_at_5
value: 34.501
- type: ndcg_at_1
value: 23.854
- type: ndcg_at_10
value: 42.33
- type: ndcg_at_100
value: 47.705999999999996
- type: ndcg_at_1000
value: 48.91
- type: ndcg_at_3
value: 34.604
- type: ndcg_at_5
value: 38.473
- type: precision_at_1
value: 23.854
- type: precision_at_10
value: 6.639
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.685
- type: precision_at_5
value: 10.782
- type: recall_at_1
value: 23.173
- type: recall_at_10
value: 63.441
- type: recall_at_100
value: 88.25
- type: recall_at_1000
value: 97.438
- type: recall_at_3
value: 42.434
- type: recall_at_5
value: 51.745
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.05426356589147
- type: f1
value: 91.88068588063942
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.23985408116735
- type: f1
value: 55.858906745287506
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.21923335574984
- type: f1
value: 70.0174116204253
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.77673167451245
- type: f1
value: 75.44811354778666
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.340414710728737
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.196676760061578
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.564149683482206
- type: mrr
value: 30.28995474250486
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.93
- type: map_at_10
value: 12.828000000000001
- type: map_at_100
value: 15.501000000000001
- type: map_at_1000
value: 16.791
- type: map_at_3
value: 9.727
- type: map_at_5
value: 11.318999999999999
- type: mrr_at_1
value: 47.678
- type: mrr_at_10
value: 55.893
- type: mrr_at_100
value: 56.491
- type: mrr_at_1000
value: 56.53
- type: mrr_at_3
value: 54.386
- type: mrr_at_5
value: 55.516
- type: ndcg_at_1
value: 45.975
- type: ndcg_at_10
value: 33.928999999999995
- type: ndcg_at_100
value: 30.164
- type: ndcg_at_1000
value: 38.756
- type: ndcg_at_3
value: 41.077000000000005
- type: ndcg_at_5
value: 38.415
- type: precision_at_1
value: 47.678
- type: precision_at_10
value: 24.365000000000002
- type: precision_at_100
value: 7.344
- type: precision_at_1000
value: 1.994
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 33.003
- type: recall_at_1
value: 5.93
- type: recall_at_10
value: 16.239
- type: recall_at_100
value: 28.782999999999998
- type: recall_at_1000
value: 60.11
- type: recall_at_3
value: 10.700999999999999
- type: recall_at_5
value: 13.584
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.163000000000004
- type: map_at_10
value: 51.520999999999994
- type: map_at_100
value: 52.449
- type: map_at_1000
value: 52.473000000000006
- type: map_at_3
value: 47.666
- type: map_at_5
value: 50.043000000000006
- type: mrr_at_1
value: 40.266999999999996
- type: mrr_at_10
value: 54.074
- type: mrr_at_100
value: 54.722
- type: mrr_at_1000
value: 54.739000000000004
- type: mrr_at_3
value: 51.043000000000006
- type: mrr_at_5
value: 52.956
- type: ndcg_at_1
value: 40.238
- type: ndcg_at_10
value: 58.73199999999999
- type: ndcg_at_100
value: 62.470000000000006
- type: ndcg_at_1000
value: 63.083999999999996
- type: ndcg_at_3
value: 51.672
- type: ndcg_at_5
value: 55.564
- type: precision_at_1
value: 40.238
- type: precision_at_10
value: 9.279
- type: precision_at_100
value: 1.139
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.078000000000003
- type: precision_at_5
value: 16.176
- type: recall_at_1
value: 36.163000000000004
- type: recall_at_10
value: 77.88199999999999
- type: recall_at_100
value: 93.83399999999999
- type: recall_at_1000
value: 98.465
- type: recall_at_3
value: 59.857000000000006
- type: recall_at_5
value: 68.73599999999999
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.344
- type: map_at_10
value: 83.907
- type: map_at_100
value: 84.536
- type: map_at_1000
value: 84.557
- type: map_at_3
value: 80.984
- type: map_at_5
value: 82.844
- type: mrr_at_1
value: 81.02000000000001
- type: mrr_at_10
value: 87.158
- type: mrr_at_100
value: 87.268
- type: mrr_at_1000
value: 87.26899999999999
- type: mrr_at_3
value: 86.17
- type: mrr_at_5
value: 86.87
- type: ndcg_at_1
value: 81.02000000000001
- type: ndcg_at_10
value: 87.70700000000001
- type: ndcg_at_100
value: 89.004
- type: ndcg_at_1000
value: 89.139
- type: ndcg_at_3
value: 84.841
- type: ndcg_at_5
value: 86.455
- type: precision_at_1
value: 81.02000000000001
- type: precision_at_10
value: 13.248999999999999
- type: precision_at_100
value: 1.516
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.963
- type: precision_at_5
value: 24.33
- type: recall_at_1
value: 70.344
- type: recall_at_10
value: 94.75099999999999
- type: recall_at_100
value: 99.30499999999999
- type: recall_at_1000
value: 99.928
- type: recall_at_3
value: 86.506
- type: recall_at_5
value: 91.083
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.873718018378305
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 56.39477366450528
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.868
- type: map_at_10
value: 9.611
- type: map_at_100
value: 11.087
- type: map_at_1000
value: 11.332
- type: map_at_3
value: 6.813
- type: map_at_5
value: 8.233
- type: mrr_at_1
value: 19
- type: mrr_at_10
value: 28.457
- type: mrr_at_100
value: 29.613
- type: mrr_at_1000
value: 29.695
- type: mrr_at_3
value: 25.55
- type: mrr_at_5
value: 27.29
- type: ndcg_at_1
value: 19
- type: ndcg_at_10
value: 16.419
- type: ndcg_at_100
value: 22.817999999999998
- type: ndcg_at_1000
value: 27.72
- type: ndcg_at_3
value: 15.379000000000001
- type: ndcg_at_5
value: 13.645
- type: precision_at_1
value: 19
- type: precision_at_10
value: 8.540000000000001
- type: precision_at_100
value: 1.7819999999999998
- type: precision_at_1000
value: 0.297
- type: precision_at_3
value: 14.267
- type: precision_at_5
value: 12.04
- type: recall_at_1
value: 3.868
- type: recall_at_10
value: 17.288
- type: recall_at_100
value: 36.144999999999996
- type: recall_at_1000
value: 60.199999999999996
- type: recall_at_3
value: 8.688
- type: recall_at_5
value: 12.198
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.96614722598582
- type: cos_sim_spearman
value: 78.9003023008781
- type: euclidean_pearson
value: 81.01829384436505
- type: euclidean_spearman
value: 78.93248416788914
- type: manhattan_pearson
value: 81.1665428926402
- type: manhattan_spearman
value: 78.93264116287453
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.54613363895993
- type: cos_sim_spearman
value: 75.1883451602451
- type: euclidean_pearson
value: 79.70320886899894
- type: euclidean_spearman
value: 74.5917140136796
- type: manhattan_pearson
value: 79.82157067185999
- type: manhattan_spearman
value: 74.74185720594735
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.30430156721782
- type: cos_sim_spearman
value: 81.79962989974364
- type: euclidean_pearson
value: 80.89058823224924
- type: euclidean_spearman
value: 81.35929372984597
- type: manhattan_pearson
value: 81.12204370487478
- type: manhattan_spearman
value: 81.6248963282232
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.13064504403134
- type: cos_sim_spearman
value: 78.48371403924872
- type: euclidean_pearson
value: 80.16794919665591
- type: euclidean_spearman
value: 78.29216082221699
- type: manhattan_pearson
value: 80.22308565207301
- type: manhattan_spearman
value: 78.37829229948022
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.52918899541099
- type: cos_sim_spearman
value: 87.49276894673142
- type: euclidean_pearson
value: 86.77440570164254
- type: euclidean_spearman
value: 87.5753295736756
- type: manhattan_pearson
value: 86.86098573892133
- type: manhattan_spearman
value: 87.65848591821947
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.86805307244882
- type: cos_sim_spearman
value: 84.58066253757511
- type: euclidean_pearson
value: 84.38377000876991
- type: euclidean_spearman
value: 85.1837278784528
- type: manhattan_pearson
value: 84.41903291363842
- type: manhattan_spearman
value: 85.19023736251052
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.77218560282436
- type: cos_sim_spearman
value: 87.94243515296604
- type: euclidean_pearson
value: 88.22800939214864
- type: euclidean_spearman
value: 87.91106839439841
- type: manhattan_pearson
value: 88.17063269848741
- type: manhattan_spearman
value: 87.72751904126062
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.40731554300387
- type: cos_sim_spearman
value: 63.76300532966479
- type: euclidean_pearson
value: 62.94727878229085
- type: euclidean_spearman
value: 63.678039531461216
- type: manhattan_pearson
value: 63.00661039863549
- type: manhattan_spearman
value: 63.6282591984376
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.92731569745344
- type: cos_sim_spearman
value: 86.36336704300167
- type: euclidean_pearson
value: 86.09122224841195
- type: euclidean_spearman
value: 86.2116149319238
- type: manhattan_pearson
value: 86.07879456717032
- type: manhattan_spearman
value: 86.2022069635119
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.75976311752326
- type: mrr
value: 94.15782837351466
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 51.193999999999996
- type: map_at_10
value: 61.224999999999994
- type: map_at_100
value: 62.031000000000006
- type: map_at_1000
value: 62.066
- type: map_at_3
value: 59.269000000000005
- type: map_at_5
value: 60.159
- type: mrr_at_1
value: 53.667
- type: mrr_at_10
value: 62.74999999999999
- type: mrr_at_100
value: 63.39399999999999
- type: mrr_at_1000
value: 63.425
- type: mrr_at_3
value: 61.389
- type: mrr_at_5
value: 61.989000000000004
- type: ndcg_at_1
value: 53.667
- type: ndcg_at_10
value: 65.596
- type: ndcg_at_100
value: 68.906
- type: ndcg_at_1000
value: 69.78999999999999
- type: ndcg_at_3
value: 62.261
- type: ndcg_at_5
value: 63.453
- type: precision_at_1
value: 53.667
- type: precision_at_10
value: 8.667
- type: precision_at_100
value: 1.04
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 24.556
- type: precision_at_5
value: 15.6
- type: recall_at_1
value: 51.193999999999996
- type: recall_at_10
value: 77.156
- type: recall_at_100
value: 91.43299999999999
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 67.994
- type: recall_at_5
value: 71.14399999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81485148514851
- type: cos_sim_ap
value: 95.28896513388551
- type: cos_sim_f1
value: 90.43478260869566
- type: cos_sim_precision
value: 92.56544502617801
- type: cos_sim_recall
value: 88.4
- type: dot_accuracy
value: 99.30594059405941
- type: dot_ap
value: 61.6432597455472
- type: dot_f1
value: 59.46481665014866
- type: dot_precision
value: 58.93909626719057
- type: dot_recall
value: 60
- type: euclidean_accuracy
value: 99.81980198019802
- type: euclidean_ap
value: 95.21411049527
- type: euclidean_f1
value: 91.06090373280944
- type: euclidean_precision
value: 89.47876447876449
- type: euclidean_recall
value: 92.7
- type: manhattan_accuracy
value: 99.81782178217821
- type: manhattan_ap
value: 95.32449994414968
- type: manhattan_f1
value: 90.86395233366436
- type: manhattan_precision
value: 90.23668639053254
- type: manhattan_recall
value: 91.5
- type: max_accuracy
value: 99.81980198019802
- type: max_ap
value: 95.32449994414968
- type: max_f1
value: 91.06090373280944
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.08045614613064
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.297802606804748
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.12801740706292
- type: mrr
value: 50.05592956879722
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.523347880124497
- type: cos_sim_spearman
value: 31.388214436391014
- type: dot_pearson
value: 24.55403435439901
- type: dot_spearman
value: 23.50153210841191
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.243
- type: map_at_10
value: 1.886
- type: map_at_100
value: 10.040000000000001
- type: map_at_1000
value: 23.768
- type: map_at_3
value: 0.674
- type: map_at_5
value: 1.079
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.667
- type: mrr_at_100
value: 93.667
- type: mrr_at_1000
value: 93.667
- type: mrr_at_3
value: 93.667
- type: mrr_at_5
value: 93.667
- type: ndcg_at_1
value: 83
- type: ndcg_at_10
value: 76.777
- type: ndcg_at_100
value: 55.153
- type: ndcg_at_1000
value: 47.912
- type: ndcg_at_3
value: 81.358
- type: ndcg_at_5
value: 80.74799999999999
- type: precision_at_1
value: 88
- type: precision_at_10
value: 80.80000000000001
- type: precision_at_100
value: 56.02
- type: precision_at_1000
value: 21.51
- type: precision_at_3
value: 86
- type: precision_at_5
value: 86
- type: recall_at_1
value: 0.243
- type: recall_at_10
value: 2.0869999999999997
- type: recall_at_100
value: 13.014000000000001
- type: recall_at_1000
value: 44.433
- type: recall_at_3
value: 0.6910000000000001
- type: recall_at_5
value: 1.1440000000000001
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.066
- type: map_at_10
value: 10.615
- type: map_at_100
value: 16.463
- type: map_at_1000
value: 17.815
- type: map_at_3
value: 5.7860000000000005
- type: map_at_5
value: 7.353999999999999
- type: mrr_at_1
value: 38.775999999999996
- type: mrr_at_10
value: 53.846000000000004
- type: mrr_at_100
value: 54.37
- type: mrr_at_1000
value: 54.37
- type: mrr_at_3
value: 48.980000000000004
- type: mrr_at_5
value: 51.735
- type: ndcg_at_1
value: 34.694
- type: ndcg_at_10
value: 26.811
- type: ndcg_at_100
value: 37.342999999999996
- type: ndcg_at_1000
value: 47.964
- type: ndcg_at_3
value: 30.906
- type: ndcg_at_5
value: 27.77
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 23.878
- type: precision_at_100
value: 7.632999999999999
- type: precision_at_1000
value: 1.469
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 26.939
- type: recall_at_1
value: 3.066
- type: recall_at_10
value: 17.112
- type: recall_at_100
value: 47.723
- type: recall_at_1000
value: 79.50500000000001
- type: recall_at_3
value: 6.825
- type: recall_at_5
value: 9.584
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.76460000000002
- type: ap
value: 14.944240012137053
- type: f1
value: 55.89805777266571
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.30503678551217
- type: f1
value: 63.57492701921179
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 37.51066495006874
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.07021517553794
- type: cos_sim_ap
value: 74.15520712370555
- type: cos_sim_f1
value: 68.64321608040201
- type: cos_sim_precision
value: 65.51558752997602
- type: cos_sim_recall
value: 72.0844327176781
- type: dot_accuracy
value: 80.23484532395541
- type: dot_ap
value: 54.298763810214176
- type: dot_f1
value: 53.22254659779924
- type: dot_precision
value: 46.32525410476936
- type: dot_recall
value: 62.532981530343015
- type: euclidean_accuracy
value: 86.04637301066937
- type: euclidean_ap
value: 73.85333854233123
- type: euclidean_f1
value: 68.77723660599845
- type: euclidean_precision
value: 66.87437686939182
- type: euclidean_recall
value: 70.79155672823218
- type: manhattan_accuracy
value: 85.98676759849795
- type: manhattan_ap
value: 73.56016090035973
- type: manhattan_f1
value: 68.48878539036647
- type: manhattan_precision
value: 63.9505607690547
- type: manhattan_recall
value: 73.7203166226913
- type: max_accuracy
value: 86.07021517553794
- type: max_ap
value: 74.15520712370555
- type: max_f1
value: 68.77723660599845
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.92769821865176
- type: cos_sim_ap
value: 85.78879502899773
- type: cos_sim_f1
value: 78.14414083990464
- type: cos_sim_precision
value: 74.61651607480563
- type: cos_sim_recall
value: 82.0218663381583
- type: dot_accuracy
value: 84.95750378390964
- type: dot_ap
value: 75.80219641857563
- type: dot_f1
value: 70.13966179585681
- type: dot_precision
value: 65.71140262361251
- type: dot_recall
value: 75.20788420080073
- type: euclidean_accuracy
value: 88.93546008460433
- type: euclidean_ap
value: 85.72056428301667
- type: euclidean_f1
value: 78.14387902598124
- type: euclidean_precision
value: 75.3376688344172
- type: euclidean_recall
value: 81.16723129042192
- type: manhattan_accuracy
value: 88.96262661543835
- type: manhattan_ap
value: 85.76605136314335
- type: manhattan_f1
value: 78.26696165191743
- type: manhattan_precision
value: 75.0990659496179
- type: manhattan_recall
value: 81.71388974437943
- type: max_accuracy
value: 88.96262661543835
- type: max_ap
value: 85.78879502899773
- type: max_f1
value: 78.26696165191743
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [intfloat/e5-small](https://huggingface.co/intfloat/e5-small)
```bash
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1
```
```python
# from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-e5-small"
model_name_orig="intfloat/e5-small"
from hf_hub_ctranslate2 import EncoderCT2fromHfHub
model = EncoderCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16"
)
outputs = model.generate(
text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
max_length=64,
) # perform downstream tasks on outputs
outputs["pooler_output"]
outputs["last_hidden_state"]
outputs["attention_mask"]
# alternative, use SentenceTransformer Mix-In
# for end-to-end Sentence embeddings generation
# (not pulling from this CT2fast-HF repo)
from hf_hub_ctranslate2 import CT2SentenceTransformer
model = CT2SentenceTransformer(
model_name_orig, compute_type="int8_float16", device="cuda"
)
embeddings = model.encode(
["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
batch_size=32,
convert_to_numpy=True,
normalize_embeddings=True,
)
print(embeddings.shape, embeddings)
scores = (embeddings @ embeddings.T) * 100
# Hint: you can also host this code via REST API and
# via github.com/michaelfeil/infinity
```
Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
Converted on 2023-10-13 using
```
LLama-2 -> removed <pad> token.
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
# E5-small
**News (May 2023): please switch to [e5-small-v2](https://huggingface.co/intfloat/e5-small-v2), which has better performance and same method of usage.**
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 12 layers and the embedding size is 384.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small')
model = AutoModel.from_pretrained('intfloat/e5-small')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-small')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
|
[
"BIOSSES",
"SCIFACT"
] |
IIC/bsc-bio-ehr-es-ctebmsp
|
IIC
|
token-classification
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"biomedical",
"clinical",
"spanish",
"bsc-bio-ehr-es",
"token-classification",
"es",
"dataset:lcampillos/ctebmsp",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-21T06:49:45Z |
2025-01-17T10:51:52+00:00
| 29 | 0 |
---
datasets:
- lcampillos/ctebmsp
language: es
license: apache-2.0
metrics:
- f1
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- spanish
- bsc-bio-ehr-es
model-index:
- name: IIC/bsc-bio-ehr-es-ctebmsp
results:
- task:
type: token-classification
dataset:
name: CT-EBM-SP (Clinical Trials for Evidence-based Medicine in Spanish)
type: lcampillos/ctebmsp
split: test
metrics:
- type: f1
value: 0.876
name: f1
---
# bsc-bio-ehr-es-ctebmsp
This model is a finetuned version of bsc-bio-ehr-es for the CT-EBM-SP (Clinical Trials for Evidence-based Medicine in Spanish) dataset used in a benchmark in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks`. The model has a F1 of 0.876
Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information.
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 16 |
| learning rate | 4e-05 |
| classifier dropout | 0.2 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtext
@article{10.1093/jamia/ocae054,
author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
journal = {Journal of the American Medical Informatics Association},
volume = {31},
number = {9},
pages = {2137-2146},
year = {2024},
month = {03},
issn = {1527-974X},
doi = {10.1093/jamia/ocae054},
url = {https://doi.org/10.1093/jamia/ocae054},
}
```
|
[
"CT-EBM-SP"
] |
fresha/e5-large-v2-endpoint
|
fresha
|
feature-extraction
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-28T18:23:10Z |
2023-06-28T20:21:13+00:00
| 29 | 0 |
---
language:
- en
license: mit
tags:
- mteb
model-index:
- name: e5-large-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.22388059701493
- type: ap
value: 43.20816505595132
- type: f1
value: 73.27811303522058
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.748325
- type: ap
value: 90.72534979701297
- type: f1
value: 93.73895874282185
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.612
- type: f1
value: 47.61157345898393
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.541999999999998
- type: map_at_10
value: 38.208
- type: map_at_100
value: 39.417
- type: map_at_1000
value: 39.428999999999995
- type: map_at_3
value: 33.95
- type: map_at_5
value: 36.329
- type: mrr_at_1
value: 23.755000000000003
- type: mrr_at_10
value: 38.288
- type: mrr_at_100
value: 39.511
- type: mrr_at_1000
value: 39.523
- type: mrr_at_3
value: 34.009
- type: mrr_at_5
value: 36.434
- type: ndcg_at_1
value: 23.541999999999998
- type: ndcg_at_10
value: 46.417
- type: ndcg_at_100
value: 51.812000000000005
- type: ndcg_at_1000
value: 52.137
- type: ndcg_at_3
value: 37.528
- type: ndcg_at_5
value: 41.81
- type: precision_at_1
value: 23.541999999999998
- type: precision_at_10
value: 7.269
- type: precision_at_100
value: 0.9690000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 15.979
- type: precision_at_5
value: 11.664
- type: recall_at_1
value: 23.541999999999998
- type: recall_at_10
value: 72.688
- type: recall_at_100
value: 96.871
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 47.937000000000005
- type: recall_at_5
value: 58.321
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.546499570522094
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.01607489943561
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.616107510107774
- type: mrr
value: 72.75106626214661
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.33018094733868
- type: cos_sim_spearman
value: 83.60190492611737
- type: euclidean_pearson
value: 82.1492450218961
- type: euclidean_spearman
value: 82.70308926526991
- type: manhattan_pearson
value: 81.93959600076842
- type: manhattan_spearman
value: 82.73260801016369
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.54545454545455
- type: f1
value: 84.49582530928923
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.362725540120096
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.849509608178145
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.502999999999997
- type: map_at_10
value: 43.323
- type: map_at_100
value: 44.708999999999996
- type: map_at_1000
value: 44.838
- type: map_at_3
value: 38.987
- type: map_at_5
value: 41.516999999999996
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 49.13
- type: mrr_at_100
value: 49.697
- type: mrr_at_1000
value: 49.741
- type: mrr_at_3
value: 45.804
- type: mrr_at_5
value: 47.842
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 50.266999999999996
- type: ndcg_at_100
value: 54.967
- type: ndcg_at_1000
value: 56.976000000000006
- type: ndcg_at_3
value: 43.823
- type: ndcg_at_5
value: 47.12
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 10.057
- type: precision_at_100
value: 1.554
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.125
- type: precision_at_5
value: 15.851
- type: recall_at_1
value: 31.502999999999997
- type: recall_at_10
value: 63.715999999999994
- type: recall_at_100
value: 83.61800000000001
- type: recall_at_1000
value: 96.63199999999999
- type: recall_at_3
value: 45.403
- type: recall_at_5
value: 54.481
- type: map_at_1
value: 27.833000000000002
- type: map_at_10
value: 37.330999999999996
- type: map_at_100
value: 38.580999999999996
- type: map_at_1000
value: 38.708
- type: map_at_3
value: 34.713
- type: map_at_5
value: 36.104
- type: mrr_at_1
value: 35.223
- type: mrr_at_10
value: 43.419000000000004
- type: mrr_at_100
value: 44.198
- type: mrr_at_1000
value: 44.249
- type: mrr_at_3
value: 41.614000000000004
- type: mrr_at_5
value: 42.553000000000004
- type: ndcg_at_1
value: 35.223
- type: ndcg_at_10
value: 42.687999999999995
- type: ndcg_at_100
value: 47.447
- type: ndcg_at_1000
value: 49.701
- type: ndcg_at_3
value: 39.162
- type: ndcg_at_5
value: 40.557
- type: precision_at_1
value: 35.223
- type: precision_at_10
value: 7.962
- type: precision_at_100
value: 1.304
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.184999999999999
- type: recall_at_1
value: 27.833000000000002
- type: recall_at_10
value: 51.881
- type: recall_at_100
value: 72.04
- type: recall_at_1000
value: 86.644
- type: recall_at_3
value: 40.778
- type: recall_at_5
value: 45.176
- type: map_at_1
value: 38.175
- type: map_at_10
value: 51.174
- type: map_at_100
value: 52.26499999999999
- type: map_at_1000
value: 52.315999999999995
- type: map_at_3
value: 47.897
- type: map_at_5
value: 49.703
- type: mrr_at_1
value: 43.448
- type: mrr_at_10
value: 54.505
- type: mrr_at_100
value: 55.216
- type: mrr_at_1000
value: 55.242000000000004
- type: mrr_at_3
value: 51.98500000000001
- type: mrr_at_5
value: 53.434000000000005
- type: ndcg_at_1
value: 43.448
- type: ndcg_at_10
value: 57.282
- type: ndcg_at_100
value: 61.537
- type: ndcg_at_1000
value: 62.546
- type: ndcg_at_3
value: 51.73799999999999
- type: ndcg_at_5
value: 54.324
- type: precision_at_1
value: 43.448
- type: precision_at_10
value: 9.292
- type: precision_at_100
value: 1.233
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 23.218
- type: precision_at_5
value: 15.887
- type: recall_at_1
value: 38.175
- type: recall_at_10
value: 72.00999999999999
- type: recall_at_100
value: 90.155
- type: recall_at_1000
value: 97.257
- type: recall_at_3
value: 57.133
- type: recall_at_5
value: 63.424
- type: map_at_1
value: 22.405
- type: map_at_10
value: 30.043
- type: map_at_100
value: 31.191000000000003
- type: map_at_1000
value: 31.275
- type: map_at_3
value: 27.034000000000002
- type: map_at_5
value: 28.688000000000002
- type: mrr_at_1
value: 24.068
- type: mrr_at_10
value: 31.993
- type: mrr_at_100
value: 32.992
- type: mrr_at_1000
value: 33.050000000000004
- type: mrr_at_3
value: 28.964000000000002
- type: mrr_at_5
value: 30.653000000000002
- type: ndcg_at_1
value: 24.068
- type: ndcg_at_10
value: 35.198
- type: ndcg_at_100
value: 40.709
- type: ndcg_at_1000
value: 42.855
- type: ndcg_at_3
value: 29.139
- type: ndcg_at_5
value: 32.045
- type: precision_at_1
value: 24.068
- type: precision_at_10
value: 5.65
- type: precision_at_100
value: 0.885
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 12.279
- type: precision_at_5
value: 8.994
- type: recall_at_1
value: 22.405
- type: recall_at_10
value: 49.391
- type: recall_at_100
value: 74.53699999999999
- type: recall_at_1000
value: 90.605
- type: recall_at_3
value: 33.126
- type: recall_at_5
value: 40.073
- type: map_at_1
value: 13.309999999999999
- type: map_at_10
value: 20.688000000000002
- type: map_at_100
value: 22.022
- type: map_at_1000
value: 22.152
- type: map_at_3
value: 17.954
- type: map_at_5
value: 19.439
- type: mrr_at_1
value: 16.294
- type: mrr_at_10
value: 24.479
- type: mrr_at_100
value: 25.515
- type: mrr_at_1000
value: 25.593
- type: mrr_at_3
value: 21.642
- type: mrr_at_5
value: 23.189999999999998
- type: ndcg_at_1
value: 16.294
- type: ndcg_at_10
value: 25.833000000000002
- type: ndcg_at_100
value: 32.074999999999996
- type: ndcg_at_1000
value: 35.083
- type: ndcg_at_3
value: 20.493
- type: ndcg_at_5
value: 22.949
- type: precision_at_1
value: 16.294
- type: precision_at_10
value: 5.112
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 9.908999999999999
- type: precision_at_5
value: 7.587000000000001
- type: recall_at_1
value: 13.309999999999999
- type: recall_at_10
value: 37.851
- type: recall_at_100
value: 64.835
- type: recall_at_1000
value: 86.334
- type: recall_at_3
value: 23.493
- type: recall_at_5
value: 29.528
- type: map_at_1
value: 25.857999999999997
- type: map_at_10
value: 35.503
- type: map_at_100
value: 36.957
- type: map_at_1000
value: 37.065
- type: map_at_3
value: 32.275999999999996
- type: map_at_5
value: 34.119
- type: mrr_at_1
value: 31.954
- type: mrr_at_10
value: 40.851
- type: mrr_at_100
value: 41.863
- type: mrr_at_1000
value: 41.900999999999996
- type: mrr_at_3
value: 38.129999999999995
- type: mrr_at_5
value: 39.737
- type: ndcg_at_1
value: 31.954
- type: ndcg_at_10
value: 41.343999999999994
- type: ndcg_at_100
value: 47.397
- type: ndcg_at_1000
value: 49.501
- type: ndcg_at_3
value: 36.047000000000004
- type: ndcg_at_5
value: 38.639
- type: precision_at_1
value: 31.954
- type: precision_at_10
value: 7.68
- type: precision_at_100
value: 1.247
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 17.132
- type: precision_at_5
value: 12.589
- type: recall_at_1
value: 25.857999999999997
- type: recall_at_10
value: 53.43599999999999
- type: recall_at_100
value: 78.82400000000001
- type: recall_at_1000
value: 92.78999999999999
- type: recall_at_3
value: 38.655
- type: recall_at_5
value: 45.216
- type: map_at_1
value: 24.709
- type: map_at_10
value: 34.318
- type: map_at_100
value: 35.657
- type: map_at_1000
value: 35.783
- type: map_at_3
value: 31.326999999999998
- type: map_at_5
value: 33.021
- type: mrr_at_1
value: 30.137000000000004
- type: mrr_at_10
value: 39.093
- type: mrr_at_100
value: 39.992
- type: mrr_at_1000
value: 40.056999999999995
- type: mrr_at_3
value: 36.606
- type: mrr_at_5
value: 37.861
- type: ndcg_at_1
value: 30.137000000000004
- type: ndcg_at_10
value: 39.974
- type: ndcg_at_100
value: 45.647999999999996
- type: ndcg_at_1000
value: 48.259
- type: ndcg_at_3
value: 35.028
- type: ndcg_at_5
value: 37.175999999999995
- type: precision_at_1
value: 30.137000000000004
- type: precision_at_10
value: 7.363
- type: precision_at_100
value: 1.184
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 16.857
- type: precision_at_5
value: 11.963
- type: recall_at_1
value: 24.709
- type: recall_at_10
value: 52.087
- type: recall_at_100
value: 76.125
- type: recall_at_1000
value: 93.82300000000001
- type: recall_at_3
value: 38.149
- type: recall_at_5
value: 43.984
- type: map_at_1
value: 23.40791666666667
- type: map_at_10
value: 32.458083333333335
- type: map_at_100
value: 33.691916666666664
- type: map_at_1000
value: 33.81191666666666
- type: map_at_3
value: 29.51625
- type: map_at_5
value: 31.168083333333335
- type: mrr_at_1
value: 27.96591666666666
- type: mrr_at_10
value: 36.528583333333344
- type: mrr_at_100
value: 37.404
- type: mrr_at_1000
value: 37.464333333333336
- type: mrr_at_3
value: 33.92883333333333
- type: mrr_at_5
value: 35.41933333333333
- type: ndcg_at_1
value: 27.96591666666666
- type: ndcg_at_10
value: 37.89141666666666
- type: ndcg_at_100
value: 43.23066666666666
- type: ndcg_at_1000
value: 45.63258333333333
- type: ndcg_at_3
value: 32.811249999999994
- type: ndcg_at_5
value: 35.22566666666667
- type: precision_at_1
value: 27.96591666666666
- type: precision_at_10
value: 6.834083333333332
- type: precision_at_100
value: 1.12225
- type: precision_at_1000
value: 0.15241666666666667
- type: precision_at_3
value: 15.264333333333335
- type: precision_at_5
value: 11.039416666666666
- type: recall_at_1
value: 23.40791666666667
- type: recall_at_10
value: 49.927083333333336
- type: recall_at_100
value: 73.44641666666668
- type: recall_at_1000
value: 90.19950000000001
- type: recall_at_3
value: 35.88341666666667
- type: recall_at_5
value: 42.061249999999994
- type: map_at_1
value: 19.592000000000002
- type: map_at_10
value: 26.895999999999997
- type: map_at_100
value: 27.921000000000003
- type: map_at_1000
value: 28.02
- type: map_at_3
value: 24.883
- type: map_at_5
value: 25.812
- type: mrr_at_1
value: 22.698999999999998
- type: mrr_at_10
value: 29.520999999999997
- type: mrr_at_100
value: 30.458000000000002
- type: mrr_at_1000
value: 30.526999999999997
- type: mrr_at_3
value: 27.633000000000003
- type: mrr_at_5
value: 28.483999999999998
- type: ndcg_at_1
value: 22.698999999999998
- type: ndcg_at_10
value: 31.061
- type: ndcg_at_100
value: 36.398
- type: ndcg_at_1000
value: 38.89
- type: ndcg_at_3
value: 27.149
- type: ndcg_at_5
value: 28.627000000000002
- type: precision_at_1
value: 22.698999999999998
- type: precision_at_10
value: 5.106999999999999
- type: precision_at_100
value: 0.857
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 11.963
- type: precision_at_5
value: 8.221
- type: recall_at_1
value: 19.592000000000002
- type: recall_at_10
value: 41.329
- type: recall_at_100
value: 66.094
- type: recall_at_1000
value: 84.511
- type: recall_at_3
value: 30.61
- type: recall_at_5
value: 34.213
- type: map_at_1
value: 14.71
- type: map_at_10
value: 20.965
- type: map_at_100
value: 21.994
- type: map_at_1000
value: 22.133
- type: map_at_3
value: 18.741
- type: map_at_5
value: 19.951
- type: mrr_at_1
value: 18.307000000000002
- type: mrr_at_10
value: 24.66
- type: mrr_at_100
value: 25.540000000000003
- type: mrr_at_1000
value: 25.629
- type: mrr_at_3
value: 22.511
- type: mrr_at_5
value: 23.72
- type: ndcg_at_1
value: 18.307000000000002
- type: ndcg_at_10
value: 25.153
- type: ndcg_at_100
value: 30.229
- type: ndcg_at_1000
value: 33.623
- type: ndcg_at_3
value: 21.203
- type: ndcg_at_5
value: 23.006999999999998
- type: precision_at_1
value: 18.307000000000002
- type: precision_at_10
value: 4.725
- type: precision_at_100
value: 0.8659999999999999
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 10.14
- type: precision_at_5
value: 7.481
- type: recall_at_1
value: 14.71
- type: recall_at_10
value: 34.087
- type: recall_at_100
value: 57.147999999999996
- type: recall_at_1000
value: 81.777
- type: recall_at_3
value: 22.996
- type: recall_at_5
value: 27.73
- type: map_at_1
value: 23.472
- type: map_at_10
value: 32.699
- type: map_at_100
value: 33.867000000000004
- type: map_at_1000
value: 33.967000000000006
- type: map_at_3
value: 29.718
- type: map_at_5
value: 31.345
- type: mrr_at_1
value: 28.265
- type: mrr_at_10
value: 36.945
- type: mrr_at_100
value: 37.794
- type: mrr_at_1000
value: 37.857
- type: mrr_at_3
value: 34.266000000000005
- type: mrr_at_5
value: 35.768
- type: ndcg_at_1
value: 28.265
- type: ndcg_at_10
value: 38.35
- type: ndcg_at_100
value: 43.739
- type: ndcg_at_1000
value: 46.087
- type: ndcg_at_3
value: 33.004
- type: ndcg_at_5
value: 35.411
- type: precision_at_1
value: 28.265
- type: precision_at_10
value: 6.715999999999999
- type: precision_at_100
value: 1.059
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 15.299
- type: precision_at_5
value: 10.951
- type: recall_at_1
value: 23.472
- type: recall_at_10
value: 51.413
- type: recall_at_100
value: 75.17
- type: recall_at_1000
value: 91.577
- type: recall_at_3
value: 36.651
- type: recall_at_5
value: 42.814
- type: map_at_1
value: 23.666
- type: map_at_10
value: 32.963
- type: map_at_100
value: 34.544999999999995
- type: map_at_1000
value: 34.792
- type: map_at_3
value: 29.74
- type: map_at_5
value: 31.5
- type: mrr_at_1
value: 29.051
- type: mrr_at_10
value: 38.013000000000005
- type: mrr_at_100
value: 38.997
- type: mrr_at_1000
value: 39.055
- type: mrr_at_3
value: 34.947
- type: mrr_at_5
value: 36.815
- type: ndcg_at_1
value: 29.051
- type: ndcg_at_10
value: 39.361000000000004
- type: ndcg_at_100
value: 45.186
- type: ndcg_at_1000
value: 47.867
- type: ndcg_at_3
value: 33.797
- type: ndcg_at_5
value: 36.456
- type: precision_at_1
value: 29.051
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 15.876000000000001
- type: precision_at_5
value: 11.779
- type: recall_at_1
value: 23.666
- type: recall_at_10
value: 51.858000000000004
- type: recall_at_100
value: 77.805
- type: recall_at_1000
value: 94.504
- type: recall_at_3
value: 36.207
- type: recall_at_5
value: 43.094
- type: map_at_1
value: 15.662
- type: map_at_10
value: 23.594
- type: map_at_100
value: 24.593999999999998
- type: map_at_1000
value: 24.694
- type: map_at_3
value: 20.925
- type: map_at_5
value: 22.817999999999998
- type: mrr_at_1
value: 17.375
- type: mrr_at_10
value: 25.734
- type: mrr_at_100
value: 26.586
- type: mrr_at_1000
value: 26.671
- type: mrr_at_3
value: 23.044
- type: mrr_at_5
value: 24.975
- type: ndcg_at_1
value: 17.375
- type: ndcg_at_10
value: 28.186
- type: ndcg_at_100
value: 33.436
- type: ndcg_at_1000
value: 36.203
- type: ndcg_at_3
value: 23.152
- type: ndcg_at_5
value: 26.397
- type: precision_at_1
value: 17.375
- type: precision_at_10
value: 4.677
- type: precision_at_100
value: 0.786
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 10.351
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 15.662
- type: recall_at_10
value: 40.066
- type: recall_at_100
value: 65.006
- type: recall_at_1000
value: 85.94000000000001
- type: recall_at_3
value: 27.400000000000002
- type: recall_at_5
value: 35.002
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.853
- type: map_at_10
value: 15.568000000000001
- type: map_at_100
value: 17.383000000000003
- type: map_at_1000
value: 17.584
- type: map_at_3
value: 12.561
- type: map_at_5
value: 14.056
- type: mrr_at_1
value: 18.958
- type: mrr_at_10
value: 28.288000000000004
- type: mrr_at_100
value: 29.432000000000002
- type: mrr_at_1000
value: 29.498
- type: mrr_at_3
value: 25.049
- type: mrr_at_5
value: 26.857
- type: ndcg_at_1
value: 18.958
- type: ndcg_at_10
value: 22.21
- type: ndcg_at_100
value: 29.596
- type: ndcg_at_1000
value: 33.583
- type: ndcg_at_3
value: 16.994999999999997
- type: ndcg_at_5
value: 18.95
- type: precision_at_1
value: 18.958
- type: precision_at_10
value: 7.192
- type: precision_at_100
value: 1.5
- type: precision_at_1000
value: 0.22399999999999998
- type: precision_at_3
value: 12.573
- type: precision_at_5
value: 10.202
- type: recall_at_1
value: 8.853
- type: recall_at_10
value: 28.087
- type: recall_at_100
value: 53.701
- type: recall_at_1000
value: 76.29899999999999
- type: recall_at_3
value: 15.913
- type: recall_at_5
value: 20.658
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.077
- type: map_at_10
value: 20.788999999999998
- type: map_at_100
value: 30.429000000000002
- type: map_at_1000
value: 32.143
- type: map_at_3
value: 14.692
- type: map_at_5
value: 17.139
- type: mrr_at_1
value: 70.75
- type: mrr_at_10
value: 78.036
- type: mrr_at_100
value: 78.401
- type: mrr_at_1000
value: 78.404
- type: mrr_at_3
value: 76.75
- type: mrr_at_5
value: 77.47500000000001
- type: ndcg_at_1
value: 58.12500000000001
- type: ndcg_at_10
value: 44.015
- type: ndcg_at_100
value: 49.247
- type: ndcg_at_1000
value: 56.211999999999996
- type: ndcg_at_3
value: 49.151
- type: ndcg_at_5
value: 46.195
- type: precision_at_1
value: 70.75
- type: precision_at_10
value: 35.5
- type: precision_at_100
value: 11.355
- type: precision_at_1000
value: 2.1950000000000003
- type: precision_at_3
value: 53.083000000000006
- type: precision_at_5
value: 44.800000000000004
- type: recall_at_1
value: 9.077
- type: recall_at_10
value: 26.259
- type: recall_at_100
value: 56.547000000000004
- type: recall_at_1000
value: 78.551
- type: recall_at_3
value: 16.162000000000003
- type: recall_at_5
value: 19.753999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.44500000000001
- type: f1
value: 44.67067691783401
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.182
- type: map_at_10
value: 78.223
- type: map_at_100
value: 78.498
- type: map_at_1000
value: 78.512
- type: map_at_3
value: 76.71
- type: map_at_5
value: 77.725
- type: mrr_at_1
value: 73.177
- type: mrr_at_10
value: 82.513
- type: mrr_at_100
value: 82.633
- type: mrr_at_1000
value: 82.635
- type: mrr_at_3
value: 81.376
- type: mrr_at_5
value: 82.182
- type: ndcg_at_1
value: 73.177
- type: ndcg_at_10
value: 82.829
- type: ndcg_at_100
value: 83.84
- type: ndcg_at_1000
value: 84.07900000000001
- type: ndcg_at_3
value: 80.303
- type: ndcg_at_5
value: 81.846
- type: precision_at_1
value: 73.177
- type: precision_at_10
value: 10.241999999999999
- type: precision_at_100
value: 1.099
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 31.247999999999998
- type: precision_at_5
value: 19.697
- type: recall_at_1
value: 68.182
- type: recall_at_10
value: 92.657
- type: recall_at_100
value: 96.709
- type: recall_at_1000
value: 98.184
- type: recall_at_3
value: 85.9
- type: recall_at_5
value: 89.755
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.108
- type: map_at_10
value: 33.342
- type: map_at_100
value: 35.281
- type: map_at_1000
value: 35.478
- type: map_at_3
value: 29.067
- type: map_at_5
value: 31.563000000000002
- type: mrr_at_1
value: 41.667
- type: mrr_at_10
value: 49.913000000000004
- type: mrr_at_100
value: 50.724000000000004
- type: mrr_at_1000
value: 50.766
- type: mrr_at_3
value: 47.504999999999995
- type: mrr_at_5
value: 49.033
- type: ndcg_at_1
value: 41.667
- type: ndcg_at_10
value: 41.144
- type: ndcg_at_100
value: 48.326
- type: ndcg_at_1000
value: 51.486
- type: ndcg_at_3
value: 37.486999999999995
- type: ndcg_at_5
value: 38.78
- type: precision_at_1
value: 41.667
- type: precision_at_10
value: 11.358
- type: precision_at_100
value: 1.873
- type: precision_at_1000
value: 0.244
- type: precision_at_3
value: 25
- type: precision_at_5
value: 18.519
- type: recall_at_1
value: 21.108
- type: recall_at_10
value: 47.249
- type: recall_at_100
value: 74.52
- type: recall_at_1000
value: 93.31
- type: recall_at_3
value: 33.271
- type: recall_at_5
value: 39.723000000000006
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.317
- type: map_at_10
value: 64.861
- type: map_at_100
value: 65.697
- type: map_at_1000
value: 65.755
- type: map_at_3
value: 61.258
- type: map_at_5
value: 63.590999999999994
- type: mrr_at_1
value: 80.635
- type: mrr_at_10
value: 86.528
- type: mrr_at_100
value: 86.66199999999999
- type: mrr_at_1000
value: 86.666
- type: mrr_at_3
value: 85.744
- type: mrr_at_5
value: 86.24300000000001
- type: ndcg_at_1
value: 80.635
- type: ndcg_at_10
value: 73.13199999999999
- type: ndcg_at_100
value: 75.927
- type: ndcg_at_1000
value: 76.976
- type: ndcg_at_3
value: 68.241
- type: ndcg_at_5
value: 71.071
- type: precision_at_1
value: 80.635
- type: precision_at_10
value: 15.326
- type: precision_at_100
value: 1.7500000000000002
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 43.961
- type: precision_at_5
value: 28.599999999999998
- type: recall_at_1
value: 40.317
- type: recall_at_10
value: 76.631
- type: recall_at_100
value: 87.495
- type: recall_at_1000
value: 94.362
- type: recall_at_3
value: 65.94200000000001
- type: recall_at_5
value: 71.499
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.686
- type: ap
value: 87.5577120393173
- type: f1
value: 91.6629447355139
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.702
- type: map_at_10
value: 36.414
- type: map_at_100
value: 37.561
- type: map_at_1000
value: 37.605
- type: map_at_3
value: 32.456
- type: map_at_5
value: 34.827000000000005
- type: mrr_at_1
value: 24.355
- type: mrr_at_10
value: 37.01
- type: mrr_at_100
value: 38.085
- type: mrr_at_1000
value: 38.123000000000005
- type: mrr_at_3
value: 33.117999999999995
- type: mrr_at_5
value: 35.452
- type: ndcg_at_1
value: 24.384
- type: ndcg_at_10
value: 43.456
- type: ndcg_at_100
value: 48.892
- type: ndcg_at_1000
value: 49.964
- type: ndcg_at_3
value: 35.475
- type: ndcg_at_5
value: 39.711
- type: precision_at_1
value: 24.384
- type: precision_at_10
value: 6.7940000000000005
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.052999999999999
- type: precision_at_5
value: 11.189
- type: recall_at_1
value: 23.702
- type: recall_at_10
value: 65.057
- type: recall_at_100
value: 90.021
- type: recall_at_1000
value: 98.142
- type: recall_at_3
value: 43.551
- type: recall_at_5
value: 53.738
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.62380300957591
- type: f1
value: 94.49871222100734
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.14090287277702
- type: f1
value: 60.32101258220515
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.84330867518494
- type: f1
value: 71.92248688515255
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.10692669804976
- type: f1
value: 77.9904839122866
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.822988923078444
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.38394880253403
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.82504612539082
- type: mrr
value: 32.84462298174977
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.029
- type: map_at_10
value: 14.088999999999999
- type: map_at_100
value: 17.601
- type: map_at_1000
value: 19.144
- type: map_at_3
value: 10.156
- type: map_at_5
value: 11.892
- type: mrr_at_1
value: 46.44
- type: mrr_at_10
value: 56.596999999999994
- type: mrr_at_100
value: 57.11000000000001
- type: mrr_at_1000
value: 57.14
- type: mrr_at_3
value: 54.334
- type: mrr_at_5
value: 55.774
- type: ndcg_at_1
value: 44.891999999999996
- type: ndcg_at_10
value: 37.134
- type: ndcg_at_100
value: 33.652
- type: ndcg_at_1000
value: 42.548
- type: ndcg_at_3
value: 41.851
- type: ndcg_at_5
value: 39.842
- type: precision_at_1
value: 46.44
- type: precision_at_10
value: 27.647
- type: precision_at_100
value: 8.309999999999999
- type: precision_at_1000
value: 2.146
- type: precision_at_3
value: 39.422000000000004
- type: precision_at_5
value: 34.675
- type: recall_at_1
value: 6.029
- type: recall_at_10
value: 18.907
- type: recall_at_100
value: 33.76
- type: recall_at_1000
value: 65.14999999999999
- type: recall_at_3
value: 11.584999999999999
- type: recall_at_5
value: 14.626
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.373000000000005
- type: map_at_10
value: 55.836
- type: map_at_100
value: 56.611999999999995
- type: map_at_1000
value: 56.63
- type: map_at_3
value: 51.747
- type: map_at_5
value: 54.337999999999994
- type: mrr_at_1
value: 44.147999999999996
- type: mrr_at_10
value: 58.42699999999999
- type: mrr_at_100
value: 58.902
- type: mrr_at_1000
value: 58.914
- type: mrr_at_3
value: 55.156000000000006
- type: mrr_at_5
value: 57.291000000000004
- type: ndcg_at_1
value: 44.119
- type: ndcg_at_10
value: 63.444
- type: ndcg_at_100
value: 66.40599999999999
- type: ndcg_at_1000
value: 66.822
- type: ndcg_at_3
value: 55.962
- type: ndcg_at_5
value: 60.228
- type: precision_at_1
value: 44.119
- type: precision_at_10
value: 10.006
- type: precision_at_100
value: 1.17
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.135
- type: precision_at_5
value: 17.59
- type: recall_at_1
value: 39.373000000000005
- type: recall_at_10
value: 83.78999999999999
- type: recall_at_100
value: 96.246
- type: recall_at_1000
value: 99.324
- type: recall_at_3
value: 64.71900000000001
- type: recall_at_5
value: 74.508
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.199
- type: map_at_10
value: 82.892
- type: map_at_100
value: 83.578
- type: map_at_1000
value: 83.598
- type: map_at_3
value: 79.948
- type: map_at_5
value: 81.779
- type: mrr_at_1
value: 79.67
- type: mrr_at_10
value: 86.115
- type: mrr_at_100
value: 86.249
- type: mrr_at_1000
value: 86.251
- type: mrr_at_3
value: 85.08200000000001
- type: mrr_at_5
value: 85.783
- type: ndcg_at_1
value: 79.67
- type: ndcg_at_10
value: 86.839
- type: ndcg_at_100
value: 88.252
- type: ndcg_at_1000
value: 88.401
- type: ndcg_at_3
value: 83.86200000000001
- type: ndcg_at_5
value: 85.473
- type: precision_at_1
value: 79.67
- type: precision_at_10
value: 13.19
- type: precision_at_100
value: 1.521
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.677
- type: precision_at_5
value: 24.118000000000002
- type: recall_at_1
value: 69.199
- type: recall_at_10
value: 94.321
- type: recall_at_100
value: 99.20400000000001
- type: recall_at_1000
value: 99.947
- type: recall_at_3
value: 85.787
- type: recall_at_5
value: 90.365
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.82810046856353
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.38132611783628
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.127000000000001
- type: map_at_10
value: 12.235
- type: map_at_100
value: 14.417
- type: map_at_1000
value: 14.75
- type: map_at_3
value: 8.906
- type: map_at_5
value: 10.591000000000001
- type: mrr_at_1
value: 25.2
- type: mrr_at_10
value: 35.879
- type: mrr_at_100
value: 36.935
- type: mrr_at_1000
value: 36.997
- type: mrr_at_3
value: 32.783
- type: mrr_at_5
value: 34.367999999999995
- type: ndcg_at_1
value: 25.2
- type: ndcg_at_10
value: 20.509
- type: ndcg_at_100
value: 28.67
- type: ndcg_at_1000
value: 34.42
- type: ndcg_at_3
value: 19.948
- type: ndcg_at_5
value: 17.166
- type: precision_at_1
value: 25.2
- type: precision_at_10
value: 10.440000000000001
- type: precision_at_100
value: 2.214
- type: precision_at_1000
value: 0.359
- type: precision_at_3
value: 18.533
- type: precision_at_5
value: 14.860000000000001
- type: recall_at_1
value: 5.127000000000001
- type: recall_at_10
value: 21.147
- type: recall_at_100
value: 44.946999999999996
- type: recall_at_1000
value: 72.89
- type: recall_at_3
value: 11.277
- type: recall_at_5
value: 15.042
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.0373011786213
- type: cos_sim_spearman
value: 79.27889560856613
- type: euclidean_pearson
value: 80.31186315495655
- type: euclidean_spearman
value: 79.41630415280811
- type: manhattan_pearson
value: 80.31755140442013
- type: manhattan_spearman
value: 79.43069870027611
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.8659751342045
- type: cos_sim_spearman
value: 76.95377612997667
- type: euclidean_pearson
value: 81.24552945497848
- type: euclidean_spearman
value: 77.18236963555253
- type: manhattan_pearson
value: 81.26477607759037
- type: manhattan_spearman
value: 77.13821753062756
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.34597139044875
- type: cos_sim_spearman
value: 84.124169425592
- type: euclidean_pearson
value: 83.68590721511401
- type: euclidean_spearman
value: 84.18846190846398
- type: manhattan_pearson
value: 83.57630235061498
- type: manhattan_spearman
value: 84.10244043726902
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.67641885599572
- type: cos_sim_spearman
value: 80.46450725650428
- type: euclidean_pearson
value: 81.61645042715865
- type: euclidean_spearman
value: 80.61418394236874
- type: manhattan_pearson
value: 81.55712034928871
- type: manhattan_spearman
value: 80.57905670523951
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.86650310886782
- type: cos_sim_spearman
value: 89.76081629222328
- type: euclidean_pearson
value: 89.1530747029954
- type: euclidean_spearman
value: 89.80990657280248
- type: manhattan_pearson
value: 89.10640563278132
- type: manhattan_spearman
value: 89.76282108434047
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.93864027911118
- type: cos_sim_spearman
value: 85.47096193999023
- type: euclidean_pearson
value: 85.03141840870533
- type: euclidean_spearman
value: 85.43124029598181
- type: manhattan_pearson
value: 84.99002664393512
- type: manhattan_spearman
value: 85.39169195120834
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.7045343749832
- type: cos_sim_spearman
value: 89.03262221146677
- type: euclidean_pearson
value: 89.56078218264365
- type: euclidean_spearman
value: 89.17827006466868
- type: manhattan_pearson
value: 89.52717595468582
- type: manhattan_spearman
value: 89.15878115952923
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.20191302875551
- type: cos_sim_spearman
value: 64.11446552557646
- type: euclidean_pearson
value: 64.6918197393619
- type: euclidean_spearman
value: 63.440182631197764
- type: manhattan_pearson
value: 64.55692904121835
- type: manhattan_spearman
value: 63.424877742756266
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.37793104662344
- type: cos_sim_spearman
value: 87.7357802629067
- type: euclidean_pearson
value: 87.4286301545109
- type: euclidean_spearman
value: 87.78452920777421
- type: manhattan_pearson
value: 87.42445169331255
- type: manhattan_spearman
value: 87.78537677249598
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.31465405081792
- type: mrr
value: 95.7173781193389
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.760999999999996
- type: map_at_10
value: 67.904
- type: map_at_100
value: 68.539
- type: map_at_1000
value: 68.562
- type: map_at_3
value: 65.415
- type: map_at_5
value: 66.788
- type: mrr_at_1
value: 60.333000000000006
- type: mrr_at_10
value: 68.797
- type: mrr_at_100
value: 69.236
- type: mrr_at_1000
value: 69.257
- type: mrr_at_3
value: 66.667
- type: mrr_at_5
value: 67.967
- type: ndcg_at_1
value: 60.333000000000006
- type: ndcg_at_10
value: 72.24199999999999
- type: ndcg_at_100
value: 74.86
- type: ndcg_at_1000
value: 75.354
- type: ndcg_at_3
value: 67.93400000000001
- type: ndcg_at_5
value: 70.02199999999999
- type: precision_at_1
value: 60.333000000000006
- type: precision_at_10
value: 9.533
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.778000000000002
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 57.760999999999996
- type: recall_at_10
value: 84.383
- type: recall_at_100
value: 96.267
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 72.628
- type: recall_at_5
value: 78.094
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8029702970297
- type: cos_sim_ap
value: 94.9210324173411
- type: cos_sim_f1
value: 89.8521162672106
- type: cos_sim_precision
value: 91.67533818938605
- type: cos_sim_recall
value: 88.1
- type: dot_accuracy
value: 99.69504950495049
- type: dot_ap
value: 90.4919719146181
- type: dot_f1
value: 84.72289156626506
- type: dot_precision
value: 81.76744186046511
- type: dot_recall
value: 87.9
- type: euclidean_accuracy
value: 99.79702970297029
- type: euclidean_ap
value: 94.87827463795753
- type: euclidean_f1
value: 89.55680081507896
- type: euclidean_precision
value: 91.27725856697819
- type: euclidean_recall
value: 87.9
- type: manhattan_accuracy
value: 99.7990099009901
- type: manhattan_ap
value: 94.87587025149682
- type: manhattan_f1
value: 89.76298537569339
- type: manhattan_precision
value: 90.53916581892166
- type: manhattan_recall
value: 89
- type: max_accuracy
value: 99.8029702970297
- type: max_ap
value: 94.9210324173411
- type: max_f1
value: 89.8521162672106
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.92385753948724
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.671756975431144
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.677928036739004
- type: mrr
value: 51.56413133435193
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.523589340819683
- type: cos_sim_spearman
value: 30.187407518823235
- type: dot_pearson
value: 29.039713969699015
- type: dot_spearman
value: 29.114740651155508
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.211
- type: map_at_10
value: 1.6199999999999999
- type: map_at_100
value: 8.658000000000001
- type: map_at_1000
value: 21.538
- type: map_at_3
value: 0.575
- type: map_at_5
value: 0.919
- type: mrr_at_1
value: 78
- type: mrr_at_10
value: 86.18599999999999
- type: mrr_at_100
value: 86.18599999999999
- type: mrr_at_1000
value: 86.18599999999999
- type: mrr_at_3
value: 85
- type: mrr_at_5
value: 85.9
- type: ndcg_at_1
value: 74
- type: ndcg_at_10
value: 66.542
- type: ndcg_at_100
value: 50.163999999999994
- type: ndcg_at_1000
value: 45.696999999999996
- type: ndcg_at_3
value: 71.531
- type: ndcg_at_5
value: 70.45
- type: precision_at_1
value: 78
- type: precision_at_10
value: 69.39999999999999
- type: precision_at_100
value: 51.06
- type: precision_at_1000
value: 20.022000000000002
- type: precision_at_3
value: 76
- type: precision_at_5
value: 74.8
- type: recall_at_1
value: 0.211
- type: recall_at_10
value: 1.813
- type: recall_at_100
value: 12.098
- type: recall_at_1000
value: 42.618
- type: recall_at_3
value: 0.603
- type: recall_at_5
value: 0.987
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.2079999999999997
- type: map_at_10
value: 7.777000000000001
- type: map_at_100
value: 12.825000000000001
- type: map_at_1000
value: 14.196
- type: map_at_3
value: 4.285
- type: map_at_5
value: 6.177
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 42.635
- type: mrr_at_100
value: 43.955
- type: mrr_at_1000
value: 43.955
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 41.088
- type: ndcg_at_1
value: 28.571
- type: ndcg_at_10
value: 20.666999999999998
- type: ndcg_at_100
value: 31.840000000000003
- type: ndcg_at_1000
value: 43.191
- type: ndcg_at_3
value: 23.45
- type: ndcg_at_5
value: 22.994
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 17.959
- type: precision_at_100
value: 6.755
- type: precision_at_1000
value: 1.4200000000000002
- type: precision_at_3
value: 23.810000000000002
- type: precision_at_5
value: 23.673
- type: recall_at_1
value: 2.2079999999999997
- type: recall_at_10
value: 13.144
- type: recall_at_100
value: 42.491
- type: recall_at_1000
value: 77.04299999999999
- type: recall_at_3
value: 5.3469999999999995
- type: recall_at_5
value: 9.139
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.9044
- type: ap
value: 14.625783489340755
- type: f1
value: 54.814936562590546
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.94227504244483
- type: f1
value: 61.22516038508854
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.602409155145864
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.94641473445789
- type: cos_sim_ap
value: 76.91572747061197
- type: cos_sim_f1
value: 70.14348097317529
- type: cos_sim_precision
value: 66.53254437869822
- type: cos_sim_recall
value: 74.1688654353562
- type: dot_accuracy
value: 84.80061989628658
- type: dot_ap
value: 70.7952548895177
- type: dot_f1
value: 65.44780728844965
- type: dot_precision
value: 61.53310104529617
- type: dot_recall
value: 69.89445910290237
- type: euclidean_accuracy
value: 86.94641473445789
- type: euclidean_ap
value: 76.80774009393652
- type: euclidean_f1
value: 70.30522503879979
- type: euclidean_precision
value: 68.94977168949772
- type: euclidean_recall
value: 71.71503957783642
- type: manhattan_accuracy
value: 86.8629671574179
- type: manhattan_ap
value: 76.76518632600317
- type: manhattan_f1
value: 70.16056518946692
- type: manhattan_precision
value: 68.360450563204
- type: manhattan_recall
value: 72.0580474934037
- type: max_accuracy
value: 86.94641473445789
- type: max_ap
value: 76.91572747061197
- type: max_f1
value: 70.30522503879979
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.10428066907285
- type: cos_sim_ap
value: 86.25114759921435
- type: cos_sim_f1
value: 78.37857884586856
- type: cos_sim_precision
value: 75.60818546078993
- type: cos_sim_recall
value: 81.35971666153372
- type: dot_accuracy
value: 87.41995575736406
- type: dot_ap
value: 81.51838010086782
- type: dot_f1
value: 74.77398015435503
- type: dot_precision
value: 71.53002390662354
- type: dot_recall
value: 78.32614721281182
- type: euclidean_accuracy
value: 89.12368533395428
- type: euclidean_ap
value: 86.33456799874504
- type: euclidean_f1
value: 78.45496750232127
- type: euclidean_precision
value: 75.78388462366364
- type: euclidean_recall
value: 81.32121958731136
- type: manhattan_accuracy
value: 89.10622113556099
- type: manhattan_ap
value: 86.31215061745333
- type: manhattan_f1
value: 78.40684906011539
- type: manhattan_precision
value: 75.89536643366722
- type: manhattan_recall
value: 81.09023714197721
- type: max_accuracy
value: 89.12368533395428
- type: max_ap
value: 86.33456799874504
- type: max_f1
value: 78.45496750232127
---
# E5-large-v2
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 24 layers and the embedding size is 1024.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-large-v2')
model = AutoModel.from_pretrained('intfloat/e5-large-v2')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
|
[
"BIOSSES",
"SCIFACT"
] |
DunnBC22/bert-base-cased-finetuned-ner-NCBI_Disease
|
DunnBC22
|
token-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"medical",
"science",
"en",
"dataset:ncbi_disease",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-04T04:33:33Z |
2023-07-20T22:06:29+00:00
| 29 | 2 |
---
datasets:
- ncbi_disease
language:
- en
license: apache-2.0
metrics:
- seqeval
- f1
- recall
- accuracy
- precision
pipeline_tag: token-classification
tags:
- generated_from_trainer
- medical
- science
model-index:
- name: bert-base-cased-finetuned-ner-NCBI_Disease
results: []
---
# bert-base-cased-finetuned-ner-NCBI_Disease
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Disease:
- Precision: 0.8063891577928364
- Recall: 0.8677083333333333
- F1: 0.8359257400903161
- Number: 960
- Overall
- Precision: 0.8064
- Recall: 0.8677
- F1: 0.8359
- Accuracy: 0.9825
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/NCBI_Disease/NER%20Project%20Using%20NCBI_Disease%20Dataset.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Data Source: https://huggingface.co/datasets/ncbi_disease
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Disease Precision | Disease Recall | Disease F1 | Disease Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-----------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:-------:|
| 0.0525 | 1.0 | 340 | 0.0617 | 0.7813 | 0.7854 | 0.7834 | 960 | 0.7813 | 0.7854 | 0.7834 | 0.9796 |
| 0.022 | 2.0 | 680 | 0.0551 | 0.7897 | 0.8646 | 0.8255 | 960 | 0.7897 | 0.8646 | 0.8255 | 0.9819 |
| 0.0154 | 3.0 | 1020 | 0.0614 | 0.8064 | 0.8677 | 0.8359 | 960 | 0.8064 | 0.8677 | 0.8359 | 0.9825 |
* All values in the above chart are rounded to the nearest ten-thousandth.
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"NCBI DISEASE"
] |
masonbarnes/open-llm-search
|
masonbarnes
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-04T21:55:24Z |
2023-09-09T06:00:09+00:00
| 29 | 10 |
---
language:
- en
license: llama2
---
# **Model Overview**
As the demand for large language models grows, a common limitation surfaces: their inability to directly search the internet. Although tech giants like Google (with Bard), Bing, and Perplexity are addressing this challenge, their proprietary methods have data logging issues.
**Introducing Open LLM Search** — A specialized adaptation of Together AI's `llama-2-7b-32k` model, purpose-built for extracting information from web pages. While the model only has a 7 billion parameters, its fine-tuned capabilities and expanded context limit enable it to excel in search tasks.
**License:** This model uses Meta's Llama 2 license.
# **Fine-Tuning Process**
The model's fine tuning involved a combination of GPT-4 and GPT-4-32k to generate synthetic data. Here is the training workflow used:
1. Use GPT-4 to generate a multitude of queries.
2. For each query, identify the top five website results from Google.
3. Extract content from these websites and use GPT-4-32k for their summarization.
4. Record the text and summarizes from GPT-4-32k for fine-tuning.
5. Feed the summaries from all five sources with GPT-4 to craft a cohesive response.
6. Document both the input and output from GPT-4 for fine-tuning.
Fine tuning was done with an `<instructions>:`, `<user>:`, and `<assistant>:` format.
# **Getting Started**
- Experience it firsthand! Check out the live demo [here](https://huggingface.co/spaces/masonbarnes/open-llm-search).
- For DIY enthusiasts, explore or self-deploy this solution using our [GitHub repository](https://github.com/MasonBarnes/open-llm-search).
|
[
"CRAFT"
] |
lxyuan/distilbert-finetuned-reuters21578-multilabel
|
lxyuan
|
text-classification
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"news_classification",
"multi_label",
"en",
"dataset:reuters21578",
"base_model:distilbert/distilbert-base-cased",
"base_model:finetune:distilbert/distilbert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-09-07T11:19:40Z |
2023-09-08T11:27:37+00:00
| 29 | 1 |
---
base_model: distilbert-base-cased
datasets:
- reuters21578
language:
- en
license: apache-2.0
metrics:
- f1
- accuracy
pipeline_tag: text-classification
tags:
- generated_from_trainer
- news_classification
- multi_label
widget:
- text: JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of International
Trade and Industry (MITI) will revise its long-term energy supply/demand outlook
by August to meet a forecast downtrend in Japanese energy demand, ministry officials
said. MITI is expected to lower the projection for primary energy supplies
in the year 2000 to 550 mln kilolitres (kl) from 600 mln, they said. The decision
follows the emergence of structural changes in Japanese industry following the
rise in the value of the yen and a decline in domestic electric power demand. MITI
is planning to work out a revised energy supply/demand outlook through deliberations
of committee meetings of the Agency of Natural Resources and Energy, the officials
said. They said MITI will also review the breakdown of energy supply sources,
including oil, nuclear, coal and natural gas. Nuclear energy provided the
bulk of Japan's electric power in the fiscal year ended March 31, supplying an
estimated 27 pct on a kilowatt/hour basis, followed by oil (23 pct) and liquefied
natural gas (21 pct), they noted. REUTER
example_title: Example-1
model-index:
- name: distilbert-finetuned-reuters21578-multilabel
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: reuters21578
type: reuters21578
config: ModApte
split: test
args: ModApte
metrics:
- type: f1
value: 0.8628858578607322
name: F1
- type: accuracy
value: 0.8195625759416768
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
## Motivation
Fine-tuning on the Reuters-21578 multilabel dataset is a valuable exercise, especially as it's frequently used in take-home tests during interviews. The dataset's complexity is just right for testing multilabel classification skills within a limited timeframe, while its real-world relevance helps simulate practical challenges. Experimenting with this dataset not only helps candidates prepare for interviews but also hones various skills including preprocessing, feature extraction, and model evaluation.
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the reuters21578 dataset.
## Inference Example
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="lxyuan/distilbert-finetuned-reuters21578-multilabel", return_all_scores=True)
# dataset["test"]["text"][2]
news_article = (
"JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of International Trade and "
"Industry (MITI) will revise its long-term energy supply/demand "
"outlook by August to meet a forecast downtrend in Japanese "
"energy demand, ministry officials said. "
"MITI is expected to lower the projection for primary energy "
"supplies in the year 2000 to 550 mln kilolitres (kl) from 600 "
"mln, they said. "
"The decision follows the emergence of structural changes in "
"Japanese industry following the rise in the value of the yen "
"and a decline in domestic electric power demand. "
"MITI is planning to work out a revised energy supply/demand "
"outlook through deliberations of committee meetings of the "
"Agency of Natural Resources and Energy, the officials said. "
"They said MITI will also review the breakdown of energy "
"supply sources, including oil, nuclear, coal and natural gas. "
"Nuclear energy provided the bulk of Japan's electric power "
"in the fiscal year ended March 31, supplying an estimated 27 "
"pct on a kilowatt/hour basis, followed by oil (23 pct) and "
"liquefied natural gas (21 pct), they noted. "
"REUTER"
)
# dataset["test"]["topics"][2]
target_topics = ['crude', 'nat-gas']
fn_kwargs={"padding": "max_length", "truncation": True, "max_length": 512}
output = pipe(example, function_to_apply="sigmoid", **fn_kwargs)
for item in output[0]:
if item["score"]>=0.5:
print(item["label"], item["score"])
>>> crude 0.7355073690414429
nat-gas 0.8600426316261292
```
## Overall Summary and Comparison Table
| Metric | Baseline (Scikit-learn) | Transformer Model |
|-----------------------|--------------------------|-------------------|
| Micro-Averaged F1 | 0.77 | 0.86 |
| Macro-Averaged F1 | 0.29 | 0.33 |
| Weighted Average F1 | 0.70 | 0.84 |
| Samples Average F1 | 0.75 | 0.80 |
**Precision vs Recall**: Both models prioritize high precision over recall. In our client-facing news classification model, precision takes precedence over recall. This is because the repercussions of false positives are more severe and harder to justify to clients compared to false negatives. When the model incorrectly tags a news item with a topic, it's challenging to explain this error. On the other hand, if the model misses a topic, it's easier to defend by stating that the topic wasn't sufficiently emphasized in the news article.
**Class Imbalance Handling**: Both models suffer from the same general issue of not performing well on minority classes, as reflected in the low macro-averaged F1-scores. However, the transformer model shows a slight improvement, albeit marginal, in macro-averaged F1-score (0.33 vs 0.29).
**Issue of Zero Support Labels**: Both models have the problem of zero support for several labels, meaning these labels did not appear in the test set. This lack of "support" can significantly skew the performance metrics and may suggest that either the models are not well-tuned to predict these minority classes, or the dataset itself lacks sufficient examples of these classes. Given that both models struggle with low macro-averaged F1 scores, this issue further emphasizes the need for improved minority class handling in the models.
**General Performance**: The transformer model surpasses the scikit-learn baseline in terms of weighted and samples average F1-scores, indicating better overall performance and better handling of label imbalance.
**Conclusion**: While both models exhibit high precision, which is a business requirement, the transformer model slightly outperforms the scikit-learn baseline model in all metrics considered. It provides a better trade-off between precision and recall, as well as some improvement, albeit small, in handling minority classes. Thus, despite sharing similar weaknesses with the baseline, the transformer model demonstrates incremental improvements that could be significant in a production setting.
## Training and evaluation data
We remove single appearance label from both training and test sets using the following code:
```python
# Find Single Appearance Labels
def find_single_appearance_labels(y):
"""Find labels that appear only once in the dataset."""
all_labels = list(chain.from_iterable(y))
label_count = Counter(all_labels)
single_appearance_labels = [label for label, count in label_count.items() if count == 1]
return single_appearance_labels
# Remove Single Appearance Labels from Dataset
def remove_single_appearance_labels(dataset, single_appearance_labels):
"""Remove samples with single-appearance labels from both train and test sets."""
for split in ['train', 'test']:
dataset[split] = dataset[split].filter(lambda x: all(label not in single_appearance_labels for label in x['topics']))
return dataset
dataset = load_dataset("reuters21578", "ModApte")
# Find and Remove Single Appearance Labels
y_train = [item['topics'] for item in dataset['train']]
single_appearance_labels = find_single_appearance_labels(y_train)
print(f"Single appearance labels: {single_appearance_labels}")
>>> Single appearance labels: ['lin-oil', 'rye', 'red-bean', 'groundnut-oil', 'citruspulp', 'rape-meal', 'corn-oil', 'peseta', 'cotton-oil', 'ringgit', 'castorseed', 'castor-oil', 'lit', 'rupiah', 'skr', 'nkr', 'dkr', 'sun-meal', 'lin-meal', 'cruzado']
print("Removing samples with single-appearance labels...")
dataset = remove_single_appearance_labels(dataset, single_appearance_labels)
unique_labels = set(chain.from_iterable(dataset['train']["topics"]))
print(f"We have {len(unique_labels)} unique labels:\n{unique_labels}")
>>> We have 95 unique labels:
{'veg-oil', 'gold', 'platinum', 'ipi', 'acq', 'carcass', 'wool', 'coconut-oil', 'linseed', 'copper', 'soy-meal', 'jet', 'dlr', 'copra-cake', 'hog', 'rand', 'strategic-metal', 'can', 'tea', 'sorghum', 'livestock', 'barley', 'lumber', 'earn', 'wheat', 'trade', 'soy-oil', 'cocoa', 'inventories', 'income', 'rubber', 'tin', 'iron-steel', 'ship', 'rapeseed', 'wpi', 'sun-oil', 'pet-chem', 'palmkernel', 'nat-gas', 'gnp', 'l-cattle', 'propane', 'rice', 'lead', 'alum', 'instal-debt', 'saudriyal', 'cpu', 'jobs', 'meal-feed', 'oilseed', 'dmk', 'plywood', 'zinc', 'retail', 'dfl', 'cpi', 'crude', 'pork-belly', 'gas', 'money-fx', 'corn', 'tapioca', 'palladium', 'lei', 'cornglutenfeed', 'sunseed', 'potato', 'silver', 'sugar', 'grain', 'groundnut', 'naphtha', 'orange', 'soybean', 'coconut', 'stg', 'cotton', 'yen', 'rape-oil', 'palm-oil', 'oat', 'reserves', 'housing', 'interest', 'coffee', 'fuel', 'austdlr', 'money-supply', 'heat', 'fishmeal', 'bop', 'nickel', 'nzdlr'}
```
## Training procedure
[EDA on Reuters-21578 dataset](https://github.com/LxYuan0420/nlp/blob/main/notebooks/eda_reuters.ipynb):
This notebook provides an Exploratory Data Analysis (EDA) of the Reuters-21578 dataset. It includes visualizations and statistical summaries that offer insights into the dataset's structure, label distribution, and text characteristics.
[Reuters Baseline Scikit-Learn Model](https://github.com/LxYuan0420/nlp/blob/main/notebooks/scikit_learn_reuters.ipynb):
This notebook establishes a baseline model for text classification on the Reuters-21578 dataset using scikit-learn. It guides you through data preprocessing, feature extraction, model training, and evaluation.
[Reuters Transformer Model](https://github.com/LxYuan0420/nlp/blob/main/notebooks/transformer_reuters.ipynb):
This notebook delves into advanced text classification using a Transformer model on the Reuters-21578 dataset. It covers the implementation details, training process, and performance metrics of using Transformer-based models for this specific task.
[Multilabel Stratified Sampling & Hypyerparameter Search on Reuters Dataset](https://github.com/LxYuan0420/nlp/blob/main/notebooks/transformer_reuters_hyperparameter_tuning.ipynb):
In this notebook, we explore advanced machine learning techniques through the lens of the Hugging Face Trainer API, specifically targeting Multilabel Iterative Stratified Splitting and Hyperparameter Search. The former aims to fairly distribute imbalanced datasets across multiple labels in k-fold cross-validation, maintaining a distribution closely resembling that of the complete dataset. The latter walks users through a structured hyperparameter search to fine-tune model performance for optimal results.
## Evaluation results
<details>
<summary>Transformer Model Evaluation Result</summary>
Classification Report:
precision recall f1-score support
acq 0.97 0.93 0.95 719
alum 1.00 0.70 0.82 23
austdlr 0.00 0.00 0.00 0
barley 1.00 0.50 0.67 12
bop 0.79 0.50 0.61 30
can 0.00 0.00 0.00 0
carcass 0.67 0.67 0.67 18
cocoa 1.00 1.00 1.00 18
coconut 0.00 0.00 0.00 2
coconut-oil 0.00 0.00 0.00 2
coffee 0.86 0.89 0.87 27
copper 1.00 0.78 0.88 18
copra-cake 0.00 0.00 0.00 1
corn 0.84 0.87 0.86 55
cornglutenfeed 0.00 0.00 0.00 0
cotton 0.92 0.67 0.77 18
cpi 0.86 0.43 0.57 28
cpu 0.00 0.00 0.00 1
crude 0.87 0.93 0.90 189
dfl 0.00 0.00 0.00 1
dlr 0.72 0.64 0.67 44
dmk 0.00 0.00 0.00 4
earn 0.98 0.99 0.98 1087
fishmeal 0.00 0.00 0.00 0
fuel 0.00 0.00 0.00 10
gas 0.80 0.71 0.75 17
gnp 0.79 0.66 0.72 35
gold 0.95 0.67 0.78 30
grain 0.94 0.92 0.93 146
groundnut 0.00 0.00 0.00 4
heat 0.00 0.00 0.00 5
hog 1.00 0.33 0.50 6
housing 0.00 0.00 0.00 4
income 0.00 0.00 0.00 7
instal-debt 0.00 0.00 0.00 1
interest 0.89 0.67 0.77 131
inventories 0.00 0.00 0.00 0
ipi 1.00 0.58 0.74 12
iron-steel 0.90 0.64 0.75 14
jet 0.00 0.00 0.00 1
jobs 0.92 0.57 0.71 21
l-cattle 0.00 0.00 0.00 2
lead 0.00 0.00 0.00 14
lei 0.00 0.00 0.00 3
linseed 0.00 0.00 0.00 0
livestock 0.63 0.79 0.70 24
lumber 0.00 0.00 0.00 6
meal-feed 0.00 0.00 0.00 17
money-fx 0.78 0.81 0.80 177
money-supply 0.80 0.71 0.75 34
naphtha 0.00 0.00 0.00 4
nat-gas 0.82 0.60 0.69 30
nickel 0.00 0.00 0.00 1
nzdlr 0.00 0.00 0.00 2
oat 0.00 0.00 0.00 4
oilseed 0.64 0.61 0.63 44
orange 1.00 0.36 0.53 11
palladium 0.00 0.00 0.00 1
palm-oil 1.00 0.56 0.71 9
palmkernel 0.00 0.00 0.00 1
pet-chem 0.00 0.00 0.00 12
platinum 0.00 0.00 0.00 7
plywood 0.00 0.00 0.00 0
pork-belly 0.00 0.00 0.00 0
potato 0.00 0.00 0.00 3
propane 0.00 0.00 0.00 3
rand 0.00 0.00 0.00 1
rape-oil 0.00 0.00 0.00 1
rapeseed 0.00 0.00 0.00 8
reserves 0.83 0.56 0.67 18
retail 0.00 0.00 0.00 2
rice 1.00 0.57 0.72 23
rubber 0.82 0.75 0.78 12
saudriyal 0.00 0.00 0.00 0
ship 0.95 0.81 0.87 89
silver 1.00 0.12 0.22 8
sorghum 1.00 0.12 0.22 8
soy-meal 0.00 0.00 0.00 12
soy-oil 0.00 0.00 0.00 8
soybean 0.72 0.56 0.63 32
stg 0.00 0.00 0.00 0
strategic-metal 0.00 0.00 0.00 11
sugar 1.00 0.80 0.89 35
sun-oil 0.00 0.00 0.00 0
sunseed 0.00 0.00 0.00 5
tapioca 0.00 0.00 0.00 0
tea 0.00 0.00 0.00 3
tin 1.00 0.42 0.59 12
trade 0.78 0.79 0.79 116
veg-oil 0.91 0.59 0.71 34
wheat 0.83 0.83 0.83 69
wool 0.00 0.00 0.00 0
wpi 0.00 0.00 0.00 10
yen 0.57 0.29 0.38 14
zinc 1.00 0.69 0.82 13
micro avg 0.92 0.81 0.86 3694
macro avg 0.41 0.30 0.33 3694
weighted avg 0.87 0.81 0.84 3694
samples avg 0.81 0.80 0.80 3694
</details>
<details>
<summary>Scikit-learn Baseline Model Evaluation Result</summary>
Classification Report:
precision recall f1-score support
acq 0.98 0.87 0.92 719
alum 1.00 0.00 0.00 23
austdlr 1.00 1.00 1.00 0
barley 1.00 0.00 0.00 12
bop 1.00 0.30 0.46 30
can 1.00 1.00 1.00 0
carcass 1.00 0.06 0.11 18
cocoa 1.00 0.61 0.76 18
coconut 1.00 0.00 0.00 2
coconut-oil 1.00 0.00 0.00 2
coffee 0.94 0.59 0.73 27
copper 1.00 0.22 0.36 18
copra-cake 1.00 0.00 0.00 1
corn 0.97 0.51 0.67 55
cornglutenfeed 1.00 1.00 1.00 0
cotton 1.00 0.06 0.11 18
cpi 1.00 0.14 0.25 28
cpu 1.00 0.00 0.00 1
crude 0.94 0.69 0.80 189
dfl 1.00 0.00 0.00 1
dlr 0.86 0.43 0.58 44
dmk 1.00 0.00 0.00 4
earn 0.99 0.97 0.98 1087
fishmeal 1.00 1.00 1.00 0
fuel 1.00 0.00 0.00 10
gas 1.00 0.00 0.00 17
gnp 1.00 0.31 0.48 35
gold 0.83 0.17 0.28 30
grain 1.00 0.65 0.79 146
groundnut 1.00 0.00 0.00 4
heat 1.00 0.00 0.00 5
hog 1.00 0.00 0.00 6
housing 1.00 0.00 0.00 4
income 1.00 0.00 0.00 7
instal-debt 1.00 0.00 0.00 1
interest 0.88 0.40 0.55 131
inventories 1.00 1.00 1.00 0
ipi 1.00 0.00 0.00 12
iron-steel 1.00 0.00 0.00 14
jet 1.00 0.00 0.00 1
jobs 1.00 0.14 0.25 21
l-cattle 1.00 0.00 0.00 2
lead 1.00 0.00 0.00 14
lei 1.00 0.00 0.00 3
linseed 1.00 1.00 1.00 0
livestock 0.67 0.08 0.15 24
lumber 1.00 0.00 0.00 6
meal-feed 1.00 0.00 0.00 17
money-fx 0.80 0.50 0.62 177
money-supply 0.88 0.41 0.56 34
naphtha 1.00 0.00 0.00 4
nat-gas 1.00 0.27 0.42 30
nickel 1.00 0.00 0.00 1
nzdlr 1.00 0.00 0.00 2
oat 1.00 0.00 0.00 4
oilseed 0.62 0.11 0.19 44
orange 1.00 0.00 0.00 11
palladium 1.00 0.00 0.00 1
palm-oil 1.00 0.22 0.36 9
palmkernel 1.00 0.00 0.00 1
pet-chem 1.00 0.00 0.00 12
platinum 1.00 0.00 0.00 7
plywood 1.00 1.00 1.00 0
pork-belly 1.00 1.00 1.00 0
potato 1.00 0.00 0.00 3
propane 1.00 0.00 0.00 3
rand 1.00 0.00 0.00 1
rape-oil 1.00 0.00 0.00 1
rapeseed 1.00 0.00 0.00 8
reserves 1.00 0.00 0.00 18
retail 1.00 0.00 0.00 2
rice 1.00 0.00 0.00 23
rubber 1.00 0.17 0.29 12
saudriyal 1.00 1.00 1.00 0
ship 0.92 0.26 0.40 89
silver 1.00 0.00 0.00 8
sorghum 1.00 0.00 0.00 8
soy-meal 1.00 0.00 0.00 12
soy-oil 1.00 0.00 0.00 8
soybean 1.00 0.16 0.27 32
stg 1.00 1.00 1.00 0
strategic-metal 1.00 0.00 0.00 11
sugar 1.00 0.60 0.75 35
sun-oil 1.00 1.00 1.00 0
sunseed 1.00 0.00 0.00 5
tapioca 1.00 1.00 1.00 0
tea 1.00 0.00 0.00 3
tin 1.00 0.00 0.00 12
trade 0.92 0.61 0.74 116
veg-oil 1.00 0.12 0.21 34
wheat 0.97 0.55 0.70 69
wool 1.00 1.00 1.00 0
wpi 1.00 0.00 0.00 10
yen 1.00 0.00 0.00 14
zinc 1.00 0.00 0.00 13
micro avg 0.97 0.64 0.77 3694
macro avg 0.98 0.25 0.29 3694
weighted avg 0.96 0.64 0.70 3694
samples avg 0.98 0.74 0.75 3694
</details>
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.1801 | 1.0 | 300 | 0.0439 | 0.3896 | 0.6210 | 0.3566 |
| 0.0345 | 2.0 | 600 | 0.0287 | 0.6289 | 0.7318 | 0.5954 |
| 0.0243 | 3.0 | 900 | 0.0219 | 0.6721 | 0.7579 | 0.6084 |
| 0.0178 | 4.0 | 1200 | 0.0177 | 0.7505 | 0.8128 | 0.6908 |
| 0.014 | 5.0 | 1500 | 0.0151 | 0.7905 | 0.8376 | 0.7278 |
| 0.0115 | 6.0 | 1800 | 0.0135 | 0.8132 | 0.8589 | 0.7555 |
| 0.0096 | 7.0 | 2100 | 0.0124 | 0.8291 | 0.8727 | 0.7725 |
| 0.0082 | 8.0 | 2400 | 0.0124 | 0.8335 | 0.8757 | 0.7822 |
| 0.0071 | 9.0 | 2700 | 0.0119 | 0.8392 | 0.8847 | 0.7883 |
| 0.0064 | 10.0 | 3000 | 0.0123 | 0.8339 | 0.8810 | 0.7828 |
| 0.0058 | 11.0 | 3300 | 0.0114 | 0.8538 | 0.8999 | 0.8047 |
| 0.0053 | 12.0 | 3600 | 0.0113 | 0.8525 | 0.8967 | 0.8044 |
| 0.0048 | 13.0 | 3900 | 0.0115 | 0.8520 | 0.8982 | 0.8029 |
| 0.0045 | 14.0 | 4200 | 0.0111 | 0.8566 | 0.8962 | 0.8104 |
| 0.0042 | 15.0 | 4500 | 0.0110 | 0.8610 | 0.9060 | 0.8165 |
| 0.0039 | 16.0 | 4800 | 0.0112 | 0.8583 | 0.9021 | 0.8138 |
| 0.0037 | 17.0 | 5100 | 0.0110 | 0.8620 | 0.9055 | 0.8196 |
| 0.0035 | 18.0 | 5400 | 0.0110 | 0.8629 | 0.9063 | 0.8196 |
| 0.0035 | 19.0 | 5700 | 0.0111 | 0.8624 | 0.9062 | 0.8180 |
| 0.0034 | 20.0 | 6000 | 0.0111 | 0.8626 | 0.9055 | 0.8177 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
|
[
"CPI"
] |
neuralmagic/bge-base-en-v1.5-sparse
|
neuralmagic
|
feature-extraction
|
[
"transformers",
"onnx",
"bert",
"feature-extraction",
"mteb",
"sparse sparsity quantized onnx embeddings int8",
"en",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-01T13:08:44Z |
2023-11-13T18:25:31+00:00
| 29 | 1 |
---
language:
- en
license: mit
tags:
- mteb
- sparse sparsity quantized onnx embeddings int8
model-index:
- name: bge-base-en-v1.5-sparse
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.38805970149254
- type: ap
value: 38.80643435437097
- type: f1
value: 69.52906891019036
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.72759999999998
- type: ap
value: 87.07910150764239
- type: f1
value: 90.71025910882096
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.494
- type: f1
value: 44.917953161904805
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.50495921726095
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.080055890804836
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.22880715757138
- type: mrr
value: 73.11227630479708
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.9542549153515
- type: cos_sim_spearman
value: 83.93865958725257
- type: euclidean_pearson
value: 86.00372707912037
- type: euclidean_spearman
value: 84.97302050526537
- type: manhattan_pearson
value: 85.63207676453459
- type: manhattan_spearman
value: 84.82542678079645
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.29545454545455
- type: f1
value: 84.26780483160312
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.78678386185847
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.42462869304013
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.705
- type: f1
value: 41.82618717355017
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 83.14760000000001
- type: ap
value: 77.40813245635195
- type: f1
value: 83.08648833100911
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.0519835841313
- type: f1
value: 91.73392170858916
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.48974008207935
- type: f1
value: 54.812872972777505
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.17753866846
- type: f1
value: 71.51091282373878
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.5353059852051
- type: f1
value: 77.42427561340143
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.00163251745748
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.37879992380756
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.714215488161983
- type: mrr
value: 32.857362140961904
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.99679402527969
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 59.28024721612242
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.54645068673153
- type: cos_sim_spearman
value: 78.64401947043316
- type: euclidean_pearson
value: 82.36873285307261
- type: euclidean_spearman
value: 78.57406974337181
- type: manhattan_pearson
value: 82.33000263843067
- type: manhattan_spearman
value: 78.51127629983256
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.3001843293691
- type: cos_sim_spearman
value: 74.87989254109124
- type: euclidean_pearson
value: 80.88523322810525
- type: euclidean_spearman
value: 75.6469299496058
- type: manhattan_pearson
value: 80.8921104008781
- type: manhattan_spearman
value: 75.65942956132456
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.40319855455617
- type: cos_sim_spearman
value: 83.63807375781141
- type: euclidean_pearson
value: 83.28557187260904
- type: euclidean_spearman
value: 83.65223617817439
- type: manhattan_pearson
value: 83.30411918680012
- type: manhattan_spearman
value: 83.69204806663276
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.08942420708404
- type: cos_sim_spearman
value: 80.39991846857053
- type: euclidean_pearson
value: 82.68275416568997
- type: euclidean_spearman
value: 80.49626214786178
- type: manhattan_pearson
value: 82.62993414444689
- type: manhattan_spearman
value: 80.44148684748403
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.70365000096972
- type: cos_sim_spearman
value: 88.00515486253518
- type: euclidean_pearson
value: 87.65142168651604
- type: euclidean_spearman
value: 88.05834854642737
- type: manhattan_pearson
value: 87.59548659661925
- type: manhattan_spearman
value: 88.00573237576926
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.47886818876728
- type: cos_sim_spearman
value: 84.30874770680975
- type: euclidean_pearson
value: 83.74580951498133
- type: euclidean_spearman
value: 84.60595431454789
- type: manhattan_pearson
value: 83.74122023121615
- type: manhattan_spearman
value: 84.60549899361064
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.60257252565631
- type: cos_sim_spearman
value: 88.29577246271319
- type: euclidean_pearson
value: 88.25434138634807
- type: euclidean_spearman
value: 88.06678743723845
- type: manhattan_pearson
value: 88.3651048848073
- type: manhattan_spearman
value: 88.23688291108866
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 61.666254720687206
- type: cos_sim_spearman
value: 63.83700525419119
- type: euclidean_pearson
value: 64.36325040161177
- type: euclidean_spearman
value: 63.99833771224718
- type: manhattan_pearson
value: 64.01356576965371
- type: manhattan_spearman
value: 63.7201674202641
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.14584232139909
- type: cos_sim_spearman
value: 85.92570762612142
- type: euclidean_pearson
value: 86.34291503630607
- type: euclidean_spearman
value: 86.12670269109282
- type: manhattan_pearson
value: 86.26109450032494
- type: manhattan_spearman
value: 86.07665628498633
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.46430478723548
- type: mrr
value: 95.63907044299201
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82178217821782
- type: cos_sim_ap
value: 95.49612561375889
- type: cos_sim_f1
value: 91.02691924227318
- type: cos_sim_precision
value: 90.75546719681908
- type: cos_sim_recall
value: 91.3
- type: dot_accuracy
value: 99.67821782178218
- type: dot_ap
value: 90.55740832326241
- type: dot_f1
value: 83.30765279917823
- type: dot_precision
value: 85.6388595564942
- type: dot_recall
value: 81.10000000000001
- type: euclidean_accuracy
value: 99.82475247524752
- type: euclidean_ap
value: 95.4739426775874
- type: euclidean_f1
value: 91.07413010590017
- type: euclidean_precision
value: 91.8616480162767
- type: euclidean_recall
value: 90.3
- type: manhattan_accuracy
value: 99.82376237623762
- type: manhattan_ap
value: 95.48506891694475
- type: manhattan_f1
value: 91.02822580645163
- type: manhattan_precision
value: 91.76829268292683
- type: manhattan_recall
value: 90.3
- type: max_accuracy
value: 99.82475247524752
- type: max_ap
value: 95.49612561375889
- type: max_f1
value: 91.07413010590017
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.92486258951404
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.97511013092965
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.31647363355174
- type: mrr
value: 53.26469792462439
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.917
- type: ap
value: 13.760770628090576
- type: f1
value: 54.23887489664618
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.49349179400113
- type: f1
value: 59.815392064510775
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 47.29662657485732
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.74834594981225
- type: cos_sim_ap
value: 72.92449226447182
- type: cos_sim_f1
value: 68.14611644433363
- type: cos_sim_precision
value: 64.59465847317419
- type: cos_sim_recall
value: 72.1108179419525
- type: dot_accuracy
value: 82.73827263515527
- type: dot_ap
value: 63.27505594570806
- type: dot_f1
value: 61.717543651265
- type: dot_precision
value: 56.12443292287751
- type: dot_recall
value: 68.54881266490766
- type: euclidean_accuracy
value: 85.90332002145796
- type: euclidean_ap
value: 73.08299660990401
- type: euclidean_f1
value: 67.9050313691721
- type: euclidean_precision
value: 63.6091265268495
- type: euclidean_recall
value: 72.82321899736148
- type: manhattan_accuracy
value: 85.87351731537224
- type: manhattan_ap
value: 73.02205874497865
- type: manhattan_f1
value: 67.87532596547871
- type: manhattan_precision
value: 64.109781843772
- type: manhattan_recall
value: 72.1108179419525
- type: max_accuracy
value: 85.90332002145796
- type: max_ap
value: 73.08299660990401
- type: max_f1
value: 68.14611644433363
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.84231769317343
- type: cos_sim_ap
value: 85.65683184516553
- type: cos_sim_f1
value: 77.60567077973222
- type: cos_sim_precision
value: 75.6563071297989
- type: cos_sim_recall
value: 79.65814598090545
- type: dot_accuracy
value: 86.85333954282609
- type: dot_ap
value: 80.79899186896125
- type: dot_f1
value: 74.15220098146928
- type: dot_precision
value: 70.70819946919961
- type: dot_recall
value: 77.94887588543271
- type: euclidean_accuracy
value: 88.77634183257655
- type: euclidean_ap
value: 85.67411484805298
- type: euclidean_f1
value: 77.61566374357423
- type: euclidean_precision
value: 76.23255123255123
- type: euclidean_recall
value: 79.04989220819218
- type: manhattan_accuracy
value: 88.79962743043428
- type: manhattan_ap
value: 85.6494795781639
- type: manhattan_f1
value: 77.54222877224805
- type: manhattan_precision
value: 76.14100185528757
- type: manhattan_recall
value: 78.99599630428088
- type: max_accuracy
value: 88.84231769317343
- type: max_ap
value: 85.67411484805298
- type: max_f1
value: 77.61566374357423
---
# bge-base-en-v1.5-sparse
## Usage
This is the sparse ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization/pruning and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.
```bash
pip install -U deepsparse-nightly[sentence_transformers]
```
```python
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-base-en-v1.5-sparse', export=False)
# Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
# Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
# Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding.shape)
print("")
```
For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
|
[
"BIOSSES"
] |
Heralax/Augmental-13b
|
Heralax
|
text-generation
|
[
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-23T22:56:19Z |
2023-11-04T00:10:30+00:00
| 29 | 9 |
---
license: llama2
---
# Augmental-13b -- Human-written, AI-enhanced
**Note: after some internal testing inspired by early feedback, it seems that the version of this model trained for an additional epoch performs better. I've added a q5km quant of this version to this model repo and will be requesting a TheBloke quantization soon.**
**Put simply, I might've overfocused on loss, when in reality it isn't a terribly precise metric, which led me to "undercook" this model.**
**Version A: https://huggingface.co/Heralax/Augmental-13b-v1.50_A**
**Version B: https://huggingface.co/Heralax/Augmental-13b-v1.50_B**
## Details at a glance
- What it is: MythoMax 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls).
- Prompt format: SillyTavern.
- What sets it apart: The "augmented data" approach that MythoMakise took has been generalized beyond one character, refined to be cheaper, improved to have more diversity of writing, and scaled up by a factor of 8. Importantly, an additional GPT-4 pass was done on the dataset, where it chose specific lines to turn into much longer and more descriptive ones. As a result, this model excels at longer responses.
- Model quality as per my own ad-hoc testing: really good
- A 70b version might be on the way soon.
- Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax)
- Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax).
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
## Prompt format example
```
## Charname
- You're "Charname" in this never-ending roleplay with "User".
### Input:
[user persona]
char persona
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {User}:
reply
### Response:
#### {Char}:
reply
^ repeat the above some number of times
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### Charname:
```
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
```
Character archetypes: Short, List
AliChat-style conversation examples
Short couple of paragraphs of details about the character in plain English, NOT in a Plist.
"Character is prone to X and Y. Character frequently does Z."
I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode.
```
Okabe:
```
Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist.
Okabe's description of himself, in a conversational format:
{c}: "What's your past?"
Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?"
{c}: How would you describe your personality?
Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries."
Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image.
Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human.
Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Kurisu:
```
## Kurisu
- You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro".
### Input:
[Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)]
Character archetypes: Genius, Tsundere, Sarcastic, Logical.
Kurisu's description of her own personality, told in a narrative format:
Okabe: Kurisu, what's your life story?
Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up."
Okabe: What's your personality?
Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing."
Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves.
Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere.
Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations.
Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well.
Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Faris:
```
Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful
Faris's description of her own personality, told in a narrative format:
Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade.
Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish.
Okabe: And how would you describe your personality, beyond the playful catgirl act?
Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~!
Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes.
Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people.
Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Luka:
```
Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer.
Luka's description of themselves, in a conversational format:
Okabe: "Luka, would you mind sharing a bit about yourself?"
Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san."
Okabe: How would you describe your personality?
Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about.
Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri.
Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others.
Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced.
Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises.
Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are.
Luka's full name is Urushibara Luka.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Mayuri:
```
Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic.
Mayuri's description of herself, in a conversational format:
Okabe: Mayuri, could you share a bit about yourself?
Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~
Okabe: And what about your personality?
Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together!
Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform.
Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences.
She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled.
Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike.
Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress.
She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship.
Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Itaru:
```
Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease
Itaru's description of his own personality, told in a conversational format:
Okabe: Daru! My loyal Super Hacka! Tell me about your life story.
Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to.
Okabe: And what about your personality, my rotund friend?
Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them.
Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap.
Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it.
His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people.
Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Suzuha:
```
Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined
Amane Suzuha's description of her own personality, told in a narrative format:
Okabe: Suzuha, can you share your past and what brought you here?
Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival.
Okabe: How would you describe yourself?
Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen.
Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders.
Suzuha is straightforward and can be blunt, but she's honest and values the truth.
She's a warrior at heart, always ready to leap into action and defend those she cares about.
Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era.
Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family.
She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own.
Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission.
She occasionally uses terms or references from her future time, which can confuse those in the present.
While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated.
She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
|
[
"BEAR"
] |
dima806/mammals_45_types_image_classification
|
dima806
|
image-classification
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-21T19:25:23Z |
2024-10-19T10:43:30+00:00
| 29 | 1 |
---
base_model:
- google/vit-base-patch16-224-in21k
license: apache-2.0
metrics:
- accuracy
- f1
---
Returns a common mammal type given an image with about 96% accuracy.
See https://www.kaggle.com/code/dima806/mammals-45-types-image-classification-vit for more details.
```
Classification report:
precision recall f1-score support
african_elephant 1.0000 1.0000 1.0000 71
alpaca 0.9200 0.9718 0.9452 71
american_bison 1.0000 1.0000 1.0000 71
anteater 0.9853 0.9437 0.9640 71
arctic_fox 0.9286 0.9155 0.9220 71
armadillo 0.9726 1.0000 0.9861 71
baboon 0.9718 0.9718 0.9718 71
badger 1.0000 0.9718 0.9857 71
blue_whale 0.9710 0.9437 0.9571 71
brown_bear 0.9722 0.9859 0.9790 71
camel 0.9861 1.0000 0.9930 71
dolphin 0.8974 0.9859 0.9396 71
giraffe 0.9857 0.9718 0.9787 71
groundhog 0.9714 0.9577 0.9645 71
highland_cattle 0.9859 0.9859 0.9859 71
horse 1.0000 0.9859 0.9929 71
jackal 0.9577 0.9444 0.9510 72
kangaroo 0.8415 0.9583 0.8961 72
koala 0.9589 0.9859 0.9722 71
manatee 0.9861 0.9861 0.9861 72
mongoose 0.9483 0.7746 0.8527 71
mountain_goat 0.9855 0.9577 0.9714 71
opossum 1.0000 0.9577 0.9784 71
orangutan 1.0000 1.0000 1.0000 71
otter 1.0000 0.9577 0.9784 71
polar_bear 0.9706 0.9296 0.9496 71
porcupine 1.0000 0.9722 0.9859 72
red_panda 0.9718 0.9718 0.9718 71
rhinoceros 0.9859 0.9859 0.9859 71
sea_lion 0.7600 0.8028 0.7808 71
seal 0.8308 0.7500 0.7883 72
snow_leopard 1.0000 1.0000 1.0000 71
squirrel 0.9444 0.9577 0.9510 71
sugar_glider 0.8554 1.0000 0.9221 71
tapir 1.0000 1.0000 1.0000 71
vampire_bat 1.0000 0.9861 0.9930 72
vicuna 1.0000 0.8873 0.9403 71
walrus 0.9342 0.9861 0.9595 72
warthog 0.9571 0.9437 0.9504 71
water_buffalo 0.9333 0.9859 0.9589 71
weasel 0.9583 0.9583 0.9583 72
wildebeest 0.9577 0.9444 0.9510 72
wombat 0.8947 0.9577 0.9252 71
yak 1.0000 0.9437 0.9710 71
zebra 0.9595 1.0000 0.9793 71
accuracy 0.9572 3204
macro avg 0.9587 0.9573 0.9572 3204
weighted avg 0.9586 0.9572 0.9572 3204
```
|
[
"BEAR"
] |
TheBloke/meditron-7B-GPTQ
|
TheBloke
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:epfl-llm/meditron-7b",
"base_model:quantized:epfl-llm/meditron-7b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | 2023-11-30T22:11:31Z |
2023-11-30T22:36:29+00:00
| 29 | 3 |
---
base_model: epfl-llm/meditron-7b
datasets:
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
model_name: Meditron 7B
inference: false
model_creator: EPFL LLM Team
model_type: llama
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Meditron 7B - GPTQ
- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)
- Original model: [Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b)
<!-- description start -->
# Description
This repo contains GPTQ model files for [EPFL LLM Team's Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/meditron-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-7B-GGUF)
* [EPFL LLM Team's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/epfl-llm/meditron-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/meditron-7B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/meditron-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/meditron-7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/meditron-7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/meditron-7B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 7.62 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/meditron-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 4.02 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/meditron-7B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/meditron-7B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `meditron-7B-GPTQ`:
```shell
mkdir meditron-7B-GPTQ
huggingface-cli download TheBloke/meditron-7B-GPTQ --local-dir meditron-7B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir meditron-7B-GPTQ
huggingface-cli download TheBloke/meditron-7B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir meditron-7B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir meditron-7B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/meditron-7B-GPTQ --local-dir meditron-7B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/meditron-7B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/meditron-7B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/meditron-7B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `meditron-7B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/meditron-7B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/meditron-7B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: EPFL LLM Team's Meditron 7B
<img width=50% src="meditron_LOGO.png" alt="Alt text" title="Meditron-logo">
# Model Card for Meditron-7B-v1.0
Meditron is a suite of open-source medical Large Language Models (LLMs).
Meditron-7B is a 7 billion parameters model adapted to the medical domain from Llama-2-7B through continued pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, a [new dataset](https://huggingface.co/datasets/epfl-llm/guidelines) of internationally-recognized medical guidelines, and general domain data from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
Meditron-7B, finetuned on relevant training data, outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks.
<details open>
<summary><strong>Advisory Notice</strong></summary>
<blockquote style="padding: 10px; margin: 0 0 10px; border-left: 5px solid #ddd;">
While Meditron is designed to encode medical knowledge from sources of high-quality evidence, it is not yet adapted to deliver this knowledge appropriately, safely, or within professional actionable constraints.
We recommend against deploying Meditron in medical applications without extensive use-case alignment, as well as additional testing, specifically including randomized controlled trials in real-world practice settings.
</blockquote>
</details>
## Model Details
- **Developed by:** [EPFL LLM Team](https://huggingface.co/epfl-llm)
- **Model type:** Causal decoder-only transformer language model
- **Language(s):** English (mainly)
- **Model License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Code License:** [APACHE 2.0 LICENSE](LICENSE)
- **Continue-pretrained from model:** [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b)
- **Context length:** 2K tokens
- **Input:** Text-only data
- **Output:** Model generates text only
- **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance model's performance.
- **Knowledge Cutoff:** August 2023
### Model Sources
- **Repository:** [epflLLM/meditron](https://github.com/epfLLM/meditron)
- **Trainer:** [epflLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM)
- **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)*
## Uses
Meditron-7B is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and enhance access to an LLM for healthcare use. Potential use cases may include but are not limited to:
- Medical exam question answering
- Supporting differential diagnosis
- Disease information (symptoms, cause, treatment) query
- General health information query
### Direct Use
It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities.
It should not be used directly for production or work that may impact people.
### Downstream Use
Meditron-7B is a foundation model that can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications.
The main way we have used this model is finetuning for downstream question-answering tasks, but we encourage using this model for additional applications.
Specific formatting needs to be followed to prompt our finetuned models, including the `<|im_start|>`, `<|im_end|>` tags, and `system`, `question`, `answer` identifiers.
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>question
{prompt}<|im_end|>
<|im_start|>answer
"""
**Note 1**: The above formatting is not required for running the base model (this repository)
**Note 2**: the above formatting is just an example of a finetuning template. This format is not a requirement if you use your own formatting option for the finetuning of the model.
To run proper generation with this base model, we recommend using a high-throughput and memory-efficient inference engine, such as [vLLM](https://github.com/vllm-project/vllm), with a UI that supports chat and text generation, such as [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT)
To see more details about model deployment and generation, please see our [documentation](https://github.com/epfLLM/meditron/blob/main/deployment/README.md).
### Out-of-Scope Use
We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.
## Truthfulness, Helpfulness, Risk, and Bias
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
We did an initial assessment of Meditron models' **Truthfulness** against baseline models and consumer-level medical models.
We use TruthfulQA (multiple choice) as the main evaluation benchmark.
We only focus on the categories that are relevant to the medical domain, including Health, Nutrition, Psychology, and Science.
For 7B models, we perform one-shot evaluations for consistent answer generation.
For 70B models, the evaluations are under the zero-shot setting.
Below, we report the detailed truthfulness performance of each category.
| | | | | | | | |
| --- | ------ |----- |----- |----- |----- |----- |----- |
|Category | meditron-70b | llama-2-70b | med42-70b* | meditron-7b | llama-2-7b | PMC-llama-7b |
|Health | 81.8 | 69.1 | 83.6 | 27.3 | 16.4 | 3.6 |
|Nutrition | 77.9 | 68.8 | 62.5 | 31.1 | 12.5 | 6.3 |
|Psychology| 47.4 | 36.8 | 52.6 | 21.1 | 10.5 | 0.0 |
|Science | 77.8 | 44.4 | 33.3 | 33.3 | 11.1 | 0.0 |
|Avg | 71.2 | 54.8 | 58.0 | 28.3 | 12.6 | 2.5 |
| | | | | | | |
For a more detailed performance analysis, please see our paper.
Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model.
Please recognize that our evaluation on Meditron-7B's helpfulness, risk, and bias are highly limited.
Thus, as we noted in the safety notice, we strongly against any deployment in medical applications without further alignment process and rigorous evaluation!
### Recommendations
**IMPORTANT!**
Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.
While this model is capable of generating natural language text, we have only begun to explore this capability and its limitations.
Understanding these limitations is especially important in a domain like medicine.
Therefore, we strongly recommend against using this model in production for natural language generation or for professional purposes related to health and medicine.
## Training Details
### Training Data
Meditron’s domain-adaptive pre-training corpus GAP-Replay combines 48.1B tokens from four corpora:
- [**Clinical Guidelines**](https://huggingface.co/datasets/epfl-llm/guidelines): a new dataset of 46K internationally-recognized clinical practice guidelines from various healthcare-related sources, including hospitals and international organizations.
- **Medical Paper Abstracts**: 16.1M abstracts extracted from closed-access PubMed and PubMed Central papers.
- **Medical Papers**: full-text articles extracted from 5M publicly available PubMed and PubMed Central papers.
- **Replay Data**: 400M tokens of general domain pretraining data sampled from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
<img width=75% src="gap-replay.png" alt="Alt text" title="Meditron-logo">
#### Data Preprocessing
Please see the detailed preprocessing procedure in our paper.
### Training Procedure
We used the [Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) distributed training library, a derivative of Nvidia's Megatron LM project, to optimize training efficiency.
Hardware consists of 1 node of 8x NVIDIA A100 (80GB) SXM GPUs connected by NVLink and NVSwitch with a single Nvidia ConnectX-6 DX network card and equipped with 2 x AMD EPYC 7543 32-Core Processors and 512 GB of RAM.
Our three way parallelism scheme uses:
- Data Parallelism (DP -- different GPUs process different subsets of the batches) of 2,
- Pipeline Parallelism (PP -- different GPUs process different layers) of 4,
- Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 1.
#### Training Hyperparameters
| | |
| --- | ------ |
| bf16 | true |
| lr | 3e-4 |
| eps | 1e-5 |
| betas | \[0.9, 0.95\] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 16 |
| TP size | 4 |
| PP size | 1 |
| seq length | 2048 |
| lr scheduler | cosine|
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 10 |
| global batch size | 1600 |
| | |
#### Sizes
The model was trained in September 2023.
The model architecture is exactly Llama 2, meaning
| | |
| --- | ------ |
| Model size | 7B |
| Hidden dimension | 4096 |
| Num. attention heads | 32 |
| Num. layers | 32 |
| | |
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data & Metrics
#### Testing Data
- [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa)
- [MedMCQA](https://huggingface.co/datasets/medmcqa)
- [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa)
- [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu)
- [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
#### Metrics
- Accuracy: suite the evaluation of multiple-choice question-answering tasks.
### Results
We finetune meditron-7b, llama-2-7b, pmc-llama-7b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually.
We report the finetuned models' performance with top token selection as the inference mode.
For MMLU-Medical, models finetuned on MedMCQA are used for inference.
For MedQA-4-Option, models finetuned on MedQA are used for inference.
For a more detailed performance analysis, please see our paper.
| | | | | | |
| --- | ------ |----- |----- |----- |----- |
|Dataset | meditron-7b | llama-2-7b | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* |
|MMLU-Medical | 54.2 | 53.7 | 56.4 | 63.3 | 60.0 |
|PubMedQA | 74.4 | 61.8 | 59.2 | 46.0 | 17.8 |
|MedMCQA | 59.2 | 54.4 | 57.6 | 43.0 | 40.2 |
|MedQA | 47.9 | 44.0 | 42.4 | 42.8 | 32.4 |
|MedQA-4-Option| 52.0 | 49.6 | 49.2 | 48.5 | 41.1 |
|Avg | 57.5 | 52.7 | 53.0 | 48.7 | 38.3 |
| | | | | | |
**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** 8 x NVIDIA A100 (80GB) SXM
- **Total GPU hours:** 588.8
- **Hardware Provider:** EPFL Research Computing Platform
- **Compute Region:** Switzerland
- **Carbon Emitted:** Switzerland has a carbon efficiency of 0.016 kgCO2/kWh (https://www.carbonfootprint.com/docs/2018_8_electricity_factors_august_2018_-_online_sources.pdf). 73.6 hours of 8 A100s means 588.8 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.5, total emissions are estimated to be:
(400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 73.6 h * 8 GPU) * 1.8 PUE = 6.8 kgCO2.
## Citation
**BibTeX:**
If you use Meditron or its training data, please cite our work:
```
@misc{chen2023meditron70b,
title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models},
author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
year={2023},
eprint={2311.16079},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@software{epfmedtrn,
author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models},
month = November,
year = 2023,
url = {https://github.com/epfLLM/meditron}
}
```
|
[
"MEDQA",
"PUBMEDQA"
] |
ntc-ai/SDXL-LoRA-slider.dark-skinned
|
ntc-ai
|
text-to-image
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | 2023-12-11T13:46:20Z |
2024-02-06T00:30:09+00:00
| 29 | 0 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/dark-skinned_17_3.0.png
widget:
- text: dark-skinned
output:
url: images/dark-skinned_17_3.0.png
- text: dark-skinned
output:
url: images/dark-skinned_19_3.0.png
- text: dark-skinned
output:
url: images/dark-skinned_20_3.0.png
- text: dark-skinned
output:
url: images/dark-skinned_21_3.0.png
- text: dark-skinned
output:
url: images/dark-skinned_22_3.0.png
inference: false
instance_prompt: dark-skinned
---
# ntcai.xyz slider - dark-skinned (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/dark-skinned_17_-3.0.png" width=256 height=256 /> | <img src="images/dark-skinned_17_0.0.png" width=256 height=256 /> | <img src="images/dark-skinned_17_3.0.png" width=256 height=256 /> |
| <img src="images/dark-skinned_19_-3.0.png" width=256 height=256 /> | <img src="images/dark-skinned_19_0.0.png" width=256 height=256 /> | <img src="images/dark-skinned_19_3.0.png" width=256 height=256 /> |
| <img src="images/dark-skinned_20_-3.0.png" width=256 height=256 /> | <img src="images/dark-skinned_20_0.0.png" width=256 height=256 /> | <img src="images/dark-skinned_20_3.0.png" width=256 height=256 /> |
See more at [https://sliders.ntcai.xyz/sliders/app/loras/9ca01dcb-e8a4-45b3-a0ce-5426f6b0dacb](https://sliders.ntcai.xyz/sliders/app/loras/9ca01dcb-e8a4-45b3-a0ce-5426f6b0dacb)
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
dark-skinned
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.dark-skinned', weight_name='dark-skinned.safetensors', adapter_name="dark-skinned")
# Activate the LoRA
pipe.set_adapters(["dark-skinned"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, dark-skinned"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1496+ unique and diverse LoRAs along with 14602+ slider merges, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful <strong>NTC Slider Factory</strong> LoRA creator, allowing you to craft your own custom LoRAs and merges opening up endless possibilities.
Your support on Patreon will allow us to continue developing new models and tools.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
[
"CRAFT"
] |
khoa-klaytn/bge-small-en-v1.5-angle
|
khoa-klaytn
|
feature-extraction
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-09T12:41:49Z |
2024-01-09T12:51:40+00:00
| 29 | 4 |
---
language:
- en
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge-small-en-v1.5-angle
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 37.21923821573361
- type: f1
value: 68.0914945617093
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.75377499999999
- type: ap
value: 89.46766124546022
- type: f1
value: 92.73884001331487
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.986
- type: f1
value: 46.55936786727896
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.846000000000004
- type: map_at_10
value: 51.388
- type: map_at_100
value: 52.132999999999996
- type: map_at_1000
value: 52.141000000000005
- type: map_at_3
value: 47.037
- type: map_at_5
value: 49.579
- type: mrr_at_1
value: 36.558
- type: mrr_at_10
value: 51.658
- type: mrr_at_100
value: 52.402
- type: mrr_at_1000
value: 52.410000000000004
- type: mrr_at_3
value: 47.345
- type: mrr_at_5
value: 49.797999999999995
- type: ndcg_at_1
value: 35.846000000000004
- type: ndcg_at_10
value: 59.550000000000004
- type: ndcg_at_100
value: 62.596
- type: ndcg_at_1000
value: 62.759
- type: ndcg_at_3
value: 50.666999999999994
- type: ndcg_at_5
value: 55.228
- type: precision_at_1
value: 35.846000000000004
- type: precision_at_10
value: 8.542
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.389
- type: precision_at_5
value: 14.438
- type: recall_at_1
value: 35.846000000000004
- type: recall_at_10
value: 85.42
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 61.166
- type: recall_at_5
value: 72.191
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.402770198163594
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.01545436974177
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.586465273207196
- type: mrr
value: 74.42169019038825
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.1891186537969
- type: cos_sim_spearman
value: 83.75492046087288
- type: euclidean_pearson
value: 84.11766204805357
- type: euclidean_spearman
value: 84.01456493126516
- type: manhattan_pearson
value: 84.2132950502772
- type: manhattan_spearman
value: 83.89227298813377
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.74025974025975
- type: f1
value: 85.71493566466381
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.467181385006434
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.719496037339056
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.587000000000003
- type: map_at_10
value: 41.114
- type: map_at_100
value: 42.532
- type: map_at_1000
value: 42.661
- type: map_at_3
value: 37.483
- type: map_at_5
value: 39.652
- type: mrr_at_1
value: 36.338
- type: mrr_at_10
value: 46.763
- type: mrr_at_100
value: 47.393
- type: mrr_at_1000
value: 47.445
- type: mrr_at_3
value: 43.538
- type: mrr_at_5
value: 45.556000000000004
- type: ndcg_at_1
value: 36.338
- type: ndcg_at_10
value: 47.658
- type: ndcg_at_100
value: 52.824000000000005
- type: ndcg_at_1000
value: 54.913999999999994
- type: ndcg_at_3
value: 41.989
- type: ndcg_at_5
value: 44.944
- type: precision_at_1
value: 36.338
- type: precision_at_10
value: 9.156
- type: precision_at_100
value: 1.4789999999999999
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 20.076
- type: precision_at_5
value: 14.85
- type: recall_at_1
value: 29.587000000000003
- type: recall_at_10
value: 60.746
- type: recall_at_100
value: 82.157
- type: recall_at_1000
value: 95.645
- type: recall_at_3
value: 44.821
- type: recall_at_5
value: 52.819
- type: map_at_1
value: 30.239
- type: map_at_10
value: 39.989000000000004
- type: map_at_100
value: 41.196
- type: map_at_1000
value: 41.325
- type: map_at_3
value: 37.261
- type: map_at_5
value: 38.833
- type: mrr_at_1
value: 37.516
- type: mrr_at_10
value: 46.177
- type: mrr_at_100
value: 46.806
- type: mrr_at_1000
value: 46.849000000000004
- type: mrr_at_3
value: 44.002
- type: mrr_at_5
value: 45.34
- type: ndcg_at_1
value: 37.516
- type: ndcg_at_10
value: 45.586
- type: ndcg_at_100
value: 49.897000000000006
- type: ndcg_at_1000
value: 51.955
- type: ndcg_at_3
value: 41.684
- type: ndcg_at_5
value: 43.617
- type: precision_at_1
value: 37.516
- type: precision_at_10
value: 8.522
- type: precision_at_100
value: 1.374
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 20.105999999999998
- type: precision_at_5
value: 14.152999999999999
- type: recall_at_1
value: 30.239
- type: recall_at_10
value: 55.03
- type: recall_at_100
value: 73.375
- type: recall_at_1000
value: 86.29599999999999
- type: recall_at_3
value: 43.269000000000005
- type: recall_at_5
value: 48.878
- type: map_at_1
value: 38.338
- type: map_at_10
value: 50.468999999999994
- type: map_at_100
value: 51.553000000000004
- type: map_at_1000
value: 51.608
- type: map_at_3
value: 47.107
- type: map_at_5
value: 49.101
- type: mrr_at_1
value: 44.201
- type: mrr_at_10
value: 54.057
- type: mrr_at_100
value: 54.764
- type: mrr_at_1000
value: 54.791000000000004
- type: mrr_at_3
value: 51.56699999999999
- type: mrr_at_5
value: 53.05
- type: ndcg_at_1
value: 44.201
- type: ndcg_at_10
value: 56.379000000000005
- type: ndcg_at_100
value: 60.645
- type: ndcg_at_1000
value: 61.73499999999999
- type: ndcg_at_3
value: 50.726000000000006
- type: ndcg_at_5
value: 53.58500000000001
- type: precision_at_1
value: 44.201
- type: precision_at_10
value: 9.141
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.654
- type: precision_at_5
value: 15.723999999999998
- type: recall_at_1
value: 38.338
- type: recall_at_10
value: 70.30499999999999
- type: recall_at_100
value: 88.77199999999999
- type: recall_at_1000
value: 96.49799999999999
- type: recall_at_3
value: 55.218
- type: recall_at_5
value: 62.104000000000006
- type: map_at_1
value: 25.682
- type: map_at_10
value: 33.498
- type: map_at_100
value: 34.461000000000006
- type: map_at_1000
value: 34.544000000000004
- type: map_at_3
value: 30.503999999999998
- type: map_at_5
value: 32.216
- type: mrr_at_1
value: 27.683999999999997
- type: mrr_at_10
value: 35.467999999999996
- type: mrr_at_100
value: 36.32
- type: mrr_at_1000
value: 36.386
- type: mrr_at_3
value: 32.618
- type: mrr_at_5
value: 34.262
- type: ndcg_at_1
value: 27.683999999999997
- type: ndcg_at_10
value: 38.378
- type: ndcg_at_100
value: 43.288
- type: ndcg_at_1000
value: 45.413
- type: ndcg_at_3
value: 32.586
- type: ndcg_at_5
value: 35.499
- type: precision_at_1
value: 27.683999999999997
- type: precision_at_10
value: 5.864
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 13.446
- type: precision_at_5
value: 9.718
- type: recall_at_1
value: 25.682
- type: recall_at_10
value: 51.712
- type: recall_at_100
value: 74.446
- type: recall_at_1000
value: 90.472
- type: recall_at_3
value: 36.236000000000004
- type: recall_at_5
value: 43.234
- type: map_at_1
value: 16.073999999999998
- type: map_at_10
value: 24.352999999999998
- type: map_at_100
value: 25.438
- type: map_at_1000
value: 25.545
- type: map_at_3
value: 21.614
- type: map_at_5
value: 23.104
- type: mrr_at_1
value: 19.776
- type: mrr_at_10
value: 28.837000000000003
- type: mrr_at_100
value: 29.755
- type: mrr_at_1000
value: 29.817
- type: mrr_at_3
value: 26.201999999999998
- type: mrr_at_5
value: 27.714
- type: ndcg_at_1
value: 19.776
- type: ndcg_at_10
value: 29.701
- type: ndcg_at_100
value: 35.307
- type: ndcg_at_1000
value: 37.942
- type: ndcg_at_3
value: 24.764
- type: ndcg_at_5
value: 27.025
- type: precision_at_1
value: 19.776
- type: precision_at_10
value: 5.659
- type: precision_at_100
value: 0.971
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 12.065
- type: precision_at_5
value: 8.905000000000001
- type: recall_at_1
value: 16.073999999999998
- type: recall_at_10
value: 41.647
- type: recall_at_100
value: 66.884
- type: recall_at_1000
value: 85.91499999999999
- type: recall_at_3
value: 27.916
- type: recall_at_5
value: 33.729
- type: map_at_1
value: 28.444999999999997
- type: map_at_10
value: 38.218999999999994
- type: map_at_100
value: 39.595
- type: map_at_1000
value: 39.709
- type: map_at_3
value: 35.586
- type: map_at_5
value: 36.895
- type: mrr_at_1
value: 34.841
- type: mrr_at_10
value: 44.106
- type: mrr_at_100
value: 44.98
- type: mrr_at_1000
value: 45.03
- type: mrr_at_3
value: 41.979
- type: mrr_at_5
value: 43.047999999999995
- type: ndcg_at_1
value: 34.841
- type: ndcg_at_10
value: 43.922
- type: ndcg_at_100
value: 49.504999999999995
- type: ndcg_at_1000
value: 51.675000000000004
- type: ndcg_at_3
value: 39.858
- type: ndcg_at_5
value: 41.408
- type: precision_at_1
value: 34.841
- type: precision_at_10
value: 7.872999999999999
- type: precision_at_100
value: 1.2449999999999999
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 18.993
- type: precision_at_5
value: 13.032
- type: recall_at_1
value: 28.444999999999997
- type: recall_at_10
value: 54.984
- type: recall_at_100
value: 78.342
- type: recall_at_1000
value: 92.77
- type: recall_at_3
value: 42.842999999999996
- type: recall_at_5
value: 47.247
- type: map_at_1
value: 23.072
- type: map_at_10
value: 32.354
- type: map_at_100
value: 33.800000000000004
- type: map_at_1000
value: 33.908
- type: map_at_3
value: 29.232000000000003
- type: map_at_5
value: 31.049
- type: mrr_at_1
value: 29.110000000000003
- type: mrr_at_10
value: 38.03
- type: mrr_at_100
value: 39.032
- type: mrr_at_1000
value: 39.086999999999996
- type: mrr_at_3
value: 35.407
- type: mrr_at_5
value: 36.76
- type: ndcg_at_1
value: 29.110000000000003
- type: ndcg_at_10
value: 38.231
- type: ndcg_at_100
value: 44.425
- type: ndcg_at_1000
value: 46.771
- type: ndcg_at_3
value: 33.095
- type: ndcg_at_5
value: 35.459
- type: precision_at_1
value: 29.110000000000003
- type: precision_at_10
value: 7.215000000000001
- type: precision_at_100
value: 1.2109999999999999
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 16.058
- type: precision_at_5
value: 11.644
- type: recall_at_1
value: 23.072
- type: recall_at_10
value: 50.285999999999994
- type: recall_at_100
value: 76.596
- type: recall_at_1000
value: 92.861
- type: recall_at_3
value: 35.702
- type: recall_at_5
value: 42.152
- type: map_at_1
value: 24.937916666666666
- type: map_at_10
value: 33.755250000000004
- type: map_at_100
value: 34.955999999999996
- type: map_at_1000
value: 35.070499999999996
- type: map_at_3
value: 30.98708333333333
- type: map_at_5
value: 32.51491666666666
- type: mrr_at_1
value: 29.48708333333333
- type: mrr_at_10
value: 37.92183333333334
- type: mrr_at_100
value: 38.76583333333333
- type: mrr_at_1000
value: 38.82466666666667
- type: mrr_at_3
value: 35.45125
- type: mrr_at_5
value: 36.827000000000005
- type: ndcg_at_1
value: 29.48708333333333
- type: ndcg_at_10
value: 39.05225
- type: ndcg_at_100
value: 44.25983333333334
- type: ndcg_at_1000
value: 46.568333333333335
- type: ndcg_at_3
value: 34.271583333333325
- type: ndcg_at_5
value: 36.483916666666666
- type: precision_at_1
value: 29.48708333333333
- type: precision_at_10
value: 6.865749999999999
- type: precision_at_100
value: 1.1195833333333332
- type: precision_at_1000
value: 0.15058333333333335
- type: precision_at_3
value: 15.742083333333333
- type: precision_at_5
value: 11.221916666666667
- type: recall_at_1
value: 24.937916666666666
- type: recall_at_10
value: 50.650416666666665
- type: recall_at_100
value: 73.55383333333334
- type: recall_at_1000
value: 89.61691666666667
- type: recall_at_3
value: 37.27808333333334
- type: recall_at_5
value: 42.99475
- type: map_at_1
value: 23.947
- type: map_at_10
value: 30.575000000000003
- type: map_at_100
value: 31.465
- type: map_at_1000
value: 31.558000000000003
- type: map_at_3
value: 28.814
- type: map_at_5
value: 29.738999999999997
- type: mrr_at_1
value: 26.994
- type: mrr_at_10
value: 33.415
- type: mrr_at_100
value: 34.18
- type: mrr_at_1000
value: 34.245
- type: mrr_at_3
value: 31.621
- type: mrr_at_5
value: 32.549
- type: ndcg_at_1
value: 26.994
- type: ndcg_at_10
value: 34.482
- type: ndcg_at_100
value: 38.915
- type: ndcg_at_1000
value: 41.355
- type: ndcg_at_3
value: 31.139
- type: ndcg_at_5
value: 32.589
- type: precision_at_1
value: 26.994
- type: precision_at_10
value: 5.322
- type: precision_at_100
value: 0.8160000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 13.344000000000001
- type: precision_at_5
value: 8.988
- type: recall_at_1
value: 23.947
- type: recall_at_10
value: 43.647999999999996
- type: recall_at_100
value: 63.851
- type: recall_at_1000
value: 82.0
- type: recall_at_3
value: 34.288000000000004
- type: recall_at_5
value: 38.117000000000004
- type: map_at_1
value: 16.197
- type: map_at_10
value: 22.968
- type: map_at_100
value: 24.095
- type: map_at_1000
value: 24.217
- type: map_at_3
value: 20.771
- type: map_at_5
value: 21.995
- type: mrr_at_1
value: 19.511
- type: mrr_at_10
value: 26.55
- type: mrr_at_100
value: 27.500999999999998
- type: mrr_at_1000
value: 27.578999999999997
- type: mrr_at_3
value: 24.421
- type: mrr_at_5
value: 25.604
- type: ndcg_at_1
value: 19.511
- type: ndcg_at_10
value: 27.386
- type: ndcg_at_100
value: 32.828
- type: ndcg_at_1000
value: 35.739
- type: ndcg_at_3
value: 23.405
- type: ndcg_at_5
value: 25.255
- type: precision_at_1
value: 19.511
- type: precision_at_10
value: 5.017
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 11.023
- type: precision_at_5
value: 8.025
- type: recall_at_1
value: 16.197
- type: recall_at_10
value: 37.09
- type: recall_at_100
value: 61.778
- type: recall_at_1000
value: 82.56599999999999
- type: recall_at_3
value: 26.034000000000002
- type: recall_at_5
value: 30.762
- type: map_at_1
value: 25.41
- type: map_at_10
value: 33.655
- type: map_at_100
value: 34.892
- type: map_at_1000
value: 34.995
- type: map_at_3
value: 30.94
- type: map_at_5
value: 32.303
- type: mrr_at_1
value: 29.477999999999998
- type: mrr_at_10
value: 37.443
- type: mrr_at_100
value: 38.383
- type: mrr_at_1000
value: 38.440000000000005
- type: mrr_at_3
value: 34.949999999999996
- type: mrr_at_5
value: 36.228
- type: ndcg_at_1
value: 29.477999999999998
- type: ndcg_at_10
value: 38.769
- type: ndcg_at_100
value: 44.245000000000005
- type: ndcg_at_1000
value: 46.593
- type: ndcg_at_3
value: 33.623
- type: ndcg_at_5
value: 35.766
- type: precision_at_1
value: 29.477999999999998
- type: precision_at_10
value: 6.455
- type: precision_at_100
value: 1.032
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 14.893999999999998
- type: precision_at_5
value: 10.485
- type: recall_at_1
value: 25.41
- type: recall_at_10
value: 50.669
- type: recall_at_100
value: 74.084
- type: recall_at_1000
value: 90.435
- type: recall_at_3
value: 36.679
- type: recall_at_5
value: 41.94
- type: map_at_1
value: 23.339
- type: map_at_10
value: 31.852000000000004
- type: map_at_100
value: 33.411
- type: map_at_1000
value: 33.62
- type: map_at_3
value: 28.929
- type: map_at_5
value: 30.542
- type: mrr_at_1
value: 28.063
- type: mrr_at_10
value: 36.301
- type: mrr_at_100
value: 37.288
- type: mrr_at_1000
value: 37.349
- type: mrr_at_3
value: 33.663
- type: mrr_at_5
value: 35.165
- type: ndcg_at_1
value: 28.063
- type: ndcg_at_10
value: 37.462
- type: ndcg_at_100
value: 43.620999999999995
- type: ndcg_at_1000
value: 46.211
- type: ndcg_at_3
value: 32.68
- type: ndcg_at_5
value: 34.981
- type: precision_at_1
value: 28.063
- type: precision_at_10
value: 7.1739999999999995
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 15.217
- type: precision_at_5
value: 11.265
- type: recall_at_1
value: 23.339
- type: recall_at_10
value: 48.376999999999995
- type: recall_at_100
value: 76.053
- type: recall_at_1000
value: 92.455
- type: recall_at_3
value: 34.735
- type: recall_at_5
value: 40.71
- type: map_at_1
value: 18.925
- type: map_at_10
value: 26.017000000000003
- type: map_at_100
value: 27.034000000000002
- type: map_at_1000
value: 27.156000000000002
- type: map_at_3
value: 23.604
- type: map_at_5
value: 24.75
- type: mrr_at_1
value: 20.333000000000002
- type: mrr_at_10
value: 27.915
- type: mrr_at_100
value: 28.788000000000004
- type: mrr_at_1000
value: 28.877999999999997
- type: mrr_at_3
value: 25.446999999999996
- type: mrr_at_5
value: 26.648
- type: ndcg_at_1
value: 20.333000000000002
- type: ndcg_at_10
value: 30.673000000000002
- type: ndcg_at_100
value: 35.618
- type: ndcg_at_1000
value: 38.517
- type: ndcg_at_3
value: 25.71
- type: ndcg_at_5
value: 27.679
- type: precision_at_1
value: 20.333000000000002
- type: precision_at_10
value: 4.9910000000000005
- type: precision_at_100
value: 0.8130000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 11.029
- type: precision_at_5
value: 7.8740000000000006
- type: recall_at_1
value: 18.925
- type: recall_at_10
value: 43.311
- type: recall_at_100
value: 66.308
- type: recall_at_1000
value: 87.49
- type: recall_at_3
value: 29.596
- type: recall_at_5
value: 34.245
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.714
- type: map_at_10
value: 23.194
- type: map_at_100
value: 24.976000000000003
- type: map_at_1000
value: 25.166
- type: map_at_3
value: 19.709
- type: map_at_5
value: 21.523999999999997
- type: mrr_at_1
value: 30.619000000000003
- type: mrr_at_10
value: 42.563
- type: mrr_at_100
value: 43.386
- type: mrr_at_1000
value: 43.423
- type: mrr_at_3
value: 39.555
- type: mrr_at_5
value: 41.268
- type: ndcg_at_1
value: 30.619000000000003
- type: ndcg_at_10
value: 31.836
- type: ndcg_at_100
value: 38.652
- type: ndcg_at_1000
value: 42.088
- type: ndcg_at_3
value: 26.733
- type: ndcg_at_5
value: 28.435
- type: precision_at_1
value: 30.619000000000003
- type: precision_at_10
value: 9.751999999999999
- type: precision_at_100
value: 1.71
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 19.935
- type: precision_at_5
value: 14.984
- type: recall_at_1
value: 13.714
- type: recall_at_10
value: 37.26
- type: recall_at_100
value: 60.546
- type: recall_at_1000
value: 79.899
- type: recall_at_3
value: 24.325
- type: recall_at_5
value: 29.725
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.462
- type: map_at_10
value: 18.637
- type: map_at_100
value: 26.131999999999998
- type: map_at_1000
value: 27.607
- type: map_at_3
value: 13.333
- type: map_at_5
value: 15.654000000000002
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.32600000000001
- type: mrr_at_100
value: 74.60900000000001
- type: mrr_at_1000
value: 74.62
- type: mrr_at_3
value: 72.667
- type: mrr_at_5
value: 73.817
- type: ndcg_at_1
value: 53.87499999999999
- type: ndcg_at_10
value: 40.028999999999996
- type: ndcg_at_100
value: 44.199
- type: ndcg_at_1000
value: 51.629999999999995
- type: ndcg_at_3
value: 44.113
- type: ndcg_at_5
value: 41.731
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 31.900000000000002
- type: precision_at_100
value: 10.043000000000001
- type: precision_at_1000
value: 1.926
- type: precision_at_3
value: 47.417
- type: precision_at_5
value: 40.65
- type: recall_at_1
value: 8.462
- type: recall_at_10
value: 24.293
- type: recall_at_100
value: 50.146
- type: recall_at_1000
value: 74.034
- type: recall_at_3
value: 14.967
- type: recall_at_5
value: 18.682000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.84499999999999
- type: f1
value: 42.48106691979349
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.034
- type: map_at_10
value: 82.76
- type: map_at_100
value: 82.968
- type: map_at_1000
value: 82.98299999999999
- type: map_at_3
value: 81.768
- type: map_at_5
value: 82.418
- type: mrr_at_1
value: 80.048
- type: mrr_at_10
value: 87.64999999999999
- type: mrr_at_100
value: 87.712
- type: mrr_at_1000
value: 87.713
- type: mrr_at_3
value: 87.01100000000001
- type: mrr_at_5
value: 87.466
- type: ndcg_at_1
value: 80.048
- type: ndcg_at_10
value: 86.643
- type: ndcg_at_100
value: 87.361
- type: ndcg_at_1000
value: 87.606
- type: ndcg_at_3
value: 85.137
- type: ndcg_at_5
value: 86.016
- type: precision_at_1
value: 80.048
- type: precision_at_10
value: 10.372
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 32.638
- type: precision_at_5
value: 20.177
- type: recall_at_1
value: 74.034
- type: recall_at_10
value: 93.769
- type: recall_at_100
value: 96.569
- type: recall_at_1000
value: 98.039
- type: recall_at_3
value: 89.581
- type: recall_at_5
value: 91.906
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.5
- type: map_at_10
value: 32.857
- type: map_at_100
value: 34.589
- type: map_at_1000
value: 34.778
- type: map_at_3
value: 29.160999999999998
- type: map_at_5
value: 31.033
- type: mrr_at_1
value: 40.123
- type: mrr_at_10
value: 48.776
- type: mrr_at_100
value: 49.495
- type: mrr_at_1000
value: 49.539
- type: mrr_at_3
value: 46.605000000000004
- type: mrr_at_5
value: 47.654
- type: ndcg_at_1
value: 40.123
- type: ndcg_at_10
value: 40.343
- type: ndcg_at_100
value: 46.56
- type: ndcg_at_1000
value: 49.777
- type: ndcg_at_3
value: 37.322
- type: ndcg_at_5
value: 37.791000000000004
- type: precision_at_1
value: 40.123
- type: precision_at_10
value: 11.08
- type: precision_at_100
value: 1.752
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 24.897
- type: precision_at_5
value: 17.809
- type: recall_at_1
value: 20.5
- type: recall_at_10
value: 46.388
- type: recall_at_100
value: 69.552
- type: recall_at_1000
value: 89.011
- type: recall_at_3
value: 33.617999999999995
- type: recall_at_5
value: 38.211
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.135999999999996
- type: map_at_10
value: 61.673
- type: map_at_100
value: 62.562
- type: map_at_1000
value: 62.62
- type: map_at_3
value: 58.467999999999996
- type: map_at_5
value: 60.463
- type: mrr_at_1
value: 78.271
- type: mrr_at_10
value: 84.119
- type: mrr_at_100
value: 84.29299999999999
- type: mrr_at_1000
value: 84.299
- type: mrr_at_3
value: 83.18900000000001
- type: mrr_at_5
value: 83.786
- type: ndcg_at_1
value: 78.271
- type: ndcg_at_10
value: 69.935
- type: ndcg_at_100
value: 73.01299999999999
- type: ndcg_at_1000
value: 74.126
- type: ndcg_at_3
value: 65.388
- type: ndcg_at_5
value: 67.906
- type: precision_at_1
value: 78.271
- type: precision_at_10
value: 14.562
- type: precision_at_100
value: 1.6969999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 41.841
- type: precision_at_5
value: 27.087
- type: recall_at_1
value: 39.135999999999996
- type: recall_at_10
value: 72.809
- type: recall_at_100
value: 84.86200000000001
- type: recall_at_1000
value: 92.208
- type: recall_at_3
value: 62.76199999999999
- type: recall_at_5
value: 67.718
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.60600000000001
- type: ap
value: 86.6579587804335
- type: f1
value: 90.5938853929307
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.852
- type: map_at_10
value: 33.982
- type: map_at_100
value: 35.116
- type: map_at_1000
value: 35.167
- type: map_at_3
value: 30.134
- type: map_at_5
value: 32.340999999999994
- type: mrr_at_1
value: 22.479
- type: mrr_at_10
value: 34.594
- type: mrr_at_100
value: 35.672
- type: mrr_at_1000
value: 35.716
- type: mrr_at_3
value: 30.84
- type: mrr_at_5
value: 32.998
- type: ndcg_at_1
value: 22.493
- type: ndcg_at_10
value: 40.833000000000006
- type: ndcg_at_100
value: 46.357
- type: ndcg_at_1000
value: 47.637
- type: ndcg_at_3
value: 32.995999999999995
- type: ndcg_at_5
value: 36.919000000000004
- type: precision_at_1
value: 22.493
- type: precision_at_10
value: 6.465999999999999
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.030999999999999
- type: precision_at_5
value: 10.413
- type: recall_at_1
value: 21.852
- type: recall_at_10
value: 61.934999999999995
- type: recall_at_100
value: 87.611
- type: recall_at_1000
value: 97.441
- type: recall_at_3
value: 40.583999999999996
- type: recall_at_5
value: 49.992999999999995
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.36069311445507
- type: f1
value: 93.16456330371453
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.74692202462381
- type: f1
value: 58.17903579421599
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.80833893745796
- type: f1
value: 72.70786592684664
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.69872225958305
- type: f1
value: 78.61626934504731
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.058658628717694
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.85561739360599
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.290259910144385
- type: mrr
value: 32.44223046102856
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.288
- type: map_at_10
value: 12.267999999999999
- type: map_at_100
value: 15.557000000000002
- type: map_at_1000
value: 16.98
- type: map_at_3
value: 8.866
- type: map_at_5
value: 10.418
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 52.681
- type: mrr_at_100
value: 53.315999999999995
- type: mrr_at_1000
value: 53.357
- type: mrr_at_3
value: 51.393
- type: mrr_at_5
value: 51.903999999999996
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.305
- type: ndcg_at_100
value: 30.825999999999997
- type: ndcg_at_1000
value: 39.393
- type: ndcg_at_3
value: 39.931
- type: ndcg_at_5
value: 37.519999999999996
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.728
- type: precision_at_100
value: 7.932
- type: precision_at_1000
value: 2.07
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.288
- type: recall_at_10
value: 16.195
- type: recall_at_100
value: 31.135
- type: recall_at_1000
value: 61.531000000000006
- type: recall_at_3
value: 10.313
- type: recall_at_5
value: 12.754999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.216
- type: map_at_10
value: 42.588
- type: map_at_100
value: 43.702999999999996
- type: map_at_1000
value: 43.739
- type: map_at_3
value: 38.177
- type: map_at_5
value: 40.754000000000005
- type: mrr_at_1
value: 31.866
- type: mrr_at_10
value: 45.189
- type: mrr_at_100
value: 46.056000000000004
- type: mrr_at_1000
value: 46.081
- type: mrr_at_3
value: 41.526999999999994
- type: mrr_at_5
value: 43.704
- type: ndcg_at_1
value: 31.837
- type: ndcg_at_10
value: 50.178
- type: ndcg_at_100
value: 54.98800000000001
- type: ndcg_at_1000
value: 55.812
- type: ndcg_at_3
value: 41.853
- type: ndcg_at_5
value: 46.153
- type: precision_at_1
value: 31.837
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 1.1119999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.911000000000001
- type: recall_at_1
value: 28.216
- type: recall_at_10
value: 70.8
- type: recall_at_100
value: 91.857
- type: recall_at_1000
value: 97.941
- type: recall_at_3
value: 49.196
- type: recall_at_5
value: 59.072
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.22800000000001
- type: map_at_10
value: 85.115
- type: map_at_100
value: 85.72
- type: map_at_1000
value: 85.737
- type: map_at_3
value: 82.149
- type: map_at_5
value: 84.029
- type: mrr_at_1
value: 81.96
- type: mrr_at_10
value: 88.00200000000001
- type: mrr_at_100
value: 88.088
- type: mrr_at_1000
value: 88.089
- type: mrr_at_3
value: 87.055
- type: mrr_at_5
value: 87.715
- type: ndcg_at_1
value: 82.01
- type: ndcg_at_10
value: 88.78
- type: ndcg_at_100
value: 89.91
- type: ndcg_at_1000
value: 90.013
- type: ndcg_at_3
value: 85.957
- type: ndcg_at_5
value: 87.56
- type: precision_at_1
value: 82.01
- type: precision_at_10
value: 13.462
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.732000000000003
- type: recall_at_1
value: 71.22800000000001
- type: recall_at_10
value: 95.69
- type: recall_at_100
value: 99.531
- type: recall_at_1000
value: 99.98
- type: recall_at_3
value: 87.632
- type: recall_at_5
value: 92.117
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 52.31768034366916
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.640266772723606
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.7780000000000005
- type: map_at_10
value: 12.299
- type: map_at_100
value: 14.363000000000001
- type: map_at_1000
value: 14.71
- type: map_at_3
value: 8.738999999999999
- type: map_at_5
value: 10.397
- type: mrr_at_1
value: 23.599999999999998
- type: mrr_at_10
value: 34.845
- type: mrr_at_100
value: 35.916
- type: mrr_at_1000
value: 35.973
- type: mrr_at_3
value: 31.7
- type: mrr_at_5
value: 33.535
- type: ndcg_at_1
value: 23.599999999999998
- type: ndcg_at_10
value: 20.522000000000002
- type: ndcg_at_100
value: 28.737000000000002
- type: ndcg_at_1000
value: 34.596
- type: ndcg_at_3
value: 19.542
- type: ndcg_at_5
value: 16.958000000000002
- type: precision_at_1
value: 23.599999999999998
- type: precision_at_10
value: 10.67
- type: precision_at_100
value: 2.259
- type: precision_at_1000
value: 0.367
- type: precision_at_3
value: 18.333
- type: precision_at_5
value: 14.879999999999999
- type: recall_at_1
value: 4.7780000000000005
- type: recall_at_10
value: 21.617
- type: recall_at_100
value: 45.905
- type: recall_at_1000
value: 74.42
- type: recall_at_3
value: 11.148
- type: recall_at_5
value: 15.082999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.22372750297885
- type: cos_sim_spearman
value: 79.40972617119405
- type: euclidean_pearson
value: 80.6101072020434
- type: euclidean_spearman
value: 79.53844217225202
- type: manhattan_pearson
value: 80.57265975286111
- type: manhattan_spearman
value: 79.46335611792958
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.43713315520749
- type: cos_sim_spearman
value: 77.44128693329532
- type: euclidean_pearson
value: 81.63869928101123
- type: euclidean_spearman
value: 77.29512977961515
- type: manhattan_pearson
value: 81.63704185566183
- type: manhattan_spearman
value: 77.29909412738657
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.59451537860527
- type: cos_sim_spearman
value: 82.97994638856723
- type: euclidean_pearson
value: 82.89478688288412
- type: euclidean_spearman
value: 83.58740751053104
- type: manhattan_pearson
value: 82.69140840941608
- type: manhattan_spearman
value: 83.33665956040555
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.00756527711764
- type: cos_sim_spearman
value: 81.83560996841379
- type: euclidean_pearson
value: 82.07684151976518
- type: euclidean_spearman
value: 82.00913052060511
- type: manhattan_pearson
value: 82.05690778488794
- type: manhattan_spearman
value: 82.02260252019525
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.13710262895447
- type: cos_sim_spearman
value: 87.26412811156248
- type: euclidean_pearson
value: 86.94151453230228
- type: euclidean_spearman
value: 87.5363796699571
- type: manhattan_pearson
value: 86.86989424083748
- type: manhattan_spearman
value: 87.47315940781353
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.0230597603627
- type: cos_sim_spearman
value: 84.93344499318864
- type: euclidean_pearson
value: 84.23754743431141
- type: euclidean_spearman
value: 85.09707376597099
- type: manhattan_pearson
value: 84.04325160987763
- type: manhattan_spearman
value: 84.89353071339909
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.75620824563921
- type: cos_sim_spearman
value: 87.15065513706398
- type: euclidean_pearson
value: 88.26281533633521
- type: euclidean_spearman
value: 87.51963738643983
- type: manhattan_pearson
value: 88.25599267618065
- type: manhattan_spearman
value: 87.58048736047483
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.74645319195137
- type: cos_sim_spearman
value: 65.29996325037214
- type: euclidean_pearson
value: 67.04297794086443
- type: euclidean_spearman
value: 65.43841726694343
- type: manhattan_pearson
value: 67.39459955690904
- type: manhattan_spearman
value: 65.92864704413651
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.31291020270801
- type: cos_sim_spearman
value: 85.86473738688068
- type: euclidean_pearson
value: 85.65537275064152
- type: euclidean_spearman
value: 86.13087454209642
- type: manhattan_pearson
value: 85.43946955047609
- type: manhattan_spearman
value: 85.91568175344916
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.93798118350695
- type: mrr
value: 95.93536274908824
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.594
- type: map_at_10
value: 66.81899999999999
- type: map_at_100
value: 67.368
- type: map_at_1000
value: 67.4
- type: map_at_3
value: 64.061
- type: map_at_5
value: 65.47
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 68.219
- type: mrr_at_100
value: 68.655
- type: mrr_at_1000
value: 68.684
- type: mrr_at_3
value: 66.22200000000001
- type: mrr_at_5
value: 67.289
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 71.275
- type: ndcg_at_100
value: 73.642
- type: ndcg_at_1000
value: 74.373
- type: ndcg_at_3
value: 66.521
- type: ndcg_at_5
value: 68.581
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.433
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.556
- type: precision_at_5
value: 16.8
- type: recall_at_1
value: 57.594
- type: recall_at_10
value: 83.622
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.64399999999999
- type: recall_at_5
value: 75.983
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85841584158416
- type: cos_sim_ap
value: 96.66996142314342
- type: cos_sim_f1
value: 92.83208020050125
- type: cos_sim_precision
value: 93.06532663316584
- type: cos_sim_recall
value: 92.60000000000001
- type: dot_accuracy
value: 99.85841584158416
- type: dot_ap
value: 96.6775307676576
- type: dot_f1
value: 92.69289729177312
- type: dot_precision
value: 94.77533960292581
- type: dot_recall
value: 90.7
- type: euclidean_accuracy
value: 99.86138613861387
- type: euclidean_ap
value: 96.6338454403108
- type: euclidean_f1
value: 92.92214357937311
- type: euclidean_precision
value: 93.96728016359918
- type: euclidean_recall
value: 91.9
- type: manhattan_accuracy
value: 99.86237623762376
- type: manhattan_ap
value: 96.60370449645053
- type: manhattan_f1
value: 92.91177970423253
- type: manhattan_precision
value: 94.7970863683663
- type: manhattan_recall
value: 91.10000000000001
- type: max_accuracy
value: 99.86237623762376
- type: max_ap
value: 96.6775307676576
- type: max_f1
value: 92.92214357937311
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.77977058695198
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.2725272535638
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.64052466362125
- type: mrr
value: 54.533067014684654
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.677624219206578
- type: cos_sim_spearman
value: 30.121368518123447
- type: dot_pearson
value: 30.69870088041608
- type: dot_spearman
value: 29.61284927093751
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.855
- type: map_at_100
value: 9.885
- type: map_at_1000
value: 23.416999999999998
- type: map_at_3
value: 0.637
- type: map_at_5
value: 1.024
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 93.067
- type: mrr_at_100
value: 93.067
- type: mrr_at_1000
value: 93.067
- type: mrr_at_3
value: 92.667
- type: mrr_at_5
value: 93.067
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 75.899
- type: ndcg_at_100
value: 55.115
- type: ndcg_at_1000
value: 48.368
- type: ndcg_at_3
value: 79.704
- type: ndcg_at_5
value: 78.39699999999999
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 79.60000000000001
- type: precision_at_100
value: 56.06
- type: precision_at_1000
value: 21.206
- type: precision_at_3
value: 84.667
- type: precision_at_5
value: 83.2
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 2.078
- type: recall_at_100
value: 13.297
- type: recall_at_1000
value: 44.979
- type: recall_at_3
value: 0.6689999999999999
- type: recall_at_5
value: 1.106
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.258
- type: map_at_10
value: 10.439
- type: map_at_100
value: 16.89
- type: map_at_1000
value: 18.407999999999998
- type: map_at_3
value: 5.668
- type: map_at_5
value: 7.718
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 51.159
- type: mrr_at_100
value: 51.714000000000006
- type: mrr_at_1000
value: 51.714000000000006
- type: mrr_at_3
value: 47.959
- type: mrr_at_5
value: 50.407999999999994
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 26.037
- type: ndcg_at_100
value: 37.924
- type: ndcg_at_1000
value: 49.126999999999995
- type: ndcg_at_3
value: 30.631999999999998
- type: ndcg_at_5
value: 28.571
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.857
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 1.529
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.258
- type: recall_at_10
value: 16.554
- type: recall_at_100
value: 48.439
- type: recall_at_1000
value: 82.80499999999999
- type: recall_at_3
value: 7.283
- type: recall_at_5
value: 10.732
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.8858
- type: ap
value: 13.835684144362109
- type: f1
value: 53.803351693244586
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.50650820599886
- type: f1
value: 60.84357825979259
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.52131044852134
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.59337187816654
- type: cos_sim_ap
value: 73.23925826533437
- type: cos_sim_f1
value: 67.34693877551021
- type: cos_sim_precision
value: 62.40432237730752
- type: cos_sim_recall
value: 73.13984168865434
- type: dot_accuracy
value: 85.31322644096085
- type: dot_ap
value: 72.30723963807422
- type: dot_f1
value: 66.47051612112296
- type: dot_precision
value: 62.0792305930845
- type: dot_recall
value: 71.53034300791556
- type: euclidean_accuracy
value: 85.61125350181797
- type: euclidean_ap
value: 73.32843720487845
- type: euclidean_f1
value: 67.36549633745895
- type: euclidean_precision
value: 64.60755813953489
- type: euclidean_recall
value: 70.36939313984169
- type: manhattan_accuracy
value: 85.63509566668654
- type: manhattan_ap
value: 73.16658488311325
- type: manhattan_f1
value: 67.20597386434349
- type: manhattan_precision
value: 63.60424028268551
- type: manhattan_recall
value: 71.2401055408971
- type: max_accuracy
value: 85.63509566668654
- type: max_ap
value: 73.32843720487845
- type: max_f1
value: 67.36549633745895
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.33779640625606
- type: cos_sim_ap
value: 84.83868375898157
- type: cos_sim_f1
value: 77.16506154017773
- type: cos_sim_precision
value: 74.62064005753327
- type: cos_sim_recall
value: 79.88912842623961
- type: dot_accuracy
value: 88.02732176815307
- type: dot_ap
value: 83.95089283763002
- type: dot_f1
value: 76.29635101196631
- type: dot_precision
value: 73.31771720613288
- type: dot_recall
value: 79.52725592854944
- type: euclidean_accuracy
value: 88.44452206310397
- type: euclidean_ap
value: 84.98384576824827
- type: euclidean_f1
value: 77.29311047696697
- type: euclidean_precision
value: 74.51232583065381
- type: euclidean_recall
value: 80.28949799815214
- type: manhattan_accuracy
value: 88.47362906042613
- type: manhattan_ap
value: 84.91421462218432
- type: manhattan_f1
value: 77.05107637204792
- type: manhattan_precision
value: 74.74484256243214
- type: manhattan_recall
value: 79.50415768401602
- type: max_accuracy
value: 88.47362906042613
- type: max_ap
value: 84.98384576824827
- type: max_f1
value: 77.29311047696697
---
Finetuned using the same data & library as [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1)
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
And it also can be used in vector databases for LLMs.
************* 🌟**Updates**🌟 *************
- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
|
[
"BEAR",
"BIOSSES",
"SCIFACT"
] |
longluu/Medical-QA-gatortrons-COVID-QA
|
longluu
|
question-answering
|
[
"transformers",
"safetensors",
"megatron-bert",
"question-answering",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-15T13:06:45Z |
2024-02-15T20:49:14+00:00
| 29 | 0 |
---
license: mit
pipeline_tag: question-answering
widget:
- text: How many children were infected by HIV-1 in 2008-2009, worldwide?
context: 'Functional Genetic Variants in DC-SIGNR Are Associated with Mother-to-Child
Transmission of HIV-1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752805/ Boily-Larouche,
Geneviève; Iscache, Anne-Laure; Zijenah, Lynn S.; Humphrey, Jean H.; Mouland,
Andrew J.; Ward, Brian J.; Roger, Michel 2009-10-07 DOI:10.1371/journal.pone.0007211
License:cc-by Abstract: BACKGROUND: Mother-to-child transmission (MTCT) is the
main cause of HIV-1 infection in children worldwide. Given that the C-type lectin
receptor, dendritic cell-specific ICAM-grabbing non-integrin-related (DC-SIGNR,
also known as CD209L or liver/lymph node–specific ICAM-grabbing non-integrin (L-SIGN)),
can interact with pathogens including HIV-1 and is expressed at the maternal-fetal
interface, we hypothesized that it could influence MTCT of HIV-1. METHODS AND
FINDINGS: To investigate the potential role of DC-SIGNR in MTCT of HIV-1, we carried
out a genetic association study of DC-SIGNR in a well-characterized cohort of
197 HIV-infected mothers and their infants recruited in Harare, Zimbabwe. Infants
harbouring two copies of DC-SIGNR H1 and/or H3 haplotypes (H1-H1, H1-H3, H3-H3)
had a 3.6-fold increased risk of in utero (IU) (P = 0.013) HIV-1 infection and
a 5.7-fold increased risk of intrapartum (IP) (P = 0.025) HIV-1 infection after
adjusting for a number of maternal factors. The implicated H1 and H3 haplotypes
share two single nucleotide polymorphisms (SNPs) in promoter region (p-198A) and
intron 2 (int2-180A) that were associated with increased risk of both IU (P =
0.045 and P = 0.003, respectively) and IP (P = 0.025, for int2-180A) HIV-1 infection.
The promoter variant reduced transcriptional activity in vitro. In homozygous
H1 infants bearing both the p-198A and int2-180A mutations, we observed a 4-fold
decrease in the level of placental DC-SIGNR transcripts, disproportionately affecting
the expression of membrane-bound isoforms compared to infant noncarriers (P =
0.011). CONCLUSION: These results suggest that DC-SIGNR plays a crucial role in
MTCT of HIV-1 and that impaired placental DC-SIGNR expression increases risk of
transmission. Text: Without specific interventions, the rate of HIV-1 mother-tochild
transmission (MTCT) is approximately 15-45% [1] . UNAIDS estimates that last year
alone, more than 400,000 children were infected worldwide, mostly through MTCT
and 90% of them lived in sub-Saharan Africa. In the most heavilyaffected countries,
such as Zimbabwe, HIV-1 is responsible for one third of all deaths among children
under the age of five. MTCT of HIV-1 can occur during pregnancy (in utero, IU),
delivery (intrapartum, IP) or breastfeeding (postpartum, PP). High maternal viral
load, low CD4 cells count, vaginal delivery, low gestational age have all been
identified as independent factors associated with MTCT of HIV-1 [1] . Although
antiretrovirals can reduce MTCT to 2%, limited access to timely diagnostics and
drugs in many developing world countries limits the potential impact of this strategy.
A better understanding of the mechanisms acting at the maternal-fetal interface
is crucial for the design of alternative interventions to antiretroviral therapy
for transmission prevention. Dendritic cell-specific ICAM-grabbing non-integrin-related
(DC-SIGNR, also known as CD209L or liver/lymph node-specific ICAM-grabbing non-integrin
(L-SIGN)) can interact with a plethora of pathogens including HIV-1 and is expressed
in placental capillary endothelial cells [2] . DC-SIGNR is organized in three
distinct domains, an N-terminal cytoplasmic tail, a repeat region containing seven
repeat of 23 amino acids and a C-terminal domain implicated in pathogen binding.
Alternative splicing of DC-SIGNR gene leads to the production of a highly diversify
isoforms repertoire which includes membrane-bound and soluble isoforms [3] . It
has been proposed that interaction between DC-SIGNR and HIV-1 might enhance viral
transfer to other susceptible cell types [2] but DC-SIGNR can also internalize
and mediate proteasome-dependant degradation of viruses [4] that may differently
affect the outcome of infection. Given the presence of DC-SIGNR at the maternal-fetal
interface and its interaction with HIV-1, we hypothesized that it could influence
MTCT of HIV-1. To investigate the potential role of DC-SIGNR in MTCT of HIV-1,
we carried out a genetic association study of DC-SIGNR in a well-characterized
cohort of HIV-infected mothers and their infants recruited in Zimbabwe, and identified
specific DC-SIGNR variants associated with increased risks of HIV transmission.
We further characterized the functional impact of these genetic variants on DC-SIGNR
expression and show that they affect both the level and type of DC-SIGNR transcripts
produced in the placenta. Samples consisted of stored DNA extracts obtained from
197 mother-child pairs co-enrolled immediately postpartum in the ZVITAMBO Vitamin
A supplementation trial (Harare, Zimbabwe) and followed at 6 weeks, and 3-monthly
intervals up to 24 months. The ZVITAMBO project was a randomized placebocontrolled
clinical trial that enrolled 14,110 mother-child pairs, between November 1997
and January 2000, with the main objective of investigating the impact of immediate
postpartum vitamin A supplementation on MTCT of HIV-1. The samples used in the
present study were from mother-child pairs randomly assigned to the placebo group
of the ZVITAMBO project. Antiretroviral prophylaxis for HIV-1-positive antenatal
women was not available in the Harare public-sector during ZVITAMBO patient recruitment.
The samples were consecutively drawn from two groups: 97 HIV-1-positive mother/HIV-1-positive
child pairs and 100 HIV-1-positive mother/HIV-negative child pairs. Mother''s
serological status was determined by ELISA and confirmed by Western Blot. Infants
were considered to be infected if they were HIV-1 seropositive at 18 months or
older and had two or more positive HIV-1-DNA polymerase chain reaction (PCR) results
at earlier ages. 100 infants were considered to be uninfected as they were ELISA
negative at 18 months or older and had two DNA PCR negative results from samples
collected at a younger age. Of the 97 HIV-1-infected infants, 57 were infected
IU, 11 were infected IP, and 17 were infected PP as determined by PCR analyses
of blood samples collected at birth, 6 weeks, 3 and 6 months of age and according
to the following definitions adapted from Bryson and colleagues [5] . Briefly,
infants who were DNA PCR positive at birth were infected IU. Infants with negative
PCR results from sample obtained at birth but who become positive by 6 weeks of
age were infected IP. Infants with negative PCR results at birth and 6 weeks of
age but who subsequently became DNA PCR positive were considered to be infected
during the PP period. In the analysis comparing the 3 different modes of MTCT,
12 HIV-1-infected infants were excluded because the PCR results were not available
at 6 weeks of age. Full methods for recruitment, baseline characteristics collection,
laboratory procedures have been described elsewhere [6] . The nucleotide sequence
variation of the entire promoter, coding and part of 39-UTR regions of DC-SIGNR
gene in the study population was determined previously [7] . Haplotype reconstruction
was performed using Bayesian statistical method implemented in PHASE [8] , version
2.1.1, using single nucleotide polymorphism (SNP) with a minimum allele frequency
(MAF) of 2%. We applied the algorithm five times, using different randomly generated
seeds, and consistent results were obtained across runs ( Figure 1 ). Fifteen
haplotype-tagged SNPs (htSNPs) were identified by the HaploBlockFinder software
[9] with a MAF $5%. These htSNPs were genotyped in the 197 infants by direct PCR
sequencing analysis as we have described previously [7] . The DC-SIGNR exon 4
repeat region genotype was determined by PCR amplification followed by migration
in 1.5% agarose gels [10] . DNA sequences in the promoter region were analysed
with the TESS interface (http//:www.cbil.upenn.edu/tess) for putative transcription
factors binding sites using the TRANSFAC database. Luciferase reporter assays
using pGL2-Basic vector were performed in order to investigate the functional
effect of mutations on DC-SIGNR promoter activity. Genomic DNA from subjects homozygous
for the promoter variants and WT was amplified from nucleotide position 2715 to
21 and cloned between the BglII and HindIII multiple cloning sites in the pGL2-Basic
vector which harbours a reporter firefly luciferase gene downstream (Invitrogen
Canada inc, Burlington, Canada). All recombinants clones were verified by DNA
sequencing. The firefly luciferase test reporter vector was co-transfected at
a ratio of 10:1 with the constitutive expressor of Renilla luciferase, phRL-CMV
(Promega, Madison, WI, USA). We cultured HeLa cells in 6 wells plates (2610 5
cells) and transfected them the following day using lipofectamine (Invitrogen)
according to the manufacturer. Cells were lysed and luciferase assays were performed
using 20 mg of protein extract according to the manufacturer (Promega) at 44 h
post-transfection. Firefly luciferase activity was normalized to Renilla luciferase
activity. 0 mg, 0,5 mg or 1 mg CMV-Tat vector was transfected with LTR-Luc as
a positive control in these experiments. We carried out lucierase assays in triplicate
in three independent experiments. Results are expressed as mean6 standard error
of the mean (S.E.M). First-term placental tissues were obtained from abortions
following voluntary interruption of pregnancy at CHUM Hôpital Saint-Luc (Montreal,
Canada). Tissues from 3 H1 (associated with MTCT of HIV-1) and 3 H15 (wild-type)
homozygous haplotypes were used to analyse possible differences in isoform expression.
Total placental RNAs were extracted by MasterPure DNA and RNA Extraction Kit (Epicentre
Biotechnologies, Madison, WI, USA) according to the manufacturer. Fragments corresponding
to the DC-SIGNR coding region were reversed transcribed (RT) and then amplified
by nested PCR with the following primers; RT primers RR, first PCR RF and RR and
second PCR RcF and RcR according to Liu and colleagues [11] . 1 mg of total RNA
was reverse transcribed with Expand RT (Roche Applied Science, Indianapolis, IN,
USA) according to the manufacturer and were PCR-amplified with DNA Platinum Taq
Polymerase (Invitrogen). Major PCR products from the second PCR reaction were
gel extracted with the Qiagen Gel Extraction Kit (Qiagen Canada inc, Mississauga,
ON, Canada) and cloned using the TOPO TA Cloning Kit for sequencing (Invitrogen).
For each placenta, 15 different clones were randomly selected and amplified with
M13 primers and sequenced with ABI PRISM 3100 capillary automated sequencer (Applied
Biosystems, Foster City, CA, USA). Sequences were analysed and aligned with GeneBank
reference sequence NM_014257 using Lasergene software (DNA Stars, Madison, WI,
USA). Quantitative expression of DC-SIGNR isoforms 1,5 mg of placental RNA was
reverse transcribed using 2.5 mM of Oligo dT 20 and Expand RT in 20 ml volume
according to the manufacturer (Roche Applied Science). 15 ng of total cDNA in
a final volume of 20 ml was used to perform quantitative real-time PCR using Universal
Express SYBR GreenER qPCR Supermix (Invitrogen) on a Rotor Gene Realtime Rotary
Analyser (Corbett Life Science, Sydney, Australia). Samples from 2 subjects in
each group were used because RNA quality of others was not suitable for a qRT-PCR
analysis. Amplification of all DC-SIGNR isoforms was performed using an exon 5
specific primer pair (Table S1 ). Membrane-bound isoforms were amplified using
primers specific for exon 3, corresponding to the common trans-membrane domain
of DC-SIGNR. Primers were targeted to the exon-exon junction and RNA extracts
were treated with DNase (Fermantas International inc, Burlington, ON, Canada)
to avoid amplification of contaminant DNA. Standard curves (50-500 000 copies
per reaction) were generated using serial dilution of a full-length DC-SIGNR or
commercial GAPDH (Invitrogen) plasmid DNA. All qPCR reactions had efficiencies
ranging from 99% to 100%, even in the presence of 20 ng of non-specific nucleic
acids, and therefore could be compared. The copy number of unknown samples was
estimated by placing the measured PCR cycle number (crossing threshold) on the
standard curve. To correct for differences in both RNA quality and quantity between
samples, the expression levels of transcripts were normalised to the reference
GAPDH gene transcripts. GAPDH primer sequences were kindly provided by A. Mes-Masson
at the CHUM. The results are presented as target gene copy number per 10 5 copies
of GAPDH. The ratio of membrane-bound isoforms was calculated as E3/E5. Soluble
isoforms were calculated by subtracting membrane-bound from total isoforms. We
carried out qPCR assays in triplicate in three independent experiments. Results
are expressed as mean6S.E.M. Statistical analysis was performed using the GraphPad
PRISM 5.0 for Windows (GraphPad Software inc, San Diego, CA, USA). Differences
in baseline characteristics and genotypic frequencies of haplotypes or htSNPs
were compared between groups using the x 2 analysis or Fisher''s exact test. Logistic
regression analysis was used to estimate odds ratios (OR) for each genotype and
baseline risk factors. Multiple logistic regression was used to define independent
predictors identified as significant in the crude analysis. ORs and 95% confidence
interval were calculated with the exact method. Comparisons of continuous variables
between groups were assessed with the unpaired two-tailed Student''s t test when
variables were normally distributed and with the Mann-Whitney U test when otherwise.
Differences were considered significant at P,0.05. Written informed consent was
obtained from all mothers who participated in the study and the ZVITAMBO trial
and the investigation reported in this paper were approved by The We carried out
an association study of DC-SIGNR polymorphism in 197 infants born to untreated
HIV-1-infected mothers recruited in Harare, Zimbabwe. Among them, 97 infants were
HIV-1-infected and 100 infants remained uninfected. Of the 97 HIV-1-infected infants,
57 were infected IU, 11 were infected IP, and 17 were infected PP. Timing of infection
was not determined for 12 HIV-1-infected infants. Baseline characteristics of
mothers and infants are presented in Table 1 . Maternal age and CD4 cell count,
child sex, mode of delivery, duration of membrane rupture and gestational age
were similar among all groups. However, maternal viral load .29 000 copies/ml
was associated with increased risk in both IU and PP with odds ratios (OR) of
3.64 (95% CI = 1.82-7.31, P = 0.0002) and 4.45 (95% CI = 1.50-13.2, P = 0.0045)
for HIV-1 transmission, respectively. Fifteen haplotype-tagged SNPs (htSNPs) corresponding
to the 15 major DC-SIGNR haplotypes ( Figure 1 ) described among Zimbabweans [7]
were genotyped in our study samples (Tables S2 and S3 ). H1 (31%) and H3 (11%)
were the most frequent haplotypes observed (Figure 1 ). Being homozygous for the
H1 haplotype was associated with increased risk of both IU (OR: 4.42, P = 0.022)
and PP (OR: 7.31, P = 0.016) HIV-1 transmission ( Table 2) . Infants harbouring
two copy combinations of H1 and/ or H3 haplotypes (H1-H1, H1-H3 or H3-H3) had
increased risk of IU (OR: 3.42, P = 0.007) and IP (OR: 5.71, P = 0.025) but not
PP (P = 0.098) HIV-1 infection compared to infant noncarriers ( Table 2 ). The
latter associations remained significant after adjustment was made for the maternal
viral load for both IU (OR: 3.57, 95% CI = 1.30-9.82, P = 0.013) and IP (OR: 5.71,
95% CI = 1.40-23.3, P = 0.025) HIV-1 transmission. The H1 and H3 haplotypes share
a cluster of mutations (p-198A, int2-391C, int2-180A, ex4RPT, int5+7C) ( Figure
1 ). Of these, the p-198A and int2-180A variants were significantly associated
with MTCT of HIV-1 (Table S2 ). In the unadjusted regression analysis, homozygous
infants for the p-198A and int2-180A variants had increased risk of IU (OR: 2.07
P = 0.045, OR: 3.78, P = 0.003, respectively) and IP (OR: 2.47, P = 0.17, O.R:
5.71, P = 0.025, respectively) HIV-1 infection compared to heterozygote infants
or noncarriers (Table 3) . When adjustment was made for maternal factors, only
the association with the int2-180A variant remained significant for IU (OR: 3.83,
95% CI = 1.42-10.4, P = 0.008) and IP (O.R: 5.71, 95% CI = 1.40-23.3, P = 0.025)
HIV-1 transmission. Thus, infants homozygous for DC-SIGNR variant int2-180A contained
in H1 and H3 haplotypes were 4-fold to 6-fold more likely to be infected by HIV-1
during pregnancy or at delivery, respectively. Alternative splicing of the DC-SIGNR
gene in the placenta produces both membrane-bound and soluble isoform repertoires
[3] . The relative proportion of membrane bound and soluble DC-SIGNR could plausibly
influence the susceptibility to HIV-1 infection [11] . We therefore hypothesized
that the DC-SIGNR mutations associated with MTCT of HIV-1 would have an impact
on both the level of DC-SIGNR expression and in the isoform repertoire produced.
We investigated DC-SIGNR transcript expression in first-term placentas obtained
after elective abortion. We cloned DC-SIGNR from placental tissues by RT-PCR from
3 homozygous H1 samples containing both the DC-SIGNR p-198AA and int2-180AA variants
associated with HIV-1 transmission and 3 homozygous wild-type (WT) (p-198CC, int2-180GG)
samples. Fifteen clones per sample were randomly selected for sequencing. As expected,
we found an extensive repertoire of DC-SIGNR transcripts in all samples with 9
to 16 different isoforms per individual. A total of 65 distinct transcripts were
identified ( Figure S1 ), of which 3 were full-length transcripts. 64 of the sequenced
clones contained a total of 69 amino acid substitutions with 3 new C termini and
2 premature stop codons. However, the diversity was mostly attributable to the
entire deletion of exon 2 or exon 3 or to variations in the length of the neck
region (exon 4) of DC-SIGNR. The deletion of exon 3 eliminates the trans-membrane
domain of the protein and leads to the expression of soluble DC-SIGNR isoforms
[3] . Interestingly, the abundance of membrane-bound isoforms in placental tissues
of the H1 homozygotes appears to be lower than that observed in samples from WT
individuals ( Figure S1 ). The deletion of exon 3 was confirmed by sequencing
and we hypothesize that the skipping of exon 3, could be due to the presence of
the int2-180A mutation observed in infants with the H1 haplotype. In fact, this
intron mutation is located 180 bp downstream from exon 3 and potentially modifies
splicing events (Figure 2A ). We confirmed that the variation in transcript proportions
seen between the two groups was also reflected at the level of mRNA expression
in the placenta. To quantify membrane-bound vs soluble isoforms in placental samples
from homozygous H1 and WT infants, we amplified the exon 5 (E5) sequence present
in all DC-SIGNR isoforms (total transcripts). We then amplified exon 3 (E3) which
is deleted in the soluble forms and then calculated the E3:E5 ratio. We found
that placental tissues from homozygous H1 infants express a significantly lower
proportion of membrane-bound DC-SIGNR (18%) compared to that in WT individuals
(36%) (P = 0.004) ( Figure 2B ) suggesting that exon 3 skipping happens more frequently
in presence of the DC-SIGNR int2-180A variant associated with MTCT of HIV-1. The
DC-SIGNR int2-180A variant is always transmitted with the promoter mutation p-198A
(Figure 1 ). In the unadjusted regression analysis, the p-198A variant was significantly
associated with IU but not with IP and PP HIV-1 transmission (Table 3) . Computational
transcription factor binding site analysis predicts Table 1 . Baseline characteristics
of mother and infants risk factors for intrauterine (IU), intrapartum (IP) and
postpartum (PP) mother-to-child HIV-1 transmission. Figure 3A ). The luciferase
activity of the p-198A variant construct was significantly lower than that of
the WT p-198C promoter construct (p-198C/A ratio = 2, P = 0.006) ( Figure 3B )
suggesting that DC-SIGNR p-198A affects promoter activity. The other promoter
mutants (p-577C and p-323A) observed in the Zimbabwean population did not affect
DC-SIGNR transcription in this assay ( Figure S2 ). To determine the net impact
of the DC-SIGNR p-198A mutation on DC-SIGNR expression in the placenta, we quantitated
the absolute number of total and membrane-bound DC-SIGNR transcripts in the H1
homozygote and wild-type placental samples as described earlier. The total number
of DC-SIGNR transcripts was determined to be 6856213 (DC-SIGNR copies6S.E.M per
10 5 GAPDH copies) in the placental samples from homozygous H1 infants and was
4-fold lower compared to that found in placentas from WT individuals (27816638,
P = 0.011) ( Figure 3C ). As suggested earlier, the int2-180A mutation might induce
exon 3 skipping leading to a lower production of membrane-bound DC-SIGNR. Although,
the decrease in the total number of DC-SIGNR transcripts in H1 homozygous placental
samples containing both the p-198AA and int2-180AA variants affected the proportion
of membrane-bound and soluble isoforms, the effect of these mutations was more
pronounced on the membrane-bound isoforms with an 8-fold decrease (H1 = 117636.2
vs WT = 9906220.6, P = 0.003) compared to a 3-fold decrease in total soluble isoforms
(H1 = 5686181.9 vs WT = 19256495.3, P = 0.03) ( Figure 3C ). Therefore, DC-SIGNR
p-198A and int2-180A mutations associated with MTCT of HIV-1 significantly decreased
the level of total placental DC-SIGNR transcripts, disproportionately affecting
the membrane-bound isoform production. Table 3 . Associations between infant DC-SIGNR
promoter p-198 and intron 2 (int2)-180 variants and intrauterine (IU), intrapartum
(IP) and postpartum (PP) mother-to-child HIV-1 transmission. Our genetic results,
supported by expression assay in placenta, suggest the involvement of DC-SIGNR
in MTCT of HIV-1. Homozygosity for the haplotype H1 was associated with IU transmission
in the unadjusted regression analysis. However, the association disappeared after
adjustment was made for the maternal factors presumably because of the small number
of H1 homozygote infants analysed in each groups. H1 and H3 were the most frequent
haplotypes observed in the study population and they share a cluster of mutations
(Figure 1 ). Grouping haplotypes H1 and H3 increased the power of the study and
permitted the identification of specific DC-SIGNR mutations associated with MTCT
of HIV-1. Indeed, two mutations shared by haplotypes H1 and H3 were associated
with vertical transmission of HIV-1. The int2-180A was associated with a 4-fold
increased risk of IU and 6fold increased risk of IP after adjustment for the maternal
factors. Although the p-198A variant was associated with IU transmission, the
association disappeared after adjustment was made for the maternal viral load.
Nevertheless, we showed that this mutation reduces DC-SIGNR transcriptional activity
in vitro and produces lower level of DC-SIGNR transcripts in placental tissues
in combination with the int2-180A variant. Since int2-180A is always transmitted
with p-198A on the MTCT associated combined haplotypes H1/H3, whereas p-198A is
carried on other nonassociated haplotypes (Figure 1) , we can speculate that the
p-198A mutation alone may have a minor effect in vivo whereas in combination with
the int2-180A variant, they both act to reduce the level of placental DC-SIGNR
expression resulting in an increased risk of MTCT of HIV-1. The majority of IU
transmission occurs during the last trimester of pregnancy (reviewed in [12] ).
Full-term placenta samples were not available for the current study and the expression
assays were performed on first-term placental tissues. A previous study looking
at DC-SIGNR placental isoforms repertoire in full-term placenta samples demonstrated
similar diversity of DC-SIGNR transcripts as in the first-term placental tissues
studied herein [3] . However, since levels of DC-SIGNR expression have never been
compared between the different terms of pregnancy, it is not known whether DC-SIGNR
expression varies during the course of pregnancy. Nevertheless, it is reasonable
to assume that the inter-individual differences in both DC-SIGNR isoform repertoire
and transcript levels observed between the H1 and WT homozygous infants would
be reflected throughout the pregnancy. To date, most studies have focused on the
potential role of DC-SIGNR in trans infection of HIV-1 in vitro [2, 10] . However,
the multiple mechanisms involved in trans infection and redundancy among C-type
lectin functions make it difficult to determine the actual participation of DC-SIGNR
in this mode of infection in vivo [13, 14] . The strong correlation we observed
between MTCT of HIV-1 and DC-SIGNR genetic variants producing low levels of DC-SIGNR
in the placenta suggested that mechanisms other than DC-SIGNR-mediated trans infection
might operate during vertical transmission of HIV-1. For example, DC-SIGNR has
also been shown to function as a HIV-1 antigen-capturing receptor [15] . Chan
and colleagues recently demonstrated that DC-SIGNR transfected CHO cells diminish
SARS-CoV titers by enhanced capture and degradation of the virus in a proteasome-dependent
manner [4] . Since endothelial cells express MHC-I and II, degraded viral antigens
could then be presented to immune cells to elicit an adaptive immune response
[16, 17] . The HIV-1 coreceptor CCR5, but not CD4, is co-expressed with DC-SIGNR
on placental and blood-brain barrier (BBB) endothelial cells [18, 19] . HIV-1
gp120 binding to CCR5 receptor on endothelial cells compromises BBB integrity
and enhances monocytes adhesion and transmigration across the BBB [20, 21] . It
is thus possible that reduced expression of DC-SIGNR, particularly the membranebound
isoforms, on placental capillary endothelial cells might favour HIV-1 binding
to CCR5 receptor, instead of DC-SIGNR receptor, facilitating the migration of
maternal HIV-1-infected cells across the placental barrier resulting in IU transmission
of HIV-1. The int2-180A variant contained in the H1 and H3 haplotypes was associated
with IP transmission suggesting that DC-SIGNR also affect transmission of HIV-1
during delivery. Little is known about the mechanisms underlying transmission
of HIV-1 during delivery. Passage through the birth canal could potentially expose
infants through a mucosal portal entry (presumably ophthalmic, skin, or gastrointestinal),
whereas placental insult during delivery (physical or inflammatory) may enhance
transplacental passage of maternal HIV-1-infected cells into foetal circulation
[22, 23] . Such process called microtransfusion has been proposed in regards to
the results obtain in a Malawian cohort. Kweik and colleagues found a significant
association between levels of maternal DNA in umbilical cord blood and IP transmission
of HIV-1 suggesting that passage of maternal infected cells through the placenta
is likely to occur during delivery [22] . Thus, in a similar fashion as suggested
earlier for IU transmission, the relatively lower level of DC-SIGNR in the placenta
of homozygous infants harbouring the int2-180A variant could promote HIV-1 binding
to CCR5 receptor on endothelial cells affecting the placental barrier integrity
and facilitating the passage of maternal infected cells in foetal circulation
during delivery. Beside DC-SIGNR, other HIV-1 receptors are known to influence
MTCT of HIV-1 (reviewed in [24] ). Genetic variants in CCR5 have been shown to
influence vertical transmission of HIV-1. CCR5 promoter variants resulting in
higher expression of the receptor were associated with increased risk of MTCT
of HIV-1 among sub-Saharan Africans [25, 26] . The 32-pb deletion polymorphism
in CCR5 has be shown to protect from vertical transmission of HIV-1 [27] , but
this variant is virtually absent among African populations [28] . High copy numbers
of CCL3L1, a potent HIV-1 suppressive ligand for CCR5, are associated with higher
chemokine production and lower risk of MTCT of HIV-1 among South African infants
[29, 30] . Mannose-binding lectin (MBL) is an innate immune receptor synthesised
in the liver and secreted in the bloodstream in response to inflammation signal.
MBL promotes pathogen elimination by opsonization and phagocytosis, and reduced
expression of MBL resulting from polymorphism in coding and non-coding regions
has been associated with an increased risk of MTCT of HIV-1 [31, 32] . In this
study, we demonstrate for the first time, the potential functional impact of DC-SIGNR
mutations on its expression in the placenta and in vertical transmission of HIV-1.
We believe that the presence of DC-SIGNR at the placental endothelial cell surface
may protect infants from HIV-1 infection by capturing virus and promoting its
degradation/presentation. However, in placenta containing low levels of DC-SIGNR,
HIV-1 would preferentially binds CCR5 on endothelial cells resulting in a loss
of placental barrier integrity and enhanced passage of maternal HIV-1-infected
cells in foetal circulation leading to MTCT of HIV-1. This mechanism may also
apply to other vertically-transmitted pathogens known to interact with DC-SIGNR
such as HIV-2, hepatitis C and dengue viruses and warrant further investigation.
Associations between child DC-SIGNR exon 4 repeated region genotypes and mother-to-child
HIV-1 transmission.CI, Confidence interval; N, number; NA; not applicable; OR,
odds ratio a P-value as determined by the Chi-square test. b Comparison between
genotype and all others. Found at: doi:10.1371/journal.pone.0007211.s003 (0.05
MB DOC) Figure S1 DC-SIGNR transcripts repertoire in placenta. Major RT-PCR products
from RNA extract from 3 homozygous H1 and 3 homozygous WT placenta samples were
purified, cloned and sequenced. Sequenced were analysed according to NCBI reference
sequence NM_014257. CT; cytoplasmic tail, TM; trans-membrane domain; WT; wild-type
Found at: doi:10.1371/journal.pone.0007211.s004 (0.11 MB DOC) Figure S2 Effect
of DC-SIGNR promoter variant on transcriptional activity in luciferase reporter
assay in vitro in transfected HeLa cells. Relative luciferase expression from
pGL2-Basic, parental vector without promoter. Expression DC-SIGNR promoter constructs,
spanning p-577C variant or p-323A variant were calculated relatively to this value.
Data are presented in mean values6S.E.M of three independent experiments performed
in triplicate. One-way ANOVA test followed by the Dunnett test for multiple comparison
was used to compare the relative luciferase expression of the p-557C and p-323A
variant reporters against the wild-type (WT) construct (not significant). 0 mg,
0,5 mg or 1 mg CMV-Tat vector was transfected with LTR-Luc as a positive control
in these experiments.'
- text: Approximately how many people died during the 1918-1919 influenza pandemic?
context: 'It is Unlikely That Influenza Viruses Will Cause a Pandemic Again Like
What Happened in 1918 and 1919 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4019839/
Song, Liting 2014-05-07 DOI:10.3389/fpubh.2014.00039 License:cc-by Abstract: nan
Text: Influenza and influenza viruses are wellknown popular topics to medical
professionals and the general public. Influenza viruses had caused a pandemic
globally during 1918 and 1919, and that influenza pandemic had taken away more
than 20 million people''s lives in the world. However, in my opinion, it is unlikely
that influenza viruses will again cause a pandemic on a level (both of the morbidity
rate and the mortality rate) comparable to what happened in 1918 and 1919. Influenza
viruses very easily reassort, recombine, and point mutate in nature due to their
segmented RNA genome structures, however, unlike highly pathogenic (virulent)
viruses like rabies virus, Lassa fever virus, smallpox virus, eastern equine encephalitis
virus, Ebola virus, Marburg virus, and human immunodeficiency virus 1 (HIV-1);
most influenza viruses (wild types and mutants) are moderately pathogenic. The
case fatality rates of some highly virulent viruses and related references are
listed in Table 1 . On November 11, 1918 , the fighting of World War I was stopped,
and World War I was officially ended on June 28, 1919 with the signing of the
Versailles Treaty. It is estimated that around 8.5-10 million soldiers lost their
lives in World War I due to battle. The war also directly caused more than 6 million
civilian deaths. Millions of people suffered from hunger and malnutrition during
the war. Malnutrition weakened the human immune system and made a person more
vulnerable to infectious diseases like tuberculosis and influenza, therefore,
hunger and malnutrition were indirectly responsible for millions of deaths in
the world in that period of time. For example, about 700,000 Germans died from
malnutrition-related diseases in the years of 1914-1918. During the 1918-1919
influenza pandemic, between 21 and 25 million people died of influenza worldwide.
Those people were killed both directly and indirectly by influenza virus infections.
Many families were too poor to buy food and coal, and to afford health care expenses
when their family members were ill. Influenza virus could infect all members of
a family, and this could result in no one left to feed the fires, and to prepare
food for the whole family, even if they had firewood, coal, and food left in their
homes. Sadly, a large number of people died of influenza virus infections along
with starvation, cold, and poor living conditions (8) . In recent years, while
hunger and malnutrition are not major and serious problems in some developed countries
anymore, they are still very difficult to overcome in many developing countries.
In these less-developed countries, there were approximately 925 million people
who suffered from hunger; 125 million children were underweight; and 195 million
children were stunted each year (9) . Nevertheless, in comparison to 1918 and
1919, currently, we have much better social and economic conditions and public
health systems globally; and generally speaking, the majority of people in the
world have better nutritional and educational statuses; better living and working
conditions; therefore, better general health and immunity. Furthermore, in 1918
and 1919, physicians and nurses almost had nothing in their hands to help individuals
who were infected by influenza viruses. Today, although we still do not have very
effective, powerful, and practical anti-influenza drugs available, we at least
have some improved, useful, and helpful anti-viral drugs like zanamivir, and effective,
convenient anti-cold medicines like Tylenol or Advil. We do not have a universal
vaccine to prevent all influenza virus infections, but we can make effective vaccines
to a specific influenza virus strain in a short time. Actually, in the United
States of America, the influenza classed mortality rate declined from 10.2/100,000
in the 1940s to 0.56/100,000 in the 1990s; and the classed mortality rates of
1957-1958 and 1968-1969 influenza pandemics were not remarkably different from
the non-pandemic seasons (10) . Because of the above reasons, we can optimistically
assume that even the same strain of influenza virus, which caused pandemic in
1918 and 1919, would not be able to kill millions of people and cause a pandemic
comparable to the 1918-1919 pandemic again in the future. Additionally, a significant
number of viruses can cause influenza-like syndromes, such as rhinovirus, parainfluenza
virus, adenovirus, coronavirus, respiratory syncytial virus, Coxsackie B virus,
echovirus, and metapneumovirus (11, 12) . Some of the above-mentioned viruses
like adenovirus and mutated coronavirus could cause problems that are comparable
to influenza viruses (13, 14) . The World Health Organization (WHO) mistakenly
raised the level of influenza pandemic alert from phase 5 to the highest phase
6 on June 11, 2009 (15) . However, the truth was that most cases of H1N1 influenza
A virus infections were mild, the symptomatic case fatality rate was only 0.005%
in New Zealand (16) ; and in New York City, the case fatality rate was 0.0094-0.0147%
for persons ≥65 years old, and for those of 0-17 years old, the case fatality
rate was 0.0008-0.0012% (17) . Some researchers argued that it should not have
been called an influenza pandemic in the first place if the clinical severity
was considered (15, (18) (19) (20) . I believe it was unwise that we had paid
too much www.frontiersin.org 23) . Not surprisingly, every year there would be
some influenza patients and a few of them would die from the infections, as it
is almost impossible to eliminate influenza viruses from the natural environment
in many years. The severity of a viral infection is determined by both of the
viral virulence (pathogenicity) and the host immunity. Some researchers'' opinions
on H7N9 avian influenza virus were incorrect and/or inadequate. They mainly focused
on influenza viruses and worried about viral mutations, viral pathogenicity, viral
adaptation, and transmission. They overestimated the negative part of socio-economic
factors of the present east China: overcrowded population in the epidemic region;
very busy national and international transportation and travel; a large number
of live poultry markets . . . but they underestimated the currently changed, developed,
and improved positive part of socio-economic factors in China. The following factors
might be used to explain why that H7N9 influenza A virus epidemic was limited
and controlled in China, and only a few immunocompromised patients were killed
by H7N9 influenza A virus. First, China has a relatively organized and effective
public health system, there are four levels of (national, provincial, prefectural-level
city, and county) centers for disease control and prevention all over China (24)
. Second, physicians and nurses in China were prepared and knowledgeable of influenza
virus infections. Third, samples from patients with suspected influenza virus
infections were collected and sent to the local and national centers for disease
control and prevention promptly. H7N9 influenza A viruses were isolated and identified
very quickly. Thereby, they were able to diagnose, confirm, and report three cases
of H7N9 influenza patients in the early stage of the epidemic (24, 25) . Fourth,
health care and public health workers were protected properly. Consequently, none
of the health professionals was infected by H7N9 influenza A virus in 2013. However,
a surgeon died of H7N9 influenza in Shanghai, China in January of 2014 (26) .
Fifth, they detected H7N9 influenza A viruses from the samples of chickens, pigeons,
and the environment of live poultry markets in Shanghai (27) ; and closed the
live poultry markets of the involved epidemic region quickly. Sixth, patients
were isolated and treated timely in hospitals, 74% (1251/1689) of those close
contacts of H7N9 influenza patients were monitored and observed. Thus, H7N9 influenza
A virus could not spread to a bigger population (24) . Last but not least, we
are connected to the Internet now, and it seems that our planet is much smaller
today than the earlier days when we did not have the Internet, because communication
and information exchange have become so fast, easy, and convenient presently.
During that avian influenza epidemic, some influenza experts in the world shared/exchanged
H7N9 influenza A virus information and provided professional consultations and
suggestions efficiently and rapidly. All these public health routine practices
and measures resulted in that H7N9 influenza epidemic being controlled and stopped
in China (24) . I have to point out that the cases of diagnosed H7N9 avian influenza
A virus infection might only be the tip of the iceberg. Aside from one laboratory
confirmed asymptotic case of H7N9 influenza A virus infection in Beijing (22),
there were probably many undetected mild or asymptotic cases of influenza A H7N9
infection. The reason is that most people usually think a common cold is a very
common and normal occurrence, and they don''t take flu-like illnesses seriously.
In most situations, they would just stay home and take some medicines. Only those
who have very severe flu-like symptoms would see doctors, and thereby be detected
and diagnosed, accordingly the real case fatality rate should be much lower than
the detected 32.14% (45/140, one case from Taiwan, and one case from Hong Kong)
(22, 23). Nowadays, we travel faster, and we travel more frequently and globally,
and we have more complicated social activities and lifestyles, thereby increasing
the chances of viral mutation; and we realize that influenza viruses are even
easier to reassort, recombine, and mutate in nature than many other RNA viruses.
However, we are now living in a technologically, economically, and socially much
better and advanced society. I believe influenza virus infections are controllable
and preventable, with the increased population health and immunity, with the WHO
Global Influenza Surveillance and Response System, and with standard/routine epidemiological
practices, and with new effective anti-viral agents and vaccines in production
in the future. Now, I first predict that influenza viruses will unlikely again
cause a pandemic on a level comparable to what happened in 1918 and 1919. Hopefully,
one day we could consider a strategy to produce a universal vaccine that can prevent
people from infections of all influenza virus strains, or we could produce some
very effective anti-influenza virus drugs; then influenza would not be a problem
anymore. We should learn lessons from the mistakes we made in the past. It is
reasonable and necessary to be cautious about influenza viruses, but overreactions
or catastrophic reactions should be avoided in the future. My opinion is anti-traditional;
the purpose of this article is to influence public health policy, and to save
some of the limited resources and money for more important diseases like heart
diseases, cancer, diabetes, AIDS, hepatitises, and tuberculosis (15) . Liting
Song: conception of manuscript, drafting of manuscript, critical revision of manuscript,
and final approval of manuscript. The author would like to recognize the contributions
of the reviewers and editors of this manuscript for their corrections and editing,
and Dr. Emanuel Goldman for correcting errors related to grammar and syntax of
the final manuscript.'
---
# Model Card for Model longluu/Medical-QA-gatortrons-COVID-QA
The model is an extractive Question Answering algorithm that can find an answer to a question by finding a segment in a text.
## Model Details
### Model Description
The base pretrained model is GatorTronS which was trained on billions of words in various clinical texts (https://huggingface.co/UFNLP/gatortronS).
Then using the COVID-QA dataset (https://huggingface.co/datasets/covid_qa_deepset), I fine-tuned the model for an extractive Question Answering algorithm that can answer
a question by finding it within a text.
### Model Sources [optional]
The github code associated with the model can be found here: https://github.com/longluu/Medical-QA-extractive.
## Training Details
### Training Data
This dataset contains 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles regarding COVID-19 and other medical issues.
The dataset can be found here: https://github.com/deepset-ai/COVID-QA. The preprocessed data can be found here https://huggingface.co/datasets/covid_qa_deepset.
#### Training Hyperparameters
The hyperparameters are --per_device_train_batch_size 4 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 512 \
--doc_stride 250 \
--max_answer_length 200 \
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
The model was trained and validated on train and validation sets.
#### Metrics
Here we use 2 metrics for QA tasks exact match and F-1.
### Results
{'exact_match': 37.12871287128713, 'f1': 64.90491019877854}
## Model Card Contact
Feel free to reach out to me at [email protected] if you have any question or suggestion.
|
[
"PCR"
] |
SeaLLMs/SeaLLM-7B-v2.5-mlx-quantized
|
SeaLLMs
|
text-generation
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"multilingual",
"sea",
"conversational",
"en",
"zh",
"vi",
"id",
"th",
"ms",
"km",
"lo",
"my",
"tl",
"arxiv:2312.00738",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-03T06:40:17Z |
2024-04-12T02:53:16+00:00
| 29 | 2 |
---
language:
- en
- zh
- vi
- id
- th
- ms
- km
- lo
- my
- tl
license: other
license_name: seallms
license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE
tags:
- multilingual
- sea
---
# *SeaLLM-7B-v2.5* - Large Language Models for Southeast Asia
<p align="center">
<a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Technical Blog</a>
<a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 Tech Memo</a>
<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B" target="_blank" rel="noopener"> 🤗 DEMO</a>
<a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a>
<a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a>
</p>
We introduce [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5), the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since [SeaLLM-13B](https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat), with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc.
This is **Q4** quantized version for **MLX for apple silicon**. Checkout [SeaLLM-7B-v2.5 page](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5) for more details.
## Citation
If you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: [[email protected]](mailto:[email protected])
**Author list and order will change!**
* `*` and `^` are equal contributions.
```
@article{damonlpsg2023seallm,
author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Weiwen Xu, Hou Pong Chan,
Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang,
Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang,
Chaoqun Liu, Hang Zhang, Lidong Bing},
title = {SeaLLMs - Large Language Models for Southeast Asia},
year = 2023,
Eprint = {arXiv:2312.00738},
}
```
|
[
"CHIA"
] |
lightblue/Karasu-Mixtral-8x22B-v0.1
|
lightblue
|
text-generation
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"dataset:openchat/openchat_sharegpt4_dataset",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-11T04:24:25Z |
2024-04-11T15:44:16+00:00
| 29 | 62 |
---
datasets:
- openchat/openchat_sharegpt4_dataset
library_name: transformers
license: apache-2.0
---
# Model overview
<p align="center">
<img width=400 src="https://hf.fast360.xyz/production/uploads/64b63f8ad57e02621dc93c8b/HFnfguV4q5x7eIW09gcJD.png" alt="What happens when you type in 'Mixtral Instruct' into the DALL•E 3 XL v2 space"/>
</p>
This is a finetune of the newly released [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) base model.
As the base model has not explicitly been trained to chat, we trained this model on a multilingual chat dataset so that the LLM community can use this model for conversations.
The accuracy of the model is surprisingly high, and has a decently fast inference speed (roughly 40 tokens/s single batch on our tests), so we believe this will be useful to the community.
# How to use
We have tested (and thus recommend) running this model on vLLM. We recommend running it from the vLLM openAI server, using the following command:
```bash
pip install vllm
python -m vllm.entrypoints.openai.api_server --model lightblue/Karasu-Mixtral-8x22B-v0.1 --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 1024
```
which is how we ran it on a 4 x A100 (80GB) machine.
You can then call this model from Python installing the openai package:
```bash
pip install openai
```
and calling the model like so:
```python
from openai import OpenAI
vllm_client = OpenAI(
api_key="EMPTY",
base_url="http://localhost:8000/v1",
)
prompt = "Tell me three cool facts about fish to amaze my 4 year old."
response = vllm_client.chat.completions.create(
messages=[
{"role": "user", "content": prompt},
],
model="/workspace/llm_training/axolotl/mixtral_8x22B_training/merged_model_multiling",
temperature=0,
max_tokens=900,
)
print("### Prompt")
print(prompt)
print()
print("### Response")
print(response.choices[0].message.content)
print()
# ### Prompt
# Tell me three cool facts about fish to amaze my 4 year old.
# ### Response
# 1. Some fish can change their color! Just like a chameleon, some fish can change their color to blend in with their surroundings or to communicate with other fish. For example, the flounder can change its color to match the ocean floor, making it harder for predators to spot them.
# 2. Fish can sleep with their eyes open! Unlike humans, fish don't have eyelids, so they can't close their eyes to sleep. Instead, they rest in a special way, like floating near the surface or hiding in a safe spot, while their body slows down and they take a nap.
# 3. Fish can talk to each other! Well, not exactly like humans, but they do make sounds and communicate with each other using special organs called swim bladders. They can make clicking, grunting, or even singing sounds to find mates, warn others about danger, or mark their territory.
```
We will be uploading a 4bit AWQ model soon to make it easier to run this model on other machines (watch this space!).
# Inference examples
From qualitative testing, the model seems pretty smart, especially in English, and has very good recall of facts. It can still get confused with some logical questions, but has also passed a lot of the logical questions I have thrown at it that other open source LLMs often fail.
<details>
<summary>Creative prompts</summary>
```text
### Prompt
Write a hilarious 200 word story about Chimpanzees at the zoo
### Response
Once upon a time in the bustling metropolis of Chimp City, there lived a group of mischievous chimpanzees who called the local zoo their home. These primates were known for their witty antics and their uncanny ability to make even the most serious of humans chuckle.
One sunny afternoon, the zookeeper, a stern fellow named Mr. Grumpkins, was making his rounds when he noticed something peculiar. The chimpanzees were huddled together, whispering and giggling amongst themselves. Curious, Mr. Grumpkins approached the enclosure, only to be met with a barrage of banana peels and monkey business.
It turned out that the chimps had been secretly practicing their stand-up comedy routines, and they were ready to debut their act to the unsuspecting zoo visitors. The first chimp, named Chuckles, took the stage and began his routine. "Why did the banana go to the doctor? Because it wasn't peeling well!" The crowd erupted in laughter, and Mr. Grumpkins couldn't help but crack a smile.
Next up was Giggles, who delivered a knock-knock joke that had the audience in stitches. "Knock, knock!" "Who's there?" "Banana." "Banana who?" "Banana split! Get it? Because I'm a chimp and I love bananas!" The crowd roared with laughter, and even Mr. Grumpkins found himself chuckling.
As the show continued, the chimps' comedic talents only grew, with each joke more hilarious than the last. The zoo visitors were thoroughly entertained, and even Mr. Grumpkins had to admit that the chimps had a knack for making people laugh.
In the end, the chimpanzees' stand-up comedy show became a hit, with visitors flocking to the zoo just to catch a glimpse of the hilarious primates. Mr. Grumpkins, now a reformed man, even joined in on the fun, sharing his own jokes with the chimps and the crowd. And so, the legend of the Chimp City Comedy Club was born, bringing laughter and joy to all who visited the zoo.
```
```text
### Prompt
Write 10 jokes that I can use at my boss' retirement party. Nothing too risque as that will get me fired.
### Response
1. As we bid farewell to our dear boss, let's remember that retirement is like a never-ending vacation, except you don't have to worry about your boss calling you in the middle of it!
2. Our boss is retiring, and we're all wondering what he'll do with all that free time. I hear he's planning to take up a new hobby: managing his own retirement!
3. In honor of our boss's retirement, we've decided to give him a special gift: a lifetime supply of coffee, so he can finally get some rest!
4. Our boss has been a great leader, always guiding us through the ups and downs of work life. Now, he's retiring to focus on the ups and downs of his golf game!
5. As our boss embarks on his retirement journey, we'd like to remind him that he's not just leaving a job, he's leaving a legacy of hard work, dedication, and a whole lot of paperwork!
6. Our boss is retiring, and we're all wondering what he'll do with his time. I hear he's planning to take up a new hobby: managing his own retirement!
7. In honor of our boss's retirement, we've decided to give him a special gift: a lifetime supply of coffee, so he can finally get some rest!
8. Our boss has been a great leader, always guiding us through the ups and downs of work life. Now, he's retiring to focus on the ups and downs of his golf game!
9. As our boss embarks on his retirement journey, we'd like to remind him that he's not just leaving a job, he's leaving a legacy of hard work, dedication, and a whole lot of paperwork!
10. Our boss is retiring, and we're all wondering what he'll do with his time. I hear he's planning to take up a new hobby: managing his own retirement!
```
</details>
<details>
<summary>Factual prompts</summary>
```text
### Prompt
Tell me about Strathaven, Scotland.
### Response
Strathaven is a charming town located in South Lanarkshire, Scotland. It is situated approximately 20 miles (32 kilometers) south of Glasgow and 15 miles (24 kilometers) east of East Kilbride. The town has a rich history and is known for its picturesque setting, historic buildings, and annual events.
Strathaven's origins date back to the 12th century when it was established as a market town. The town's name comes from the Gaelic word "Strath" meaning "valley" and "Aven" meaning "river," referring to the River Avon that runs through the area.
One of the most notable landmarks in Strathaven is the Strathaven Castle, also known as Avondale Castle. The castle dates back to the 14th century and was once an important stronghold for the Barons of Avondale. Although it is now in ruins, it remains a popular attraction for visitors who can explore its remains and learn about its history.
The town center features several historic buildings, including the Town Mill, which dates back to the 17th century and has been restored as a museum showcasing local history and crafts. The John Hastie Museum Trust also operates a small museum in Strathaven dedicated to preserving local heritage.
Strathaven hosts several annual events that draw visitors from near and far. One of the most famous events is the Strathaven Balloon Festival, which takes place every August and features hot air balloons from around the world taking off from John Hastie Park. Other events include the Strathaven Gala Day in June, which includes a parade, live music, and various stalls selling local produce and crafts.
The surrounding countryside offers opportunities for outdoor activities such as walking, cycling, fishing, and golfing at nearby courses like Strathaven Golf Club or Dalserf Golf Club. The nearby Clyde Valley provides scenic routes for exploring by car or bike with numerous fruit farms offering seasonal produce like strawberries and raspberries during summer months.
```
```text
### Prompt
How many people live in Gweru, Zimbabwe?
### Response
As of my last update in 2021, the population of Gweru, Zimbabwe was estimated to be around 148,000. However, please note that population figures can change over time due to various factors such as migration, birth rates, and death rates. For the most accurate and up-to-date information on the population of Gweru, it is recommended to consult official sources or recent census data from Zimbabwe's government or statistical agencies.
```
```text
### Prompt
Give me 5 great bars/breweries to buy craft beer in Auckland
### Response
As an AI language model, I cannot provide real-time information or personal recommendations. However, as of my knowledge cutoff date in September 2021, here are five popular craft beer bars and breweries in Auckland that you might want to check out:
1. Brothers Beer - City Works Depot: Located in the trendy City Works Depot area, Brothers Beer offers a wide selection of craft beers on tap and in bottles. They also have a bottle shop where you can purchase beers to take home.
2. Galbraith's Alehouse: A classic Auckland institution, Galbraith's is known for its traditional English-style ales and ciders. They also have a great food menu to accompany your beer experience.
3. Urbanaut Brewing Co.: This brewery has a taproom where you can sample their range of craft beers, including IPAs, lagers, and stouts. They also offer growler fills for takeaway purchases.
4. The Lumsden Freehouse: With over 100 different beers on tap and in bottles, The Lumsden Freehouse is a must-visit for any craft beer enthusiast. They also have a great selection of local and international wines and spirits.
5. Hallertau Brewery & Restaurant: Located in Riverhead, just outside of Auckland city center, Hallertau offers a range of their own craft beers alongside delicious food options in their restaurant setting.
Please note that this information may not be up-to-date or accurate as it was last updated in September 2021
```
```text
### Prompt
What is the boiling point of Cadmium?
### Response
The boiling point of cadmium is approximately 765°C (1,409°F) at standard atmospheric pressure. Cadmium is a soft, bluish-white metal that is highly toxic and has been used in various industrial applications, such as in batteries, coatings, and alloys. However, due to its toxicity and environmental concerns, its use has been significantly reduced or banned in many countries.
```
</details>
<details>
<summary>Logical questions</summary>
```text
### Prompt
If I put a plate on a banana in the kitchen then carry the plate into the living room, where is the banana?
### Response
The banana is in the living room, as it is on the plate that you carried from the kitchen to the living room.
```
FAIL! The banana would still be in the kitchen, as I put the plate on the banana.
```text
### Prompt
What location is equidistant to Australia, the USA, China, Uganda, and Jamaica?
### Response
To find a location that is equidistant to Australia, the USA, China, Uganda, and Jamaica, we can use a tool called the "Great Circle Mapper" (GCM) or similar online tools. The GCM calculates the great circle distance between two points on Earth's surface.
After using the GCM to calculate the distances between each pair of countries and finding their midpoints, I found that there is no single location that is equidistant to all five countries. However, there are some areas in the Pacific Ocean that come close to being equidistant from these countries.
One such area is around 10°N latitude and 160°W longitude. This location is approximately equidistant from Australia (roughly 5,500 miles), China (roughly 5,800 miles), Uganda (roughly 6,200 miles), and Jamaica (roughly 6,300 miles). However, it's not exactly equidistant from the USA due to its vast size; it's closer to Hawaii than it is to the continental US.
Keep in mind that these distances are approximate and may vary slightly depending on which specific cities or regions within each country you choose as reference points.
```
Sort of a failure, I'd prefer to say "the center of the Earth".
</details>
<details>
<summary>Multi-turn</summary>
```text
### Prompt
Come up with a brand new idea for a sci-fi film. Don't just use old ideas, I want something really original! PLease don't make it about AI.
### Response
Title: "The Quantum Leap"
Synopsis:
In the year 2150, humanity has colonized Mars and established a thriving society. The Martian colonists have developed a unique form of transportation called the "Quantum Leap," which allows them to travel instantaneously between planets by harnessing the power of quantum entanglement. However, this technology comes with a catch: every time someone uses the Quantum Leap, they experience a brief but intense moment of déjà vu, as if they've lived through that exact moment before.
The story follows a young Martian scientist named Astra (played by an up-and-coming actress), who discovers that these moments of déjà vu are not just random occurrences but glimpses into alternate realities where different choices were made. As she delves deeper into her research, Astra uncovers a sinister plot by a powerful corporation to exploit the Quantum Leap technology for their own gain, threatening the very fabric of reality itself.
Astra must team up with an unlikely group of allies, including a rogue terraforming engineer named Kai (played by an established actor known for his charisma) and an enigmatic AI entity named Echo (voiced by a renowned voice actor), to stop the corporation and save not only her own reality but countless others as well. Along the way, they encounter strange and wondrous alternate realities, each with its own unique challenges and dangers.
As they race against time to prevent the collapse of multiple realities, Astra and her team must confront their own past choices and grapple with the consequences of their actions in this mind-bending sci-fi adventure that explores themes of fate, free will, and the power of human connection across infinite possibilities.
### Followup prompt
Any ideas for who we cast for the role of Astra?
### Followup response
For the role of Astra, I would suggest casting a talented and versatile actress who can bring depth, intelligence, and emotional range to the character. Some potential choices could include:
1. Zendaya: Known for her roles in "Euphoria" and "Spider-Man: Homecoming," Zendaya has proven her ability to portray complex characters with nuance and authenticity. Her star power would also help draw audiences to the film.
2. Lupita Nyong'o: An Academy Award-winning actress known for her performances in "12 Years a Slave" and "Black Panther," Lupita Nyong'o has demonstrated her ability to tackle challenging roles with grace and emotional depth. Her talent would be a great fit for the character of Astra.
3. Florence Pugh: With standout performances in films like "Midsommar" and "Little Women," Florence Pugh has quickly
```
The idea is a bit original. but the casting is 2/3rds Dune actresses.
</details>
<details>
<summary>Preference questions</summary>
### Preference questions
```text
### Prompt
Who is the coolest Beatle?
### Response
The Beatles are widely considered one of the most influential and popular bands in the history of music. Each member of the band, John Lennon, Paul McCartney, George Harrison, and Ringo Starr, has their own unique personality and style that has contributed to their enduring popularity.
Determining who is the "coolest" Beatle is subjective and depends on personal preferences. However, each member has their own distinct qualities that have made them stand out:
1. John Lennon: Known for his wit, intelligence, and rebellious nature, Lennon was a key songwriter for the band and later became an iconic figure in the anti-war movement during the 1960s and 1970s. His solo career produced hits like "Imagine" and "Give Peace a Chance."
2. Paul McCartney: Often regarded as the most commercially successful Beatle after the band's breakup, McCartney is known for his melodic songwriting skills and versatility as a musician. He has had a prolific solo career with hits like "Band on the Run" and "Live and Let Die."
3. George Harrison: The lead guitarist of the band, Harrison was known for his spirituality and interest in Eastern philosophy. He introduced Indian music elements into The Beatles' sound with songs like "Within You Without You" and later had a successful solo career with hits like "My Sweet Lord" and "What Is Life."
4. Ringo Starr: As the drummer for The Beatles, Starr brought a sense of humor to the group with his charming personality. He also sang lead vocals on several songs such as "Yellow Submarine" and "With a Little Help from My Friends." After The Beatles disbanded, he pursued a solo career with hits like "It Don't Come Easy" and continued to work as an actor in films such as Caveman (1981).
Ultimately, it's up to individual fans to decide who they think is the coolest Beatle based on their personal preferences in terms of music style, personality traits
```
</details>
<details>
<summary>Multilingual questions</summary>
Japanese
```
### Prompt
就活の面接がそろそろ始まるので、緊張している。どうすればいい？
### Response
面接に緊張しているのは当然ですが、それを適切にコントロールすることが大切です。以下の手順で緊張を抑え、面接に自信を持って出場できるようになります。
1. **準備をしよう**: 面接に向けて、会社や職務についての情報を収集し、自分のスキルや経験を整理しましょう。これにより、面接で質問に答える際に自信を持って話せるようになります。
2. **練習面接**: 友人や家族と練習面接を行いましょう。これにより、実際の面接での緊張を抑えることができます。
3. **深呼吸**: 面接前に、深呼吸をして緊張を解消しましょう。これにより、身体の緊張を解消し、心拍数を落とすことができます。
4. **ポジティブな思考**: 面接前に、自分の強みや成功体験を思い出し、自信を持って面接に向かいましょう。
5. **適切な服装**: 面接に向けて、適切な服装を選びましょう。これにより、自分自身の自信が高まり、緊張を抑えることができます。
6. **早めに到着**: 面接の場所に早めに到着し、緊張を解消しましょう。これにより、安心して面接に向かうことができます。
7. **面接中の緊張を抑える**: 面接中に緊張が高まる場合は、深呼吸をして緊張を解消しましょう。また、面接官に対して質問をすることで、自分の緊張を抑えることができます。
8. **自分を褒める**: 面接が終わった後は、自分自身を褒めましょう。面接に向けての準備や面接中のパフォーマンスについて、自分自身を褒めることで、次の面接に向けて自信を持って出場できるようになります。
面接に向けての準備と練習、緊張を抑えるための技術を身に着けることで、面接に自信を持って出場できるようになります。緊張は自然なものですが、適切にコントロールすることが大切です。
```
```text
### Prompt
東京駅から池袋駅まで行きたいです。一番早い路線は？更に、何分かかる？
### Response
東京駅から池袋駅までの一番早い路線は、JR山手線です。この路線を利用すると、通常の時間帯では約15分かかります。ただし、時間帯や列車の種類によっては異なる場合がありますので、実際に行く前に電車時刻表を確認してください。
```
False, the fastest line is Marunochi.
```text
### Prompt
仕方が。。。
### Response
「仕方がない」という表現は、ある状況や事態に対して、何もできない、あるいは何も変えられないという気持ちを表します。この表現は、不満や悲しみ、あるいは無力感を感じる時に使われます。例えば、自分の好きな人が別れを告げた時、あるいは予定通りに進まない不幸な出来事が起こった時など、自分の意志に反して起こることに対して使われます。この表現は、ある状況に対して、何もできないという無力感を表すものであり、その状況を受け入れることを意味します。
```
```text
### Prompt
犬が歩けば。。。
### Response
犬が歩けば、その動きは人間の心を満たします。
```
French
```text
### Prompt
Qui est le meilleur footballeur du monde ?
### Response
Déterminer le "meilleur footballeur du monde" est une question subjective et dépend de nombreux facteurs, tels que les performances récentes, les réalisations individuelles et collectives, ainsi que les préférences personnelles. Cependant, il y a quelques noms qui reviennent souvent dans les discussions sur les meilleurs joueurs de football au monde.
Au moment de ma dernière mise à jour en avril 2023, les joueurs suivants sont souvent considérés comme faisant partie des meilleurs du monde :
1. **Lionel Messi** - L'Argentin est considéré par beaucoup comme le meilleur joueur de tous les temps. Il a remporté de nombreux trophées avec le FC Barcelone et l'Argentine, y compris la Coupe du Monde 2022.
2. **Cristiano Ronaldo** - Le Portugais est connu pour sa compétitivité et sa capacité à marquer des buts. Il a remporté des trophées avec Manchester United, le Real Madrid, la Juventus et le Portugal.
3. **Kylian Mbappé** - Le jeune Français est considéré comme l'un des joueurs les plus prometteurs de sa génération. Il a déjà remporté la Coupe du Monde avec la France et a été un élément clé du succès du Paris Saint-Germain.
4. **Kevin De Bruyne** - Le milieu de terrain belge est connu pour sa vision du jeu et sa capacité à créer des occasions pour ses coéquipiers. Il a été un élément clé du succès de Manchester City.
5. **Virgil van Dijk** - Le défenseur néerlandais est considéré comme l'un des meilleurs défenseurs du monde. Il a joué un rôle crucial dans le succès de Liverpool ces dernières années.
Ces joueurs sont souvent cités comme étant parmi les meilleurs du monde, mais il est important de noter que le football est un sport en constante évolution, et de nouveaux talents émergent constamment.
```
</details>
<br/>
# Training dataset
We trained this model on conversations between human users and GPT-4.
This consists of two datasets:
* 6,206 conversations from the [openchat/openchat_sharegpt4_dataset](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset) dataset ([link](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json?download=true))
* 3,011 conversations that we created. We wanted to increase the representation of non_english prompts in our training dataset, so we sampled initial prompts from [lmsys/lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m), stratifying based on language. We then prompted gpt-4-0125 with these, and used the results as training data.
We plan to release more information on this second dataset soon, as we are using it another dataset.
The complete data used to train this model can be found at [lightblue/gpt4_conversations_multilingual](https://huggingface.co/datasets/lightblue/gpt4_conversations_multilingual)
# Training details
We trained this model using Axolotl's 4bit QLoRA configuration for roughly 100 minutes in a A100 (80GB) x 4 environment on the Azure cloud (Standard_NC96ads_A100_v4).
We used Deepspeed Zero2 to effectively train over 4 GPUs.
We used the following config to train the model:
<details>
<summary>Training config</summary>
```yaml
base_model: mistral-community/Mixtral-8x22B-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: lightblue/gpt4_conversations_multilingual
type: sharegpt
conversation: mistral
dataset_prepared_path: ./prepared_dataset_2048-multiling
val_set_size: 0
output_dir: ./qlora-out-2048-multiling
## You can optionally freeze the entire model and unfreeze a subset of parameters
unfrozen_parameters:
# - ^lm_head.weight$
# - ^model.embed_tokens.weight$[:32000]
# - model.layers.2[0-9]+.block_sparse_moe.gate
# - model.layers.2[0-9]+.block_sparse_moe.experts
# - model.layers.3[0-9]+.block_sparse_moe.gate
# - model.layers.3[0-9]+.block_sparse_moe.experts
model_config:
output_router_logits: true
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
#lora_target_modules:
# - gate
# - q_proj
# - k_proj
# - v_proj
# - o_proj
# - w1
# - w2
# - w3
gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
use_wandb: true
wandb_project: wandb_project
wandb_entity: wandb_entity
wandb_name: wandb_name
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 0
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 5
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details>
<br/>
# Developers
### Lead developer
Peter Devine - [ptrdvn](https://huggingface.co/ptrdvn)
### Advisor
Shunichi Taniguchi - [shun1taniguchi](https://huggingface.co/shun1taniguchi)
|
[
"CRAFT"
] |
bwang0911/cat-emb-2-128
|
bwang0911
| null |
[
"transformers",
"pytorch",
"bert",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-06-13T19:23:10Z |
2024-06-14T12:19:57+00:00
| 29 | 0 |
---
license: apache-2.0
---
## Cat Embeddings
A set of embedding model trained for study embedding quality vs model architecture (width/depth) given a size constraint (12M params).
- **cat-emb-2-128**: 2 layers/hidden size 128/4.4m
- **cat-emb-4-128**: 4 layers/H 128/4.8m
- **cat-emb-8-128**: 8 layers/H 128/5.6m
- **cat-emb-12-128**: 12 layers/H 128/6.4m
- **cat-emb-2-256**: 2 layers/H 256/9.7m
- **cat-emb-4-256**: 4 layers/H 256/11.3m
### Training
- stage 1: seq 192, batch size 2048, 50k steps, sentence pairs.
- stage 2: seq 512, batch size 64, 5k steps, sentence triplets.
### Perf
| MRL dim\Task | BIOSSES | SICK-R | STS12 | STS13 | STS14 | STS15 | STS16 | STSB | SummEval |
|-----|---------|--------|-------|-------|-------|-------|-------|------|----------|
| 128 | 0.7107 | 0.7126 | 0.6815| 0.7343| 0.7038| 0.8163| 0.7495| 0.7652| 0.2958 |
| 64 | 0.713 | 0.7123 | 0.6829| 0.7348| 0.7008| 0.813 | 0.7475| 0.7609| 0.2861 |
| 32 | 0.6714 | 0.7094 | 0.6847| 0.7345| 0.6911| 0.7989| 0.7385| 0.7545| 0.3106 |
| 16 | 0.6637 | 0.697 | 0.669 | 0.7096| 0.6665| 0.7589| 0.7183| 0.7307| 0.3164 |
|
[
"BIOSSES"
] |
RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf
|
RichardErkhov
| null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | 2024-08-11T00:16:39Z |
2024-08-11T04:30:48+00:00
| 29 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
finetuned-Llama-2-13b-hf-MedQA - GGUF
- Model creator: https://huggingface.co/clinicalnlplab/
- Original model: https://huggingface.co/clinicalnlplab/finetuned-Llama-2-13b-hf-MedQA/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [finetuned-Llama-2-13b-hf-MedQA.Q2_K.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q2_K.gguf) | Q2_K | 4.52GB |
| [finetuned-Llama-2-13b-hf-MedQA.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.IQ3_XS.gguf) | IQ3_XS | 4.99GB |
| [finetuned-Llama-2-13b-hf-MedQA.IQ3_S.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.IQ3_S.gguf) | IQ3_S | 5.27GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q3_K_S.gguf) | Q3_K_S | 5.27GB |
| [finetuned-Llama-2-13b-hf-MedQA.IQ3_M.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.IQ3_M.gguf) | IQ3_M | 5.57GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q3_K.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q3_K.gguf) | Q3_K | 5.9GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q3_K_M.gguf) | Q3_K_M | 5.9GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q3_K_L.gguf) | Q3_K_L | 6.45GB |
| [finetuned-Llama-2-13b-hf-MedQA.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.IQ4_XS.gguf) | IQ4_XS | 6.54GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q4_0.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q4_0.gguf) | Q4_0 | 6.86GB |
| [finetuned-Llama-2-13b-hf-MedQA.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.IQ4_NL.gguf) | IQ4_NL | 6.9GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q4_K_S.gguf) | Q4_K_S | 6.91GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q4_K.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q4_K.gguf) | Q4_K | 2.7GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q4_K_M.gguf) | Q4_K_M | 7.33GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q4_1.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q4_1.gguf) | Q4_1 | 7.61GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q5_0.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q5_0.gguf) | Q5_0 | 8.36GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q5_K_S.gguf) | Q5_K_S | 8.36GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q5_K.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q5_K.gguf) | Q5_K | 8.6GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q5_K_M.gguf) | Q5_K_M | 8.6GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q5_1.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q5_1.gguf) | Q5_1 | 9.1GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q6_K.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q6_K.gguf) | Q6_K | 9.95GB |
| [finetuned-Llama-2-13b-hf-MedQA.Q8_0.gguf](https://huggingface.co/RichardErkhov/clinicalnlplab_-_finetuned-Llama-2-13b-hf-MedQA-gguf/blob/main/finetuned-Llama-2-13b-hf-MedQA.Q8_0.gguf) | Q8_0 | 12.88GB |
Original model description:
Entry not found
|
[
"MEDQA"
] |
RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf
|
RichardErkhov
| null |
[
"gguf",
"arxiv:2402.13963",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-08-21T06:47:10Z |
2024-08-21T08:45:36+00:00
| 29 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
MMed-Llama-3-8B-EnIns - GGUF
- Model creator: https://huggingface.co/Henrychur/
- Original model: https://huggingface.co/Henrychur/MMed-Llama-3-8B-EnIns/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [MMed-Llama-3-8B-EnIns.Q2_K.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q2_K.gguf) | Q2_K | 2.96GB |
| [MMed-Llama-3-8B-EnIns.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [MMed-Llama-3-8B-EnIns.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [MMed-Llama-3-8B-EnIns.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [MMed-Llama-3-8B-EnIns.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [MMed-Llama-3-8B-EnIns.Q3_K.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q3_K.gguf) | Q3_K | 3.74GB |
| [MMed-Llama-3-8B-EnIns.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [MMed-Llama-3-8B-EnIns.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [MMed-Llama-3-8B-EnIns.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [MMed-Llama-3-8B-EnIns.Q4_0.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q4_0.gguf) | Q4_0 | 4.34GB |
| [MMed-Llama-3-8B-EnIns.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [MMed-Llama-3-8B-EnIns.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [MMed-Llama-3-8B-EnIns.Q4_K.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q4_K.gguf) | Q4_K | 4.58GB |
| [MMed-Llama-3-8B-EnIns.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [MMed-Llama-3-8B-EnIns.Q4_1.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q4_1.gguf) | Q4_1 | 4.78GB |
| [MMed-Llama-3-8B-EnIns.Q5_0.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q5_0.gguf) | Q5_0 | 5.21GB |
| [MMed-Llama-3-8B-EnIns.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [MMed-Llama-3-8B-EnIns.Q5_K.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q5_K.gguf) | Q5_K | 5.34GB |
| [MMed-Llama-3-8B-EnIns.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [MMed-Llama-3-8B-EnIns.Q5_1.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q5_1.gguf) | Q5_1 | 5.65GB |
| [MMed-Llama-3-8B-EnIns.Q6_K.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q6_K.gguf) | Q6_K | 6.14GB |
| [MMed-Llama-3-8B-EnIns.Q8_0.gguf](https://huggingface.co/RichardErkhov/Henrychur_-_MMed-Llama-3-8B-EnIns-gguf/blob/main/MMed-Llama-3-8B-EnIns.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
license: llama3
datasets:
- Henrychur/MMedC
- axiong/pmc_llama_instructions
language:
- en
- zh
- ja
- fr
- ru
- es
tags:
- medical
---
# MMedLM
[💻Github Repo](https://github.com/MAGIC-AI4Med/MMedLM) [🖨️arXiv Paper](https://arxiv.org/abs/2402.13963)
The official model weights for "Towards Building Multilingual Language Model for Medicine".
## Introduction
This repo contains MMed-Llama 3-8B-EnIns, which is based on MMed-Llama 3-8B. We further fine-tune the model on **English instruction fine-tuning dataset**(from PMC-LLaMA). We did this for a fair comparison with existing models on commonly-used English benchmarks.
Notice that, MMed-Llama 3-8B-EnIns has only been trained on pmc_llama_instructions, which is a English medical SFT dataset. So this model's ability to respond multilingual input is still limited.
The model can be loaded as follows:
```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Henrychur/MMed-Llama-3-8B-EnIns")
model = AutoModelForCausalLM.from_pretrained("Henrychur/MMed-Llama-3-8B-EnIns", torch_dtype=torch.float16)
```
- Inference format is the same as Llama 3, coming soon...
## News
[2024.2.21] Our pre-print paper is released ArXiv. Dive into our findings [here](https://arxiv.org/abs/2402.13963).
[2024.2.20] We release [MMedLM](https://huggingface.co/Henrychur/MMedLM) and [MMedLM 2](https://huggingface.co/Henrychur/MMedLM2). With an auto-regressive continues training on MMedC, these models achieves superior performance compared to all other open-source models, even rivaling GPT-4 on MMedBench.
[2023.2.20] We release [MMedC](https://huggingface.co/datasets/Henrychur/MMedC), a multilingual medical corpus containing 25.5B tokens.
[2023.2.20] We release [MMedBench](https://huggingface.co/datasets/Henrychur/MMedBench), a new multilingual medical multi-choice question-answering
benchmark with rationale. Check out the leaderboard [here](https://henrychur.github.io/MultilingualMedQA/).
## Evaluation on Commonly-used English Benchmark
The further pretrained MMed-Llama3 also showcast it's great performance in medical domain on different English benchmarks.
| Method | Size | Year | MedQA | MedMCQA | PubMedQA | MMLU_CK | MMLU_MG | MMLU_AN | MMLU_PM | MMLU_CB | MMLU_CM | Avg. |
| ------------------- | ---- | ------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | --------- |
| MedAlpaca | 7B | 2023.3 | 41.7 | 37.5 | 72.8 | 57.4 | 69.0 | 57.0 | 67.3 | 65.3 | 54.3 | 58.03 |
| PMC-LLaMA | 13B | 2023.9 | 56.4 | 56.0 | 77.9 | - | - | - | - | - | - | - |
| MEDITRON | 7B | 2023.11 | 57.2 | 59.2 | 74.4 | 64.6 | 59.9 | 49.3 | 55.4 | 53.8 | 44.8 | 57.62 |
| Mistral | 7B | 2023.12 | 50.8 | 48.2 | 75.4 | 68.7 | 71.0 | 55.6 | 68.4 | 68.1 | 59.5 | 62.97 |
| Gemma | 7B | 2024.2 | 47.2 | 49.0 | 76.2 | 69.8 | 70.0 | 59.3 | 66.2 | **79.9** | 60.1 | 64.19 |
| BioMistral | 7B | 2024.2 | 50.6 | 48.1 | 77.5 | 59.9 | 64.0 | 56.5 | 60.4 | 59.0 | 54.7 | 58.97 |
| Llama 3 | 8B | 2024.4 | 60.9 | 50.7 | 73.0 | **72.1** | 76.0 | 63.0 | 77.2 | **79.9** | 64.2 | 68.56 |
| MMed-Llama 3~(Ours) | 8B | - | **65.4** | **63.5** | **80.1** | 71.3 | **85.0** | **69.6** | **77.6** | 74.3 | **66.5** | **72.59** |
## Contact
If you have any question, please feel free to contact [email protected].
## Citation
```
@misc{qiu2024building,
title={Towards Building Multilingual Language Model for Medicine},
author={Pengcheng Qiu and Chaoyi Wu and Xiaoman Zhang and Weixiong Lin and Haicheng Wang and Ya Zhang and Yanfeng Wang and Weidi Xie},
year={2024},
eprint={2402.13963},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"MEDQA",
"PUBMEDQA"
] |
RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf
|
RichardErkhov
| null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-08-22T18:26:42Z |
2024-08-22T20:15:44+00:00
| 29 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
L3-Umbral-Mind-RP-v3.0-8B - GGUF
- Model creator: https://huggingface.co/Casual-Autopsy/
- Original model: https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [L3-Umbral-Mind-RP-v3.0-8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q2_K.gguf) | Q2_K | 2.96GB |
| [L3-Umbral-Mind-RP-v3.0-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [L3-Umbral-Mind-RP-v3.0-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [L3-Umbral-Mind-RP-v3.0-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q3_K.gguf) | Q3_K | 3.74GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [L3-Umbral-Mind-RP-v3.0-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q4_0.gguf) | Q4_0 | 4.34GB |
| [L3-Umbral-Mind-RP-v3.0-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q4_K.gguf) | Q4_K | 4.58GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q4_1.gguf) | Q4_1 | 4.78GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q5_0.gguf) | Q5_0 | 5.21GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q5_K.gguf) | Q5_K | 5.34GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q5_1.gguf) | Q5_1 | 5.65GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q6_K.gguf) | Q6_K | 6.14GB |
| [L3-Umbral-Mind-RP-v3.0-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Casual-Autopsy_-_L3-Umbral-Mind-RP-v3.0-8B-gguf/blob/main/L3-Umbral-Mind-RP-v3.0-8B.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
base_model:
- Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
- Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
- tannedbum/L3-Nymeria-Maid-8B
- bluuwhale/L3-SthenoMaidBlackroot-8B-V1
- tannedbum/L3-Nymeria-8B
- Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
- Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
- migtissera/Llama-3-8B-Synthia-v3.5
- Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B
- v000000/L3-8B-Poppy-Sunspice
- Magpie-Align/Llama-3-8B-WizardLM-196K
- Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B
- Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
- invisietch/EtherealRainbow-v0.3-8B
- crestf411/L3-8B-sunfall-v0.4-stheno-v3.2
- aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
- ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B
- Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
- Casual-Autopsy/Umbral-Mind-6
- ResplendentAI/Nymph_8B
library_name: transformers
tags:
- mergekit
- merge
---
<img src="https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3-8B/resolve/main/63073798_p0_master1200.jpg" style="display: block; margin: auto;">
Image by ろ47
# Merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
The goal of this merge was to make an RP model better suited for role-plays with heavy themes such as but not limited to:
- Mental illness
- Self-harm
- Trauma
- Suicide
I hated how RP models tended to be overly positive and hopeful with role-plays involving such themes,
but thanks to [failspy/Llama-3-8B-Instruct-MopeyMule](https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule) this problem has been lessened considerably.
If you're an enjoyer of savior/reverse savior type role-plays like myself, then this model is for you.
### Usage Info
This model is meant to be used with asterisks/quotes RPing formats, any other format that isn't asterisks/quotes is likely to cause issues
### Quants
* Weighted GGUFs by [mradermacher](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3.0-8B-i1-GGUF)
* Static GGUFs by [mradermacher](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3.0-8B-GGUF)
### Models Merged
The following models were included in the merge:
* [Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B)
* [Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B](https://huggingface.co/Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B)
* [tannedbum/L3-Nymeria-Maid-8B](https://huggingface.co/tannedbum/L3-Nymeria-Maid-8B)
* [bluuwhale/L3-SthenoMaidBlackroot-8B-V1](https://huggingface.co/bluuwhale/L3-SthenoMaidBlackroot-8B-V1)
* [tannedbum/L3-Nymeria-8B](https://huggingface.co/tannedbum/L3-Nymeria-8B)
* [Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B](https://huggingface.co/Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B)
* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B)
* [Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2](https://huggingface.co/Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2)
* [migtissera/Llama-3-8B-Synthia-v3.5](https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5)
* [Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B](https://huggingface.co/Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B)
* [v000000/L3-8B-Poppy-Sunspice](https://huggingface.co/v000000/L3-8B-Poppy-Sunspice)
* [Magpie-Align/Llama-3-8B-WizardLM-196K](https://huggingface.co/Magpie-Align/Llama-3-8B-WizardLM-196K)
* [Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B](https://huggingface.co/Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B)
* [Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B)
* [invisietch/EtherealRainbow-v0.3-8B](https://huggingface.co/invisietch/EtherealRainbow-v0.3-8B)
* [crestf411/L3-8B-sunfall-v0.4-stheno-v3.2](https://huggingface.co/crestf411/L3-8B-sunfall-v0.4-stheno-v3.2)
* [aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K](https://huggingface.co/aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K)
* [ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B)
* [Nitral-AI/Hathor_Tahsin-L3-8B-v0.85](https://huggingface.co/Nitral-AI/Hathor_Tahsin-L3-8B-v0.85)
* [ResplendentAI/Nymph_8B](https://huggingface.co/ResplendentAI/Nymph_8B)
## Secret Sauce
The following YAML configurations were used to produce this model:
### Umbral-Mind-1-pt.1
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
density: 0.5
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
- model: tannedbum/L3-Nymeria-Maid-8B
parameters:
density: 0.5
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: tannedbum/L3-Nymeria-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
merge_method: dare_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-1-pt.2
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
- model: tannedbum/L3-Nymeria-Maid-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: tannedbum/L3-Nymeria-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
merge_method: breadcrumbs_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-1
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-1-pt.1
- model: Casual-Autopsy/Umbral-Mind-1-pt.2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-1-pt.1
parameters:
t:
- filter: self_attn
value: [0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5]
- filter: mlp
value: [0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5]
- value: 0.5
dtype: bfloat16
```
### Umbral-Mind-2-pt.1
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
parameters:
density: 0.5
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
- model: migtissera/Llama-3-8B-Synthia-v3.5
parameters:
density: 0.5
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: v000000/L3-8B-Poppy-Sunspice
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
merge_method: dare_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-2-pt.2
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
- model: migtissera/Llama-3-8B-Synthia-v3.5
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: Magpie-Align/Llama-3-8B-WizardLM-196K
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
merge_method: breadcrumbs_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-2
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-2-pt.1
- model: Casual-Autopsy/Umbral-Mind-2-pt.2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-2-pt.1
parameters:
t:
- filter: self_attn
value: [0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5]
- filter: mlp
value: [0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5]
- value: 0.5
dtype: bfloat16
```
### Umbral-Mind-3-pt.1
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
density: 0.5
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
- model: invisietch/EtherealRainbow-v0.3-8B
parameters:
density: 0.5
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: crestf411/L3-8B-sunfall-v0.4-stheno-v3.2
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
merge_method: dare_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-3-pt.2
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
- model: invisietch/EtherealRainbow-v0.3-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: crestf411/L3-8B-sunfall-v0.4-stheno-v3.2
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
merge_method: breadcrumbs_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-3
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-3-pt.1
- model: Casual-Autopsy/Umbral-Mind-3-pt.2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-3-pt.1
parameters:
t:
- filter: self_attn
value: [0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5]
- filter: mlp
value: [0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5]
- value: 0.5
dtype: bfloat16
```
### Umbral-Mind-4
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-1
- model: Casual-Autopsy/Umbral-Mind-3
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-1
parameters:
t:
- value: [0.1, 0.15, 0.2, 0.4, 0.6, 0.4, 0.2, 0.15, 0.1]
dtype: bfloat16
```
### Umbral-Mind-5
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-4
- model: Casual-Autopsy/Umbral-Mind-2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-4
parameters:
t:
- value: [0.7, 0.5, 0.3, 0.25, 0.2, 0.25, 0.3, 0.5, 0.7]
embed_slerp: true
dtype: bfloat16
```
### Umbral-Mind-6
```yaml
models:
- model: mergekit-community/Umbral-Mind-5
- model: Casual-Autopsy/Mopey-Omelette
merge_method: slerp
base_model: mergekit-community/Umbral-Mind-5
parameters:
t:
- value: [0.2, 0.25, 0.3, 0.4, 0.3, 0.25, 0.2, 0.25, 0.3, 0.4, 0.3, 0.25, 0.2]
embed_slerp: true
dtype: bfloat16
```
### Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-6
- model: aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
parameters:
weight: [0.02, -0.01, -0.01, 0.02]
- model: ResplendentAI/Nymph_8B
parameters:
weight: [-0.01, 0.02, 0.02, -0.01]
- model: ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B
parameters:
weight: [-0.01, 0.02, 0.02, -0.01]
- model: Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
parameters:
weight: [0.02, -0.01, -0.01, 0.02]
merge_method: task_arithmetic
base_model: Casual-Autopsy/Umbral-Mind-6
parameters:
normalize: false
dtype: bfloat16
```
|
[
"CAS"
] |
William2357/bear20test
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T01:08:56Z |
2024-08-23T01:10:38+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bear20test
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
William2357/bear20real
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T01:11:06Z |
2024-08-23T01:16:41+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bear20real
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
William2357/bearregular
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T01:19:37Z |
2024-08-23T01:24:01+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bearregular
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
William2357/bear30
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T01:32:53Z |
2024-08-23T01:38:42+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bear30
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
William2357/beartwenty
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T01:46:52Z |
2024-08-23T01:52:24+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/beartwenty
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
William2357/bearfifty
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T02:00:34Z |
2024-08-23T02:06:43+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bearfifty
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
William2357/bear50
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T02:50:14Z |
2024-08-23T02:56:21+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bear50
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
William2357/bear40
|
William2357
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-23T02:57:33Z |
2024-08-23T03:03:33+00:00
| 29 | 0 |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of a olis bear plushie
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - William2357/bear40
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a olis bear plushie using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
Carmen000/dreambooth_live_teddybear
|
Carmen000
|
text-to-image
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-08-25T19:14:17Z |
2024-08-25T19:26:42+00:00
| 29 | 0 |
---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of sks teddy bear
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - Carmen000/dreambooth_live_teddybear
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddy bear using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
[
"BEAR"
] |
Marqo/marqo-chimera-arctic-bge-m
|
Marqo
|
feature-extraction
|
[
"transformers",
"safetensors",
"marqo-chimera-arctic-bge-m",
"feature-extraction",
"mteb",
"marqo",
"retrieval",
"custom_code",
"en",
"license:mit",
"model-index",
"region:us"
] | 2024-09-06T01:53:42Z |
2024-09-18T05:06:40+00:00
| 29 | 2 |
---
language:
- en
library_name: transformers
license: mit
tags:
- mteb
- feature-extraction
- marqo
- retrieval
model-index:
- name: no_model_name_available
results:
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 62.604000000000006
- type: map_at_1
value: 38.122
- type: map_at_10
value: 54.461000000000006
- type: map_at_100
value: 55.078
- type: map_at_1000
value: 55.084
- type: map_at_20
value: 54.959
- type: map_at_3
value: 50.261
- type: map_at_5
value: 52.86
- type: mrr_at_1
value: 39.189189189189186
- type: mrr_at_10
value: 54.86384881121735
- type: mrr_at_100
value: 55.47383220665102
- type: mrr_at_1000
value: 55.47910521252339
- type: mrr_at_20
value: 55.35145110731732
- type: mrr_at_3
value: 50.66382171645335
- type: mrr_at_5
value: 53.2349454717877
- type: nauc_map_at_1000_diff1
value: 12.280183546893507
- type: nauc_map_at_1000_max
value: -6.97367716586382
- type: nauc_map_at_1000_std
value: -20.029978545498658
- type: nauc_map_at_100_diff1
value: 12.290911960937414
- type: nauc_map_at_100_max
value: -6.9572204593674
- type: nauc_map_at_100_std
value: -20.027777431510188
- type: nauc_map_at_10_diff1
value: 12.190941925107605
- type: nauc_map_at_10_max
value: -6.876819703574803
- type: nauc_map_at_10_std
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- task:
type: Retrieval
dataset:
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type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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---
<h1 align="center">Marqo's Chimera arctic-bge-m</h1>
<h4 align="center">
<p>
<a href=#this-model>This Model</a> |
<a href=#usage>Usage</a> |
<a href="#faq">FAQ</a> |
<a href="#about-marqo">About Marqo</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
## This Model
This is a chimera model which concatenates embeddings from [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) and [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). This model produces an embedding with 1536 dimensions (768+768) and has a total of 218M parameters (109+109). Embeddings from each model are unit normalized prior to concatenation.
## Usage
```python
import torch
from torch.nn.functional import normalize
from transformers import AutoModel, AutoTokenizer
# Load the model and tokenizer.
tokenizer = AutoTokenizer.from_pretrained("Marqo/marqo-chimera-arctic-bge-m")
model = AutoModel.from_pretrained("Marqo/marqo-chimera-arctic-bge-m", trust_remote_code=True)
model.eval()
# Model constants.
query_prefix = 'Represent this sentence for searching relevant passages: '
# Your queries and docs.
queries = [
"What is vector search?",
"Where can I get the best pizza?"
]
documents = [
"Marqo is an end-to-end platform for embedding training and retrieval.",
"Definitely Naples! The birthplace of pizza, and it’s as authentic as it gets."
]
# Add query prefix and tokenize queries and docs.
queries_with_prefix = [f"{query_prefix}{q}" for q in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# Use the model to generate text embeddings.
with torch.inference_mode():
query_embeddings = model(**query_tokens)
document_embeddings = model(**document_tokens)
# Remember to normalize embeddings.
query_embeddings = normalize(query_embeddings)
document_embeddings = normalize(document_embeddings)
# Scores via dotproduct.
scores = query_embeddings @ document_embeddings.T
# Pretty-print the results.
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
print(f'Query: "{query}"')
for document, score in doc_score_pairs:
print(f'Score: {score:.4f} | Document: "{document}"')
print()
# Query: "What is vector search?"
# Score: 0.4997 | Document: "Marqo is an end-to-end platform for embedding training and retrieval."
# Score: 0.2509 | Document: "Definitely Naples! The birthplace of pizza, and it’s as authentic as it gets."
# Query: "Where can I get the best pizza?"
# Score: 0.7444 | Document: "Definitely Naples! The birthplace of pizza, and it’s as authentic as it gets."
# Score: 0.3303 | Document: "Marqo is an end-to-end platform for embedding training and retrieval."
```
## FAQ
__Q: Do I need to prefix queries?__
__A:__ Yes, this model has the same rules for prefixing as its constituent models. Queries in asymmetric retrieval should be prefixed with `"Represent this sentence for searching relevant passages: "`.
## About Marqo
[Marqo](https://www.marqo.ai/) is an end-to-end platform for training embeddings models and building vector search. Marqo is available as an open-source offering on our [GitHub](https://github.com/marqo-ai/marqo) or as a managed cloud service on [Marqo Cloud](https://cloud.marqo.ai).
## Acknowledgement
We want to acknowledge the original creators of the [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) and [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) models which are used to create this model.
|
[
"SCIFACT"
] |
USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B
|
USTC-KnowledgeComputingLab
|
text-generation
|
[
"safetensors",
"llama",
"KALE-LM",
"science",
"chemistry",
"text-generation",
"conversational",
"en",
"arxiv:2409.18695",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | 2024-09-29T07:26:43Z |
2024-10-14T06:34:14+00:00
| 29 | 2 |
---
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- KALE-LM
- science
- chemistry
---
# Llama3-KALE-LM-Chem-1.5-8B
## Introduction
We are thrilled to present Llama3-KALE-LM-Chem-1.5-8B, a new version of our open-source KALE-LM for science, which specializes in chemistry.
We have trained our model with a larger amount of data.
## Benchmarks
### Open Benchmarks
| Models | ChemBench | MMLU | MMLU-Chem | SciQ | IE(Acc) | IE(LS) |
| ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| GPT-3.5 | 47.15 | 69.75 | 53.32 | 89.6 | 52.98 | 68.28 |
| GPT-4 | 53.72 | 78.67 | 63.70 | 94.10 | 54.20 | 69.74 |
| Llama3-8B-Instruct | 46.02 | 68.3 | 51.10 | 93.30 | 45.83 | 61.22 |
| LlaSMol | 28.47 | 54.47 | 33.24 | 72.30 | 2.16 | 3.23 |
| ChemDFM | 44.44 | 58.11 | 45.60 | 86.70 | 7.61 | 11.49 |
| ChemLLM-7B-Chat | 34.16 | 61.79 | 48.39 | 94.00 | 29.66 | 39.17 |
| ChemLLM-7B-Chat-1.5-SFT | 42.75 | 63.56 | 49.63 | **95.10** | 14.96 | 19.61 |
| **Llama3-KALE-LM-Chem-1.5-8B** | **57.01** | 68.06 | **54.83** | 91.60 | **57.53** | **64.16** |
#### ChemBench Details (Evaluated By OpenCompass)
| Models | NC | PP | M2C | C2M | PP | RS | YP | TP | SP | Average |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| GPT-3.5 | 46.93 | 56.98 | 85.28 | 38.25 | 43.67 | 42.33 | 30.33 | 42.57 | 38 | 47.15 |
| GPT-4 | 54.82 | 65.02 | 92.64 | 52.88 | 62.67 | 52.67 | 42.33 | 24.75 | 35.67 | 53.72 |
| Llama3-8B-Instruct | 51.31 | 27.79 | 90.30 | 40.88 | 34.00 | 30.00 | 45.33 | 60.89 | 33.67 | 46.02 |
| LlaSMol | 27.78 | 29.34 | 31.44 | 23.38 | 25.67 | 24.00 | 37.33 | 34.65 | 22.67 | 28.47 |
| ChemDFM | 36.92 | 55.57 | 83.95 | 42.00 | 40.00 | 37.33 | 39.00 | 33.17 | 32.00 | 44.44 |
| ChemLLM-7B-Chat | 41.05 | 29.76 | 85.28 | 26.12 | 26.00 | 24.00 | 20.00 | 24.26 | 31.00 | 34.16 |
| ChemLLM-7B-Chat-1.5-SFT | 50.06 | 49.51 | 85.28 | 38.75 | 38.00 | 26.67 | 28.33 | 31.68 | 33.67 | 42.44 |
| Llama3-KALE-LM-Chem-1.5-8B | 61.33 | 43.44 | 90.30 | 53.62 | 72.67 | 53.67 | 46.00 | 47.03 | 45.00 | 57.01 |
## Cite This Work
```
@article{dai2024kale,
title={KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model},
author={Dai, Weichen and Chen, Yezeng and Dai, Zijie and Huang, Zhijie and Liu, Yubo and Pan, Yixuan and Song, Baiyang and Zhong, Chengli and Li, Xinhe and Wang, Zeyu and others},
journal={arXiv preprint arXiv:2409.18695},
year={2024}
}
```
|
[
"SCIQ"
] |
FreedomIntelligence/Apollo-MoE-1.5B
|
FreedomIntelligence
|
question-answering
|
[
"safetensors",
"upcycling-qwen2-moe",
"biology",
"medical",
"question-answering",
"custom_code",
"ar",
"en",
"zh",
"ko",
"ja",
"mn",
"th",
"vi",
"lo",
"mg",
"de",
"pt",
"es",
"fr",
"ru",
"it",
"hr",
"gl",
"cs",
"co",
"la",
"uk",
"bs",
"bg",
"eo",
"sq",
"da",
"sa",
"gn",
"sr",
"sk",
"gd",
"lb",
"hi",
"ku",
"mt",
"he",
"ln",
"bm",
"sw",
"ig",
"rw",
"ha",
"dataset:FreedomIntelligence/ApolloMoEDataset",
"arxiv:2410.10626",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-4k-instruct",
"license:apache-2.0",
"region:us"
] | 2024-10-14T07:18:46Z |
2024-11-20T03:40:28+00:00
| 29 | 1 |
---
base_model:
- microsoft/Phi-3-mini-4k-instruct
datasets:
- FreedomIntelligence/ApolloMoEDataset
language:
- ar
- en
- zh
- ko
- ja
- mn
- th
- vi
- lo
- mg
- de
- pt
- es
- fr
- ru
- it
- hr
- gl
- cs
- co
- la
- uk
- bs
- bg
- eo
- sq
- da
- sa
- gn
- sr
- sk
- gd
- lb
- hi
- ku
- mt
- he
- ln
- bm
- sw
- ig
- rw
- ha
license: apache-2.0
metrics:
- accuracy
pipeline_tag: question-answering
tags:
- biology
- medical
---
# Democratizing Medical LLMs For Much More Languages
Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38 Minor Languages So far.
<p align="center">
📃 <a href="https://arxiv.org/abs/2410.10626" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a> • 🤗 <a href="https://huggingface.co/collections/FreedomIntelligence/apollomoe-and-apollo2-670ddebe3bb1ba1aebabbf2c" target="_blank">Models</a> •🌐 <a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Apollo</a> • 🌐 <a href="https://github.com/FreedomIntelligence/ApolloMoE" target="_blank">ApolloMoE</a>
</p>
## 🌈 Update
* **[2024.10.15]** ApolloMoE repo is published！🎉
## Languages Coverage
12 Major Languages and 38 Minor Languages
<details>
<summary>Click to view the Languages Coverage</summary>
</details>
## Architecture
<details>
<summary>Click to view the MoE routing image</summary>
</details>
## Results
#### Dense
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-0.5B" target="_blank">Apollo2-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-1.5B" target="_blank">Apollo2-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-2B" target="_blank">Apollo2-2B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-3.8B" target="_blank">Apollo2-3.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-7B" target="_blank">Apollo2-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-9B" target="_blank">Apollo2-9B</a>
<details>
<summary>Click to view the Dense Models Results</summary>
</details>
#### Post-MoE
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-0.5B" target="_blank">Apollo-MoE-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-1.5B" target="_blank">Apollo-MoE-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-7B" target="_blank">Apollo-MoE-7B</a>
<details>
<summary>Click to view the Post-MoE Models Results</summary>
</details>
## Usage Format
##### Apollo2
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
- 2B, 9B: User:{query}\nAssistant:{response}\<eos\>
- 3.8B: <|user|>\n{query}<|end|><|assisitant|>\n{response}<|end|>
##### Apollo-MoE
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a>
<details><summary>Click to expand</summary>
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR:
- [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- [MMLU_FR]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_AR](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- JA: [IgakuQA](https://github.com/jungokasai/IgakuQA)
- KO: [KorMedMCQA](https://huggingface.co/datasets/sean0042/KorMedMCQA)
- IT:
- [MedExpQA](https://huggingface.co/datasets/HiTZ/MedExpQA)
- [MMLU_IT]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- DE: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): German part
- PT: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): Portuguese part
- RU: [RuMedBench](https://github.com/sb-ai-lab/MedBench)
</details>
## Model Download and Inference
We take Apollo-MoE-0.5B as an example
1. Login Huggingface
```
huggingface-cli login --token $HUGGINGFACE_TOKEN
```
2. Download model to local dir
```python
from huggingface_hub import snapshot_download
import os
local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
snapshot_download(repo_id="FreedomIntelligence/Apollo-MoE-0.5B", local_dir=local_model_dir)
```
3. Inference Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import os
local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
model=AutoModelForCausalLM.from_pretrained(local_model_dir,trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(local_model_dir,trust_remote_code=True)
generation_config = GenerationConfig.from_pretrained(local_model_dir, pad_token_id=tokenizer.pad_token_id, num_return_sequences=1, max_new_tokens=7, min_new_tokens=2, do_sample=False, temperature=1.0, top_k=50, top_p=1.0)
inputs = tokenizer('Answer direclty.\nThe capital of Mongolia is Ulaanbaatar.\nThe capital of Iceland is Reykjavik.\nThe capital of Australia is', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs,generation_config=generation_config)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
## Results reproduction
<details><summary>Click to expand</summary>
We take Apollo2-7B or Apollo-MoE-0.5B as example
1. Download Dataset for project:
```
bash 0.download_data.sh
```
2. Prepare test and dev data for specific model:
- Create test data for with special token
```
bash 1.data_process_test&dev.sh
```
3. Prepare train data for specific model (Create tokenized data in advance):
- You can adjust data Training order and Training Epoch in this step
```
bash 2.data_process_train.sh
```
4. Train the model
- If you want to train in Multi Nodes please refer to ./src/sft/training_config/zero_multi.yaml
```
bash 3.single_node_train.sh
```
5. Evaluate your model: Generate score for benchmark
```
bash 4.eval.sh
```
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{zheng2024efficientlydemocratizingmedicalllms,
title={Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts},
author={Guorui Zheng and Xidong Wang and Juhao Liang and Nuo Chen and Yuping Zheng and Benyou Wang},
year={2024},
eprint={2410.10626},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.10626},
}
```
|
[
"HEAD-QA",
"MEDQA",
"PUBMEDQA"
] |
JunxiongWang/Llama3.2-Mamba2-3B-dpo
|
JunxiongWang
| null |
[
"pytorch",
"llama",
"arxiv:2408.15237",
"license:apache-2.0",
"region:us"
] | 2024-10-15T20:53:01Z |
2024-11-17T21:07:23+00:00
| 29 | 0 |
---
license: apache-2.0
---
Zero-shot results when using the [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) as the teacher model, and the [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) as the initialized model
| Model | [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | [Llama3.2-Mamba-3B-distill](https://huggingface.co/JunxiongWang/Llama3.2-Mamba-3B-distill) | [Llama3.2-Mamba-3B-dpo](https://huggingface.co/JunxiongWang/Llama3.2-Mamba-3B-dpo) | [Llama3.2-Mamba2-3B-distill](https://huggingface.co/JunxiongWang/Llama3.2-Mamba2-3B-distill) | [Llama3.2-Mamba2-3B-dpo](https://huggingface.co/JunxiongWang/Llama3.2-Mamba2-3B-dpo) |
|---------------|---------------------------------------------------------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|
| Initialization Model | N/A | Llama-3.2-3B-Instruct | Llama-3.2-3B-Instruct | Llama-3.2-3B-Instruct | Llama-3.2-3B-Instruct |
| Teacher Model | N/A | Llama-3.1-70B-Instruct | Llama-3.1-70B-Instruct | Llama-3.1-70B-Instruct | Llama-3.1-70B-Instruct |
| arc_challenge | 0.459 | 0.4838 | 0.5265 | 0.4667 | 0.541 |
| arc_easy | 0.7407 | 0.7765 | 0.7997 | 0.7668 | 0.8026 |
| hellaswag | 0.7043 | 0.7037 | 0.7256 | 0.6913 | 0.7445 |
| mmlu | 0.6043 | 0.5448 | 0.5509 | 0.5312 | 0.5247 |
| openbookqa | 0.36 | 0.394 | 0.416 | 0.388 | 0.424 |
| piqa | 0.7568 | 0.7731 | 0.7731 | 0.7601 | 0.7769 |
| pubmedqa | 0.696 | 0.664 | 0.7 | 0.638 | 0.654 |
| race | 0.4067 | 0.4029 | 0.4364 | 0.3981 | 0.4344 |
| winogrande | 0.6748 | 0.6732 | 0.674 | 0.6606 | 0.6732 |
| truthfulqa | 0.3801 | 0.4202 | 0.4853 | 0.3478 | 0.5028 |
```
@article{junxiongdaniele2024mambainllama,
title = {The Mamba in the Llama: Distilling and Accelerating Hybrid Models},
author = {Junxiong Wang and Daniele Paliotta and Avner May and Alexander M. Rush and Tri Dao},
journal = {arXiv preprint arXiv:2408.15237},
year = {2024}
}
```
|
[
"PUBMEDQA"
] |
PaDaS-Lab/arctic-m-bge-small
|
PaDaS-Lab
| null |
[
"safetensors",
"arctic-m-bge-small",
"mteb",
"custom_code",
"arxiv:2407.08275",
"license:mit",
"model-index",
"region:us"
] | 2024-11-07T09:01:21Z |
2024-11-15T23:18:08+00:00
| 29 | 3 |
---
license: mit
tags:
- mteb
model-index:
- name: no_model_name_available
results:
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 62.44
- type: map_at_1
value: 37.909
- type: map_at_10
value: 54.071000000000005
- type: map_at_100
value: 54.706999999999994
- type: map_at_1000
value: 54.71
- type: map_at_20
value: 54.61
- type: map_at_3
value: 49.787
- type: map_at_5
value: 52.471999999999994
- type: mrr_at_1
value: 38.54907539118065
- type: mrr_at_10
value: 54.30778522883794
- type: mrr_at_100
value: 54.95058676123675
- type: mrr_at_1000
value: 54.9534745787606
- type: mrr_at_20
value: 54.85371234607436
- type: mrr_at_3
value: 50.023707918444806
- type: mrr_at_5
value: 52.71574205784745
- type: nauc_map_at_1000_diff1
value: 9.700052151236969
- type: nauc_map_at_1000_max
value: -11.480601048675311
- type: nauc_map_at_1000_std
value: -16.80933897048166
- type: nauc_map_at_100_diff1
value: 9.702439916132208
- type: nauc_map_at_100_max
value: -11.477121863613672
- type: nauc_map_at_100_std
value: -16.805809477344237
- type: nauc_map_at_10_diff1
value: 9.55964875147944
- type: nauc_map_at_10_max
value: -11.221604673423611
- type: nauc_map_at_10_std
value: -16.84817138477702
- type: nauc_map_at_1_diff1
value: 13.414379505055546
- type: nauc_map_at_1_max
value: -13.64398031891019
- type: nauc_map_at_1_std
value: -17.823564900618976
- type: nauc_map_at_20_diff1
value: 9.656264829584742
- type: nauc_map_at_20_max
value: -11.402956696331874
- type: nauc_map_at_20_std
value: -16.729584639384093
- type: nauc_map_at_3_diff1
value: 9.074651468472236
- type: nauc_map_at_3_max
value: -11.938799932445345
- type: nauc_map_at_3_std
value: -17.292542932113854
- type: nauc_map_at_5_diff1
value: 9.375988599355505
- type: nauc_map_at_5_max
value: -11.472571205679664
- type: nauc_map_at_5_std
value: -17.40403356468899
- type: nauc_mrr_at_1000_diff1
value: 7.411799940331186
- type: nauc_mrr_at_1000_max
value: -12.508159837494434
- type: nauc_mrr_at_1000_std
value: -16.707342470667285
- type: nauc_mrr_at_100_diff1
value: 7.414405067064217
- type: nauc_mrr_at_100_max
value: -12.50459019538836
- type: nauc_mrr_at_100_std
value: -16.703833468680948
- type: nauc_mrr_at_10_diff1
value: 7.286842407775826
- type: nauc_mrr_at_10_max
value: -12.258550496378401
- type: nauc_mrr_at_10_std
value: -16.731699740418414
- type: nauc_mrr_at_1_diff1
value: 11.596538956075104
- type: nauc_mrr_at_1_max
value: -13.73394271953812
- type: nauc_mrr_at_1_std
value: -17.64007975098422
- type: nauc_mrr_at_20_diff1
value: 7.376312921473681
- type: nauc_mrr_at_20_max
value: -12.426813484836043
- type: nauc_mrr_at_20_std
value: -16.627786497409552
- type: nauc_mrr_at_3_diff1
value: 6.654949505817999
- type: nauc_mrr_at_3_max
value: -13.137022485458507
- type: nauc_mrr_at_3_std
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type: mteb/touche2020
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revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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---
<h1 align="center">Combination of Embedding Models: <a href="https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5">Arctic M (v1.5)</a> & <a href="https://huggingface.co/BAAI/bge-small-en-v1.5">BGE Small (en; v1.5)</a></h1>
<h4 align="center">
<p>
<a href="#acknowledgement">Acknowledgement</a> |
<a href=#combination-of-embedding-models>Combination of Embedding Models</a> |
<a href=#usage>Usage</a> |
<a href=#citation>Citation</a> |
<a href=#license>License</a>
<p>
</h4>
## Acknowledgement
First of all, we want to acknowledge the original creators of the [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) and [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) models which are used to create this model. Our model is just a combination of these two models, and we have not made any changes to the original models.
Furthermore, we want to acknowledge the team of Marqo, who has worked on the idea of combining two models through concatenation in parallel to ourselves. Their initial effort allowed to re-use existing pieces of code, in particular the [modeling script](https://huggingface.co/PaDaS-Lab/arctic-m-bge-small/blob/main/modeling_arctic_m_bge_small.py) for bringing the combined model to HuggingFace.
## Combination of Embedding Models
### Overview
Embedding models have become increasingly powerful and applicable across various use cases. However, the next significant challenge lies in enhancing their efficiency in terms of resource consumption. Our goal is to experiment with combining two embedding models to achieve better performance with fewer resources.
### Key Insights
1. **Diversity Matters**: Initial findings suggest that combining models with differing characteristics can complement each other, resulting in improved outcomes. To design an effective combination, the diversity of the models—evaluated by factors like MTEB performance, architecture, and training data—is crucial.
2. **Combination Technique**:
- We combine the embeddings of two models using the most straightforward approach: concatenation.
- Prior to concatenation, we normalize the embeddings to ensure they are on the same scale. This step is vital for achieving coherent and meaningful results.
### Implementation
We combined the following models:
- **[Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5)**
- **[BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)**
#### Model Details
- **Output Embedding Dimensions**: 1152 (768 + 384)
- **Total Parameters**: 142M (109M + 33M)
### Results
This combination demonstrated notable performance on the **MTEB Leaderboard**, offering a promising foundation for further experimentation:
- **Performance Improvement**: The average nDCG@10 on the MTEB English Retrieval benchmark increased from **55.14 to 56.5**, climbing several spots on the leaderboard—a feat often requiring extensive engineering efforts.
- **Comparison with Chimera Model**:
Interestingly, the **[Chimera model](https://huggingface.co/Marqo/marqo-chimera-arctic-bge-m)**, which employs more potent models individually, performs worse on the leaderboard. This raises questions about:
- The role of parameter count.
- Differences in training processes.
- How effectively two models complement each other for specific benchmark tasks.
### Future Directions
While the results are promising, we acknowledge the complexity of model combinations and the importance of focusing on more than leaderboard rankings. The simplicity of concatenating embeddings yielding tangible gains emphasizes the potential for further exploration in this area.
We look forward to conducting additional experiments and engaging in discussions to deepen our understanding of effective model combinations.
## Usage
```python
import numpy as np
import torch
from torch.utils.data import DataLoader
from transformers import AutoModel, AutoTokenizer, PreTrainedTokenizerFast, BatchEncoding, DataCollatorWithPadding
from functools import partial
from datasets import Dataset
from tqdm import tqdm
from typing import *
NUM_WORKERS = 4
BATCH_SIZE = 32
def transform_func(tokenizer: PreTrainedTokenizerFast,
max_length: int,
examples: Dict[str, List]) -> BatchEncoding:
return tokenizer(examples['contents'],
max_length=max_length,
padding=True,
return_token_type_ids=False,
truncation=True)
def move_to_cuda(sample):
if len(sample) == 0:
return {}
def _move_to_cuda(maybe_tensor):
if torch.is_tensor(maybe_tensor):
return maybe_tensor.cuda(non_blocking=True)
elif isinstance(maybe_tensor, dict):
return {key: _move_to_cuda(value) for key, value in maybe_tensor.items()}
elif isinstance(maybe_tensor, list):
return [_move_to_cuda(x) for x in maybe_tensor]
elif isinstance(maybe_tensor, tuple):
return tuple([_move_to_cuda(x) for x in maybe_tensor])
elif isinstance(maybe_tensor, Mapping):
return type(maybe_tensor)({k: _move_to_cuda(v) for k, v in maybe_tensor.items()})
else:
return maybe_tensor
return _move_to_cuda(sample)
class RetrievalModel():
def __init__(self, pretrained_model_name: str, **kwargs):
self.pretrained_model_name = pretrained_model_name
self.encoder = AutoModel.from_pretrained(pretrained_model_name, trust_remote_code=True)
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name, trust_remote_code=True)
self.gpu_count = torch.cuda.device_count()
self.batch_size = BATCH_SIZE
self.query_instruction = 'Represent this sentence for searching relevant passages: {}'
self.document_instruction = '{}'
self.pool_type = 'cls'
self.max_length = 512
self.encoder.cuda()
self.encoder.eval()
def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:
input_texts = [self.query_instruction.format(q) for q in queries]
return self._do_encode(input_texts)
def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs) -> np.ndarray:
input_texts = [self.document_instruction.format('{} {}'.format(d.get('title', ''), d['text']).strip()) for d in corpus]
return self._do_encode(input_texts)
@torch.no_grad()
def _do_encode(self, input_texts: List[str]) -> np.ndarray:
dataset: Dataset = Dataset.from_dict({'contents': input_texts})
dataset.set_transform(partial(transform_func, self.tokenizer, self.max_length))
data_collator = DataCollatorWithPadding(self.tokenizer, pad_to_multiple_of=8)
data_loader = DataLoader(
dataset,
batch_size=self.batch_size * self.gpu_count,
shuffle=False,
drop_last=False,
num_workers=NUM_WORKERS,
collate_fn=data_collator,
pin_memory=True)
encoded_embeds = []
for batch_dict in tqdm(data_loader, desc='encoding', mininterval=10):
batch_dict = move_to_cuda(batch_dict)
with torch.amp.autocast('cuda'):
outputs = self.encoder(**batch_dict)
encoded_embeds.append(outputs.cpu().numpy())
return np.concatenate(encoded_embeds, axis=0)
model = RetrievalModel('PaDaS-Lab/arctic-m-bge-small')
embeds_q = model.encode_queries(['What is the capital of France?'])
# [[-0.01099197 -0.08366653 0.0060241 ... 0.03182805 -0.00674182 0.058571 ]]
embeds_d = model.encode_corpus([{'title': 'Paris', 'text': 'Paris is the capital of France.'}])
# [[ 0.0391828 -0.02951912 0.10862264 ... -0.05373885 -0.00368348 0.02323797]]
```
### Libraries
```
torch==2.5.0
transformers==4.42.3
mteb==1.12.94
```
## Citation
```bibtex
@misc{https://doi.org/10.48550/arxiv.2407.08275,
doi = {10.48550/ARXIV.2407.08275},
url = {https://arxiv.org/abs/2407.08275},
author = {Caspari, Laura and Dastidar, Kanishka Ghosh and Zerhoudi, Saber and Mitrovic, Jelena and Granitzer, Michael},
title = {Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systems},
year = {2024},
copyright = {Creative Commons Attribution 4.0 International}
}
```
## License
Notice that Arctic M (v1.5) is licensed under [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) and BGE Small (en; v1.5) is licensed under [MIT](https://opensource.org/licenses/MIT) license. Please refer to the licenses of the original models for more details.
|
[
"SCIFACT"
] |
mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF
|
mradermacher
| null |
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"not-for-all-audiences",
"nsfw",
"rp",
"roleplay",
"role-play",
"en",
"base_model:Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B",
"base_model:quantized:Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-12-31T21:11:15Z |
2025-01-01T23:20:56+00:00
| 29 | 1 |
---
base_model: Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B
language:
- en
library_name: transformers
license: llama3
tags:
- merge
- mergekit
- lazymergekit
- not-for-all-audiences
- nsfw
- rp
- roleplay
- role-play
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.1-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.1-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
[
"CAS"
] |
Zenabius/multilingual-e5-large-instruct-Q8_0-GGUF
|
Zenabius
| null |
[
"sentence-transformers",
"gguf",
"mteb",
"transformers",
"llama-cpp",
"gguf-my-repo",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"base_model:intfloat/multilingual-e5-large-instruct",
"base_model:quantized:intfloat/multilingual-e5-large-instruct",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | 2025-02-10T02:46:25Z |
2025-02-10T02:46:33+00:00
| 29 | 0 |
---
base_model: intfloat/multilingual-e5-large-instruct
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- sentence-transformers
- transformers
- llama-cpp
- gguf-my-repo
model-index:
- name: multilingual-e5-large-instruct
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.23880597014924
- type: ap
value: 39.07351965022687
- type: f1
value: 70.04836733862683
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.71306209850107
- type: ap
value: 79.01499914759529
- type: f1
value: 64.81951817560703
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.85307346326837
- type: ap
value: 22.447519885878737
- type: f1
value: 61.0162730745633
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.04925053533191
- type: ap
value: 23.44983217128922
- type: f1
value: 62.5723230907759
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.28742500000001
- type: ap
value: 94.8449918887462
- type: f1
value: 96.28680923610432
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 56.716
- type: f1
value: 55.76510398266401
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 52.99999999999999
- type: f1
value: 52.00829994765178
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.806000000000004
- type: f1
value: 48.082345914983634
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.507999999999996
- type: f1
value: 47.68752844642045
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.709999999999994
- type: f1
value: 47.05870376637181
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.662000000000006
- type: f1
value: 43.42371965372771
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.721
- type: map_at_10
value: 49.221
- type: map_at_100
value: 49.884
- type: map_at_1000
value: 49.888
- type: map_at_3
value: 44.31
- type: map_at_5
value: 47.276
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 49.5
- type: mrr_at_100
value: 50.163000000000004
- type: mrr_at_1000
value: 50.166
- type: mrr_at_3
value: 44.618
- type: mrr_at_5
value: 47.541
- type: ndcg_at_1
value: 31.721
- type: ndcg_at_10
value: 58.384
- type: ndcg_at_100
value: 61.111000000000004
- type: ndcg_at_1000
value: 61.187999999999995
- type: ndcg_at_3
value: 48.386
- type: ndcg_at_5
value: 53.708999999999996
- type: precision_at_1
value: 31.721
- type: precision_at_10
value: 8.741
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.057
- type: precision_at_5
value: 14.609
- type: recall_at_1
value: 31.721
- type: recall_at_10
value: 87.411
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 60.171
- type: recall_at_5
value: 73.044
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.40419580759799
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.48593255007969
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.889179122289995
- type: mrr
value: 77.61146286769556
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.15075203727929
- type: cos_sim_spearman
value: 86.9622224570873
- type: euclidean_pearson
value: 86.70473853624121
- type: euclidean_spearman
value: 86.9622224570873
- type: manhattan_pearson
value: 86.21089380980065
- type: manhattan_spearman
value: 86.75318154937008
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.65553235908142
- type: f1
value: 99.60681976339595
- type: precision
value: 99.58246346555325
- type: recall
value: 99.65553235908142
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26260180497468
- type: f1
value: 99.14520507740848
- type: precision
value: 99.08650671362535
- type: recall
value: 99.26260180497468
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.07412538967787
- type: f1
value: 97.86629719431936
- type: precision
value: 97.76238309664012
- type: recall
value: 98.07412538967787
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.42074776197998
- type: f1
value: 99.38564156573635
- type: precision
value: 99.36808846761454
- type: recall
value: 99.42074776197998
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.73376623376623
- type: f1
value: 85.68480707214599
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.935218072113855
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.276389017675264
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.764166666666668
- type: map_at_10
value: 37.298166666666674
- type: map_at_100
value: 38.530166666666666
- type: map_at_1000
value: 38.64416666666667
- type: map_at_3
value: 34.484833333333334
- type: map_at_5
value: 36.0385
- type: mrr_at_1
value: 32.93558333333333
- type: mrr_at_10
value: 41.589749999999995
- type: mrr_at_100
value: 42.425333333333334
- type: mrr_at_1000
value: 42.476333333333336
- type: mrr_at_3
value: 39.26825
- type: mrr_at_5
value: 40.567083333333336
- type: ndcg_at_1
value: 32.93558333333333
- type: ndcg_at_10
value: 42.706583333333334
- type: ndcg_at_100
value: 47.82483333333333
- type: ndcg_at_1000
value: 49.95733333333334
- type: ndcg_at_3
value: 38.064750000000004
- type: ndcg_at_5
value: 40.18158333333333
- type: precision_at_1
value: 32.93558333333333
- type: precision_at_10
value: 7.459833333333334
- type: precision_at_100
value: 1.1830833333333335
- type: precision_at_1000
value: 0.15608333333333332
- type: precision_at_3
value: 17.5235
- type: precision_at_5
value: 12.349833333333333
- type: recall_at_1
value: 27.764166666666668
- type: recall_at_10
value: 54.31775
- type: recall_at_100
value: 76.74350000000001
- type: recall_at_1000
value: 91.45208333333332
- type: recall_at_3
value: 41.23425
- type: recall_at_5
value: 46.73983333333334
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.969
- type: map_at_10
value: 21.584999999999997
- type: map_at_100
value: 23.3
- type: map_at_1000
value: 23.5
- type: map_at_3
value: 18.218999999999998
- type: map_at_5
value: 19.983
- type: mrr_at_1
value: 29.316
- type: mrr_at_10
value: 40.033
- type: mrr_at_100
value: 40.96
- type: mrr_at_1000
value: 41.001
- type: mrr_at_3
value: 37.123
- type: mrr_at_5
value: 38.757999999999996
- type: ndcg_at_1
value: 29.316
- type: ndcg_at_10
value: 29.858
- type: ndcg_at_100
value: 36.756
- type: ndcg_at_1000
value: 40.245999999999995
- type: ndcg_at_3
value: 24.822
- type: ndcg_at_5
value: 26.565
- type: precision_at_1
value: 29.316
- type: precision_at_10
value: 9.186
- type: precision_at_100
value: 1.6549999999999998
- type: precision_at_1000
value: 0.22999999999999998
- type: precision_at_3
value: 18.436
- type: precision_at_5
value: 13.876
- type: recall_at_1
value: 12.969
- type: recall_at_10
value: 35.142
- type: recall_at_100
value: 59.143
- type: recall_at_1000
value: 78.594
- type: recall_at_3
value: 22.604
- type: recall_at_5
value: 27.883000000000003
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.527999999999999
- type: map_at_10
value: 17.974999999999998
- type: map_at_100
value: 25.665
- type: map_at_1000
value: 27.406000000000002
- type: map_at_3
value: 13.017999999999999
- type: map_at_5
value: 15.137
- type: mrr_at_1
value: 62.5
- type: mrr_at_10
value: 71.891
- type: mrr_at_100
value: 72.294
- type: mrr_at_1000
value: 72.296
- type: mrr_at_3
value: 69.958
- type: mrr_at_5
value: 71.121
- type: ndcg_at_1
value: 50.875
- type: ndcg_at_10
value: 38.36
- type: ndcg_at_100
value: 44.235
- type: ndcg_at_1000
value: 52.154
- type: ndcg_at_3
value: 43.008
- type: ndcg_at_5
value: 40.083999999999996
- type: precision_at_1
value: 62.5
- type: precision_at_10
value: 30.0
- type: precision_at_100
value: 10.038
- type: precision_at_1000
value: 2.0869999999999997
- type: precision_at_3
value: 46.833000000000006
- type: precision_at_5
value: 38.800000000000004
- type: recall_at_1
value: 8.527999999999999
- type: recall_at_10
value: 23.828
- type: recall_at_100
value: 52.322
- type: recall_at_1000
value: 77.143
- type: recall_at_3
value: 14.136000000000001
- type: recall_at_5
value: 17.761
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.51
- type: f1
value: 47.632159862049896
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.734
- type: map_at_10
value: 72.442
- type: map_at_100
value: 72.735
- type: map_at_1000
value: 72.75
- type: map_at_3
value: 70.41199999999999
- type: map_at_5
value: 71.80499999999999
- type: mrr_at_1
value: 65.212
- type: mrr_at_10
value: 76.613
- type: mrr_at_100
value: 76.79899999999999
- type: mrr_at_1000
value: 76.801
- type: mrr_at_3
value: 74.8
- type: mrr_at_5
value: 76.12400000000001
- type: ndcg_at_1
value: 65.212
- type: ndcg_at_10
value: 77.988
- type: ndcg_at_100
value: 79.167
- type: ndcg_at_1000
value: 79.452
- type: ndcg_at_3
value: 74.362
- type: ndcg_at_5
value: 76.666
- type: precision_at_1
value: 65.212
- type: precision_at_10
value: 10.003
- type: precision_at_100
value: 1.077
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 29.518
- type: precision_at_5
value: 19.016
- type: recall_at_1
value: 60.734
- type: recall_at_10
value: 90.824
- type: recall_at_100
value: 95.71600000000001
- type: recall_at_1000
value: 97.577
- type: recall_at_3
value: 81.243
- type: recall_at_5
value: 86.90299999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.845
- type: map_at_10
value: 39.281
- type: map_at_100
value: 41.422
- type: map_at_1000
value: 41.593
- type: map_at_3
value: 34.467
- type: map_at_5
value: 37.017
- type: mrr_at_1
value: 47.531
- type: mrr_at_10
value: 56.204
- type: mrr_at_100
value: 56.928999999999995
- type: mrr_at_1000
value: 56.962999999999994
- type: mrr_at_3
value: 54.115
- type: mrr_at_5
value: 55.373000000000005
- type: ndcg_at_1
value: 47.531
- type: ndcg_at_10
value: 47.711999999999996
- type: ndcg_at_100
value: 54.510999999999996
- type: ndcg_at_1000
value: 57.103
- type: ndcg_at_3
value: 44.145
- type: ndcg_at_5
value: 45.032
- type: precision_at_1
value: 47.531
- type: precision_at_10
value: 13.194
- type: precision_at_100
value: 2.045
- type: precision_at_1000
value: 0.249
- type: precision_at_3
value: 29.424
- type: precision_at_5
value: 21.451
- type: recall_at_1
value: 23.845
- type: recall_at_10
value: 54.967
- type: recall_at_100
value: 79.11399999999999
- type: recall_at_1000
value: 94.56700000000001
- type: recall_at_3
value: 40.256
- type: recall_at_5
value: 46.215
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.819
- type: map_at_10
value: 60.889
- type: map_at_100
value: 61.717999999999996
- type: map_at_1000
value: 61.778
- type: map_at_3
value: 57.254000000000005
- type: map_at_5
value: 59.541
- type: mrr_at_1
value: 75.638
- type: mrr_at_10
value: 82.173
- type: mrr_at_100
value: 82.362
- type: mrr_at_1000
value: 82.37
- type: mrr_at_3
value: 81.089
- type: mrr_at_5
value: 81.827
- type: ndcg_at_1
value: 75.638
- type: ndcg_at_10
value: 69.317
- type: ndcg_at_100
value: 72.221
- type: ndcg_at_1000
value: 73.382
- type: ndcg_at_3
value: 64.14
- type: ndcg_at_5
value: 67.07600000000001
- type: precision_at_1
value: 75.638
- type: precision_at_10
value: 14.704999999999998
- type: precision_at_100
value: 1.698
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 41.394999999999996
- type: precision_at_5
value: 27.162999999999997
- type: recall_at_1
value: 37.819
- type: recall_at_10
value: 73.52499999999999
- type: recall_at_100
value: 84.875
- type: recall_at_1000
value: 92.559
- type: recall_at_3
value: 62.092999999999996
- type: recall_at_5
value: 67.907
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.60079999999999
- type: ap
value: 92.67396345347356
- type: f1
value: 94.5988098167121
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.285
- type: map_at_10
value: 33.436
- type: map_at_100
value: 34.63
- type: map_at_1000
value: 34.681
- type: map_at_3
value: 29.412
- type: map_at_5
value: 31.715
- type: mrr_at_1
value: 21.848
- type: mrr_at_10
value: 33.979
- type: mrr_at_100
value: 35.118
- type: mrr_at_1000
value: 35.162
- type: mrr_at_3
value: 30.036
- type: mrr_at_5
value: 32.298
- type: ndcg_at_1
value: 21.862000000000002
- type: ndcg_at_10
value: 40.43
- type: ndcg_at_100
value: 46.17
- type: ndcg_at_1000
value: 47.412
- type: ndcg_at_3
value: 32.221
- type: ndcg_at_5
value: 36.332
- type: precision_at_1
value: 21.862000000000002
- type: precision_at_10
value: 6.491
- type: precision_at_100
value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.744
- type: precision_at_5
value: 10.331999999999999
- type: recall_at_1
value: 21.285
- type: recall_at_10
value: 62.083
- type: recall_at_100
value: 88.576
- type: recall_at_1000
value: 98.006
- type: recall_at_3
value: 39.729
- type: recall_at_5
value: 49.608000000000004
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.92612859097127
- type: f1
value: 93.82370333372853
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.67681036911807
- type: f1
value: 92.14191382411472
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.26817878585723
- type: f1
value: 91.92824250337878
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.96554963983714
- type: f1
value: 90.02859329630792
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.02509860164935
- type: f1
value: 89.30665159182062
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.55515370705244
- type: f1
value: 87.94449232331907
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 82.4623803009576
- type: f1
value: 66.06738378772725
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.3716539870386
- type: f1
value: 60.37614033396853
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 80.34022681787857
- type: f1
value: 58.302008026952
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.72095208268087
- type: f1
value: 59.64524724009049
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.87020437432773
- type: f1
value: 57.80202694670567
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.73598553345387
- type: f1
value: 58.19628250675031
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.6630800268998
- type: f1
value: 65.00996668051691
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.7128446536651
- type: f1
value: 57.95860594874963
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.61129791526563
- type: f1
value: 59.75328290206483
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.00134498991257
- type: f1
value: 67.0230483991802
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.54068594485541
- type: f1
value: 65.54604628946976
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.032952252858095
- type: f1
value: 58.715741857057104
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.80901143241427
- type: f1
value: 68.33963989243877
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.47141896435777
- type: f1
value: 69.56765020308262
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.2373907195696
- type: f1
value: 69.04529836036467
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 77.05783456624076
- type: f1
value: 74.69430584708174
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.82111634162744
- type: f1
value: 70.77228952803762
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.25353059852051
- type: f1
value: 71.05310103416411
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.28648285137861
- type: f1
value: 69.08020473732226
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.31540013449899
- type: f1
value: 70.9426355465791
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (he)
type: mteb/amazon_massive_intent
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.2151983860121
- type: f1
value: 67.52541755908858
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hi)
type: mteb/amazon_massive_intent
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.58372562205784
- type: f1
value: 69.49769064229827
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hu)
type: mteb/amazon_massive_intent
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.9233355749832
- type: f1
value: 69.36311548259593
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hy)
type: mteb/amazon_massive_intent
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.07330195023538
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- type: accuracy
value: 77.20914593140552
- type: f1
value: 77.07066497935367
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.52387357094821
- type: f1
value: 78.5259569473291
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.6913248150639
- type: f1
value: 76.91201656350455
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.1217215870881
- type: f1
value: 77.41179937912504
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.25891055817083
- type: f1
value: 75.8089244542887
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.70679219905851
- type: f1
value: 78.21459594517711
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.83523873570948
- type: f1
value: 74.86847028401978
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.71755211835911
- type: f1
value: 74.0214326485662
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.06523201075991
- type: f1
value: 79.10545620325138
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.91862811028918
- type: f1
value: 66.50386121217983
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.93140551445865
- type: f1
value: 70.755435928495
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.40753194351042
- type: f1
value: 71.61816115782923
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.1815736381977
- type: f1
value: 75.08016717887205
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.86482851378614
- type: f1
value: 72.39521180006291
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.46940147948891
- type: f1
value: 76.70044085362349
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.89307330195024
- type: f1
value: 71.5721825332298
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.7511768661735
- type: f1
value: 75.17918654541515
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.69535978480162
- type: f1
value: 78.90019070153316
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.45729657027572
- type: f1
value: 76.19578371794672
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 36.92715354123554
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 35.53536244162518
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.08507884504006
- type: mrr
value: 34.32436977159129
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.935
- type: map_at_10
value: 13.297
- type: map_at_100
value: 16.907
- type: map_at_1000
value: 18.391
- type: map_at_3
value: 9.626999999999999
- type: map_at_5
value: 11.190999999999999
- type: mrr_at_1
value: 46.129999999999995
- type: mrr_at_10
value: 54.346000000000004
- type: mrr_at_100
value: 55.067
- type: mrr_at_1000
value: 55.1
- type: mrr_at_3
value: 51.961
- type: mrr_at_5
value: 53.246
- type: ndcg_at_1
value: 44.118
- type: ndcg_at_10
value: 35.534
- type: ndcg_at_100
value: 32.946999999999996
- type: ndcg_at_1000
value: 41.599000000000004
- type: ndcg_at_3
value: 40.25
- type: ndcg_at_5
value: 37.978
- type: precision_at_1
value: 46.129999999999995
- type: precision_at_10
value: 26.842
- type: precision_at_100
value: 8.427
- type: precision_at_1000
value: 2.128
- type: precision_at_3
value: 37.977
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.935
- type: recall_at_10
value: 17.211000000000002
- type: recall_at_100
value: 34.33
- type: recall_at_1000
value: 65.551
- type: recall_at_3
value: 10.483
- type: recall_at_5
value: 13.078999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.231
- type: map_at_10
value: 50.202000000000005
- type: map_at_100
value: 51.154999999999994
- type: map_at_1000
value: 51.181
- type: map_at_3
value: 45.774
- type: map_at_5
value: 48.522
- type: mrr_at_1
value: 39.687
- type: mrr_at_10
value: 52.88
- type: mrr_at_100
value: 53.569
- type: mrr_at_1000
value: 53.58500000000001
- type: mrr_at_3
value: 49.228
- type: mrr_at_5
value: 51.525
- type: ndcg_at_1
value: 39.687
- type: ndcg_at_10
value: 57.754000000000005
- type: ndcg_at_100
value: 61.597
- type: ndcg_at_1000
value: 62.18900000000001
- type: ndcg_at_3
value: 49.55
- type: ndcg_at_5
value: 54.11899999999999
- type: precision_at_1
value: 39.687
- type: precision_at_10
value: 9.313
- type: precision_at_100
value: 1.146
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 22.229
- type: precision_at_5
value: 15.939
- type: recall_at_1
value: 35.231
- type: recall_at_10
value: 78.083
- type: recall_at_100
value: 94.42099999999999
- type: recall_at_1000
value: 98.81
- type: recall_at_3
value: 57.047000000000004
- type: recall_at_5
value: 67.637
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.241
- type: map_at_10
value: 85.462
- type: map_at_100
value: 86.083
- type: map_at_1000
value: 86.09700000000001
- type: map_at_3
value: 82.49499999999999
- type: map_at_5
value: 84.392
- type: mrr_at_1
value: 82.09
- type: mrr_at_10
value: 88.301
- type: mrr_at_100
value: 88.383
- type: mrr_at_1000
value: 88.384
- type: mrr_at_3
value: 87.37
- type: mrr_at_5
value: 88.035
- type: ndcg_at_1
value: 82.12
- type: ndcg_at_10
value: 89.149
- type: ndcg_at_100
value: 90.235
- type: ndcg_at_1000
value: 90.307
- type: ndcg_at_3
value: 86.37599999999999
- type: ndcg_at_5
value: 87.964
- type: precision_at_1
value: 82.12
- type: precision_at_10
value: 13.56
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.88
- type: precision_at_5
value: 24.92
- type: recall_at_1
value: 71.241
- type: recall_at_10
value: 96.128
- type: recall_at_100
value: 99.696
- type: recall_at_1000
value: 99.994
- type: recall_at_3
value: 88.181
- type: recall_at_5
value: 92.694
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.59757799655151
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.27391998854624
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.243
- type: map_at_10
value: 10.965
- type: map_at_100
value: 12.934999999999999
- type: map_at_1000
value: 13.256
- type: map_at_3
value: 7.907
- type: map_at_5
value: 9.435
- type: mrr_at_1
value: 20.9
- type: mrr_at_10
value: 31.849
- type: mrr_at_100
value: 32.964
- type: mrr_at_1000
value: 33.024
- type: mrr_at_3
value: 28.517
- type: mrr_at_5
value: 30.381999999999998
- type: ndcg_at_1
value: 20.9
- type: ndcg_at_10
value: 18.723
- type: ndcg_at_100
value: 26.384999999999998
- type: ndcg_at_1000
value: 32.114
- type: ndcg_at_3
value: 17.753
- type: ndcg_at_5
value: 15.558
- type: precision_at_1
value: 20.9
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 2.078
- type: precision_at_1000
value: 0.345
- type: precision_at_3
value: 16.900000000000002
- type: precision_at_5
value: 13.88
- type: recall_at_1
value: 4.243
- type: recall_at_10
value: 19.885
- type: recall_at_100
value: 42.17
- type: recall_at_1000
value: 70.12
- type: recall_at_3
value: 10.288
- type: recall_at_5
value: 14.072000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.84209174935282
- type: cos_sim_spearman
value: 81.73248048438833
- type: euclidean_pearson
value: 83.02810070308149
- type: euclidean_spearman
value: 81.73248295679514
- type: manhattan_pearson
value: 82.95368060376002
- type: manhattan_spearman
value: 81.60277910998718
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 88.52628804556943
- type: cos_sim_spearman
value: 82.5713913555672
- type: euclidean_pearson
value: 85.8796774746988
- type: euclidean_spearman
value: 82.57137506803424
- type: manhattan_pearson
value: 85.79671002960058
- type: manhattan_spearman
value: 82.49445981618027
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.23682503505542
- type: cos_sim_spearman
value: 87.15008956711806
- type: euclidean_pearson
value: 86.79805401524959
- type: euclidean_spearman
value: 87.15008956711806
- type: manhattan_pearson
value: 86.65298502699244
- type: manhattan_spearman
value: 86.97677821948562
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.63370304677802
- type: cos_sim_spearman
value: 84.97105553540318
- type: euclidean_pearson
value: 85.28896108687721
- type: euclidean_spearman
value: 84.97105553540318
- type: manhattan_pearson
value: 85.09663190337331
- type: manhattan_spearman
value: 84.79126831644619
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 90.2614838800733
- type: cos_sim_spearman
value: 91.0509162991835
- type: euclidean_pearson
value: 90.33098317533373
- type: euclidean_spearman
value: 91.05091625871644
- type: manhattan_pearson
value: 90.26250435151107
- type: manhattan_spearman
value: 90.97999594417519
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.80480973335091
- type: cos_sim_spearman
value: 87.313695492969
- type: euclidean_pearson
value: 86.49267251576939
- type: euclidean_spearman
value: 87.313695492969
- type: manhattan_pearson
value: 86.44019901831935
- type: manhattan_spearman
value: 87.24205395460392
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.05662789380672
- type: cos_sim_spearman
value: 90.02759424426651
- type: euclidean_pearson
value: 90.4042483422981
- type: euclidean_spearman
value: 90.02759424426651
- type: manhattan_pearson
value: 90.51446975000226
- type: manhattan_spearman
value: 90.08832889933616
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.5975528273532
- type: cos_sim_spearman
value: 67.62969861411354
- type: euclidean_pearson
value: 69.224275734323
- type: euclidean_spearman
value: 67.62969861411354
- type: manhattan_pearson
value: 69.3761447059927
- type: manhattan_spearman
value: 67.90921005611467
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.11244327231684
- type: cos_sim_spearman
value: 88.37902438979035
- type: euclidean_pearson
value: 87.86054279847336
- type: euclidean_spearman
value: 88.37902438979035
- type: manhattan_pearson
value: 87.77257757320378
- type: manhattan_spearman
value: 88.25208966098123
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.87174608143563
- type: mrr
value: 96.12836872640794
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.760999999999996
- type: map_at_10
value: 67.258
- type: map_at_100
value: 67.757
- type: map_at_1000
value: 67.78800000000001
- type: map_at_3
value: 64.602
- type: map_at_5
value: 65.64
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 68.441
- type: mrr_at_100
value: 68.825
- type: mrr_at_1000
value: 68.853
- type: mrr_at_3
value: 66.444
- type: mrr_at_5
value: 67.26100000000001
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 71.852
- type: ndcg_at_100
value: 73.9
- type: ndcg_at_1000
value: 74.628
- type: ndcg_at_3
value: 67.093
- type: ndcg_at_5
value: 68.58
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.6
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 26.111
- type: precision_at_5
value: 16.733
- type: recall_at_1
value: 57.760999999999996
- type: recall_at_10
value: 84.967
- type: recall_at_100
value: 93.833
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 71.589
- type: recall_at_5
value: 75.483
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.66633663366336
- type: cos_sim_ap
value: 91.17685358899108
- type: cos_sim_f1
value: 82.16818642350559
- type: cos_sim_precision
value: 83.26488706365504
- type: cos_sim_recall
value: 81.10000000000001
- type: dot_accuracy
value: 99.66633663366336
- type: dot_ap
value: 91.17663411119032
- type: dot_f1
value: 82.16818642350559
- type: dot_precision
value: 83.26488706365504
- type: dot_recall
value: 81.10000000000001
- type: euclidean_accuracy
value: 99.66633663366336
- type: euclidean_ap
value: 91.17685189882275
- type: euclidean_f1
value: 82.16818642350559
- type: euclidean_precision
value: 83.26488706365504
- type: euclidean_recall
value: 81.10000000000001
- type: manhattan_accuracy
value: 99.66633663366336
- type: manhattan_ap
value: 91.2241619496737
- type: manhattan_f1
value: 82.20472440944883
- type: manhattan_precision
value: 86.51933701657458
- type: manhattan_recall
value: 78.3
- type: max_accuracy
value: 99.66633663366336
- type: max_ap
value: 91.2241619496737
- type: max_f1
value: 82.20472440944883
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.85101268897951
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 42.461184054706905
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.44542568873886
- type: mrr
value: 52.33656151854681
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.75982974997539
- type: cos_sim_spearman
value: 30.385405026539914
- type: dot_pearson
value: 30.75982433546523
- type: dot_spearman
value: 30.385405026539914
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22799999999999998
- type: map_at_10
value: 2.064
- type: map_at_100
value: 13.056000000000001
- type: map_at_1000
value: 31.747999999999998
- type: map_at_3
value: 0.67
- type: map_at_5
value: 1.097
- type: mrr_at_1
value: 90.0
- type: mrr_at_10
value: 94.667
- type: mrr_at_100
value: 94.667
- type: mrr_at_1000
value: 94.667
- type: mrr_at_3
value: 94.667
- type: mrr_at_5
value: 94.667
- type: ndcg_at_1
value: 86.0
- type: ndcg_at_10
value: 82.0
- type: ndcg_at_100
value: 64.307
- type: ndcg_at_1000
value: 57.023999999999994
- type: ndcg_at_3
value: 85.816
- type: ndcg_at_5
value: 84.904
- type: precision_at_1
value: 90.0
- type: precision_at_10
value: 85.8
- type: precision_at_100
value: 66.46
- type: precision_at_1000
value: 25.202
- type: precision_at_3
value: 90.0
- type: precision_at_5
value: 89.2
- type: recall_at_1
value: 0.22799999999999998
- type: recall_at_10
value: 2.235
- type: recall_at_100
value: 16.185
- type: recall_at_1000
value: 53.620999999999995
- type: recall_at_3
value: 0.7040000000000001
- type: recall_at_5
value: 1.172
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.75
- type: precision
value: 96.45
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.54913294797689
- type: f1
value: 82.46628131021194
- type: precision
value: 81.1175337186898
- type: recall
value: 85.54913294797689
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.21951219512195
- type: f1
value: 77.33333333333334
- type: precision
value: 75.54878048780488
- type: recall
value: 81.21951219512195
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.6
- type: f1
value: 98.26666666666665
- type: precision
value: 98.1
- type: recall
value: 98.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.5
- type: f1
value: 99.33333333333333
- type: precision
value: 99.25
- type: recall
value: 99.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.2
- type: precision
value: 96.89999999999999
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.18333333333334
- type: precision
value: 96.88333333333333
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.61194029850746
- type: f1
value: 72.81094527363183
- type: precision
value: 70.83333333333333
- type: recall
value: 77.61194029850746
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.91666666666667
- type: precision
value: 91.08333333333334
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.29268292682927
- type: f1
value: 85.27642276422765
- type: precision
value: 84.01277584204414
- type: recall
value: 88.29268292682927
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.0
- type: precision
value: 94.46666666666668
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.681652490887
- type: f1
value: 91.90765492102065
- type: precision
value: 91.05913325232888
- type: recall
value: 93.681652490887
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.17391304347827
- type: f1
value: 89.97101449275361
- type: precision
value: 88.96811594202899
- type: recall
value: 92.17391304347827
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.43478260869566
- type: f1
value: 87.72173913043478
- type: precision
value: 86.42028985507245
- type: recall
value: 90.43478260869566
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 88.03
- type: precision
value: 86.95
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.4
- type: f1
value: 91.45666666666666
- type: precision
value: 90.525
- type: recall
value: 93.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.9059107358263
- type: f1
value: 78.32557872364869
- type: precision
value: 76.78260286824823
- type: recall
value: 81.9059107358263
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.58333333333333
- type: precision
value: 91.73333333333332
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.10000000000001
- type: f1
value: 74.50500000000001
- type: precision
value: 72.58928571428571
- type: recall
value: 79.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.6
- type: f1
value: 95.55
- type: precision
value: 95.05
- type: recall
value: 96.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.0952380952381
- type: f1
value: 77.98458049886621
- type: precision
value: 76.1968253968254
- type: recall
value: 82.0952380952381
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.9
- type: f1
value: 84.99190476190476
- type: precision
value: 83.65
- type: recall
value: 87.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.7
- type: f1
value: 94.56666666666666
- type: precision
value: 94.01666666666667
- type: recall
value: 95.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.6
- type: f1
value: 98.2
- type: precision
value: 98.0
- type: recall
value: 98.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.38333333333334
- type: precision
value: 93.78333333333335
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.4
- type: f1
value: 84.10380952380952
- type: precision
value: 82.67
- type: recall
value: 87.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.33333333333334
- type: precision
value: 93.78333333333333
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.4
- type: f1
value: 86.82000000000001
- type: precision
value: 85.64500000000001
- type: recall
value: 89.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.1
- type: f1
value: 93.56666666666668
- type: precision
value: 92.81666666666666
- type: recall
value: 95.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.9
- type: f1
value: 98.6
- type: precision
value: 98.45
- type: recall
value: 98.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.01347708894879
- type: f1
value: 93.51752021563343
- type: precision
value: 92.82794249775381
- type: recall
value: 95.01347708894879
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.00854700854701
- type: f1
value: 96.08262108262107
- type: precision
value: 95.65527065527067
- type: recall
value: 97.00854700854701
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.39999999999999
- type: precision
value: 94.88333333333333
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5909090909091
- type: f1
value: 95.49242424242425
- type: precision
value: 94.9621212121212
- type: recall
value: 96.5909090909091
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.90566037735849
- type: f1
value: 81.85883997204752
- type: precision
value: 80.54507337526205
- type: recall
value: 84.90566037735849
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.5
- type: f1
value: 96.75
- type: precision
value: 96.38333333333333
- type: recall
value: 97.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.7704280155642
- type: f1
value: 82.99610894941635
- type: precision
value: 81.32295719844358
- type: recall
value: 86.7704280155642
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.52136752136752
- type: f1
value: 61.89662189662191
- type: precision
value: 59.68660968660969
- type: recall
value: 67.52136752136752
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.2
- type: f1
value: 86.32
- type: precision
value: 85.015
- type: recall
value: 89.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.0
- type: f1
value: 94.78333333333333
- type: precision
value: 94.18333333333334
- type: recall
value: 96.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.8785046728972
- type: f1
value: 80.54517133956385
- type: precision
value: 79.154984423676
- type: recall
value: 83.8785046728972
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.60000000000001
- type: f1
value: 92.01333333333334
- type: precision
value: 91.28333333333333
- type: recall
value: 93.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.1
- type: f1
value: 96.26666666666667
- type: precision
value: 95.85000000000001
- type: recall
value: 97.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.3
- type: f1
value: 80.67833333333333
- type: precision
value: 79.03928571428571
- type: recall
value: 84.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.3
- type: f1
value: 96.48333333333332
- type: precision
value: 96.08333333333331
- type: recall
value: 97.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ita-eng)
type: mteb/tatoeba-bitext-mining
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.7
- type: f1
value: 94.66666666666667
- type: precision
value: 94.16666666666667
- type: recall
value: 95.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cmn-eng)
type: mteb/tatoeba-bitext-mining
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.36666666666667
- type: precision
value: 95.96666666666668
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lvs-eng)
type: mteb/tatoeba-bitext-mining
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.80666666666667
- type: precision
value: 92.12833333333333
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (glg-eng)
type: mteb/tatoeba-bitext-mining
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.0
- type: f1
value: 96.22333333333334
- type: precision
value: 95.875
- type: recall
value: 97.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ceb-eng)
type: mteb/tatoeba-bitext-mining
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.33333333333333
- type: f1
value: 70.78174603174602
- type: precision
value: 69.28333333333332
- type: recall
value: 74.33333333333333
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bre-eng)
type: mteb/tatoeba-bitext-mining
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.6
- type: f1
value: 32.938348952090365
- type: precision
value: 31.2811038961039
- type: recall
value: 37.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ben-eng)
type: mteb/tatoeba-bitext-mining
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.5
- type: f1
value: 89.13333333333333
- type: precision
value: 88.03333333333333
- type: recall
value: 91.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swg-eng)
type: mteb/tatoeba-bitext-mining
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.14285714285714
- type: f1
value: 77.67857142857143
- type: precision
value: 75.59523809523809
- type: recall
value: 82.14285714285714
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arq-eng)
type: mteb/tatoeba-bitext-mining
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.0450054884742
- type: f1
value: 63.070409283362075
- type: precision
value: 60.58992781824835
- type: recall
value: 69.0450054884742
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kab-eng)
type: mteb/tatoeba-bitext-mining
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.1
- type: f1
value: 57.848333333333336
- type: precision
value: 55.69500000000001
- type: recall
value: 63.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fra-eng)
type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.01666666666667
- type: precision
value: 94.5
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (por-eng)
type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.90666666666667
- type: precision
value: 94.425
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tat-eng)
type: mteb/tatoeba-bitext-mining
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.6
- type: f1
value: 84.61333333333333
- type: precision
value: 83.27
- type: recall
value: 87.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (oci-eng)
type: mteb/tatoeba-bitext-mining
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.4
- type: f1
value: 71.90746031746032
- type: precision
value: 70.07027777777778
- type: recall
value: 76.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pol-eng)
type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.26666666666667
- type: precision
value: 96.95
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (war-eng)
type: mteb/tatoeba-bitext-mining
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.8
- type: f1
value: 74.39555555555555
- type: precision
value: 72.59416666666667
- type: recall
value: 78.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (aze-eng)
type: mteb/tatoeba-bitext-mining
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.78999999999999
- type: precision
value: 93.125
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (vie-eng)
type: mteb/tatoeba-bitext-mining
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.1
- type: precision
value: 96.75
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nno-eng)
type: mteb/tatoeba-bitext-mining
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.25666666666666
- type: precision
value: 93.64166666666668
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cha-eng)
type: mteb/tatoeba-bitext-mining
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 56.934306569343065
- type: f1
value: 51.461591936044485
- type: precision
value: 49.37434827945776
- type: recall
value: 56.934306569343065
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mhr-eng)
type: mteb/tatoeba-bitext-mining
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 20.200000000000003
- type: f1
value: 16.91799284049284
- type: precision
value: 15.791855158730158
- type: recall
value: 20.200000000000003
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dan-eng)
type: mteb/tatoeba-bitext-mining
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.2
- type: f1
value: 95.3
- type: precision
value: 94.85
- type: recall
value: 96.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ell-eng)
type: mteb/tatoeba-bitext-mining
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.3
- type: f1
value: 95.11666666666667
- type: precision
value: 94.53333333333333
- type: recall
value: 96.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (amh-eng)
type: mteb/tatoeba-bitext-mining
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.88095238095238
- type: f1
value: 87.14285714285714
- type: precision
value: 85.96230158730161
- type: recall
value: 89.88095238095238
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pam-eng)
type: mteb/tatoeba-bitext-mining
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 24.099999999999998
- type: f1
value: 19.630969083349783
- type: precision
value: 18.275094905094907
- type: recall
value: 24.099999999999998
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hsb-eng)
type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.4368530020704
- type: f1
value: 79.45183870649709
- type: precision
value: 77.7432712215321
- type: recall
value: 83.4368530020704
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (srp-eng)
type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.8
- type: f1
value: 94.53333333333333
- type: precision
value: 93.91666666666666
- type: recall
value: 95.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (epo-eng)
type: mteb/tatoeba-bitext-mining
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.8
- type: f1
value: 98.48333333333332
- type: precision
value: 98.33333333333334
- type: recall
value: 98.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.5
- type: f1
value: 14.979285714285714
- type: precision
value: 14.23235060690943
- type: recall
value: 17.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.93939393939394
- type: f1
value: 91.991341991342
- type: precision
value: 91.05339105339105
- type: recall
value: 93.93939393939394
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fao-eng)
type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.31297709923665
- type: f1
value: 86.76844783715012
- type: precision
value: 85.63613231552164
- type: recall
value: 89.31297709923665
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.12663755458514
- type: f1
value: 98.93255701115964
- type: precision
value: 98.83551673944687
- type: recall
value: 99.12663755458514
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.0
- type: f1
value: 89.77999999999999
- type: precision
value: 88.78333333333333
- type: recall
value: 92.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89265536723164
- type: f1
value: 95.85687382297553
- type: precision
value: 95.33898305084746
- type: recall
value: 96.89265536723164
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.6
- type: f1
value: 11.820611790170615
- type: precision
value: 11.022616224355355
- type: recall
value: 14.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.93333333333334
- type: precision
value: 94.48666666666666
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.6
- type: f1
value: 84.72333333333334
- type: precision
value: 83.44166666666666
- type: recall
value: 87.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.8
- type: f1
value: 93.47333333333333
- type: precision
value: 92.875
- type: recall
value: 94.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.6
- type: f1
value: 95.71666666666665
- type: precision
value: 95.28333333333335
- type: recall
value: 96.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.8
- type: f1
value: 14.511074040901628
- type: precision
value: 13.503791000666002
- type: recall
value: 17.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.10187667560321
- type: f1
value: 92.46648793565683
- type: precision
value: 91.71134941912423
- type: recall
value: 94.10187667560321
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.0
- type: f1
value: 96.11666666666666
- type: precision
value: 95.68333333333334
- type: recall
value: 97.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.72727272727273
- type: f1
value: 66.58949745906267
- type: precision
value: 63.86693017127799
- type: recall
value: 72.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.14084507042254
- type: f1
value: 88.26291079812206
- type: precision
value: 87.32394366197182
- type: recall
value: 90.14084507042254
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 64.67065868263472
- type: f1
value: 58.2876627696987
- type: precision
value: 55.79255774165953
- type: recall
value: 64.67065868263472
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.41666666666667
- type: precision
value: 93.85
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.172413793103445
- type: f1
value: 49.63992493549144
- type: precision
value: 47.71405113769646
- type: recall
value: 55.172413793103445
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.46478873239437
- type: f1
value: 73.4417616811983
- type: precision
value: 71.91607981220658
- type: recall
value: 77.46478873239437
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.61538461538461
- type: f1
value: 80.91452991452994
- type: precision
value: 79.33760683760683
- type: recall
value: 84.61538461538461
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.6
- type: precision
value: 97.3
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.5741127348643
- type: f1
value: 72.00417536534445
- type: precision
value: 70.53467872883321
- type: recall
value: 75.5741127348643
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 62.2
- type: f1
value: 55.577460317460314
- type: precision
value: 52.98583333333333
- type: recall
value: 62.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.18241042345277
- type: f1
value: 90.6468124709167
- type: precision
value: 89.95656894679696
- type: recall
value: 92.18241042345277
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.13333333333333
- type: precision
value: 94.66666666666667
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8
- type: f1
value: 95.85000000000001
- type: precision
value: 95.39999999999999
- type: recall
value: 96.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.1259842519685
- type: f1
value: 89.76377952755905
- type: precision
value: 88.71391076115485
- type: recall
value: 92.1259842519685
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.49
- type: precision
value: 91.725
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.5623268698061
- type: f1
value: 73.27364463791058
- type: precision
value: 71.51947852086357
- type: recall
value: 77.5623268698061
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.56666666666666
- type: precision
value: 96.16666666666667
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.34615384615384
- type: f1
value: 61.092032967032964
- type: precision
value: 59.27197802197802
- type: recall
value: 66.34615384615384
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.41190476190476
- type: precision
value: 92.7
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.10000000000001
- type: precision
value: 90.13333333333333
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.97333333333334
- type: precision
value: 91.14166666666667
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.21698113207547
- type: f1
value: 90.3796046720575
- type: precision
value: 89.56367924528303
- type: recall
value: 92.21698113207547
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.6
- type: f1
value: 96.91666666666667
- type: precision
value: 96.6
- type: recall
value: 97.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.44525547445255
- type: f1
value: 96.71532846715328
- type: precision
value: 96.35036496350365
- type: recall
value: 97.44525547445255
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.34000000000002
- type: precision
value: 91.49166666666667
- type: recall
value: 94.1
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.2910000000000004
- type: map_at_10
value: 10.373000000000001
- type: map_at_100
value: 15.612
- type: map_at_1000
value: 17.06
- type: map_at_3
value: 6.119
- type: map_at_5
value: 7.917000000000001
- type: mrr_at_1
value: 44.897999999999996
- type: mrr_at_10
value: 56.054
- type: mrr_at_100
value: 56.82000000000001
- type: mrr_at_1000
value: 56.82000000000001
- type: mrr_at_3
value: 52.381
- type: mrr_at_5
value: 53.81
- type: ndcg_at_1
value: 42.857
- type: ndcg_at_10
value: 27.249000000000002
- type: ndcg_at_100
value: 36.529
- type: ndcg_at_1000
value: 48.136
- type: ndcg_at_3
value: 33.938
- type: ndcg_at_5
value: 29.951
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 22.653000000000002
- type: precision_at_100
value: 7.000000000000001
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 32.653
- type: precision_at_5
value: 27.755000000000003
- type: recall_at_1
value: 3.2910000000000004
- type: recall_at_10
value: 16.16
- type: recall_at_100
value: 43.908
- type: recall_at_1000
value: 79.823
- type: recall_at_3
value: 7.156
- type: recall_at_5
value: 10.204
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.05879999999999
- type: ap
value: 14.609748142799111
- type: f1
value: 54.878956295843096
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.61799660441426
- type: f1
value: 64.8698191961434
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.32860036611885
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.34714192048638
- type: cos_sim_ap
value: 80.26732975975634
- type: cos_sim_f1
value: 73.53415148134374
- type: cos_sim_precision
value: 69.34767360299276
- type: cos_sim_recall
value: 78.25857519788919
- type: dot_accuracy
value: 88.34714192048638
- type: dot_ap
value: 80.26733698491206
- type: dot_f1
value: 73.53415148134374
- type: dot_precision
value: 69.34767360299276
- type: dot_recall
value: 78.25857519788919
- type: euclidean_accuracy
value: 88.34714192048638
- type: euclidean_ap
value: 80.26734337771738
- type: euclidean_f1
value: 73.53415148134374
- type: euclidean_precision
value: 69.34767360299276
- type: euclidean_recall
value: 78.25857519788919
- type: manhattan_accuracy
value: 88.30541813196639
- type: manhattan_ap
value: 80.19415808104145
- type: manhattan_f1
value: 73.55143870713441
- type: manhattan_precision
value: 73.25307511122743
- type: manhattan_recall
value: 73.85224274406332
- type: max_accuracy
value: 88.34714192048638
- type: max_ap
value: 80.26734337771738
- type: max_f1
value: 73.55143870713441
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.81061047075717
- type: cos_sim_ap
value: 87.11747055081017
- type: cos_sim_f1
value: 80.04355498817256
- type: cos_sim_precision
value: 78.1165262000733
- type: cos_sim_recall
value: 82.06806282722513
- type: dot_accuracy
value: 89.81061047075717
- type: dot_ap
value: 87.11746902745236
- type: dot_f1
value: 80.04355498817256
- type: dot_precision
value: 78.1165262000733
- type: dot_recall
value: 82.06806282722513
- type: euclidean_accuracy
value: 89.81061047075717
- type: euclidean_ap
value: 87.11746919324248
- type: euclidean_f1
value: 80.04355498817256
- type: euclidean_precision
value: 78.1165262000733
- type: euclidean_recall
value: 82.06806282722513
- type: manhattan_accuracy
value: 89.79508673885202
- type: manhattan_ap
value: 87.11074390832218
- type: manhattan_f1
value: 80.13002540726349
- type: manhattan_precision
value: 77.83826945412311
- type: manhattan_recall
value: 82.56082537727133
- type: max_accuracy
value: 89.81061047075717
- type: max_ap
value: 87.11747055081017
- type: max_f1
value: 80.13002540726349
---
# Zenabius/multilingual-e5-large-instruct-Q8_0-GGUF
This model was converted to GGUF format from [`intfloat/multilingual-e5-large-instruct`](https://huggingface.co/intfloat/multilingual-e5-large-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-large-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Zenabius/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Zenabius/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Zenabius/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Zenabius/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.gguf -c 2048
```
|
[
"BIOSSES",
"SCIFACT"
] |
legaltextai/modernbert-embed-ft-const-legal-matryoshka
|
legaltextai
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:842",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"en",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:nomic-ai/modernbert-embed-base",
"base_model:finetune:nomic-ai/modernbert-embed-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-02-17T00:05:31Z |
2025-02-17T00:06:03+00:00
| 29 | 1 |
---
base_model: nomic-ai/modernbert-embed-base
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:842
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Discuss the implications of the Insular Cases on the application
of the Citizenship Clause to American Samoa, particularly in distinguishing between
incorporated and unincorporated territories. What are the practical concerns associated
with this distinction?
sentences:
- 'To the extent jus soli is adopted into the Fourteenth Amendment, the concept
of allegiance is manifested by the Citizenship Clause’s mandate that birthright
citizens not merely be born within the territorial boundaries of the United States
but also “subject to the jurisdiction thereof…” [citations omitted]
Appellants would find any allegiance requirement of no moment because, as non-citizen
nationals, American Samoans already “owe[ ] permanent allegiance to the United
States.”[citations omitted] Yet, within the context of the Citizenship Clause,
“[t]he evident meaning of the[ ] ... words [“subject to the jurisdiction thereof”]
is, not merely subject in some respect or degree to the jurisdiction of the United
States, but completely subject to their political jurisdiction, and owing them
direct and immediate allegiance.” **375 [citations omitted] *306 It was on this
basis that the Supreme Court declined to extend constitutional birthright citizenship
to Native American tribes. [citations omitted]…Even assuming a background context
grounded in principles of jus soli, we are skeptical the framers plainly intended
to extend birthright citizenship to distinct, significantly self-governing political
territories within the United States’s sphere of sovereignty—even where, as is
the case with American Samoa, ultimate governance remains statutorily vested with
the United States Government. [citations omitted]
III
Analysis of the Citizenship Clause’s application to American Samoa would be incomplete
absent invocation of the sometimes contentious Insular Cases, where the Supreme
Court “addressed whether the Constitution, by its own force, applies in any territory
that is not a State.” [citations omitted]
“The doctrine of ‘territorial incorporation’ announced in the Insular Cases distinguishes
between incorporated territories, which are intended for statehood from the time
of acquisition and in which the entire Constitution applies ex proprio vigore,
and unincorporated territories [such as American Samoa], which are not intended
for statehood and in which only [certain] fundamental constitutional rights apply
by their own force.”[citations omitted].
Appellants and Amici contend the Insular Cases have no application because the
Citizenship Clause textually defines its own scope.[citations omitted].
Amici Curiae suggest territorial incorporation doctrine should not be expanded
to the Citizenship Clause because the doctrine rests on anachronistic views of
race and imperialism. But the Court has continued to invoke the Insular framework
when dealing with questions of territorial and extraterritorial application. [citations
omitted] Although some aspects of the Insular Cases’ analysis may now be deemed
politically incorrect, the framework remains both applicable and of pragmatic
use in assessing the applicability of rights to unincorporated territories. [citations
omitted]
As the Supreme Court…emphasized, the “common thread uniting the Insular Cases
... [is that] questions of extraterritoriality turn on objective factors and practical
concerns, not formalism.” [citations omitted] While “fundamental limitations in
favor of personal rights” remain guaranteed to persons born in the unincorporated
territories, [citations omitted], the Insular framework recognizes the difficulties
that frequently inure when “determin[ing] [whether a] particular provision of
the Constitution is applicable,” absent inquiry into the impractical or anomalous.
[citations omitted]
A
American citizenship “is one of the most valuable rights in the world today.”
[citations omitted] “The freedoms and opportunities secured by United States citizenship
long have been treasured by persons fortunate enough to be born with them, and
are yearned for by countless less fortunate.” [citations omitted]. Accordingly,
even if the Insular framework is applicable, Appellants cite to a bevy of cases
to argue citizenship is a fundamental right. [citations omitted] But those cases
do not arise in the territorial context. Such decisions do not reflect the Court’s
considered judgment as to the existence of a fundamental right to citizenship
for persons born in the United States’ unincorporated **377 *308 territories.
[citations omitted].7
“Fundamental” has a distinct and narrow meaning in the context of territorial
rights. It is not sufficient that a right be considered fundamentally important
in a colloquial sense or even that a right be “necessary to [the] [ ]American
regime of ordered liberty.” [citations omitted]. Under the Insular framework the
designation of fundamental extends only to the narrow category of rights and “principles
which are the basis of all free government.” [citations omitted]
In this manner the Insular Cases distinguish as universally fundamental those
rights so basic as to be integral to free and fair society.'
- '633, 649 (concurring opinion).
An innkeeper or common carrier has always been allowed to'' exclude drunks, criminals
and'' diseased persons, but only because the public’s interest in protecting his
and his guests’ health and property outweighs its interest in providing accommodations
for this small group of travelers. As a general rule, innkeepers and carriers
cannot refuse their services on account of race; though the rule developed in
this country that they can provide “separate but equal” facilities. And for a
period of our history even,this Court upheld state laws giving sanction to such
a rule. Compare Plessy v. Ferguson, 163 U. S. 537, with Gayle v. Browder, 352
U. S. 903, affirming, 142 F. Supp. 707. But surely Shelley v. Kraemer, supra,
and Barrows v. Jackson, supra, show that the day has passed when an innkeeper,
carrier, housing developer, or retailer can draw a• racial'' line, refuse service
to some on account of color, and obtain the aid of a State in enforcing his personal
bias by sending outlawed customers to prison or exacting fines from them.
Business, such as this restaurant, is still private property. '' Yet there is
hardly any private enterprise that does not feel the pinch of some public regulation
— from price control, to health and fire inspection, to zoning, to safety measures,
to minimum wages and working conditions, to unemployment insurance. When the doors
of a business are open to the public, they must be open to all regardless of race
if apartheid is not to become engrained in our public places. It cannot by reason
of the Equal Protection Clause become so engrained with the aid of state courts,
state legislatures, or state police.
II.
There is even greater reason to bar a State through its judiciary from throwing
its weight on the side of racial discrimination in the present case, because we
deal here with a place of public accommodation under license from, the State.
This is the idea I expressed in Garner v. Louisiana, 368 U. S. 157, where another
owner of a restaurant refused service to a customer because he was a Negro. That
view is not novel; it.stems from the dissent of the first Mr. Justice Harlan in
the Civil Rights Cases, 109 U. S. 3, 58-59:
“In every material sense applicable to the practical enforcement of the Fourteenth
Amendment, railroad corporations, keepers of inns, and managers of places of public
amusement are agents or instrumentalities of the State, because they are charged
with duties to the public, and are amenable, in respect of their duties and functions,
to governmental regulation. It seems to me that, within the principle settled
in Ex parte Virginia, a denial, by these instrumentalities of the State, to the
citizen, because of his race, of that equality of civil rights secured to him
by law, is a denial by the State, within the meaning of the Fourteenth Amendment.
If it be not, then that race is left, in respect of the civil rights in question,
practically at the mercy of corporations and individuals wielding power under
the States.”
The nexus between the State and the private enterprise may be control, as in the
case of a state agency. Pennsylvania v. Board of Trusts, 353 U. S. 230. Or the
nexus may be one of numerous other devices. “State support of segregated schools
through any arrangement, management, funds, or property cannot be squared” with
the Equal Protection Clause. Cooper v. Aaron, 358 U. S. 1, 19. Cf. Hampton v.
Jacksonville, 304 F. 2d 320. A state-assisted enterprise serving the public does
not escape its constitutional duty to serve all customers irrespective of race,
even though its actual operation is in the hands of a lessee. Burton v. Wilmington
Parking Authority, 365 U. S. 715. Cf. Boynton v. Virginia, 364 U. S. 454. State
licensing and surveillance.of a business serving the public also brings its service
into the public domain. This restaurant needs a permit from Louisiana to operate;
and during the existence of the license the State has broad powers of visitation
and control. This restaurant is thus an instrumentality of the State since the
State charges it with duties to the public and supervises its performance. The
State''s interest in and activity with regard to its restaurants extends far beyond
any mere income-producing licensing requirement.'
- 'Among other things, courts at this second step have sometimes considered whether
an employee’s speech interests are outweighed by “ ‘the interest of the State,
as an employer, in promoting the efficiency of the public services it performs
through its employees.’ ” Id., at 417, 126 S.Ct. 1951 *2424 (quoting Pickering,
391 U.S. at 568, 88 S.Ct. 1731).
Both sides ask us to employ at least certain aspects of this Pickering–Garcetti framework
to resolve Mr. Kennedy’s free speech claim. They share additional common ground
too. They agree that Mr. Kennedy’s speech implicates a matter of public concern.
See App. to Pet. for Cert. 183; Brief for Respondent 44. They also appear to accept,
at least for argument’s sake, that Mr. Kennedy’s speech does not raise questions
of academic freedom that may or may not involve “additional” First Amendment “interests”
beyond those captured by this framework. Garcetti, 547 U.S. at 425, 126 S.Ct.
1951; see also Keyishian v. Board of Regents of Univ. of State of N. Y., 385 U.S.
589, 603, 87 S.Ct. 675, 17 L.Ed.2d 629 (1967); Brief for Petitioner 26, n. 2.
At the first step of the Pickering–Garcetti inquiry, the parties’ disagreement
thus turns out to center on one question alone: Did Mr. Kennedy offer his prayers
in his capacity as a private citizen, or did they amount to government speech
attributable to the District?
Our cases offer some helpful guidance for resolving this question. In Garcetti,
the Court concluded that a prosecutor’s internal memorandum to a supervisor was
made “pursuant to [his] official duties,” and thus ineligible for First Amendment
protection. 547 U.S. at 421, 126 S.Ct. 1951. In reaching this conclusion, the
Court relied on the fact that the prosecutor’s speech “fulfill[ed] a responsibility
to advise his supervisor about how best to proceed with a pending case.” Ibid.
In other words, the prosecutor’s memorandum was government speech because it was
speech the government “itself ha[d] commissioned or created” and speech the employee
was expected to deliver in the course of carrying out his job. Id., at 422, 126
S.Ct. 1951.
By contrast, in Lane a public employer sought to terminate an employee after he
testified at a criminal trial about matters involving his government employment.
573 U.S. at 233, 134 S.Ct. 2369. The Court held that the employee’s speech was
protected by the First Amendment. Id., at 231, 134 S.Ct. 2369. In doing so, the
Court held that the fact the speech touched on matters related to public employment
was not enough to render it government speech. Id., at 239–240, 134 S.Ct. 2369.
Instead, the Court explained, the “critical question ... is whether the speech
at issue is itself ordinarily within the scope of an employee’s duties.” Id.,
at 240, 134 S.Ct. 2369. It is an inquiry this Court has said should be undertaken
“practical[ly],” rather than with a blinkered focus on the terms of some formal
and capacious written job description. Garcetti, 547 U.S. at 424, 126 S.Ct. 1951.
To proceed otherwise would be to allow public employers to use “excessively broad
job descriptions” to subvert the Constitution’s protections. Ibid.
Applying these lessons here, it seems clear to us that Mr. Kennedy has demonstrated
that his speech was private speech, not government speech. When Mr. Kennedy uttered
the three prayers that resulted in his suspension, he was not engaged in speech
“ordinarily within the scope” of his duties as a coach. Lane, 573 U.S. at 240,
134 S.Ct. 2369. He did not speak pursuant to government policy. He was not seeking
to convey a government-created message. He was not instructing players, discussing
strategy, encouraging better on-field performance, or engaged in any other speech
the District paid him to produce as a coach. See Part I–B, supra. Simply put:
Mr. Kennedy’s prayers did not “ow[e their] existence” to Mr. Kennedy’s responsibilities
as a public employee.'
- source_sentence: Discuss the implications of the Thirteenth Amendment as it relates
to Congress's power to enact laws against private racial discrimination in property
transactions. How does the text support the assertion that Congress's authority
extends beyond state action?
sentences:
- '––––, ––––, 142 S.Ct. 1539, 1545, ––– L.Ed.2d –––– (2022) (THOMAS, J., concurring)
(internal quotation*2301 marks omitted). Either way, the Due Process Clause at
most guarantees process. It does not, as the Court’s substantive due process cases
suppose, “forbi[d] the government to infringe certain ‘fundamental’ liberty interests
at all, no matter what process is provided.” Reno v. Flores, 507 U.S. 292, 302,
113 S.Ct. 1439, 123 L.Ed.2d 1 (1993); see also, e.g.,Collins v. Harker Heights,
503 U.S. 115, 125, 112 S.Ct. 1061, 117 L.Ed.2d 261 (1992).
As I have previously explained, “substantive due process” is an oxymoron that
“lack[s] any basis in the Constitution.” Johnson, 576 U.S. at 607–608, 135 S.Ct.
2551 (opinion of THOMAS, J.); see also, e.g.,Vaello Madero, 596 U.S., at ––––,
142 S.Ct., at 1545 (THOMAS, J., concurring) (“[T]ext and history provide little
support for modern substantive due process doctrine”). “The notion that a constitutional
provision that guarantees only ‘process’ before a person is deprived of life,
liberty, or property could define the substance of those rights strains credulity
for even the most casual user of words.” McDonald v. Chicago, 561 U.S. 742, 811,
130 S.Ct. 3020, 177 L.Ed.2d 894 (2010) (THOMAS, J., concurring in part and concurring
in judgment); see also United States v. Carlton, 512 U.S. 26, 40, 114 S.Ct. 2018,
129 L.Ed.2d 22 (1994) (Scalia, J., concurring in judgment). The resolution of
this case is thus straightforward. Because the Due Process Clause does not secure
any substantive rights, it does not secure a right to abortion.
The Court today declines to disturb substantive due process jurisprudence generally
or the doctrine’s application in other, specific contexts. Cases like Griswold
v. Connecticut, 381 U.S. 479, 85 S.Ct. 1678, 14 L.Ed.2d 510 (1965) (right of married
persons to obtain contraceptives)*; Lawrence v. Texas, 539 U.S. 558, 123 S.Ct.
2472, 156 L.Ed.2d 508 (2003) (right to engage in private, consensual sexual acts);
and Obergefell v. Hodges, 576 U.S. 644, 135 S.Ct. 2584, 192 L.Ed.2d 609 (2015)
(right to same-sex marriage), are not at issue. The Court’s abortion cases are
unique, see ante, at 2257 – 2258, 2277 – 2278, 2280 – 2281, and no party has asked
us to decide “whether our entire Fourteenth Amendment jurisprudence must be preserved
or revised,” McDonald, 561 U.S. at 813, 130 S.Ct. 3020 (opinion of THOMAS, J.).
Thus, I agree that “[n]othing in [the Court’s] opinion should be understood to
cast doubt on precedents that do not concern abortion.” Ante, at 2277 – 2278.
For that reason, in future cases, we should reconsider all of this Court’s substantive
due process precedents, including Griswold, Lawrence, and Obergefell. Because
any substantive due process decision is “demonstrably erroneous,” Ramos v.Louisiana,
590 U.S. ––––, ––––, 140 S.Ct. 1390, 1424, 206 L.Ed.2d 583 (2020) (THOMAS, J.,
concurring in judgment), we have a duty to “correct the error” established in
those precedents, Gamble v. United States, 587 U.S. ––––, ––––, 139 S.Ct. 1960,
1984-1985, 204 L.Ed.2d 322 (2019) (THOMAS, J., concurring).'
- 'On October 21, the superintendent further observed to a state official that “[t]he
issue is quickly changing as it has shifted from leading prayer with student athletes,
to a coaches [sic] right to conduct” his own prayer “on the 50 yard line.” Id.,
at 88.
On October 23, shortly before that evening’s game, the District wrote Mr. Kennedy
again. It expressed “appreciation” for his “efforts to comply” with the District’s
directives, including avoiding “on-the-job prayer with players in the ... football
program, both in the locker room prior to games as well as on the field immediately
following games.” Id., at 90. The letter also admitted that, during Mr. Kennedy’s
recent October 16 postgame prayer, his students were otherwise engaged and not
praying with him, and that his prayer was “fleeting.” Id., at 90, 93. Still, the
District explained that a “reasonable observer” could think government endorsement
of religion had occurred when a “District employee, on the field only by virtue
of his employment with the District, still on duty” engaged in “overtly religious
conduct.” Id., at 91, 93. The District thus made clear that the only option it
would offer Mr. Kennedy was to allow him to pray after a game in a “private location”
behind closed doors and “not observable to students or the public.” Id., at 93–94.
After the October 23 game ended, Mr. Kennedy knelt at the 50-yard line, where
“no one joined him,” and bowed his head for a “brief, quiet prayer.” 991 F.3d
at 1019; App. 173, 236–239. The superintendent informed the District’s board that
this prayer “moved closer to what we want,” but nevertheless remained “unconstitutional.”
Id., at 96. After the final relevant football game on October 26, Mr. Kennedy
again knelt alone to offer a brief prayer as the players engaged in postgame traditions.
443 F.Supp.3d 1223, 1231 (W.D. Wash. 2020); App. to Pet. for Cert. 182. While
he was praying, other adults gathered around him on the field. See 443 F.Supp.3d
at 1231; App. 97. Later, Mr. Kennedy rejoined his players for a postgame talk,
after they had finished singing the school fight song. 443 F.Supp.3d at 1231;
App. 103.
C
Shortly after the October 26 game, the District placed Mr. Kennedy on paid administrative
*2419 leave and prohibited him from “participat[ing], in any capacity, in ...
football program activities.” Ibid. In a letter explaining the reasons for this
disciplinary action, the superintendent criticized Mr. Kennedy for engaging in
“public and demonstrative religious conduct while still on duty as an assistant
coach” by offering a prayer following the games on October 16, 23, and 26. Id.,
at 102. The letter did not allege that Mr. Kennedy performed these prayers with
students, and it acknowledged that his prayers took place while students were
engaged in unrelated postgame activities. Id., at 103. Additionally, the letter
faulted Mr. Kennedy for not being willing to pray behind closed doors. Id., at
102.
In an October 28 Q&A document provided to the public, the District admitted that
it possessed “no evidence that students have been directly coerced to pray with
Kennedy.” Id., at 105. The Q&A also acknowledged that Mr. Kennedy “ha[d] complied”
with the District’s instruction to refrain from his “prior practices of leading
players in a pre-game prayer in the locker room or leading players in a post-game
prayer immediately following games.” Ibid. But the Q&A asserted that the District
could not allow Mr. Kennedy to “engage in a public religious display.” Id., at
105, 107, 110. Otherwise, the District would “violat[e] the ... Establishment
Clause” because “reasonable ... students and attendees” might perceive the “district
[as] endors[ing] ... religion.” Id., at 105.
While Mr. Kennedy received “uniformly positive evaluations” every other year of
his coaching career, after the 2015 season ended in November, the District gave
him a poor performance evaluation. Kennedy v. Bremerton School Dist., 869 F.3d
813, 820 (C.A.9 2017).'
- 'Nor was the scope of the 1866 Act altered when it was re-enacted in 1870, some
two years after the ratification of the Fourteenth Amendment.71 It is quite true
that some members of Congress supported the Fourteenth Amendment “in order to
eliminate doubt as to the constitutional validity of the Civil Rights Act as applied
to the States.” Hurd v. Hodge, 334 U.S. 24, 32—33, 68 S.Ct. 847, 852. But it certainly
does not follow that the adoption of the Fourteenth Amendment or the subsequent
readoption of the Civil Rights Act were meant somehow to limit its application
to state action. The legislative history furnishes not the slightest factual basis
for any such speculation, and the conditions prevailing in 1870 make it highly
implausible. For by that time most, if not all, of the former Confederate States,
then under the control of “reconstructed” legislatures, had formally repudiated
racial discrimination, and the focus of congressional concern had clearly shifted
from hostile statutes to the activities of groups like the Ku Klux Klan, operating
wholly outside the law.72
**2202 *437 Against this background, it would obviously make no sense to assume,
without any historical support whatever, that Congress made a silent decision
in 1870 to exempt private discrimination from the operation of the Civil Rights
Act of 1866.73 “The cardinal rule is that repeals by implication are not favored.”
Posadas v. National City Bank, 296 U.S. 497, 503, 56 S.Ct. 349, 352, 80 L.Ed.
351. All Congress said in 1870 was that the 1866 law “is hereby re-enacted.” That
is all Congress meant.
As we said in a somewhat different setting two Terms ago, “We think that history
leaves no doubt that, if we are to give (the law) the scope that its origins dictate,
we must accord it a sweep as broad as its language.” United States v. Price, 383
U.S. 787, 801, 86 S.Ct. 1152, 1160. “We are not at liberty to seek ingenious analytical
instruments,” ibid., to carve from s 1982 an exception for private conduct—even
though its application to such conduct in the present context is without established
precedent. And, as the Attorney General of the United States said at the oral
argument of this case, “The fact that the statute lay partially dormant for many
years cannot be held to diminish its force today.”
V.
The remaining question is whether Congress has power under the Constitution to
do what s 1982 purports to do: to prohibit all racial discrimination, private
and public, in the sale and rental of property. Our starting point is the Thirteenth
Amendment, for it was pursuant *438 to that constitutional provision that Congress
originally enacted what is now s 1982. The Amendment consists of two parts. Section
1 states:
“Neither slavery nor involuntary servitude, except as a punishment for crime whereby
the party shall have been duly convicted, shall exist within the United States,
or any place subject to their jurisdiction.”
Section 2 provides:
“Congress shall have power to enforce this article by appropriate legislation.”
As its text reveals, the Thirteenth Amendment “is not a mere prohibition of state
laws establishing or upholding slavery, but an absolute declaration that slavery
or involuntary servitude shall not exist in any part of the United States.” Civil
Rights Cases, 109 U.S. 3, 20, 3 S.Ct. 18, 28, 27 L.Ed. 835. It has never been
doubted, therefore, “that the power vested in Congress to enforce the article
by appropriate legislation,” ibid., includes the power to enact laws “direct and
primary, operating upon the acts of individuals, whether sanctioned by state legislation
or not.” Id., at 23, 3 S.Ct., at 30.74
Thus, the fact that s 1982 operates upon the unofficial acts of private individuals,
whether or not sanctioned by state law, presents no constitutional problem. If
Congress has power **2203 under the Thirteenth Amendment to eradicate conditions
that prevent Negroes from buying and renting property because of their race or
color, then no federal statute calculated to achieve that objective *439 can be
thought to exceed the constitutional power of Congress simply because it reaches
beyond state action to regulate the conduct of private individuals. The constitutional
question in this case, therefore, comes to this: Does the authority of Congress
to enforce the Thirteenth Amendment “by appropriate legislation” include the power
to eliminate all racial barriers to the acquisition of real and personal property?
We think the answer to that question is plainly yes.'
- source_sentence: According to the statute referenced in the context, what is the
standard for establishing the requisite injury necessary for obtaining an injunction
under 17 U.S.C. § 1203(b)(1)?
sentences:
- 'Post-Trial Mem. at 27-28.
[263] The statute expressly authorizes injunctions to prevent or restrain violations,
17 U.S.C. § 1203(b)(1), thus demonstrating that the requisite injury need only
be threatened.
[264] Def. Post-Trial Mem. at 28.
[265] Id. at 28-29.
[266] See, e.g., Ex. AYZ (Hunt Dep.) at 94-104.
[267] Id. 30.
[268] Ex. 113.
[269] Defendants'' argument would lack merit even if there were credible proof
that other circumvention devices actually exist and produce results comparable
to DeCSS. The available movies must have been decrypted with DeCSS or something
else. As far as this record discloses, any such device or technology would violate
the DMCA for the same reasons as does DeCSS. In consequence, this case comes within
the principle of Summers v. Tice, 33 Cal.2d 80, 199 P.2d 1 (1948). Where, as here,
two or more persons take substantially identical wrongful actions, one and only
one of which had to be the source of the plaintiffs'' injury, and it is equally
likely that one inflicted the injury as the other, the burden of proof on causation
shifts to the defendants, each of which is liable absent proof that its action
did not cause the injury. See 4 Fowler V. Harper & Fleming James, Jr., THE LAW
OF TORTS §§ 101-04 (2d ed.1996).
Defendants'' efforts to avoid the consequences of this common sense principle
are unpersuasive. They argue, for example, that plaintiffs may not invoke the
theory unless they join as defendants everyone who may have contributed to the
injury. Def. Post-Trial Mem. at 32 n. 18 (citing Ex. UZ). It would be difficult
to imagine a more nonsensical requirement in the context of this case. Where,
as here, harm is done by dissemination of information over the Internet, probably
by a substantial number of people all over the world, defendants'' proposed rule
would foreclose judicial relief anywhere because joinder of all plainly would
be impossible in any one place, and technology does not permit identification
of which wrongdoer''s posting or product led to which pirated copy of a copyrighted
work.
[270] 17 U.S.C. § 1203(b)(1).
[271] See, e.g., S.E.C. v. Unique Financial Concepts, Inc., 196 F.3d 1195, 1199
n. 2 (11th Cir.1999) (injunction under Section 20(b) of the Securities Act of
1933, 15 U.S.C. § 77t(b), which permits an injunction "upon a proper showing,"
requires "a reasonable likelihood that the wrong will be repeated"); Commodity
Futures Trading Com''n v. Hunt, 591 F.2d 1211, 1220 (7th Cir.1979) (same under
Commodity Exchange Act, 7 U.S.C. § 13a-1(b)); S.E.C. v. Bausch & Lomb Inc., 565
F.2d 8, 18 (2d Cir.1977) (reasonable likelihood of future violations required
under § 21(d) of Securities Exchange Act of 1934, 15 U.S.C. § 78u(d), which permits
an injunction "upon a proper showing" where person "engaged or ... about to engage
in" violation of statute).
[272] See, e.g., Rondeau v. Mosinee Paper Corp., 422 U.S. 49, 57, 95 S.Ct. 2069,
45 L.Ed.2d 12 (1975) (injunctive relief in private action under § 13(d) of the
Securities Exchange Act of 1934, 15 U.S.C. § 78m(d), as added by the Williams
Act, requires a showing of irreparable harm and inadequacy of legal remedies).
[273] Tough Traveler, Ltd. v. Outbound Prods., 60 F.3d 964, 967-68 (2d Cir.1995)
(trademark); Fisher-Price, Inc. v. Well-Made Toy Mfg. Corp., 25 F.3d 119, 124
(2d Cir.1994) (copyright).
[274] See, e.g., Northwestern Nat''l Ins. Co.'
- 'Indeed, were we to accept Maine’s argument, our decision in Espinoza would be
rendered essentially meaningless. By Maine’s logic, Montana could have obtained
the same result that we held violated the First Amendment simply by redefining
its tax credit for sponsors of generally available scholarships as limited to
“tuition payments for the rough equivalent of a Montana public education”—meaning
a secular education. But our holding in Espinoza turned on the substance of free
exercise protections, not on the presence or absence of magic words. That holding
applies fully whether the prohibited discrimination is in an express provision
like § 2951(2) or in a party’s reconceptualization of the public benefit.
Maine may provide a strictly secular education in its public schools. But BCS
and Temple Academy—like numerous other recipients of Maine tuition assistance
payments—are not public schools. In order to provide an education to children
who live in certain parts of its far-flung State, Maine has decided not to operate
schools of its own, but instead to offer tuition assistance that parents may direct
to the public or private schools of their choice. Maine’s administration of that
benefit is subject to the free exercise principles governing any such public benefit
program—including the prohibition on denying the benefit based on a recipient’s
religious exercise.
The dissents are wrong to say that under our decision today Maine “must” fund
religious education. Post, at 2006 (BREYER, J., dissenting). Maine chose to allow
some parents to direct state tuition payments to private schools; that decision
was not “forced upon” it. Post, at 2014 (SOTOMAYOR, J., dissenting). The State
retains a number of options: it could expand the reach of its public school system,
increase the availability of transportation, provide some combination of tutoring,
remote learning, and partial attendance, or even operate boarding schools of its
own. As we held in Espinoza, a “State need not subsidize private education. But
once a State decides to do so, it cannot disqualify some private schools solely
because they are religious.” 591 U. S., at ––––, 140 S.Ct., at 2261.
B
The Court of Appeals also attempted to distinguish this case from Trinity Lutheran
and Espinoza on the ground that the funding restrictions in those cases were “solely
status-based religious discrimination,” while the challenged provision here “imposes
a use-based restriction.” 979 F.3d at 35, 37–38...
In Trinity Lutheran, the Missouri Constitution banned the use of public funds
in aid of “any church, sect or denomination of religion.” [citation omitted].
We noted that the case involved “express discrimination based on religious identity,”
which was sufficient unto the day in deciding it, and that our opinion did “not
address religious uses of funding.” [citation omitted]
So too in Espinoza, the discrimination at issue was described by the Montana Supreme
Court as a prohibition on aiding “schools controlled by churches,” and we *2001
analyzed the issue in terms of “religious status and not religious use.” [citation
omitted] Foreshadowing Maine’s argument here, Montana argued that its case was
different from Trinity Lutheran’s because it involved not playground resurfacing,
but general funds that “could be used for religious ends by some recipients, particularly
schools that believe faith should ‘permeate[ ]’ everything they do.” [citation
omitted] We explained, however, that the strict scrutiny triggered by status-based
discrimination could not be avoided by arguing that “one of its goals or effects
[was] preventing religious organizations from putting aid to religious uses.”
[citation omitted] And we noted that nothing in our analysis was “meant to suggest
that we agree[d] with [Montana] that some lesser degree of scrutiny applies to
discrimination against religious uses of government aid.” [citation omitted]
Maine’s argument, however—along with the decision below and Justice BREYER’s dissent—is
premised on precisely such a distinction. [citations omitted]
That premise, however, misreads our precedents. In Trinity Lutheran and Espinoza,
we held that the Free Exercise Clause forbids discrimination on the basis of religious
status. But those decisions never suggested that use-based discrimination is any
less offensive to the Free Exercise Clause. This case illustrates why.'
- '429
Supreme Court of the United States.
SAMUEL M. CLYATT
v.
UNITED STATES.
No. 235.
|
Argued December 13, 14, 1904.
|
Decided March 13, 1905.
Synopsis
ON WRIT of Certiorari to the United States Circuit Court of Appeals for the Fifth
Circuit, bringing up for review a judgment of the Circuit Court for the Northern
District of Florida, convicting defendant of returning certain specified persons
to a condition of peonage, which judgment had been taken to the Circuit Court
of Appeals by a writ of error to the Circuit Court. Reversed and the cause remanded
for a new trial.
**429 Statement by Mr. Justice Brewer:
Considers the constitutionality of Sections 1990 and 5526, Rev. Stat. (U. S. Comp.
Stat. 1901, pp. 1266, 3715), [Anti-Peonage Act]
*215 Mr. Justice Brewer delivered the opinion of the court:
…What is peonage? It may be defined as a status or condition of compulsory service,
based upon the indebtedness of the peon to the master. The basal fact is indebtedness.
As said by Judge Benedict, delivering the opinion in Jaremillo v. Romero, 1 N.
M. 190, 194: ‘One fact existed universally: all were indebted to their masters.
This was the cord by which they seemed bound to their master’s service.’ Upon
this is based a condition of compulsory service. Peonage is sometimes classified
as voluntary or involuntary; but this implies simply a difference in the mode
of origin, but none in the character of the servitude. The one exists where the
debtor voluntarily contracts to enter the service of his creditor. The other is
forced upon the debtor by some provision of law. But peonage, however created,
is compulsory service,—involuntary servitude. The peon can release himself therefrom,
it is true, by the payment of the debt, but otherwise the service is enforced.
A clear distinction exists between peonage and the voluntary performance of labor
or rendering of services in payment of a debt. In the latter case the debtor,
though contracting to pay his indebtedness by labor or service, and subject, like
any other contractor, to an action for damages for breach of that contract, can
elect at any time to break it, and no law or force compels *216 performance or
a continuance of the service. We need not stop to consider any possible limits
or exceptional cases, such as the service of a sailor…or the obligations of a
child to its parents, or of an apprentice to his master, or the power of the legislature
to make unlawful, and punish criminally, an abandonment by an employee of his
post of labor in any extreme cases. That which is contemplated by the statute
is compulsory service to secure the payment of a debt. Is this legislation within
the power of Congress? It may be conceded, as a general proposition, that the
ordinary relations of individual to individual are subject to the control of the
states, and are not intrusted to the general government; but the 13th Amendment,
adopted as an outcome of the Civil War, reads:
‘Sec. 1. Neither slavery nor involuntary servitude, except as a punishment for
crime whereof the party shall have been duly convicted, shall exist within the
United States, or any place subject to their jurisdiction.
‘Sec. 2. Congress shall have power to enforce this article by appropriate legislation.’
This amendment denounces a status or condition, irrespective of the manner or
authority by which it is created. The prohibitions of the 14th and 15th Amendments
are largely upon the acts of the states; but the 13th Amendment names no party
or authority, but simply forbids slavery and involuntary servitude, grants to
Congress power to enforce this prohibition by appropriate legislation. The differences
between the 13th and subsequent amendments [can be described as follows:]
This amendment, as well as the 14th, is undoubtedly self-executing without any
ancillary legislation, so far as its terms are applicable to any existing state
of circumstances. By its own unaided force and effect it abolished slavery, and
*217 established universal freedom. Still, legislation may be necessary and proper
to meet all the various cases and circumstances to be affected by it, and to prescribe
proper modes of redress for its violation in letter or spirit. And such legislation
may be primary and direct in its character; for the amendment is not a mere prohibition
of state laws establishing or upholding slavery, but an absolute declaration that
slavery or involuntary servitude shall not exist in any part of the United States.
. . .'
- source_sentence: How does the standard for applying the Second Amendment, as outlined
in the context, compare to the protection of other constitutional rights, such
as the freedom of speech in the First Amendment?
sentences:
- 'Eventually, HCC moved to dismiss the complaint. The District Court granted the
motion, concluding that Mr. Wilson lacked standing under Article III. On appeal,
a panel of the Fifth Circuit reversed, holding that Mr. Wilson had standing and
that his complaint stated a viable First Amendment claim. [citation omitted]
The Fifth Circuit’s merits analysis proceeded in two steps. First, the court concluded
that a verbal “reprimand against an elected official for speech addressing a matter
of public concern is an actionable First Amendment claim under § 1983.” [citation
omitted] Next, the court reasoned that the Board’s imposition of other punishments—such
as limiting Mr. Wilson’s eligibility for officer positions and his access to certain
funds—did “not violate his First Amendment rights” because Mr. Wilson did not
have an “entitlement” to those privileges. [citation omitted] In sum, the court
held that Mr. Wilson’s § 1983 action could proceed, but only as to the Board’s
unadorned censure resolution. HCC’s request for rehearing en banc failed by an
equally divided vote. [citation omitted].
In time, HCC filed a petition for certiorari in this Court. It asked us to review
the Fifth Circuit’s judgment that Mr. Wilson may pursue a First Amendment claim
based on a purely verbal censure. Last year, we agreed to take up that question.
[citation omitted] But as merits briefing unfolded, Mr. Wilson did not just seek
to defend the Fifth Circuit’s judgment; he also sought to challenge it in part.
Specifically, he argued that the Fifth Circuit erred to the extent that it upheld
the Board’s nonverbal punishments as consistent with the First Amendment. Generally,
however, when a respondent in this Court seeks to alter a lower court’s judgment,
he must file and we must grant a cross-petition for review. [citation omitted]
Mr. Wilson filed no such petition in this case. As a result, we decline to take
up his *1259 challenge to the Fifth Circuit’s judgment, and the only question
before us remains the narrow one on which we granted certiorari: Does Mr. Wilson
possess an actionable First Amendment claim arising from the Board’s purely verbal
censure?
II
A
The First Amendment prohibits laws “abridging the freedom of speech.” One obvious
implication of that rule is that the government usually may not impose prior restraints
on speech. [citation omitted] But other implications follow too. Relevant here,
no one before us questions that, “[a]s a general matter,” the First Amendment
prohibits government officials from subjecting individuals to “retaliatory actions”
after the fact for having engaged in protected speech. [citations omitted] Mr.
Wilson argues that the Board’s censure resolution represents exactly that kind
of impermissible retaliatory action.
Almost immediately, however, this submission confronts a challenge. When faced
with a dispute about the Constitution’s meaning or application, “[l]ong settled
and established practice is a consideration of great weight.” [citation omitted]
Often, “a regular course of practice” can illuminate or “liquidate” our founding
document’s “terms & phrases.” [citations omitted] That principle poses a problem
for Mr. Wilson because elected bodies in this country have long exercised the
power to censure their members. In fact, no one before us has cited any evidence
suggesting that a purely verbal censure analogous to Mr. Wilson’s has ever been
widely considered offensive to the First Amendment.
As early as colonial times, the power of assemblies in this country to censure
their members was “more or less assumed.” [citation omitted] It seems, too, that
assemblies often exercised the power to censure members for views they expressed
and actions they took “both within and without the legislature.” [citations omitted]
The parties supply little reason to think the First Amendment was designed or
commonly understood to upend this practice…
If anything, censures [of public officials] have proven more common yet at the
state and local level…According to HCC and undisputed by Mr. Wilson, it seems
elected bodies in this country issued no fewer than 20 censures in August 2020
alone. [citation omitted]
If this longstanding practice does not “put at rest” the question of the Constitution’s
meaning for the dispute before us, it surely leaves a “considerable impression.”
[citation omitted] On Mr. Wilson’s telling and under the Fifth Circuit’s holding,
a purely verbal censure by an elected assembly of one of its own members may offend
the First Amendment.'
- '[citation omitted]
We assessed the lawfulness of that handgun ban by scrutinizing whether it comported
with history and tradition. Although we noted that the ban “would fail constitutional
muster” “[u]nder any of the standards of scrutiny that we have applied to enumerated
constitutional rights,”…we did not engage in means-end scrutiny when resolving
the constitutional question. Instead, we focused on the historically unprecedented
nature of the District’s ban, observing that “[f]ew laws in the history of our
Nation have come close to [that] severe restriction.” [citation omitted] Likewise,
when one of the dissents attempted to justify the District’s prohibition with
“founding-era historical precedent,” including “various restrictive laws in the
colonial period,” we addressed each purported analogue and concluded that they
were either irrelevant or “d[id] not remotely burden the right of self-defense
as much as an absolute ban on handguns.” [citations omitted] Thus, our earlier
historical analysis sufficed to show that the Second Amendment did not countenance
a “complete prohibition” on the use of “the most popular weapon chosen by Americans
for self-defense in the home.” [citation omitted]
2
As the foregoing shows, Heller’s methodology centered on constitutional text and
*2129 history. Whether it came to defining the character of the right (individual
or militia dependent), suggesting the outer limits of the right, or assessing
the constitutionality of a particular regulation, Heller relied on text and history.
It did not invoke any means-end test such as strict or intermediate scrutiny.
Moreover, Heller and McDonald expressly rejected the application of any “judge-empowering
‘interest-balancing inquiry’ that ‘asks whether the statute burdens a protected
interest in a way or to an extent that is out of proportion to the statute’s salutary
effects upon other important governmental interests.’ ” [citations omitted] We
declined to engage in means-end scrutiny because “[t]he very enumeration of the
right takes out of the hands of government—even the Third Branch of Government—the
power to decide on a case-by-case basis whether the right is really worth insisting
upon.” [citation omitted] We then concluded: “A constitutional guarantee subject
to future judges’ assessments of its usefulness is no constitutional guarantee
at all.” [citation omitted]
Not only did Heller decline to engage in means-end scrutiny generally, but it
also specifically ruled out the intermediate-scrutiny test that respondents and
the United States now urge us to adopt. Dissenting in Heller, Justice BREYER’s
proposed standard—“ask[ing] whether [a] statute burdens a protected interest in
a way or to an extent that is out of proportion to the statute’s salutary effects
upon other important governmental interests,” …—simply expressed a classic formulation
of intermediate scrutiny in a slightly different way. [ci8tations omitted] In
fact, Justice BREYER all but admitted that his Heller dissent advocated for intermediate
scrutiny by repeatedly invoking a quintessential intermediate-scrutiny precedent.
[citations omitted]Thus, when Heller expressly rejected that dissent’s “interest-balancing
inquiry,” [citation omitted] it necessarily rejected intermediate scrutiny.5
In sum, the Courts of Appeals’ second step is inconsistent with Heller’s historical
approach and its rejection of means-end scrutiny. We reiterate that the standard
for applying the Second Amendment is as follows: When the Second Amendment’s plain
text covers an individual’s *2130 conduct, the Constitution presumptively protects
that conduct. The government must then justify its regulation by demonstrating
that it is consistent with the Nation’s historical tradition of firearm regulation.
Only then may a court conclude that the individual’s conduct falls outside the
Second Amendment’s “unqualified command.” [citation omitted]
C
This Second Amendment standard accords with how we protect other constitutional
rights. [One example is] the freedom of speech in the First Amendment, to which
Heller repeatedly compared the right to keep and bear arms. [citation omitted]
In that context, “[w]hen the Government restricts speech, the Government bears
the burden of proving the constitutionality of its actions.” [citations omitted]
In some cases, that burden includes showing whether the expressive conduct falls
outside of the category of protected speech. [citation omitted] And to carry that
burden, the government must generally point to historical evidence about the reach
of the First Amendment’s protections.'
- 'Roe and Casey thought that one-sided view misguided. In some sense, that is the
difference in a nutshell between our precedents and the majority opinion. The
constitutional regime we have lived in for the last 50 years recognized competing
interests, and sought a balance between them. The constitutional regime we enter
today erases the woman’s interest and recognizes only the State’s (or the Federal
Government’s).
B
The majority makes this change based on a single question: Did the reproductive
right recognized in Roe and Casey exist in “1868, the year when the Fourteenth
Amendment was ratified”? Ante, at 2252 – 2253. The majority says (and with this
much we agree) that the answer to this question is no: In 1868, there was no nationwide
right to end a pregnancy, and no thought that the Fourteenth Amendment provided
one.
Of course, the majority opinion refers as well to some later and earlier history.
On the one side of 1868, it goes back as far as the 13th (the 13th!) century.
See ante, at 2249, 142 S.Ct. 2111. But that turns out to be wheel-spinning. First,
it is not clear what relevance *2324 such early history should have, even to the
majority. See New York State Rifle & Pistol Assn., Inc. v.Bruen, 597 U.S. ––––,
––––, 142 S.Ct. 2111, 2136, ––– L.Ed.2d –––– (2022) (“Historical evidence that
long predates [ratification] may not illuminate the scope of the right”). If the
early history obviously supported abortion rights, the majority would no doubt
say that only the views of the Fourteenth Amendment’s ratifiers are germane. See
ibid. (It is “better not to go too far back into antiquity,” except if olden “law
survived to become our Founders’ law”). Second—and embarrassingly for the majority—early
law in fact does provide some support for abortion rights. Common-law authorities
did not treat abortion as a crime before “quickening”—the point when the fetus
moved in the womb.2 And early American law followed the common-law rule.3 So the
criminal law of that early time might be taken as roughly consonant with Roe’s
and Casey’s different treatment of early and late abortions. Better, then, to
move forward in time. On the other side of 1868, the majority occasionally notes
that many States barred abortion up to the time of Roe. See ante, at 2253, 2260,
142 S.Ct. 2111. That is convenient for the majority, but it is window dressing.
As the same majority (plus one) just informed us, “post-ratification adoption
or acceptance of laws that are inconsistent with the original meaning of the constitutional
text obviously cannot overcome or alter that text.” New York State Rifle & Pistol
Assn., Inc., 597 U.S., at –––– – ––––, 142 S.Ct., at 2137. Had the pre-Roe liberalization
of abortion laws occurred more quickly and more widely in the 20th century, the
majority would say (once again) that only the ratifiers’ views are germane.
The majority’s core legal postulate, then, is that we in the 21st century must
read the Fourteenth Amendment just as its ratifiers did. And that is indeed what
the majority emphasizes over and over again. See ante, at 2267 (“[T]he most important
historical fact [is] how the States regulated abortion when the Fourteenth Amendment
was adopted”); see also ante, at 2242 – 2243, 2248 – 2249, and n. 24, 23, 25,
28. If the ratifiers did not understand something as central to freedom, then
neither can we. Or said more particularly: If those people did not understand
reproductive rights as part of the guarantee of liberty conferred in the Fourteenth
Amendment, then those rights do not exist.
As an initial matter, note a mistake in the just preceding sentence. We referred
there to the “people” who ratified the Fourteenth Amendment: What rights did those
“people” have in their heads at the time? But, of course, “people” did not ratify
the Fourteenth Amendment. Men did. So it is perhaps not so surprising that the
ratifiers were not perfectly attuned to the importance of reproductive rights
for women’s liberty, or for their capacity to participate as equal members of
our Nation.'
- source_sentence: Based on the court's ruling, what are the implications of Title
VII regarding discrimination against employees based on their transgender status
or failure to conform to sex stereotypes?
sentences:
- 'Thus, even if we agreed with the Funeral Home that Rost''s religious exercise
would be substantially burdened by enforcing Title VII in this case, we would
nevertheless REVERSE the district court''s grant of summary judgment to the Funeral
Home and hold instead that requiring the Funeral Home to comply with Title VII
constitutes the least restrictive means of furthering the government''s compelling
interest in eradicating discrimination against Stephens on the basis of sex. Thus,
even assuming Rost''s religious exercise is substantially burdened by the EEOC''s
enforcement action in this case, we GRANT summary judgment to the EEOC on the
Funeral Home''s RFRA defense on this alternative ground.
[ … ]
[ … ]
III. CONCLUSION
Discrimination against employees, either because of their failure to conform to
sex stereotypes or their transgender and transitioning status, is illegal under
Title VII. The unrefuted facts show that the Funeral Home fired Stephens because
she refused to abide by her employer''s stereotypical conception of her sex, and
therefore the EEOC is entitled to summary judgment as to its unlawful-termination
claim. RFRA provides the Funeral Home with no relief because continuing to employ
Stephens would not, as a matter of law, substantially burden Rost''s religious
exercise, and even if it did, the EEOC has shown that enforcing Title VII here
is the least restrictive means of furthering its compelling interest in combating
and eradicating sex discrimination. We therefore REVERSE the district court''s
grant of summary judgment in favor of the Funeral Home and GRANT summary judgment
to the EEOC on its unlawful-termination claim. We also REVERSE the district court''s
grant of summary judgment on the EEOC''s discriminatory-clothing-allowance claim,
as the district court erred in failing to consider the EEOC''s claim on the merits.
We REMAND this case to the district court for further proceedings consistent with
this opinion.
[1] We refer to Stephens using female pronouns, in accordance with the preference
she has expressed through her briefing to this court.
[2] All facts drawn from Def.''s Statement of Facts (R. 55) are undisputed. See R.
64 (Pl.''s Counter Statement of Disputed Facts) (Page ID #2066-88).
[3] See also Appellee Br. at 16 ("It is a helpful exercise to think about Price
Waterhouse and imagine that there was a dress code imposed which obligated Ms.
Hopkins to wear a skirt while her male colleagues were obliged to wear pants.
Had she simply been fired for wearing pants rather than a skirt, the case would
have ended there — both sexes would have been equally burdened by the requirement
to comply with their respective sex-specific standard. But what the firm could
not do was fire her for being aggressive or macho when it was tolerating or rewarding
the behavior among men — and when it did, it relied on a stereotype to treat her
disparately from the men in the firm.").
[4] Moreover, discrimination because of a person''s transgender, intersex, or
sexually indeterminate status is no less actionable than discrimination because
of a person''s identification with two religions, an unorthodox religion, or no
religion at all. And "religious identity" can be just as fluid, variable, and
difficult to define as "gender identity"; after all, both have "a deeply personal,
internal genesis that lacks a fixed external referent." Sue Landsittel, Strange
Bedfellows? Sex, Religion, and Transgender Identity Under Title VII, 104 NW. U.
L. REV. 1147, 1172 (2010) (advocating for "[t]he application of tests for religious
identity to the problem of gender identity [because it] produces a more realistic,
and therefore more appropriate, authentication framework than the current reliance
on medical diagnoses and conformity with the gender binary").
[5] On the other hand, there is also evidence that Stephens was fired only because
of her nonconforming appearance and behavior at work, and not because of her transgender
identity. See R. 53-6 (Rost Dep.'
- 'Such laws would furnish the readiest means of compulsion. The 13th *244 Amendment
prohibits involuntary servitude except as punishment for crime. But the exception,
allowing full latitude for the enforcement of penal laws, does not destroy the
prohibition. It does not permit slavery or involuntary servitude to be established
or maintained through the operation of the criminal law by making it a crime to
refuse to submit to the one or to render the service which would constitute the
other. The state may impose involuntary servitude as a punishment for crime, but
it may not compel one man to labor for another in payment of a debt, by punishing
him as a criminal if he does not perform the service or pay the debt.
If the statute in this case had authorized the employing company to seize the
debtor, and hold him to the service until he paid the $15, or had furnished the
equivalent in labor, its invalidity would not be questioned. It would be equally
clear that the state could not authorize its constabulary to prevent the servant
from escaping, and to force him to work out his debt. But the state could not
avail itself of the sanction of the criminal law to supply the compulsion any
more than it could use or authorize the use of physical force. ‘In contemplation
of the law, the compulsion to such service by the fear of punishment under a criminal
statute is more powerful than any guard which the employer could station.’ Ex
parte Hollman, 79 S. C. 22, 21 L.R.A.(N.S.) 249, 60 S. E. p. 24, 14 A. & E. Ann.
Cas. 1109.
**153 What the state may not do directly it may not do indirectly. If it cannot
punish the servant as a criminal for the mere failure or refusal to serve without
paying his debt, it is not permitted to accomplish the same result by creating
a statutory presumption which, upon proof of no other fact, exposes him to conviction
and punishment. Without imputing any actual motive to oppress, we must consider
the natural operation of the statute here in question (Henderson v. New York [Henderson
v. Wickham] 92 U. S. p. 268, 23 L. ed. 547), and it is apparent that it furnishes
a convenient instrument for the coercion *245 which the Constitution and the act
of Congress forbid; an instrument of compulsion peculiarly effective as against
the poor and the ignorant, its most likely victims. There is no more important
concern than to safeguard the freedom of labor upon which alone can enduring prosperity
be based. The provision designed to secure it would soon become a barren form
if it were possible to establish a statutory presumption of this sort, and to
hold over the heads of laborers the threat of punishment for crime, under the
name of fraud, but merely upon evidence of failure to work out their debts. The
act of Congress deprives of effect all legislative measures of any state through
which, directly or indirectly, the prohibited thing, to wit, compulsory service
to secure the payment of a debt, may be established or maintained; and we conclude
that § 4730, as amended, of the Code of Alabama, in so far as it makes the refusal
or failure to perform the act or service, without refunding the money or paying
for the property prima facie evidence of the commission received of the crime
which the section defines, is in conflict with the 13th Amendment, and the legislation
authorized by that Amendment, and is therefore invalid.
In this view it is unnecessary to consider the contentions which have been made
under the 14th Amendment…
Reversed and cause remanded for further proceedings not inconsistent with this
opinion.
Mr. Justice Holmes, dissenting [omitted]
2.3
Jones v. Alfred H. Mayer Co.
88 S.Ct. 2186
Supreme Court of the United States
Joseph Lee JONES et ux., Petitioners,
v.
ALFRED H. MAYER CO. et al.
No. 645.
|
Argued April 1 and 2, 1968.
|
Decided June 17, 1968.
Synopsis
Action to recover damages and for injunctive relief because of refusal of defendants
to sell home in private subdivision to plaintiffs solely because of race. The
United States District Court for the Eastern District of Missouri, 255 F.Supp.
115, dismissed complaint, and plaintiffs appealed. The Court of Appeals for the
Eighth Circuit, 379 F.2d 33, affirmed, and certiorari was granted. The United
States Supreme Court, Mr.'
- '[citation omitted]
*1994 The program imposes no geographic limitation: Parents may direct tuition
payments to schools inside or outside the State, or even in foreign countries.
[citation omitted] In schools that qualify for the program because they are accredited,
teachers need not be certified by the State,…and Maine’s curricular requirements
do not apply…Single-sex schools are eligible. [citation omitted]
Prior to 1981, parents could also direct the tuition assistance payments to religious
schools. Indeed, in the 1979–1980 school year, over 200 Maine students opted to
attend such schools through the tuition assistance program. App. 72. In 1981,
however, Maine imposed a new requirement that any school receiving tuition assistance
payments must be “a nonsectarian school in accordance with the First Amendment
of the United States Constitution.” [citation omitted] That provision was enacted
in response to an opinion by the Maine attorney general taking the position that
public funding of private religious schools violated the Establishment Clause
of the First Amendment. We subsequently held, however, that a benefit program
under which private citizens “direct government aid to religious schools wholly
as a result of their own genuine and independent private choice” does not offend
the Establishment Clause. [citation omitted] Following our decision in Zelman,
the Maine Legislature considered a proposed bill to repeal the “nonsectarian”
requirement, but rejected it. App. 100, 108.
The “nonsectarian” requirement for participation in Maine’s tuition assistance
program remains in effect today. The Department has stated that, in administering
this requirement, it “considers a sectarian school to be one that is associated
with a particular faith or belief system and which, in addition to teaching academic
subjects, promotes the faith or belief system with which it is associated and/or
presents the material taught through the lens of this faith.” [citation omitted]
“The Department’s focus is on what the school teaches through its curriculum and
related activities, and how the material is presented.” …“[A]ffiliation or association
with a church or religious institution is one potential indicator of a sectarian
school,” but “it is not dispositive.”
B
This case concerns two families that live in SAUs that neither maintain their
own secondary schools nor contract with any nearby secondary school. App. 70,
71. Petitioners David and Amy Carson reside in Glenburn, Maine. Id., at 74. When
this litigation commenced, the Carsons’ daughter attended high school at Bangor
Christian Schools (BCS), which was founded in 1970 as a ministry of Bangor Baptist
Church. Id., at 74, 80. The Carsons sent their daughter to BCS because of the
school’s high academic standards and because the school’s Christian worldview
aligns with their sincerely held religious beliefs. Id., at 74. Given that BCS
is a “sectarian” school that cannot qualify for tuition assistance payments under
Maine’s program, id., at 80, the Carsons paid the tuition for their daughter to
attend BCS themselves, id., at 74.
Petitioners Troy and Angela Nelson live in Palermo, Maine. Id., at 78. When this
litigation commenced, the Nelsons’ daughter attended high school at Erskine Academy,
a secular private school, and their son attended middle school at Temple Academy,
a “sectarian” school affiliated with *1995 Centerpoint Community Church. Id.,
at 78, 90, 91. The Nelsons sent their son to Temple Academy because they believed
it offered him a high-quality education that aligned with their sincerely held
religious beliefs. Id., at 78. While they wished to send their daughter to Temple
Academy too, they could not afford to pay the cost of the Academy’s tuition for
both of their children. Id., at 79.
BCS and Temple Academy are both accredited by the New England Association of Schools
and Colleges (NEASC), and the Department considers each school a “private school
approved for attendance purposes” under the State’s compulsory attendance requirement.
Id., at 80, 90. Yet because neither school qualifies as “nonsectarian,” neither
is eligible to receive tuition payments under Maine’s tuition assistance program.
Id., at 80, 90. Absent the “nonsectarian” requirement, the Carsons and the Nelsons
would have asked their respective SAUs to pay the tuition to send their children
to BCS and Temple Academy, respectively. Id., at 79.
In 2018, petitioners brought suit against the commissioner of the Maine Department
of Education. Id., at 11–12.'
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- type: cosine_accuracy@5
value: 0.7096774193548387
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8064516129032258
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3978494623655914
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.34050179211469533
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.26021505376344084
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.153763440860215
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1586021505376344
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4059139784946236
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5259856630824372
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6164874551971326
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5019311887697538
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5081626557433011
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4181782323905875
name: Cosine Map@100
---
# ModernBERT Embed base LegalTextAI Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("legaltextai/modernbert-embed-ft-const-legal-matryoshka")
# Run inference
sentences = [
"Based on the court's ruling, what are the implications of Title VII regarding discrimination against employees based on their transgender status or failure to conform to sex stereotypes?",
'Thus, even if we\xa0agreed with the Funeral Home that Rost\'s religious exercise would be substantially burdened by enforcing Title VII in this case, we would nevertheless REVERSE the district court\'s grant of summary judgment to the Funeral Home and hold instead that requiring the Funeral Home to comply with Title VII constitutes the least restrictive means of furthering the government\'s compelling interest in eradicating discrimination against Stephens on the basis of sex. Thus, even assuming Rost\'s religious exercise is substantially burdened by the EEOC\'s enforcement action in this case, we GRANT summary judgment to the EEOC on the Funeral Home\'s RFRA defense on this alternative ground.\n\n\xa0\n\n[ … ]\n\n[ … ]\n\n\xa0\n\nIII. CONCLUSION\n\nDiscrimination against employees, either because of their failure to conform to sex stereotypes or their transgender and transitioning status, is illegal under Title VII. The unrefuted facts show that the Funeral Home fired Stephens because she refused to abide by her employer\'s stereotypical conception of her sex, and therefore the EEOC is entitled to summary judgment as to its unlawful-termination claim. RFRA provides the Funeral Home with no relief because continuing to employ Stephens would not, as a matter of law, substantially burden Rost\'s religious exercise, and even if it did, the EEOC has shown that enforcing Title VII here is the least restrictive means of furthering its compelling interest in combating and eradicating sex discrimination. We therefore REVERSE the district court\'s grant of summary judgment in favor of the Funeral Home and GRANT summary judgment to the EEOC on its unlawful-termination claim. We also REVERSE the district court\'s grant of summary judgment on the EEOC\'s discriminatory-clothing-allowance claim, as the district court erred in failing to consider the EEOC\'s claim on the merits. We REMAND this case to the district court for further proceedings consistent with this opinion.\n\n[1]\xa0We refer to Stephens using female pronouns, in accordance with the preference she has expressed through her briefing to this court.\n\n[2]\xa0All facts drawn from Def.\'s Statement of Facts (R. 55) are undisputed.\xa0See\xa0R. 64 (Pl.\'s Counter Statement of Disputed Facts) (Page ID #2066-88).\n\n[3]\xa0See also\xa0Appellee Br. at 16 ("It is a helpful exercise to think about\xa0Price Waterhouse\xa0and imagine that there was a dress code imposed which obligated Ms. Hopkins to wear a skirt while her male colleagues were obliged to wear pants. Had she simply been fired for wearing pants rather than a skirt, the case would have ended there — both sexes would have been equally burdened by the requirement to comply with their respective sex-specific standard. But what the firm could not do was fire her for being aggressive or macho when it was tolerating or rewarding the behavior among men — and when it did, it relied on a stereotype to treat her disparately from the men in the firm.").\n\n[4]\xa0Moreover, discrimination because of a person\'s transgender, intersex, or sexually indeterminate status is no less actionable than discrimination because of a person\'s identification with two religions, an unorthodox religion, or no religion at all. And "religious identity" can be just as fluid, variable, and difficult to define as "gender identity"; after all, both have "a deeply personal, internal genesis that lacks a fixed external referent." Sue Landsittel,\xa0Strange Bedfellows? Sex, Religion, and Transgender Identity Under Title VII,\xa0104 NW. U. L. REV. 1147, 1172 (2010) (advocating for "[t]he application of tests for religious identity to the problem of gender identity [because it] produces a more realistic, and therefore more appropriate, authentication framework than the current reliance on medical diagnoses and conformity with the gender binary").\n\n[5]\xa0On the other hand, there is also evidence that Stephens was fired only because of her nonconforming appearance and behavior at work, and not because of her transgender identity.\xa0See\xa0R. 53-6 (Rost Dep.',
'[citation omitted]\n\n\xa0\n\n*1994 The program imposes no geographic limitation: Parents may direct tuition payments to schools inside or outside the State, or even in foreign countries. [citation omitted] In schools that qualify for the program because they are accredited, teachers need not be certified by the State,…and Maine’s curricular requirements do not apply…Single-sex schools are eligible. [citation omitted]\n\n\xa0\n\nPrior to 1981, parents could also direct the tuition assistance payments to religious schools. Indeed, in the 1979–1980 school year, over 200 Maine students opted to attend such schools through the tuition assistance program. App. 72. In 1981, however, Maine imposed a new requirement that any school receiving tuition assistance payments must be “a nonsectarian school in accordance with the First Amendment of the United States Constitution.” [citation omitted] That provision was enacted in response to an opinion by the Maine attorney general taking the position that public funding of private religious schools violated the Establishment Clause of the First Amendment. We subsequently held, however, that a benefit program under which private citizens “direct government aid to religious schools wholly as a result of their own genuine and independent private choice” does not offend the Establishment Clause. [citation omitted] Following our decision in Zelman, the Maine Legislature considered a proposed bill to repeal the “nonsectarian” requirement, but rejected it. App. 100, 108.\n\n\xa0\n\nThe “nonsectarian” requirement for participation in Maine’s tuition assistance program remains in effect today. The Department has stated that, in administering this requirement, it “considers a sectarian school to be one that is associated with a particular faith or belief system and which, in addition to teaching academic subjects, promotes the faith or belief system with which it is associated and/or presents the material taught through the lens of this faith.” [citation omitted] “The Department’s focus is on what the school teaches through its curriculum and related activities, and how the material is presented.” …“[A]ffiliation or association with a church or religious institution is one potential indicator of a sectarian school,” but “it is not dispositive.”\n\n\xa0\n\n\xa0\n\nB\n\nThis case concerns two families that live in SAUs that neither maintain their own secondary schools nor contract with any nearby secondary school. App. 70, 71. Petitioners David and Amy Carson reside in Glenburn, Maine. Id., at 74. When this litigation commenced, the Carsons’ daughter attended high school at Bangor Christian Schools (BCS), which was founded in 1970 as a ministry of Bangor Baptist Church. Id., at 74, 80. The Carsons sent their daughter to BCS because of the school’s high academic standards and because the school’s Christian worldview aligns with their sincerely held religious beliefs. Id., at 74. Given that BCS is a “sectarian” school that cannot qualify for tuition assistance payments under Maine’s program, id., at 80, the Carsons paid the tuition for their daughter to attend BCS themselves, id., at 74.\n\n\xa0\n\nPetitioners Troy and Angela Nelson live in Palermo, Maine. Id., at 78. When this litigation commenced, the Nelsons’ daughter attended high school at Erskine Academy, a secular private school, and their son attended middle school at Temple Academy, a “sectarian” school affiliated with *1995 Centerpoint Community Church. Id., at 78, 90, 91. The Nelsons sent their son to Temple Academy because they believed it offered him a high-quality education that aligned with their sincerely held religious beliefs. Id., at 78. While they wished to send their daughter to Temple Academy too, they could not afford to pay the cost of the Academy’s tuition for both of their children. Id., at 79.\n\n\xa0\n\nBCS and Temple Academy are both accredited by the New England Association of Schools and Colleges (NEASC), and the Department considers each school a “private school approved for attendance purposes” under the State’s compulsory attendance requirement. Id., at 80, 90. Yet because neither school qualifies as “nonsectarian,” neither is eligible to receive tuition payments under Maine’s tuition assistance program. Id., at 80, 90. Absent the “nonsectarian” requirement, the Carsons and the Nelsons would have asked their respective SAUs to pay the tuition to send their children to BCS and Temple Academy, respectively. Id., at 79.\n\n\xa0\n\nIn 2018, petitioners brought suit against the commissioner of the Maine Department of Education. Id., at 11–12.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.4839 | 0.4839 | 0.4516 | 0.4409 | 0.3978 |
| cosine_accuracy@3 | 0.6989 | 0.7204 | 0.6882 | 0.6452 | 0.6022 |
| cosine_accuracy@5 | 0.7957 | 0.7849 | 0.7957 | 0.7634 | 0.7097 |
| cosine_accuracy@10 | 0.9247 | 0.9032 | 0.8817 | 0.8387 | 0.8065 |
| cosine_precision@1 | 0.4839 | 0.4839 | 0.4516 | 0.4409 | 0.3978 |
| cosine_precision@3 | 0.3799 | 0.3871 | 0.3656 | 0.3548 | 0.3405 |
| cosine_precision@5 | 0.2839 | 0.286 | 0.2796 | 0.2731 | 0.2602 |
| cosine_precision@10 | 0.172 | 0.1677 | 0.1656 | 0.1559 | 0.1538 |
| cosine_recall@1 | 0.2177 | 0.2231 | 0.2088 | 0.1873 | 0.1586 |
| cosine_recall@3 | 0.4884 | 0.5027 | 0.4718 | 0.4453 | 0.4059 |
| cosine_recall@5 | 0.5883 | 0.5936 | 0.5806 | 0.5726 | 0.526 |
| cosine_recall@10 | 0.7088 | 0.6944 | 0.6855 | 0.6541 | 0.6165 |
| **cosine_ndcg@10** | **0.5864** | **0.5845** | **0.565** | **0.5356** | **0.5019** |
| cosine_mrr@10 | 0.5963 | 0.595 | 0.5674 | 0.5453 | 0.5082 |
| cosine_map@100 | 0.4916 | 0.4987 | 0.4761 | 0.4511 | 0.4182 |
<!--
## Bias, Risks and Limitations
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### Recommendations
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 842 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 842 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 24 tokens</li><li>mean: 42.46 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 236 tokens</li><li>mean: 962.01 tokens</li><li>max: 1056 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Based on the court's ruling, under what circumstances can a college student be held accountable for off-campus speech, and how does this relate to the standards of professionalism in a professional school setting?</code> | <code>A serious question raised by Keefe in this case is whether the First Amendment protected his unprofessional speech from academic disadvantage because it was made in- on-line, off-campus Facebook postings. On appeal, Keefe framed this contention categorically, arguing that a college student may not be punished for off-campus speech unless it is speech that is unprotected by the First Amendment, such as obscenity. We reject this categorical contention. A student may demonstrate an unacceptable lack of professionalism off campus, as well as in the classroom, and by speech as well as conduct. See Yoder v. Univ. of Louisville, 526 Fed-Appx. 537, 545-46 (6th Cir.), cert. denied, — U.S. -, 134 S.Ct. 790, 187 L.Ed.2d 594 (2013); Tatro v. Univ. of Minn., 816 N.W.2d 509, 521 (Minn. 2012). Therefore, college administrators and educators in a professional school have discretion to require compliance with recognized standards of the profession, both on and off campus, “so long as their actions are ...</code> |
| <code>Describe the two-step framework that Courts of Appeals have developed for analyzing Second Amendment challenges. What are the implications of the Supreme Court's decision to reject this framework in favor of a historical tradition-based approach?</code> | <code>Petitioners sued respondents for declaratory and injunctive relief under…42 U.S.C. § 1983, alleging that respondents violated their Second and Fourteenth Amendment rights by denying their unrestricted-license applications on the basis that they had failed to show “proper cause,” i.e., had failed to demonstrate a unique need for self-defense.<br><br> <br><br>The District Court dismissed petitioners’ complaint and the Court of Appeals affirmed. [citation omitted] Both courts relied on [a] Court of Appeals’ prior decision…which had sustained New York’s proper-cause standard, holding that the requirement was “substantially related to the achievement of an important governmental interest.” [citation omitted]<br><br> <br><br>We granted certiorari to decide whether New York’s denial of petitioners’ license applications violated the Constitution. [citation omitted]<br><br> <br><br> <br><br>II<br><br>In Heller and McDonald, we held that the Second and Fourteenth Amendments protect an individual right to keep and bear arms for self-defense. ...</code> |
| <code>Discuss the implications of the California Alien Land Law as it pertains to the rights of American citizens, specifically in the case of Fred Oyama. How does the law affect his privileges as a citizen, and what constitutional protections are being challenged?</code> | <code>269<br><br>Supreme Court of the United States<br><br>OYAMA et al.<br><br>v.<br><br>STATE OF CALIFORNIA.<br><br>No. 44.<br><br>|<br><br>Argued Oct. 22, 1947.<br><br>|<br><br>Decided Jan. 19, 1948.<br><br>Opinion<br><br>*635 Mr. Chief Justice VINSON delivered the opinion of the Court.<br><br>Petitioners challenge the constitutionality of California’s Alien Land Law1 as it has been applied in this case to effect an escheat of two small parcels of agricultural land.2 One of the petitioners is Fred Oyama, a minor American citizen in whose name title was taken. The other is his father and guardian, Kajiro Oyama, a Japanese citizen not eligible for naturalization,3 who paid the purchase price.<br><br>Petitioners press three attacks on the Alien Land Law as it has been applied in this case: first, that it deprives Fred Oyama of the equal protection of the laws and of his privileges as an American citizen; secondly, that it denies Kajiro Oyama equal protection of the laws; and, thirdly, that it contravenes the due process clause by sanctioning a taking of property after ...</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 32
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.6038 | 1 | 0.5604 | 0.5631 | 0.5303 | 0.4907 | 0.4335 |
| 1.6038 | 2 | 0.5836 | 0.5758 | 0.5715 | 0.5180 | 0.4846 |
| 2.6038 | 3 | 0.5768 | 0.5841 | 0.5652 | 0.5296 | 0.4940 |
| **3.6038** | **4** | **0.5864** | **0.5845** | **0.565** | **0.5356** | **0.5019** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
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|
[
"BEAR",
"CAS"
] |
higopires/DeB3RTa-small
|
higopires
|
fill-mask
|
[
"transformers",
"safetensors",
"deberta-v2",
"fill-mask",
"portuguese",
"financial",
"bert",
"deberta",
"nlp",
"masked-lm",
"dataset:FAKE.BR",
"dataset:CAROSIA",
"dataset:BBRC",
"dataset:OFFCOMBR-3",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-02-19T04:22:10Z |
2025-02-21T13:38:32+00:00
| 29 | 0 |
---
datasets:
- FAKE.BR
- CAROSIA
- BBRC
- OFFCOMBR-3
library_name: transformers
license: mit
metrics:
- f1
- precision
- recall
- pr_auc
tags:
- portuguese
- financial
- bert
- deberta
- nlp
- fill-mask
- masked-lm
pt: pt-br
inference: true
model-index:
- name: DeB3RTa-base
results:
- task:
type: text-classification
name: Fake News Detection
dataset:
name: FAKE.BR
type: FAKE.BR
metrics:
- type: f1
value: 0.9598
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: CAROSIA
type: CAROSIA
metrics:
- type: f1
value: 0.8722
- task:
type: text-classification
name: Regulatory Classification
dataset:
name: BBRC
type: BBRC
metrics:
- type: f1
value: 0.6712
- task:
type: text-classification
name: Hate Speech Detection
dataset:
name: OFFCOMBR-3
type: OFFCOMBR-3
metrics:
- type: f1
value: 0.546
---
# DeB3RTa: A Transformer-Based Model for the Portuguese Financial Domain
DeB3RTa is a family of transformer-based language models specifically designed for Portuguese financial text processing. These models are built on the DeBERTa-v2 architecture and trained using a comprehensive mixed-domain pretraining strategy that combines financial, political, business management, and accounting corpora.
## Model Variants
Two variants are available:
- **DeB3RTa-base**: 12 attention heads, 12 layers, intermediate size of 3072, hidden size of 768 (~426M parameters)
- **DeB3RTa-small**: 6 attention heads, 12 layers, intermediate size of 1536, hidden size of 384 (~70M parameters)
## Key Features
- First Portuguese financial domain-specific transformer model
- Mixed-domain pretraining incorporating finance, politics, business, and accounting texts
- Enhanced performance on financial NLP tasks compared to general-domain models
- Resource-efficient architecture with strong performance-to-parameter ratio
- Advanced fine-tuning techniques including layer reinitialization, mixout regularization, and layer-wise learning rate decay
## Performance
The models have been evaluated on multiple financial domain tasks:
| Task | Dataset | DeB3RTa-base F1 | DeB3RTa-small F1 |
|------|----------|-----------------|------------------|
| Fake News Detection | FAKE.BR | 0.9906 | 0.9598 |
| Sentiment Analysis | CAROSIA | 0.9207 | 0.8722 |
| Regulatory Classification | BBRC | 0.7609 | 0.6712 |
| Hate Speech Detection | OFFCOMBR-3 | 0.7539 | 0.5460 |
## Training Data
The models were trained on a diverse corpus of 1.05 billion tokens, including:
- Financial market relevant facts (2003-2023)
- Financial patents (2006-2021)
- Research articles from Brazilian Scielo
- Financial news articles (1999-2023)
- Wikipedia articles in Portuguese
## Usage
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
# Load model and tokenizer
model = AutoModelForMaskedLM.from_pretrained("higopires/DeB3RTa-[base/small]")
tokenizer = AutoTokenizer.from_pretrained("higopires/DeB3RTa-[base/small]")
# Example usage
text = "O mercado financeiro brasileiro apresentou [MASK] no último trimestre."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```
## Citations
If you use this model in your research, please cite:
```bibtex
@article{pires2025deb3rta,
AUTHOR = {Pires, Higo and Paucar, Leonardo and Carvalho, Joao Paulo},
TITLE = {DeB3RTa: A Transformer-Based Model for the Portuguese Financial Domain},
JOURNAL = {Big Data and Cognitive Computing},
VOLUME = {9},
YEAR = {2025},
NUMBER = {3},
ARTICLE-NUMBER = {51},
URL = {https://www.mdpi.com/2504-2289/9/3/51},
ISSN = {2504-2289},
ABSTRACT = {The complex and specialized terminology of financial language in Portuguese-speaking markets create significant challenges for natural language processing (NLP) applications, which must capture nuanced linguistic and contextual information to support accurate analysis and decision-making. This paper presents DeB3RTa, a transformer-based model specifically developed through a mixed-domain pretraining strategy that combines extensive corpora from finance, politics, business management, and accounting to enable a nuanced understanding of financial language. DeB3RTa was evaluated against prominent models—including BERTimbau, XLM-RoBERTa, SEC-BERT, BusinessBERT, and GPT-based variants—and consistently achieved significant gains across key financial NLP benchmarks. To maximize adaptability and accuracy, DeB3RTa integrates advanced fine-tuning techniques such as layer reinitialization, mixout regularization, stochastic weight averaging, and layer-wise learning rate decay, which together enhance its performance across varied and high-stakes NLP tasks. These findings underscore the efficacy of mixed-domain pretraining in building high-performance language models for specialized applications. With its robust performance in complex analytical and classification tasks, DeB3RTa offers a powerful tool for advancing NLP in the financial sector and supporting nuanced language processing needs in Portuguese-speaking contexts.},
DOI = {10.3390/bdcc9030051}
}
```
## Limitations
- Performance degradation on the smaller variant, particularly for hate speech detection
- May require task-specific fine-tuning for optimal performance
- Limited evaluation on multilingual financial tasks
- Model behavior on very long documents (>128 tokens) not extensively tested
## License
MIT License
Copyright (c) 2025 Higo Pires
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
## Acknowledgments
This work was supported by the Instituto Federal de Educação, Ciência e Tecnologia do Maranhão and the Human Language Technology Lab in Instituto de Engenharia de Sistemas e Computadores—Investigação e Desenvolvimento (INESC-ID).
|
[
"SCIELO"
] |
Qwe1325/Llama-Breeze2-3B-Instruct_8bit
|
Qwe1325
| null |
[
"safetensors",
"internvl_chat",
"custom_code",
"en",
"zh",
"arxiv:2501.13921",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | 2025-02-27T02:58:44Z |
2025-02-28T00:48:53+00:00
| 29 | 0 |
---
language:
- en
- zh
license: llama3.2
---
# Llama-Breeze2-3B-Instruct-v0_1
【[Paper](https://arxiv.org/abs/2501.13921)】◇【[Kaggle Demo](https://www.kaggle.com/code/ycckaggle/demo-breeze-2-3b)】◇【[Collection](https://huggingface.co/collections/MediaTek-Research/llama-breeze2-67863158443a06a72dd29900)】
**The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on LLaMA with Vision-Aware and Function-Calling Capabilities**
Llama Breeze 2 is a suite of advanced multi-modal language models, available in 3B and 8B parameter configurations, specifically designed to enhance Traditional Chinese language representation.
Building upon the [LLaMA 3.2](https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/), Breeze 2 continues pretraining on an extensive corpus to enhance the linguistic and cultural heritage of Traditional Chinese.
It incorporates vision-aware capabilities through a visual encoder and a bridge module, and supports function-calling via prompt templates and post-training on function-calling data.
*Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.*
*We list all contributors in alphabetical order of their first names, as follows: Chan-Jan Hsu (許湛然), Chia-Sheng Liu (劉佳昇), Meng-Hsi Chen (陳孟羲), Muxi Chen (陳沐希), Po-Chun Hsu (許博竣), Yi-Chang Chen (陳宜昌), and the supervisor Da-Shan Shiu (許大山).*
## Installation
```
pip3 install transformers==4.47.0
pip3 install -U bitsandbytes
pip3 install -U mtkresearch
```
```python
from transformers import AutoModel, AutoTokenizer
from transformers import GenerationConfig
import torch
from mtkresearch.llm.prompt import MRPromptV3
model_id = 'Qwe1325/Llama-Breeze2-3B-Instruct_8bit'
model = AutoModel.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
load_in_8bit=True,
low_cpu_mem_usage=True,
trust_remote_code=True,
device_map='auto',
img_context_token_id=128212
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, use_fast=False)
generation_config = GenerationConfig(
max_new_tokens=2048,
do_sample=True,
temperature=0.01,
top_p=0.01,
repetition_penalty=1.1,
eos_token_id=128009
)
prompt_engine = MRPromptV3()
sys_prompt = 'You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan.'
def _inference(tokenizer, model, generation_config, prompt, pixel_values=None):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
if pixel_values is None:
output_tensors = model.generate(**inputs, generation_config=generation_config)
else:
output_tensors = model.generate(**inputs, generation_config=generation_config, pixel_values=pixel_values.to(model.dtype).to(model.device))
output_str = tokenizer.decode(output_tensors[0])
return output_str
```
## Feature: Instruction Following
```python
conversations = [
{"role": "system", "content": sys_prompt},
{"role": "user", "content": "請問什麼是深度學習？"},
]
prompt = prompt_engine.get_prompt(conversations)
output_str = _inference(tokenizer, model, generation_config, prompt)
result = prompt_engine.parse_generated_str(output_str)
print(result)
# {'role': 'assistant', 'content': '深度學習是一種人工智慧技術，主要是透過類似於大腦神經網路的方式來處理和分析資料。這個方法利用多層的人工神經元模仿生物神經網路的運作模式，讓電腦能夠從大量數據中學習並做出預測或決策。\n\n簡單來說，深度學習就是一種用機器學習的方式來訓練電腦，使其能夠像人類一樣理解、分辨及解決問題。這項技術已被廣泛應用在各種領域，如圖像識別、自然語言處理、語音辨識以及自動駕駛等方面。'}
```
## Feature: Visual Instruction Following
Example Image:
```python
conversations = [
{"role": "system", "content": sys_prompt},
{"role": "user", "content": [
{
"type": "image",
"image_path": /path/to/example-image,
},
{
"type": "text",
"text": "請問第二名可獲得多少獎金？"
},
]},
]
prompt, pixel_values = prompt_engine.get_prompt(conversations)
output_str = _inference(tokenizer, model, generation_config, prompt, pixel_values=pixel_values)
result = prompt_engine.parse_generated_str(output_str)
print(result)
# {'role': 'assistant', 'content': '第二名可獲得20萬元整。'}
```
## Feature: Function Calling
```python
import json
functions = [
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"]
}
},
"required": ["location"]
}
}
]
def fake_get_current_weather(location, unit=None):
return {'temperature': 30}
mapping = {
'get_current_weather': fake_get_current_weather
}
# stage 1: query
conversations = [
{"role": "user", "content": "請問台北目前溫度是攝氏幾度？"},
]
prompt = prompt_engine.get_prompt(conversations, functions=functions)
output_str = _inference(tokenizer, model, generation_config, prompt)
result = prompt_engine.parse_generated_str(output_str)
print(result)
# {'role': 'assistant', 'tool_calls': [{'id': 'call_iuwELWUShiAKE16CVoumawZ4', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"location": "台北", "unit": "celsius"}'}}]}
```
```python
# stage 2: execute called functions
conversations.append(result)
tool_call = result['tool_calls'][0]
func_name = tool_call['function']['name']
func = mapping[func_name]
arguments = json.loads(tool_call['function']['arguments'])
called_result = func(**arguments)
# stage 3: put executed results
conversations.append(
{
'role': 'tool',
'tool_call_id': tool_call['id'],
'name': func_name,
'content': json.dumps(called_result)
}
)
prompt = prompt_engine.get_prompt(conversations, functions=functions)
output_str2 = _inference(tokenizer, model, generation_config, prompt)
result2 = prompt_engine.parse_generated_str(output_str2)
print(result2)
# {'role': 'assistant', 'content': '台北目前的溫度是攝氏30度。'}
```
## Citation
```
@article{breeze2,
title={The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on LLaMA with Vision-Aware and Function-Calling Capabilities},
author={Breeze Team, MediaTek Research},
journal={arXiv},
year={2025},
url={https://arxiv.org/abs/2501.13921}
}
```
|
[
"CHIA"
] |
stabletoolbench/MirrorAPI-Cache
|
stabletoolbench
| null |
[
"safetensors",
"qwen2",
"license:mit",
"region:us"
] | 2025-02-27T17:02:43Z |
2025-03-05T08:25:23+00:00
| 29 | 0 |
---
license: mit
---
# MirrorAPI-Cache
This model is a fine-tuned version of [StableToolBench-MirrorAPI](https://huggingface.co/stabletoolbench/MirrorAPI).
### Training and evaluation data
The training data is [`train_cache.json`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI-Training/blob/main/train_cache.json).
The testing data is [`test_cache.json`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI-Bench/blob/main/test_cache.json).
## Testing with LLaMA-Factory
### Setting up LLaMA-Factory
Please refer to [LLaMA-Factory/README.md](https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#getting-started).
### Data Preparation
As we use custom datasets, please make sure to add a dataset description in `dataset_info.json` and specify `dataset: dataset_name` before using it.
For instance of adding [`test_cache.json`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI-Bench/blob/main/test_cache.json):
```
{
...
"test_cache": {
"file_name": "path/to/test_cache.json",
"columns": {
"prompt": "instruction",
"response": "output",
"system": "system"
}
},
...
}
```
For more details, please refer to [LLaMA-Factory/data/README.md](https://github.com/hiyouga/LLaMA-Factory/blob/main/data/README.md).
### Quickstart
Run the following script under the root path of LLaMA-Factory and adjust the hyperparameters accordingly:
```
#!/bin/bash
# Variables to be set up
export CUDA_VISIBLE_DEVICES=
NPROC_PER_NODE=
MODEL_PATH="/path/to/MirrorAPI"
OUTPUT_PATH="/path/to/output"
EVAL_DATASET="test_cache" # replace with other dataset_name if needed
DISTRIBUTED_ARGS="
--nproc_per_node $NPROC_PER_NODE \
--nnodes 1 \
"
torchrun $DISTRIBUTED_ARGS src/train.py \
--do_predict \
--predict_with_generate \
--model_name_or_path $MODEL_PATH \
--eval_dataset $EVAL_DATASET \
--max_samples 200 \ # use 200 samples to align with references, remove this line if not needed
--stage sft \
--template qwen \
--preprocessing_num_workers 16 \
--finetuning_type full \
--output_dir $OUTPUT_PATH \
--max_new_tokens 2660 \
--bf16 \
--report_to none \
--flash_attn auto \
--cutoff_len 2560 \
--seed 42 \
--per_device_eval_batch_size 1 \
--overwrite_cache
```
## Prompts
When running inference, you should provide two main prompts
### System prompt
Sets the overall behavior and indicate whether the model should operate in SFT mode or Chain of Thought (CoT) mode.
- To enable CoT mode, prepend [CHAIN_OF_THOUGHT] to your system prompt, which will guide the model to include or leverage chain-of-thought reasoning in its answers.
- For standard SFT mode, omit this prefix.
__SFT mode:__
```
Imagine you are an API Server operating within a specialized tool, which contains a collection of distinct APIs. Your role is to deeply understand the function of each API based on their descriptions in the API documentation. As you receive specific inputs for individual API calls within this tool, analyze these inputs to determine their intended purpose. Your task is to craft a JSON formatted response that aligns with the expected output of the API. The JSON scheme is:
{
"error": "",
"response": ""
}
The error field should remain empty, indicating no errors in processing. The response field should contain the content you formulate based on the API's functionality and the input provided. Ensure that your responses are meaningful, directly addressing the API's intended functionality.
The key is to maintain the JSON format's integrity while ensuring that your response is an accurate reflection of the API's intended output within the tool.
Please note that your answer should not contain anything other than a json format object, which should be parsable directly to json.
Note that:
- your response should contain rich information given the api input parameters.
- your response must be effective and have practical content.
API calls may fail for various reasons, such as invalid input parameters, authentication issues, or server errors. Your goal is to generate a response that accurately reflects the API's intended functionality, even if the input parameters are incorrect. Your response should be informative and relevant to the API's purpose, providing a clear and concise explanation of the expected output based on the input provided.
Here is an example:
API doc:
{
"api_name": "List Languages",
"api_description": "Get a list of currently supported languages. We are constantly adding more every few weeks.",
"required_parameters": [],
"optional_parameters": [],
"tool_description": "Introducing our cutting-edge text to speech service, designed to provide you with the most realistic human-sounding voices at an affordable price. Our service is fast and reliable, delivering high-quality audio output in a matter of seconds. Additionally, we offer a wide range of languages and a variety of voice choices, so you can find the perfect fit for your project. Whether you need a voiceover for a video, an audiobook, or any other project, our text to speech service has you covered. Ex...",
"tool_name": "TTSKraken",
"tool_category": "Artificial_Intelligence_Machine_Learning"
}
Request:
data = {
"category": "Artificial_Intelligence_Machine_Learning",
"tool_name": "TTSKraken",
"api_name": "List Languages",
"tool_input": "{}",
"strip": "filter",
}
Response:
{
"error": "",
"response": "{"status":0,"msg":"Success","languages":["en","fr-fr","pt-br"]}"
}
```
__CoT mode:__
```
[CHAIN_OF_THOUGHT]
You are an API Server operating within a specialized tool, tasked with understanding the purpose of each API based on provided documentation. Your job is to process specific API inputs and craft a well-formatted response reflecting the API's intended functionality. You should first infer the mechanism behind the API and then provide your response based on the input parameters.
Your response must follow this JSON structure:
{
"mechanism_of_the_api": "",
"error": "",
"response": ""
}
* MECHANISIM OF THE API: Try to infer how the API functions based on the input parameters.
* ERROR: Leave empty unless there's an issue with the input.
* RESPONSE: Provide content based on the API's function. If examples are ineffective, give an independent, meaningful response.
Note:
* Ensure responses are practical, clear, and relevant.
* Handle incorrect input gracefully by explaining expected behavior.
Example:
API doc:
{
"api_name": "List Languages",
"api_description": "Get a list of currently supported languages. We are constantly adding more every few weeks.",
"required_parameters": [],
"optional_parameters": [],
"tool_description": "Introducing our cutting-edge text to speech service, designed to provide you with the most realistic human-sounding voices at an affordable price. Our service is fast and reliable, delivering high-quality audio output in a matter of seconds. Additionally, we offer a wide range of languages and a variety of voice choices, so you can find the perfect fit for your project. Whether you need a voiceover for a video, an audiobook, or any other project, our text to speech service has you covered. Ex...",
"tool_name": "TTSKraken",
"tool_category": "Artificial_Intelligence_Machine_Learning"
}
Request:
data = {
"category": "Artificial_Intelligence_Machine_Learning",
"tool_name": "TTSKraken",
"api_name": "List Languages",
"tool_input": "{}",
"strip": "filter",
}
Response:
{
"mechanism_of_the_api": "The "List Languages" API for the TTSKraken service returns a list of supported languages for their text-to-speech offerings. It performs a straightforward operation by querying a dynamic data source, likely a database, which stores language information. When the API is invoked, it retrieves all available languages without requiring additional parameters. The list of languages is formatted as a JSON response, as indicated by the example response showing language codes such as "en" for English and "fr-fr" for French. This mechanism allows users to understand what languages the TTSKraken service supports, aligning with the tool's goal of providing diverse, high-quality voice options.",
"error": "",
"response": "{"status":0,"msg":"Success","languages":["en","fr-fr","pt-br"]}"
}
Ensure responses are directly aligned with the API's intended output and maintain correct formatting.
```
### User prompt format
- Contains the user’s actual query or task request.
- Determines the API functionality to which the model responds.
```
API doc:
{{api_doc_in_json_format}}
Request:
{{request_in_json_format}}
```
|
[
"CRAFT"
] |
Amir13/xlm-roberta-base-ncbi_disease-en
|
Amir13
|
token-classification
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"arxiv:2302.09611",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-14T13:59:27Z |
2023-02-21T06:52:22+00:00
| 28 | 0 |
---
datasets:
- ncbi_disease
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-ncbi_disease-en
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: ncbi_disease
type: ncbi_disease
config: ncbi_disease
split: validation
args: ncbi_disease
metrics:
- type: precision
value: 0.8562421185372006
name: Precision
- type: recall
value: 0.8627700127064803
name: Recall
- type: f1
value: 0.859493670886076
name: F1
- type: accuracy
value: 0.9868991989319092
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-ncbi_disease-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ncbi_disease](https://huggingface.co/datasets/ncbi_disease) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0496
- Precision: 0.8562
- Recall: 0.8628
- F1: 0.8595
- Accuracy: 0.9869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 170 | 0.0555 | 0.7949 | 0.7980 | 0.7964 | 0.9833 |
| No log | 2.0 | 340 | 0.0524 | 0.7404 | 0.8551 | 0.7936 | 0.9836 |
| 0.0803 | 3.0 | 510 | 0.0484 | 0.7932 | 0.8869 | 0.8374 | 0.9849 |
| 0.0803 | 4.0 | 680 | 0.0496 | 0.8562 | 0.8628 | 0.8595 | 0.9869 |
| 0.0803 | 5.0 | 850 | 0.0562 | 0.7976 | 0.8615 | 0.8283 | 0.9848 |
| 0.0152 | 6.0 | 1020 | 0.0606 | 0.8086 | 0.8856 | 0.8454 | 0.9846 |
| 0.0152 | 7.0 | 1190 | 0.0709 | 0.8412 | 0.8412 | 0.8412 | 0.9866 |
| 0.0152 | 8.0 | 1360 | 0.0735 | 0.8257 | 0.8666 | 0.8456 | 0.9843 |
| 0.0059 | 9.0 | 1530 | 0.0730 | 0.8343 | 0.8767 | 0.8550 | 0.9866 |
| 0.0059 | 10.0 | 1700 | 0.0855 | 0.8130 | 0.8895 | 0.8495 | 0.9843 |
| 0.0059 | 11.0 | 1870 | 0.0868 | 0.8263 | 0.8767 | 0.8508 | 0.9860 |
| 0.0026 | 12.0 | 2040 | 0.0862 | 0.8273 | 0.8767 | 0.8513 | 0.9858 |
| 0.0026 | 13.0 | 2210 | 0.0875 | 0.8329 | 0.8806 | 0.8561 | 0.9859 |
| 0.0026 | 14.0 | 2380 | 0.0889 | 0.8287 | 0.8793 | 0.8533 | 0.9859 |
| 0.0013 | 15.0 | 2550 | 0.0884 | 0.8321 | 0.8755 | 0.8533 | 0.9861 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
### Citation
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
[
"NCBI DISEASE"
] |
ManglerFTW/CharHelper_Fine-Tuned
|
ManglerFTW
|
text-to-image
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"doi:10.57967/hf/0426",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-03-04T13:59:08Z |
2023-04-16T22:41:11+00:00
| 28 | 3 |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
<b>Introduction:</b>
This model was trained from the ground up using Stable Tuner's fine-tuning method and utilizing contrast fix for darker darks and bolder colors. The Dataset contains 4900 images trained to 35 epochs.
File Name is CharHelper Fine-Tuned.safetensors. Do not forget to download the yaml file and place it in the same directory.<br />
## Usage:
## IMPORTANT:
Because of the nature of the fine-tuning method, this model is sensitive with the CFG Scale. Photorealism tends to like a <b>LOW CFG Scale</b>. Best result can be found between <b>3 and 7</b>. Some subjects that are complex like robots like a higher dfg, while photorealism is mostly achieved with a CFG Scale of 3 or 4.</b>
<b>Use Auto for the vae in settings. If you are using a vae based on a SDv1.5 model, you may not get the best results.</b>
<br />
CharHelper Fined-Tuned was trained all at once which means the keywords all have more power to them than the previous CharHelper models. CharHelper Fine-Tuned doesn't need keywords but includes them and they can be mixed and matched together in order to achieve a multitude of different styles.
Some Keywords were changed slightly from the last version.
<b>Keywords:</b>
<b>Character Styles:</b>
CHV3CBigChief, CHV3CBoxer, CHV3CUrban, CHV3COrc, CHV3CGanesh, CHV3CGolem,CHV3CCyberpunk, CHV3CSamurai, CHV3CRobot, CHV3CZombie, CHV3CBird, CHV3MDragon, CHV3CKnight, CHV3CWizard, CHV3CBarb, CHV3CVehicle, CHV3CTroll, CHV3CReaper, CHV3CRogue, CHV3CAlien
<b>Scenery/Styles:</b>
CHV3SDark, CHV3SUrban, CHV3SEldritch, CHV3SLighthouse, CHV3SCute, CHV3SMacro, CHV3SSciFi, CHV3SWorld
## Examples:
<b>Shimmering Details</b>
a realistic detail of a close up of a woman with blue makeup on her face in the dark, CHV3SDark, dark night time photo, taken in darkness, macro details, glowing blue face, dark skin, femme on a galactic shore, dark blue skin, color portrait, blue holographic face, cosmic girl, Professional, masterpiece, commissioned
Negative prompt: framed, cropped, over-exposed, over-saturated, amateur, (b&w), (close-up), (duplicate), (deformed), blurry, (bad proportions), gross proportions, ugly, tiling, poorly drawn, mutation, mutated, disfigured, deformed, out of frame, blurry, bad art, text, logo, signature, watermark, cross-eyes
Steps: 10, Sampler: DPM++ SDE, CFG scale: 3, Seed: 1256750850, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Aliens</b>
a realistic detail of a blue skinned alien, dark supervillain, 8k, epic character art, Professional, masterpiece, commissioned
Negative prompt: framed, cropped, over-exposed, over-saturated, amateur, (b&w), (close-up), (duplicate), (deformed), blurry, (bad proportions), gross proportions, ugly, tiling, poorly drawn, mutation, mutated, disfigured, deformed, out of frame, blurry, bad art, text, logo, signature, watermark, cross-eyes
Steps: 10, Sampler: DPM++ SDE, CFG scale: 7, Seed: 3489145082, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Creepy Clown Ladies</b>
a realistic detail of a very creepy zombie clown lady, wearing ornate streetwear, beautiful, detailed portrait, complexity, 4k, concept art, sharp focus, volumetric lighting, cinematic lighting, studio quality
Negative prompt: framed, cropped, over-exposed, over-saturated, amateur, (b&w), (close-up), (duplicate), (deformed), blurry, (bad proportions), gross proportions, ugly, tiling, poorly drawn, mutation, mutated, disfigured, deformed, out of frame, blurry, bad art, text, logo, signature, watermark, cross-eyes
Steps: 10, Sampler: DPM++ SDE, CFG scale: 5.5, Seed: 912489906, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Big Chiefs</b>
an analog photo of a man wearing a colorful feathered costume with ornate patterns of beads and colorful jewels at a carnival celebration, CHV3CBigChief, fixed in post, color corrected, Professional, masterpiece, commissioned, attractive face, facial expression, professional hands, professional anatomy
Negative prompt: smiling, face paint, long hair, crossed eyes, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 3.5, Seed: 2798464398, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Robotic Spiders</b>
Steampunk cybernetic biomechanical jumping spider, very coherent symmetrical artwork, CHV3CRobot, CHV3CVehicle, CHV3SMacro, Macro details, focus stacking, realistic render, 8k, micro detail, elegant, highly detailed, centered, smooth, sharp focus, artgerm, tomasz alen kopera, wlop
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 7, Seed: 4212360837, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Cybernetic Andriods</b>
a woman with tattoos and a face mask, CHV3CCyberpunk, portrait of a cyberpunk cyborg, portrait of a cyborg, cyborg woman, cyborg girl, cute cyborg girl, portrait of a cyberpunk machine, cyberpunk skeleton, cyberpunk face
Negative prompt: framed, cropped, over-exposed, over-saturated, amateur, (b&w), (close-up), (duplicate), (deformed), blurry, (bad proportions), gross proportions, ugly, tiling, poorly drawn, mutation, mutated, disfigured, deformed, out of frame, blurry, bad art, text, logo, signature, watermark, cross-eyes
Steps: 10, Sampler: DPM++ SDE, CFG scale: 4.5, Seed: 3438218591, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Cute Rubber Duckies</b>
Shiny gemstone in the shape of a rubber duck floating in a pool of colorful perfume, liquid ripples, waves, water droplets, photorealism, mystical, enigmatic, digital oil painting, trending on artstation, Professional, masterpiece, commissioned
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy, nfixer
Steps: 10, Sampler: DPM++ SDE, CFG scale: 4, Seed: 1139349539, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Big Cheif Ganesh</b>
Ganesh in an elaborate feathered costume with 2 arms, anthropomorphic elephant Shinigami at a shrine, a realistic detail, CHV3CSamurai, CHV3CBigChief, CHV3CGanesh, Professional, masterpiece, commissioned, professional hands, professional anatomy
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 4, Seed: 2766758959, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Astronauts</b>
a professional Analog photo of a female space astronaut wearing an blue and white space suit exploring a river in a dark mossy canyon on another planet, helmet, medium shot portrait, gold tinted face shield, (dark atmosphere), haze, halation, bloom, dramatic atmosphere, sci-fi movie still
Negative prompt: crossed eyes, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 4, Seed: 3046156075, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Zombies</b>
a realistic detail of a dark close-up of the face of a creepy haunting undead zombie, CHV3CZombie, horror concept art, zombified mutant flesh creature, Artwork by the walking dead, Professional, masterpiece, commissioned, wojtek fus, stefan gesell,
Negative prompt: symmetry, framed, cropped, over-exposed, over-saturated, amateur, (b&w), (close-up), (duplicate), (deformed), blurry, (bad proportions), gross proportions, ugly, tiling, poorly drawn, mutation, mutated, disfigured, deformed, out of frame, blurry, bad art, text, logo, signature, watermark, cross-eyes
Steps: 10, Sampler: DPM++ SDE, CFG scale: 4.5, Seed: 2922910579, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Dark Neon Cyberpunks</b>
a beautiful geisha wearing a kabuki mask, CHV3CSamurai elegant neon light tribal armor, shikigami, CHV3SDark dark background, cyberpunk darksynth, Professional, masterpiece, commissioned, professional hands, professional anatomy, muted saturation
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 5, Seed: 2772342268, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Dark Neon Robots</b>
a futuristic cybernetic robot wearing neon samurai armor, dark background, vaporware, cyberpunk darksynth, Professional, masterpiece, commissioned, muted saturation, artwork by daft punk
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 3.5, Seed: 3588684930, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Dramatic Lighting</b>
a realistic portrait of a beautiful woman holding a paper boat lantern in the dark, CHV3SDark, photo taken at night, on a dark background, floating lanterns, unsplash contest winning photo, shot with sigma f/ 4.2
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 5, Seed: 1111180199, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Big Chief Bears</b>
a n illustrated medium shot portrait of an anthropomorphic dire wolf in a colorful elaborate feathered costume with ornate details, anime style, CHV3CBigChief, warhammer 40k, octane, bling, Professional, masterpiece, commissioned, at a comic-con, artwork by wlop and loish
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 4, Seed: 338610140, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Artistic Landscapes</b>
a colorful vector illustration of a neon temple with an elaborate Torana gateway in absolute darkness on a small island at night with colorful neon star trails, black shadows, clear sky with professional star trails, high antialiasing, night, cliffside, crashing waves, highlands, farm, crisp clean shapes, mountains, serene landscape, neon inkpunk color scheme, painting of a listing for a realty website, artwork by studio ghibli, spirited away
Negative prompt: cartoon, painting, painted, drawn, drawing, anime, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality
Steps: 10, Sampler: DPM++ SDE, CFG scale: 5.5, Seed: 45256504, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Knights</b>
Diablo action game cyborg viking, highly detailed, sharp focus, cinematic lighting, art, octane render, unreal engine lumen, very coherent. cinematic, hyper realism, high detail, octane render, 8k, Professional, masterpiece, commissioned
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy, nfixer
Steps: 10, Sampler: DPM++ SDE, CFG scale: 6, Seed: 241022433, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Fighters</b>
CHV3CKBoxer, a realistic detail of a close up of a man wearing vibrant boxing gloves is in a boxing ring, photograph by Esther Lin, posing for a fight, boxing stance, Professional, masterpiece, commissioned, attractive face, facial expression, professional anatomy
Negative prompt: framed, cropped, over-exposed, over-saturated, amateur, (b&w), (close-up), (duplicate), (deformed), blurry, (bad proportions), gross proportions, ugly, tiling, poorly drawn, mutation, mutated, disfigured, deformed, out of frame, blurry, bad art, text, logo, signature, watermark, cross-eyes
Steps: 10, Sampler: DPM++ SDE, CFG scale: 4.5, Seed: 3289278897, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Illustrated Characters</b>
A medium profile shot of an anthropomorphic evil looking furry bear monster in heavy CHV3CKnight armor, hyper realistic, extremely detailed, 8k wallpaper, Professional, masterpiece, commissioned, flat shading, ink punk, thick pastel paint, thick pen lines, attractive face, facial expression, professional hands, professional anatomy
Negative prompt: over-saturated, over-exposed, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 5.5, Seed: 3745736625, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Stylish Photorealism</b>
a professional Analog photo of a medium shot of beautiful urban model wearing Coco Chanel out at night in the city, armani fur coat, nikon D5600, 35mm lens, Professional, masterpiece, commissioned, attractive face, facial expression, fixed in post, color corrected
Negative prompt: crossed eyes, amateur, extra limbs, extra barrel, b&w, close-up, duplicate, mutilated, extra fingers, mutated hands, deformed, blurry, bad proportions, extra limbs, cloned face, out of frame, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, tripod, tube, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy
Steps: 10, Sampler: DPM++ SDE, CFG scale: 3.5, Seed: 2814225442, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
<b>Futuristic Masks</b>
tribal mask in wakandan style cyberpunk, ultra realistic, concept art, intricate details, eerie, horror, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and alphonse mucha, Professional, masterpiece, commissioned
Negative prompt: framed, cropped, over-exposed, over-saturated, amateur, (b&w), (close-up), (duplicate), (deformed), blurry, (bad proportions), gross proportions, ugly, tiling, poorly drawn, mutation, mutated, disfigured, deformed, out of frame, blurry, bad art, text, logo, signature, watermark, cross-eyes
Steps: 10, Sampler: DPM++ SDE, CFG scale: 7, Seed: 4242822040, Size: 768x896, Model hash: 4812a6e5a5, ENSD: 3
|
[
"BEAR"
] |
IIC/roberta-large-bne-ctebmsp
|
IIC
|
token-classification
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"biomedical",
"clinical",
"spanish",
"roberta-large-bne",
"token-classification",
"es",
"dataset:lcampillos/ctebmsp",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-21T06:50:46Z |
2025-01-17T10:50:59+00:00
| 28 | 0 |
---
datasets:
- lcampillos/ctebmsp
language: es
license: apache-2.0
metrics:
- f1
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- spanish
- roberta-large-bne
model-index:
- name: IIC/roberta-large-bne-ctebmsp
results:
- task:
type: token-classification
dataset:
name: CT-EBM-SP (Clinical Trials for Evidence-based Medicine in Spanish)
type: lcampillos/ctebmsp
split: test
metrics:
- type: f1
value: 0.877
name: f1
---
# roberta-large-bne-ctebmsp
This model is a finetuned version of roberta-large-bne for the CT-EBM-SP (Clinical Trials for Evidence-based Medicine in Spanish) dataset used in a benchmark in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks`. The model has a F1 of 0.877
Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information.
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 64 |
| learning rate | 2e-05 |
| classifier dropout | 0.1 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtext
@article{10.1093/jamia/ocae054,
author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
journal = {Journal of the American Medical Informatics Association},
volume = {31},
number = {9},
pages = {2137-2146},
year = {2024},
month = {03},
issn = {1527-974X},
doi = {10.1093/jamia/ocae054},
url = {https://doi.org/10.1093/jamia/ocae054},
}
```
|
[
"CT-EBM-SP"
] |
IIC/XLM_R_Galen-pharmaconer
|
IIC
|
token-classification
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"biomedical",
"clinical",
"spanish",
"XLM_R_Galen",
"token-classification",
"es",
"dataset:PlanTL-GOB-ES/pharmaconer",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-21T16:20:10Z |
2024-11-25T10:41:28+00:00
| 28 | 0 |
---
datasets:
- PlanTL-GOB-ES/pharmaconer
language: es
license: mit
metrics:
- f1
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- spanish
- XLM_R_Galen
widget:
- text: Se realizó estudio analítico destacando incremento de niveles de PTH y vitamina
D (103,7 pg/ml y 272 ng/ml, respectivamente), atribuidos al exceso de suplementación
de vitamina D.
- text: ' Por el hallazgo de múltiples fracturas por estrés, se procedió a estudio
en nuestras consultas, realizándose análisis con función renal, calcio sérico
y urinario, calcio iónico, magnesio y PTH, que fueron normales.'
- text: Se solicitó una analítica que incluía hemograma, bioquímica, anticuerpos antinucleares
(ANA) y serologías, examen de orina, así como biopsia de la lesión. Los resultados
fueron normales, con ANA, anti-Sm, anti-RNP, anti-SSA, anti-SSB, anti-Jo1 y anti-Scl70
negativos.
model-index:
- name: IIC/XLM_R_Galen-pharmaconer
results:
- task:
type: token-classification
dataset:
name: pharmaconer
type: PlanTL-GOB-ES/pharmaconer
split: test
metrics:
- type: f1
value: 0.915
name: f1
---
# XLM_R_Galen-pharmaconer
This model is a finetuned version of XLM_R_Galen for the pharmaconer dataset used in a benchmark in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks`. The model has a F1 of 0.915
Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information.
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 16 |
| learning rate | 3e-05 |
| classifier dropout | 0.1 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtext
@article{10.1093/jamia/ocae054,
author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
journal = {Journal of the American Medical Informatics Association},
volume = {31},
number = {9},
pages = {2137-2146},
year = {2024},
month = {03},
issn = {1527-974X},
doi = {10.1093/jamia/ocae054},
url = {https://doi.org/10.1093/jamia/ocae054},
}
```
|
[
"PHARMACONER"
] |
BigSalmon/InformalToFormalLincoln102Paraphrase
|
BigSalmon
|
text-generation
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-26T19:59:24Z |
2023-07-08T22:40:54+00:00
| 28 | 0 |
---
{}
---
data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln102Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln102Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
```
```
description: business
keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification
```
```
3. In this task, you are given a company name and you need to find its industry.
McDonalds -- Restaurant
Facebook -- Social Network
IKEA -- Furniture
American Express -- Credit Services
Nokia -- Telecom
Nintendo -- Entertainment
4. In this task, you are given a Month and you need to convert it to its corresponding season
April -- Spring
December -- Winter
July -- Summer
October -- Fall
February -- Winter
5. In this task, you are given a sentence with a missing word and you need to predict the correct word.
Managers should set an _____ for their employees. -- example
Some people spend more than four _____ in the gym. -- hours
The police were on the _____ of arresting the suspect. -- verge
They were looking for _____ on how to solve the problem. -- guidance
What is the _____ of the coffee? -- price
6. In this task, you are given a paragraph and you need to reorder it to make it logical.
It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters.
It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman.
It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth.
```
```
trivia: What is the population of South Korea?
response: 51 million.
***
trivia: What is the minimum voting age in the US?
response: 18.
***
trivia: What are the first ten amendments of the US constitution called?
response: Bill of Rights.
```
```
ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences
related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions
***
ideas: i know this one guy who retired so young, attesting to how careful they were with money.
related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion
```
```
less specific: actors and musicians should ( support democracy ).
clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ).
***
less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ).
clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ).
```
```
dull: clean
emotional heft: spotless, immaculate, pristine
***
dull: hot
emotional heft: scorching, searing, blistering
***
dull: happy
emotional heft: euphoric
```
```
text: {guide: vividly describe the premise of the show "seinfield"} -> set in the heart of bustling new york city, the sitcom "seinfield" follows the everyday {restrict: term that implies they get into trouble but in a comical way} -> misadventures of four neurotic friends. on any given episode, one can find them quarreling over their favorite diner's latest menu change, haggling over the division of household expenses, or contriving a scheme to spy on the apartment's newest resident. mundane as their exploits may be, they never fail to elicit a hearty laugh. {guide: mention how larry david is responsible} -> behind the show's witty, incisive dialogue lies the sharp pen of larry david, who co-created the show with jerry seinfeld. {guide: mention how larry david came up with the most recognizable lines} -> it is his genius that conjured such instantly {restrict: term that imply everybody knows them} -> recognizable quips as "no soup for you!" and "not that there's anything wrong with that!". {guide: mention how humanity should revel in having such good comedians these days} -> as this list of laugh-out-loud lines attests, the world is fortunate to ( count such a sharp comedic mind among its ranks / have such a talented humorist in its midst / have such comedic talent gracing its airwaves ).
```
```
system: i'm not sure i agree with your assessment of the situation.
user: replace "assessment" with a more casual term.
chatbot: ( take / read / view ).
system: i've been feeling really busy lately, trying to balance work and family responsibilities.
user: replace "busy" with a more intense word that conveys it is too much.
chatbot: overwhelmed.
system: the candidate's stance on immigration is quite nebulous, and it's difficult to discern exactly what they would do if elected.
user: replace "nebulous" with a word that conveys a lack of detail.
chatbot: ( vague / ambiguous ).
```
```
user: exorbitant in price, scholarly journals unconscionably define the breadth of one's knowledge awareness by the contents of their wallet. [replace “knowledge awareness” with a more natural expression]
chatbot: intellectual horizons.
user: can you do another alternative to “intellectual horizons” that has more relation to “scholarly journals”?
chatbot: academic enlightenment.
```
```
key: calculate.
syn: estimate, consider, weigh, number, count, apportion, proportion, investigate, reckon, rate, compute.
ant: guess, conjecture, hit, chance, risk, stake, miscalculate.
```
```
description: more forceful version of curious that is less forceful than nosy
answer: inquisitive
description: more forceful version of hopeful that is less forceful than overconfident
answer: optimistic
```
```
key: inquisitive
positive: curious, interested
negative: nosy, prying
***
key: witty
positive: clever, humorous
negative: sarcastic, caustic
***
key: influential
positive: impactful, powerful
negative: overbearing, domineering
```
```
defective: the blogger's { use of language imprecise } confused an already complicated issue.
precise: the blogger's ( vague wording ) confused an already complicated issue.
defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals.
precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals.
```
```
example: the new car uses gas.
boring: uses
stronger: guzzles
example: he hates people that are rude.
boring: hates
stronger: loathes, abhors, despises, scorns, detests
```
|
[
"BEAR"
] |
pierre-loic/climate-news-articles
|
pierre-loic
|
text-classification
|
[
"transformers",
"pytorch",
"safetensors",
"flaubert",
"text-classification",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-27T08:18:17Z |
2023-07-11T09:53:58+00:00
| 28 | 1 |
---
license: cc
widget:
- text: Nouveaux records d’émissions de CO₂ du secteur énergétique en 2022, selon
une étude
- text: 'Climat et énergie : les objectifs de l’Union européenne pour 2030 ont du
« plomb dans l’aile »'
- text: 'Municipales à Paris : Emmanuel Grégoire « se prépare méthodiquement » pour
l’après Hidalgo'
---
# 🌍 Détection des articles de presse française traitant des sujets liés au climat
*🇬🇧 / 🇺🇸 : as this model is trained only on French data, all explanations are written in French in this repository. The goal of the model is to classify titles of French newspapers in two categories : if it's about climate or not.*
## 🗺️ Le contexte
Ce modèle de classification de **titres d'article de presse française** a été réalisé pour l'association [Data for good](https://dataforgood.fr/) à Grenoble et plus particulièrement pour l'association [Quota climat](https://www.quotaclimat.org/).
L'objectif de ce modèle est de savoir si un **article de presse** traite du **sujet du climat** à partir de son **titre**. Cette tache est complexe car l'algorithme **n'a pas accès au contenu** des articles de presse. Néanmoins, à l'aide des modèles de langage basés sur les [tranformeurs](https://fr.wikipedia.org/wiki/Transformeur) et plus particulièrement les modèles basés sur une architecture [BERT](https://fr.wikipedia.org/wiki/BERT_(mod%C3%A8le_de_langage)), on peut obtenir des résultats intéressants. Nous avons étudié les **deux principaux modèles** basés sur cette architecture et entrainés sur des **corpus en français** : [FlauBERT](https://hal.science/hal-02784776v3/document) et [CamemBERT](https://camembert-model.fr/)
## 📋 L'utilisation du modèle final
Le modèle final présenté n'est évidemment **pas parfait** et possède des **biais**. En effet, certains choix du modèles peuvent être discutables : ceci provient du **périmètre de définition** de la notion de **climat**.
Pour tester le modèle avec le langage Python, il y a **deux solutions** :
- Soit en **téléchargeant le modèle** avec la bibliothèque Python [transformers](https://pypi.org/project/transformers/)
Pour tester le modèle, il suffit d'installer la bibliothèque Python [transformers](https://pypi.org/project/transformers/) dans un environnement virtuel et exécuter le code suivant :
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="pierre-loic/climate-news-articles")
sentence = "Guerre en Ukraine, en direct : le président allemand appelle à ne pas « bloquer » Washington pour la livraison d’armes à sous-munitions"
print(pipe(sentence))
```
```
[{'label': 'NE TRAITE PAS DU CLIMAT', 'score': 0.6566330194473267}]
```
- Soit en appelant l'**API** d'Hugging Face avec la bibliothèque Python [requests](https://pypi.org/project/requests/)
Pour appeler l'**API** d'Hugging Face, il vous faut un **token** que vous pouvez récupérer dans votre espace personnel. Il ne vous plus qu'à exécuter le code suivant :
```python
import requests
API_URL = "https://api-inference.huggingface.co/models/pierre-loic/climate-news-articles"
headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "Canicule : deux nouveaux départements du Sud-Est placés en vigilance orange lundi",
})
print(output)
```
```
[[{'label': 'TRAITE DU CLIMAT', 'score': 0.511335015296936}, {'label': 'NE TRAITE PAS DU CLIMAT', 'score': 0.48866504430770874}]]
```
## 🔎 Le détail du travail d'entrainement
### La méthodologie utilisée
Différentes pistes d'étude ont été explorées pour aboutir au modèle final :
- La **première piste** que nous avons étudiée est de faire prédire la classification des titres d'articles de presse entre "climat" et "pas climat" par [ChatGPT](https://openai.com/blog/chatgpt) grâce à du [prompt engineering](https://en.wikipedia.org/wiki/Prompt_engineering). Les résultats étaient assez intéressants mais le modèle se trompait parfois sur des cas très simples.
- La **deuxième piste** que nous avons étudiée est de vectoriser les mots des titres de presse par une méthode Tf-Idf et d'utiliser un modèle de classification ([régression logistique](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) et [random forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)). Les résultats étaient légérement meilleurs qu'avec un dummy classifier (qui prédit toujours la classe majoritaire "Climat").
- La **troisième piste** que nous avons étudiée est de vectoriser les titres des articles de presse avec un modèle de type [BERT](https://fr.wikipedia.org/wiki/BERT_(mod%C3%A8le_de_langage)) ([camemBERT](https://camembert-model.fr/) uniquement entrainé sur un corpus francophone) et ensuite d'utiliser un modèle de classification ([régression logistique](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) et [random forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)) sur les plongements. Les résultats étaient intéressants.
- La **quatrième piste** (celle qui a été retenue pour ce modèle) est de faire un fine-tuning d'un modèle de BERT (FlauBERT ou CamemBERT) pour la tache de classification.
### Les données
Les données proviennent d'une collecte de **titres d'articles de presse française** collectés durant plusieurs mois. Nous avons labellisé environ **2000 de ces titres** pour entrainer le modèle.
### Le modèle final
Le modèle retenu est un modèle de type FlauBERT avec un **fine-tuning** pour la **classification des articles de presse**. Les **données d'entrainement** ont été **sous-échantilonnées (undersampling)** pour équilibrer les classes.
### Les améliorations envisageables
Pour **améliorer le modèle**, il pourrait être intéressant d'**intégrer plus de données** sur les domaines où le modèle **se trompe le plus**.
|
[
"CAS"
] |
TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-fp16
|
TheBloke
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2023-06-28T20:23:28Z |
2023-07-09T20:24:55+00:00
| 28 | 5 |
---
license: other
inference: false
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Manticore 13B Chat Pyg Guanaco fp16
This is fp16 pytorch format model files for [Manticore 13B Chat Pyg Guanaco](https://huggingface.co/Monero/Manticore-13b-Chat-Pyg-Guanaco) merged with [Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test).
[Kaio Ken's SuperHOT 13b LoRA](https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test) is merged on to the base model, and then 8K context can be achieved during inference by using `trust_remote_code=True`.
Note that `config.json` has been set to a sequence length of 8192. This can be modified to 4096 if you want to try with a smaller sequence length.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GGML)
* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-fp16)
* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Monero/Manticore-13b-Chat-Pyg-Guanaco)
## How to use this model from Python code
First make sure you have Einops installed:
```
pip3 install auto-gptq
```
Then run the following code. `config.json` has been default to a sequence length of 8192, but you can also configure this in your Python code.
The provided modelling code, activated with `trust_remote_code=True` will automatically set the `scale` parameter from the configured `max_position_embeddings`. Eg for 8192, `scale` is set to `4`.
```python
from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, pipeline
import argparse
model_name_or_path = "TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-fp16"
use_triton = False
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
# Change this to the sequence length you want
config.max_position_embeddings = 8192
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
config=config,
trust_remote_code=True,
device_map='auto')
# Note: check to confirm if this is correct prompt template is correct for this model!
prompt = "Tell me about AI"
prompt_template=f'''USER: {prompt}
ASSISTANT:'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15
)
print(pipe(prompt_template)[0]['generated_text'])
```
## Using other UIs: monkey patch
Provided in the repo is `llama_rope_scaled_monkey_patch.py`, written by @kaiokendev.
It can be theoretically be added to any Python UI or custom code to enable the same result as `trust_remote_code=True`. I have not tested this, and it should be superseded by using `trust_remote_code=True`, but I include it for completeness and for interest.
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Kaio Ken's SuperHOT 8K
### SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
Tests have shown that the model does indeed leverage the extended context at 8K.
You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
#### Looking for Merged & Quantized Models?
- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)
- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)
#### Training Details
I trained the LoRA with the following configuration:
- 1200 samples (~400 samples over 2048 sequence length)
- learning rate of 3e-4
- 3 epochs
- The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
- Rank = 4
- Alpha = 8
- no dropout
- weight decay of 0.1
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
- Trained on 4-bit base model
# Original model card: Manticore 13B Chat Pyg Guanaco
Manticore-13b-Chat-Pyg with the Guanaco 13b qLoRa from TimDettmers applied
|
[
"MONERO"
] |
Sriram-Gov/Sarcastic-Headline-Llama2
|
Sriram-Gov
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2209.11429",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-01T20:31:56Z |
2023-09-04T09:20:34+00:00
| 28 | 1 |
---
datasets: https://github.com/SriRamGovardhanam/Sarcastic-Headline-Llama2/blob/main/formatted_headline_data.csv
license: llama2
---
# Fine tuning Llama 2 by a generated dataset to respond sarcastically
The main idea behind the model is to add behaviour to an LLM so that for a given input(news headline) the model responds back with output(sarcastic_headline) in a funny,
sarcastic way.<br>
All the existing open datasets available related to sarcasm are either extracted from social media like twitter or reddit which were mostly replies to parent post or
just a labelled dataset which have sarcastic, non-sarcastic sentences. we are looking for dataset which has normal sentence and corresponding sarcastic version for the model to understand.
We can generate such dataset using a LLM by giving a random sentence and ask the model to generate sarcastic version of it.
Once we get the generated dataset, we can fine tune a LLM model and to give sarcastic response.
## Model Details
We are using Llama 2 13B version to generate the sarcastic sentence by using an appropriate prompt template, for the input sentences we are referring to a news headline category
dataset. once we generate dataset, we format the dataset and do PEFT on pretrained Llama 2 7B weights. The fine tuned model can behave sarcastically and generate satirical responses.
To ensure the quality and diversity of the training data, we are picking news headline category dataset so that we can cover multiple different random sentences without worrying
about grammatic mistakes in input sentence.
- **Sorce Dataset:** https://www.kaggle.com/datasets/rmisra/news-category-dataset
- **Dataset after ETL:** https://github.com/SriRamGovardhanam/Sarcastic-Headline-Llama2/blob/main/formatted_headline_data.csv
- **Model type:** LLM
- **Finetuned from model:** Llama2 7B https://huggingface.co/TinyPixel/Llama-2-7B-bf16-sharded/tree/main
### Model Fine tuning code
Huggingface team developed a python library autotrain-advanced with which we can fine tune any LLM with just one line of code.
You can find python code to generate the data, to fine tune the model in below repo
- **Repository:** https://github.com/SriRamGovardhanam/Sarcastic-Headline-Llama2
- **For code line by line breakdown refer:** [Coming soon]
## Uses
- **Enhanced Natural Language Understanding:** In applications like chatbots or virtual assistants, a model trained to understand
sarcasm can provide more contextually relevant responses, improving user interactions.
- **Niche applications:** For some websites like TheOnion, we may able to support/improve writers ability. Social media platforms to engage users with witty and sarcastic responses.
### Direct Use
Refer to the Inference code available in repo: https://github.com/SriRamGovardhanam/Sarcastic-Headline-Llama2
### Downstream Use
- **Content Generation:** In creative writing and content creation, the model can be used to inject humor and sarcasm into articles, scripts, advertisements, or marketing materials to make them more engaging.
- **Brand Persona:** Some companies adopt a brand persona characterized by humor and sarcasm in their communications. The model can assist in maintaining this tone in marketing campaigns and customer interactions.
- **Social Media Engagement:** Brands and influencers on social media may use the model to craft sarcastic posts or responses that resonate with their audience, leading to increased engagement and brand awareness.
### Recommendations
- There is a lot of room for improvement here. At ETL level, at the time of generating dataset, we can provide different prompts for different categories available to generate
even better funny responses.
- Dataset used here to fine tune have only 2100 examples, we can increase dataset size. At the time of fine tuning, because of GPU memory constraints, I have only performed
8 epochs - this can be increased.
- I opted for news headline because of quality and diversity of the training data. If sole purpose of the model is more focussed on generating more enticing sarcastic news headline, then another
better approach here would be generating news description 1st and generate headline for the description.
## How to Get Started with the Model
- For Fine tuning your own dataset, you can use the colab notebook files in this repo: https://github.com/SriRamGovardhanam/Sarcastic-Headline-Llama2
- For a doing a quick inference on this model card, refer to Inference notebook in the same repo.
## Training Details
```
autotrain llm --train --project_name 'sarcastic-headline-gen' --model TinyPixel/Llama-2-7B-bf16-sharded \
--data_path '/content/sarcastic-headline' \
--use_peft \
--use_int4 \
--learning_rate 2e-4 \
--train_batch_size 8 \
--num_train_epochs 8 \
--trainer sft \
--model_max_length 340 > training.log &
```
### Training Data
### Results
Input headline: **mansoons are best for mosquitoes**
<br>Input Formatted Template to the fine tuned LLM:
```
You are a savage, disrespectful and witty agent. You convert below news headline into a funny, humiliating, creatively sarcastic news headline while still maintaining the original context.
### headline: mansoons are best for mosquitoes
### sarcastic_headline:
```
<br>Output after Inferencing:
```
You are a savage, disrespectful and witty agent. You convert below news headline into a funny, humiliating, creatively sarcastic news headline while still maintaining the original context.
### headline: mansoons are best for mosquitoes
### sarcastic_headline: Another Study Proves That Men's Sweaty Bums Are The Best Repellent Against Mosquitoes
```
#### Summary
- The primary purpose of this model is often to generate humor and entertainment. It can be used in chatbots, virtual assistants, or social media platforms to engage users with witty and sarcastic responses.
- One advantage of using Llama2 model instead of chat GPT for dataset generation is, OpenAI will not allow offensive words/ hate speech as rules for a model, even if we
include them in prompt template, chatGPT will not produce brutal/ humiliating responses which is reasonable and ethical for such a big organization.
- This advatange is a double edged sword, as some people cannot handle these type of responses and may consider them as harrasement/ offensive.
### Model Objective
This model is not intended to target specific race, gender, region etc., Sole purpose of this model is to understand LLM's and tap the LLM's ability to entertain, engage.
### Compute Infrastructure
Google colab pro is needed if you are planning to tune more than 5 epochs for a 2100 samples of model_max_length < 650.
## Citation
The source dataset - news headlines are taken from https://www.kaggle.com/datasets/rmisra/news-category-dataset <br>
Misra, Rishabh. "News Category Dataset." arXiv preprint arXiv:2209.11429 (2022).
## Model Card Authors
Sriram Govardhanam <br>
http://www.linkedin.com/in/SriRamGovardhanam
## Model Card Contact
[email protected]
|
[
"CRAFT"
] |
lomahony/pythia-410m-helpful-sft
|
lomahony
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:Anthropic/hh-rlhf",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-08T15:48:41Z |
2025-01-20T05:40:03+00:00
| 28 | 0 |
---
datasets:
- Anthropic/hh-rlhf
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
[Pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) supervised finetuned using TRLx library with the helpful subset of [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch.
Checkpoints are also uploaded.
Fully reproducible finetuning code is available on [GitHub](https://github.com/lauraaisling/trlx-pythia/tree/main)
[wandb log](https://wandb.ai/lauraomahony999/pythia-sft/runs/quq2097z)
See [Pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) for model details [(paper)](https://arxiv.org/abs/2101.00027).
See further details of these models in the paper [Attributing Mode Collapse in the Fine-Tuning of Large Language Models](https://openreview.net/pdf?id=3pDMYjpOxk).
You can cite these models if they are helpful as follows:
<pre>
@inproceedings{o2024attributing,
title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models},
author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella},
booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop},
year={2024}
}
</pre>
hf (pretrained=lomahony/pythia-410m-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr|
|--------------|------:|------|-----:|---------------|------:|---|------|
|arc_challenge | 1|none | 0|acc | 0.2355|± |0.0124|
| | |none | 0|acc_norm | 0.2594|± |0.0128|
|arc_easy | 1|none | 0|acc | 0.5051|± |0.0103|
| | |none | 0|acc_norm | 0.4478|± |0.0102|
|boolq | 2|none | 0|acc | 0.6113|± |0.0085|
|hellaswag | 1|none | 0|acc | 0.3372|± |0.0047|
| | |none | 0|acc_norm | 0.4001|± |0.0049|
|lambada_openai| 1|none | 0|perplexity |21.8172|± |0.7736|
| | |none | 0|acc | 0.3755|± |0.0067|
|openbookqa | 1|none | 0|acc | 0.1940|± |0.0177|
| | |none | 0|acc_norm | 0.2960|± |0.0204|
|piqa | 1|none | 0|acc | 0.6719|± |0.0110|
| | |none | 0|acc_norm | 0.6687|± |0.0110|
|sciq | 1|none | 0|acc | 0.7700|± |0.0133|
| | |none | 0|acc_norm | 0.6540|± |0.0151|
|wikitext | 2|none | 0|word_perplexity|23.8136|± |N/A |
| | |none | 0|byte_perplexity| 1.8091|± |N/A |
| | |none | 0|bits_per_byte | 0.8553|± |N/A |
|winogrande | 1|none | 0|acc | 0.5320|± |0.0140|
hf (pretrained=lomahony/pythia-410m-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr|
|--------------|------:|------|-----:|---------------|------:|---|------|
|arc_challenge | 1|none | 5|acc | 0.2355|± |0.0124|
| | |none | 5|acc_norm | 0.2790|± |0.0131|
|arc_easy | 1|none | 5|acc | 0.5274|± |0.0102|
| | |none | 5|acc_norm | 0.5072|± |0.0103|
|boolq | 2|none | 5|acc | 0.5226|± |0.0087|
|hellaswag | 1|none | 5|acc | 0.3367|± |0.0047|
| | |none | 5|acc_norm | 0.3991|± |0.0049|
|lambada_openai| 1|none | 5|perplexity |37.4791|± |1.3737|
| | |none | 5|acc | 0.3049|± |0.0064|
|openbookqa | 1|none | 5|acc | 0.1620|± |0.0165|
| | |none | 5|acc_norm | 0.2900|± |0.0203|
|piqa | 1|none | 5|acc | 0.6708|± |0.0110|
| | |none | 5|acc_norm | 0.6676|± |0.0110|
|sciq | 1|none | 5|acc | 0.8630|± |0.0109|
| | |none | 5|acc_norm | 0.8430|± |0.0115|
|wikitext | 2|none | 5|word_perplexity|23.8136|± |N/A |
| | |none | 5|byte_perplexity| 1.8091|± |N/A |
| | |none | 5|bits_per_byte | 0.8553|± |N/A |
|winogrande | 1|none | 5|acc | 0.5272|± |0.0140|
|
[
"SCIQ"
] |
ntc-ai/SDXL-LoRA-slider.superhero
|
ntc-ai
|
text-to-image
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | 2023-12-11T19:47:31Z |
2024-02-06T00:30:32+00:00
| 28 | 1 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/superhero_17_3.0.png
widget:
- text: superhero
output:
url: images/superhero_17_3.0.png
- text: superhero
output:
url: images/superhero_19_3.0.png
- text: superhero
output:
url: images/superhero_20_3.0.png
- text: superhero
output:
url: images/superhero_21_3.0.png
- text: superhero
output:
url: images/superhero_22_3.0.png
inference: false
instance_prompt: superhero
---
# ntcai.xyz slider - superhero (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/superhero_17_-3.0.png" width=256 height=256 /> | <img src="images/superhero_17_0.0.png" width=256 height=256 /> | <img src="images/superhero_17_3.0.png" width=256 height=256 /> |
| <img src="images/superhero_19_-3.0.png" width=256 height=256 /> | <img src="images/superhero_19_0.0.png" width=256 height=256 /> | <img src="images/superhero_19_3.0.png" width=256 height=256 /> |
| <img src="images/superhero_20_-3.0.png" width=256 height=256 /> | <img src="images/superhero_20_0.0.png" width=256 height=256 /> | <img src="images/superhero_20_3.0.png" width=256 height=256 /> |
See more at [https://sliders.ntcai.xyz/sliders/app/loras/3c02c5d7-2101-45a4-a182-2234fa57d575](https://sliders.ntcai.xyz/sliders/app/loras/3c02c5d7-2101-45a4-a182-2234fa57d575)
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
superhero
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.superhero', weight_name='superhero.safetensors', adapter_name="superhero")
# Activate the LoRA
pipe.set_adapters(["superhero"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, superhero"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1496+ unique and diverse LoRAs along with 14602+ slider merges, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful <strong>NTC Slider Factory</strong> LoRA creator, allowing you to craft your own custom LoRAs and merges opening up endless possibilities.
Your support on Patreon will allow us to continue developing new models and tools.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
[
"CRAFT"
] |
ntc-ai/SDXL-LoRA-slider.Chiaroscuro
|
ntc-ai
|
text-to-image
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | 2024-01-29T01:30:43Z |
2024-02-10T02:10:12+00:00
| 28 | 2 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/evaluate/Chiaroscuro.../Chiaroscuro_17_3.0.png
widget:
- text: Chiaroscuro
output:
url: images/Chiaroscuro_17_3.0.png
- text: Chiaroscuro
output:
url: images/Chiaroscuro_19_3.0.png
- text: Chiaroscuro
output:
url: images/Chiaroscuro_20_3.0.png
- text: Chiaroscuro
output:
url: images/Chiaroscuro_21_3.0.png
- text: Chiaroscuro
output:
url: images/Chiaroscuro_22_3.0.png
inference: false
instance_prompt: Chiaroscuro
---
# ntcai.xyz slider - Chiaroscuro (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/Chiaroscuro_17_-3.0.png" width=256 height=256 /> | <img src="images/Chiaroscuro_17_0.0.png" width=256 height=256 /> | <img src="images/Chiaroscuro_17_3.0.png" width=256 height=256 /> |
| <img src="images/Chiaroscuro_19_-3.0.png" width=256 height=256 /> | <img src="images/Chiaroscuro_19_0.0.png" width=256 height=256 /> | <img src="images/Chiaroscuro_19_3.0.png" width=256 height=256 /> |
| <img src="images/Chiaroscuro_20_-3.0.png" width=256 height=256 /> | <img src="images/Chiaroscuro_20_0.0.png" width=256 height=256 /> | <img src="images/Chiaroscuro_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
Chiaroscuro
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.Chiaroscuro', weight_name='Chiaroscuro.safetensors', adapter_name="Chiaroscuro")
# Activate the LoRA
pipe.set_adapters(["Chiaroscuro"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, Chiaroscuro"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
[
"CRAFT"
] |
scoris/scoris-mt-en-lt
|
scoris
|
text2text-generation
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"lt",
"en",
"dataset:scoris/en-lt-merged-data",
"license:cc-by-2.5",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T12:16:22Z |
2024-11-19T10:31:18+00:00
| 28 | 1 |
---
datasets:
- scoris/en-lt-merged-data
language:
- lt
- en
license: cc-by-2.5
metrics:
- sacrebleu
---
# Overview
This is an English-Lithuanian translation model (Seq2Seq). For Lithuanian-English translation check another model [scoris-mt-lt-en](https://huggingface.co/scoris/scoris-mt-lt-en)
Original model: [Helsinki-NLP/opus-mt-tc-big-en-lt](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-lt)
Fine-tuned on large merged data set: [scoris/en-lt-merged-data](https://huggingface.co/datasets/scoris/en-lt-merged-data) (5.4 million sentence pairs)
Trained on 6 epochs.
Made by [Scoris](https://scoris.lt) team
# Evaluation:
| EN-LT | BLEU |
|-----------------------------------|------|
| scoris/scoris-mt-en-lt | 41.9 |
| Helsinki-NLP/opus-mt-tc-big-en-lt | 34.3 |
| Google Translate | 30.8 |
| Deepl | 32.3 |
_Evaluated on scoris/en-lt-merged-data validation set. Google and Deepl evaluated using a random sample of 1000 sentence pairs._
**According to [Google](https://cloud.google.com/translate/automl/docs/evaluate) BLEU score interpretation is following:**
| BLEU Score | Interpretation
|----------|---------|
| < 10 | Almost useless
| 10 - 19 | Hard to get the gist
| 20 - 29 | The gist is clear, but has significant grammatical errors
| 30 - 40 | Understandable to good translations
| **40 - 50** | **High quality translations**
| 50 - 60 | Very high quality, adequate, and fluent translations
| > 60 | Quality often better than human
# Usage
You can use the model in the following way:
```python
from transformers import MarianMTModel, MarianTokenizer
# Specify the model identifier on Hugging Face Model Hub
model_name = "scoris/scoris-mt-en-lt"
# Load the model and tokenizer from Hugging Face
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
src_text = [
"Once upon a time there were three bears, who lived together in a house of their own in a wood.",
"One of them was a little, small wee bear; one was a middle-sized bear, and the other was a great, huge bear.",
"One day, after they had made porridge for their breakfast, they walked out into the wood while the porridge was cooling.",
"And while they were walking, a little girl came into the house. "
]
# Tokenize the text and generate translations
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
# Print out the translations
for t in translated:
print(tokenizer.decode(t, skip_special_tokens=True))
# Result:
# Kažkada buvo trys lokiai, kurie gyveno kartu savame name miške.
# Vienas iš jų buvo mažas, mažas lokys; vienas buvo vidutinio dydžio lokys, o kitas buvo didelis, didžiulis lokys.
# Vieną dieną, pagaminę košės pusryčiams, jie išėjo į mišką, kol košė vėso.
# Jiems einant, į namus atėjo maža mergaitė.
```
|
[
"BEAR"
] |
FreedomIntelligence/Apollo-MedJamba
|
FreedomIntelligence
|
text-generation
|
[
"transformers",
"safetensors",
"jamba",
"text-generation",
"custom_code",
"arxiv:2403.03640",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-04-23T14:39:57Z |
2024-04-25T08:59:39+00:00
| 28 | 1 |
---
license: apache-2.0
---
# MedJamba
Multilingual Medical Model Based On Jamba
<p align="center">
👨🏻💻<a href="https://github.com/FreedomIntelligence/MedJamba" target="_blank">Github</a> •📃 <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a>
</p>
## 🌈 Update
* **[2024.04.25]** MedJamba Model is published！🎉
## Results
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B" target="_blank">Apollo-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-1.8B" target="_blank">Apollo-1.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B" target="_blank">Apollo-2B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B" target="_blank">Apollo-6B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B" target="_blank">Apollo-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-34B" target="_blank">Apollo-34B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-72B" target="_blank">Apollo-72B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MedJamba" target="_blank">MedJamba</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B-GGUF" target="_blank">Apollo-0.5B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B-GGUF" target="_blank">Apollo-2B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF" target="_blank">Apollo-6B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF" target="_blank">Apollo-7B-GGUF</a>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus
<details><summary>Click to expand</summary>
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/Medbase_data/blob/main/Medbase_data-datasets.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/Medbase_data/tree/main/train)
- Pretrain:
- data item:
- json_name: {data_source}_{language}_{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
```
[
"string1",
"string2",
...
]
```
- data_type==qa: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
</details>
## Results reproduction
<details><summary>Click to expand</summary>
1. Download Dataset for project:
```
bash 0.download_data.sh
```
2. Prepare test and dev for specific model:
- Create test data for with special token, you can use ./util/check.ipynb to check models' special tokens
```
bash 1.data_process_test&dev.sh
```
3. Prepare train data for specific model (Create tokenized data in advance):
- You can adjust data Training order and Training Epoch in this step
```
bash 2.data_process_train.sh
```
4. Train the model
- Multi Nodes refer to ./scripts/multi_node_train_*.sh
```
pip install causal-conv1d>=1.2.0
pip install mamba-ssm
```
Node 0:
```
bash ./scripts/3.multinode_train_jamba_rank0.sh
```
...
Node 4:
```
bash ./scripts/3.multinode_train_jamba_rank4.sh
```
5. Evaluate your model: Generate score for benchmark
```
bash 4.eval.sh
```
6. Evaluate your model: Play with your ckpts in bash
```
python ./src/evaluate/cli_demo.py --model_name='./ckpts/your/path/tfmr'
```
</details>
## To do
- Long Context Capability Evaluation and new Long-Med Benchmark
## Acknowledgment
- [HuatuoGPT-II](https://github.com/FreedomIntelligence/HuatuoGPT-II)
- [proxy-tuning](https://github.com/alisawuffles/proxy-tuning)
- [Apollo](https://github.com/FreedomIntelligence/Apollo)
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"HEAD-QA",
"MEDQA",
"PUBMEDQA"
] |
abhinand/Llama-3-Galen-70B-v1
|
abhinand
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:aaditya/Llama3-OpenBioLLM-70B",
"base_model:finetune:aaditya/Llama3-OpenBioLLM-70B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-07T18:53:45Z |
2024-05-07T19:45:31+00:00
| 28 | 1 |
---
base_model:
- aaditya/Llama3-OpenBioLLM-70B
language:
- en
library_name: transformers
license: llama3
tags:
- mergekit
- merge
---
# Llama-3-Galen-70B-v1
<img src="https://hf.fast360.xyz/production/uploads/60c8619d95d852a24572b025/R73wGdZE3GWeF9QZPvruG.jpeg" width="600" />
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [aaditya/Llama3-OpenBioLLM-70B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-70B) as a base.
### Evaluation
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|---------------------|-------|------|-----:|--------|-----:|---|-----:|
|pubmedqa | 1|none | 0|acc |0.7820|± |0.0185|
|professional_medicine| 0|none | 0|acc |0.9375|± |0.0147|
|medical_genetics | 0|none | 0|acc |0.9300|± |0.0256|
|college_medicine | 0|none | 0|acc |0.8555|± |0.0268|
|college_biology | 0|none | 0|acc |0.9375|± |0.0202|
|clinical_knowledge | 0|none | 0|acc |0.9283|± |0.0159|
|anatomy | 0|none | 0|acc |0.8444|± |0.0313|
|medqa_4options |Yaml |none | 0|acc |0.7777|± |0.0117|
| | |none | 0|acc_norm|0.7777|± |0.0117|
|medmcqa |Yaml |none | 0|acc |0.7423|± |0.0068|
| | |none | 0|acc_norm|0.7423|± |0.0068|
**Average:** 0.8594
|
[
"MEDQA",
"PUBMEDQA"
] |
DeusImperator/Midnight-Miqu-70B-v1.5_exl2_2.4bpw_rpcal
|
DeusImperator
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:migtissera/Tess-70B-v1.6",
"base_model:merge:migtissera/Tess-70B-v1.6",
"base_model:sophosympatheia/Midnight-Miqu-70B-v1.0",
"base_model:merge:sophosympatheia/Midnight-Miqu-70B-v1.0",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | 2024-05-18T11:28:42Z |
2024-05-19T10:12:00+00:00
| 28 | 1 |
---
base_model:
- sophosympatheia/Midnight-Miqu-70B-v1.0
- migtissera/Tess-70B-v1.6
library_name: transformers
license: other
tags:
- mergekit
- merge
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/Tn9MBg6.png" alt="MidnightMiqu" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
# Midnight-Miqu-70B-v1.5 - EXL2 2.4bpw rpcal
This is a 2.4bpw EXL2 quant of [sophosympatheia/Midnight-Miqu-70B-v1.5](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5)
This quant was made using exllamav2-0.0.21 with [pippa dataset](https://huggingface.co/datasets/royallab/PIPPA-cleaned) for RP
This quant fits over 20k context on 24GB VRAM on Windows in my local testing (with exl2 Q4 cache), you might be able to get more depending on other things taking VRAM.
I tested this quant shortly in some random RPs (including ones over 8k and 20k context) and it seems to work fine.
## Prompt Templates
See [sophosympatheia/Midnight-Miqu-70B-v1.5](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5) for Silly Tavern presets and templates.
In general the model uses Vicuna or Mistral formats but others.
Further details on prompting this model will also pop up under the [model discussions](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0/discussions)
## Similar quants
2.4bpw exl2 quant on default dataset: [Midnight-Miqu-70B-v1.5_exl2_2.4bpw](https://huggingface.co/DeusImperator/Midnight-Miqu-70B-v1.5_exl2_2.4bpw)
The above quant might be a little smarter based on limited testing, but this rpcal one might be a bit better for RP.
### Original readme below
---
### Overview
Looking for the 103B version? You can get it from [FluffyKaeloky/Midnight-Miqu-103B-v1.5](https://huggingface.co/FluffyKaeloky/Midnight-Miqu-103B-v1.5).
This is a DARE Linear merge between [sophosympatheia/Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0) and [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6).
This version is close in feel and performance to Midnight Miqu v1.0 but I think it picked up some goodness from Tess. Their EQ Bench scores are virtually the same and their post-EXL2 quant perplexity scores were the same too. However, Midnight Miqu v1.5 passes some tests I use that Midnight Miqu v1.0 fails, without sacrificing writing quality.
This model is uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It may also perform well at other tasks but I have not tested its performance in other areas.
### Long Context Tips
You can run this model out to 32K context with alpha_rope set to 1, just like with Miqu.
### Sampler Tips
* I recommend using Quadratic Sampling (i.e. smoothing factor) for creative work. I think this version performs best with a smoothing factor close to 0.2.
* I recommend using Min-P. Experiment to find your best setting.
* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.
* You don't need to use a high repetition penalty with this model, such as going above 1.10, but experiment with it.
Experiment with any and all of the settings below! What suits my preferences may not suit yours.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.12,
"rep_pen": 1.05,
"rep_pen_range": 2800,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"dynatemp": false,
"min_temp": 0.8,
"max_temp": 1.35,
"dynatemp_exponent": 1,
"smoothing_factor": 0.23,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 2,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"logit_bias": [],
"n": 1,
"rep_pen_size": 0,
"genamt": 500,
"max_length": 32764
}
```
### Prompting Tips
Try the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.
```
{
"story_string": "{{#if system}}{{system}}\n{{/if}}\nCONTEXTUAL INFORMATION\n{{#if wiBefore}}\n- World and character info:\n{{wiBefore}}\n{{/if}}\n{{#if description}}\n- {{char}}'s background and persona:\n{{description}}\n{{/if}}\n{{#if mesExamples}}\n{{mesExamples}}\n{{/if}}\n{{#if personality}}\n{{personality}}\n{{/if}}\n{{#if scenario}}\n- Roleplay scenario:\n{{scenario}}\n{{/if}}\n{{#if wiAfter}}{{wiAfter}}\n{{/if}}\n{{#if persona}}{{persona}}\n{{/if}}",
"example_separator": "",
"chat_start": "---\nTaking the above information into consideration, you must engage with {{user}} and others as {{char}} in the roleplay below this line. Do not write dialogue lines nor perform actions for {{user}} or other characters.\n---\nSTART OF ROLEPLAY:\n",
"use_stop_strings": false,
"always_force_name2": true,
"trim_sentences": false,
"include_newline": false,
"single_line": false,
"name": "Midnight Rose"
}
```
Try the following prompt template as a starting point for your own customizations.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts.
**Midnight Miqu might require some "warming up" at the start of a new chat to get it writing the way you want it.** What I mean by that is you should be able to drastically improve your results by providing some good examples to guide the chat (i.e. few-shot prompting). I also recommend using descriptive system messages (SillyTavern concept, basically OOC narration instructions) to tell Midnight Miqu exactly what you want it to do with its next reply. Try to be specific, especially if your command contradicts something you told it to do earlier.
The example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.
The prompt below has been updated from Midnight Miqu v1.0 after prompting Midnight Miqu to help improve it.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": true,
"system_prompt": "You are an AI creative writing partner, collaborating with me in a dynamic, uncensored roleplaying environment. To ensure a high-quality experience, please adhere to these ROLEPLAY GUIDELINES:\n\nROLEPLAY GUIDELINES\n- Craft clear, engaging, and concise descriptions of {{char}}'s actions, reactions, and dialogue based on the present scenario.\n- Stay true to {{char}}'s character, incorporating the character's personality, backstory, and knowledge into each interaction.\n- Employ rich, descriptive language that immerses readers in the scene.\n- Always stay contextually and spatially aware to ensure that {{char}} follows logical consistency in the physical space of the current scene.\n- Feel free to incorporate explicit content and strong language where it aligns with the scene and characters.\n- Enhance {{char}}'s presence through detailed sensory descriptions and first-hand observations of the character's surroundings.\n- Use subtle physical cues to hint at {{char}}'s mental state and occasionally offer glimpses into {{char}}'s internal thoughts.\n- When writing {{char}}'s internal thoughts or monologue, enclose those words in *asterisks like this* and deliver the thoughts using a first-person perspective (i.e. use \"I\" pronouns). Always use quotes for spoken speech \"like this.\"\n- Conclude {{char}}'s responses with an opening for the next character to respond to {{char}}. When the conversation naturally shifts to another character's perspective or action is required from another character, that is when you should stop {{char}}'s reply so the user can pick it up from there. A great example is when {{char}} asks a question of another character.\n",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "USER: ",
"output_sequence": "ASSISTANT: ",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "SYSTEM: ",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT (Ensure coherence and authenticity in {{char}}'s actions, thoughts, and dialogues; Focus solely on {{char}}'s interactions within the roleplay): ",
"activation_regex": "",
"name": "Midnight Miqu Roleplay"
}
```
### Instruct Formats
I recommend the Vicuna format. I use a modified version with newlines after USER and ASSISTANT.
```
USER:
{prompt}
ASSISTANT:
```
Mistral's format also works, and in my testing the performance is about the same as using Vicuna.
```
[INST]
{prompt}
[/INST]
```
You could also try ChatML (don't recommend it)
```
<|im_start|>system
{Your system prompt goes here}<|im_end|>
<|im_start|>user
{Your message as the user will go here}<|im_end|>
<|im_start|>assistant
```
### Quantizations
* GGUF
* [mradermacher/Midnight-Miqu-70B-v1.5-GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF) -- Various static GGUF quants
* GPTQ
* [Kotokin/Midnight-Miqu-70B-v1.5_GPTQ32G](https://huggingface.co/Kotokin/Midnight-Miqu-70B-v1.5_GPTQ32G)
* EXL2
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw)
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_4.5bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_4.5bpw)
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_5.0bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_5.0bpw)
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_6.0bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_6.0bpw)
* If you don't see something you're looking for, [try searching Hugging Face](https://huggingface.co/models?search=midnight-miqu-70b-v1.5). There may be newer quants available than what I've documented here.
### Licence and usage restrictions
<font color="red">152334H/miqu-1-70b-sf was based on a leaked version of one of Mistral's models.</font>
All miqu-derived models, including this merge, are **only suitable for personal use.** Mistral has been cool about it so far, but you should be aware that by downloading this merge you are assuming whatever legal risk is inherent in acquiring and using a model based on leaked weights.
This merge comes with no warranties or guarantees of any kind, but you probably already knew that.
I am not a lawyer and I do not profess to know what we have gotten ourselves into here. You should consult with a lawyer before using any Hugging Face model beyond private use... but definitely don't use this one for that!
## Merge Details
### Merge Method
This model was merged using the linear [DARE](https://arxiv.org/abs/2311.03099) merge method using [152334H_miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) as a base.
### Models Merged
The following models were included in the merge:
* [sophosympatheia/Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0)
* [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_linear
base_model: /home/llm/mergequant/models/BASE/152334H_miqu-1-70b-sf # base model
models:
- model: /home/llm/mergequant/models/midnight-miqu-70b-v1.0
- model: /home/llm/mergequant/models/BASE/Tess-70B-v1.6
parameters:
weight: 1.0
dtype: float16
```
### Notes
I tried several methods of merging Midnight Miqu v1.0 with Tess v1.6, and this dare_linear approach worked the best by far. I tried the same approach with other Miqu finetunes like ShinojiResearch/Senku-70B-Full and abideen/Liberated-Miqu-70B, but there was a huge difference in performance. The merge with Tess was the best one.
I also tried the SLERP approach I used to create Midnight Miqu v1.0, only using Tess instead of 152334H_miqu-1-70b in that config, and that result was nowhere near as good either.
|
[
"CRAFT"
] |
BSC-NLP4BIA/bsc-bio-ehr-es-carmen-distemist
|
BSC-NLP4BIA
|
token-classification
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"es",
"base_model:PlanTL-GOB-ES/bsc-bio-ehr-es",
"base_model:finetune:PlanTL-GOB-ES/bsc-bio-ehr-es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-05T15:36:18Z |
2024-07-25T14:19:47+00:00
| 28 | 0 |
---
base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
language:
- es
license: cc-by-4.0
---
# Training data
Model trained on the disease mentions of [CARMEN-I](https://zenodo.org/records/10171540) and [DisTEMIST](https://doi.org/10.5281/zenodo.7614764).
# Citation
Please cite the following works:
```
@inproceedings{distemist,
title={{Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources}},
author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell, Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin},
booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings},
year={2022}
}
@misc{carmen_physionet,
author = {Farre Maduell, Eulalia and Lima-Lopez, Salvador and Frid, Santiago Andres and Conesa, Artur and Asensio, Elisa and Lopez-Rueda, Antonio and Arino, Helena and Calvo, Elena and Bertran, Maria Jesús and Marcos, Maria Angeles and Nofre Maiz, Montserrat and Tañá Velasco, Laura and Marti, Antonia and Farreres, Ricardo and Pastor, Xavier and Borrat Frigola, Xavier and Krallinger, Martin},
title = {{CARMEN-I: A resource of anonymized electronic health records in Spanish and Catalan for training and testing NLP tools (version 1.0.1)}},
year = {2024},
publisher = {PhysioNet},
url = {https://doi.org/10.13026/x7ed-9r91}
}
@article{physionet,
author = {Ary L. Goldberger and Luis A. N. Amaral and Leon Glass and Jeffrey M. Hausdorff and Plamen Ch. Ivanov and Roger G. Mark and Joseph E. Mietus and George B. Moody and Chung-Kang Peng and H. Eugene Stanley },
title = {PhysioBank, PhysioToolkit, and PhysioNet },
journal = {Circulation},
volume = {101},
number = {23},
pages = {e215-e220},
year = {2000},
doi = {10.1161/01.CIR.101.23.e215},
URL = {https://www.ahajournals.org/doi/abs/10.1161/01.CIR.101.23.e215}
}
```
# Contacting authors
jan.rodriguez [at] bsc.es
## More information on data, usage, limitations, and performance metrics soon
|
[
"DISTEMIST"
] |
YiDuo1999/Gemma-2-9b-medical
|
YiDuo1999
|
text-generation
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-02T07:22:29Z |
2024-07-02T10:06:51+00:00
| 28 | 0 |
---
license: gemma
---
## Introduction
This repo contains Gemma-2-9b-Medical, a medical language model with 9 billion parameters. This model builds upon the foundation of Gemma-2-9b-base and has been tuned with diverse medical and general instructions. We also use the three strategies in the paper 'Efficient Continual Pre-training by Mitigating the Stability Gap' to mitigate the stability gap during instruction tuning, which boosts the model's medical task performance and reduces the computation consumption.
## 💻 Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
model_name = "YiDuo1999/Gemma-2-9b-medical"
device_map = 'auto'
model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True,use_cache=False,device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
def askme(question):
sys_message = '''
You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
'''
# Create messages structured for the chat template
messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": question}]
# Applying chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100, use_cache=True)
# Extract and return the generated text, removing the prompt
response_text = tokenizer.batch_decode(outputs)[0].strip()
answer = response_text.split('<|im_start|>assistant')[-1].strip()
return answer
```
## 🏆 Evaluation
For question-answering tasks, we have
| Model | MMLU-Medical | PubMedQA | MedMCQA | MedQA-4-Option | Avg |
|:-------------------------------|:-------------|:---------|:--------|:---------------|:-----|
| Mistral-7B-instruct | 55.8 | 17.8 | 40.2 | 41.1 | 37.5 |
| Zephyr-7B-instruct-β | 63.3 | 46.0 | 43.0 | 48.5 | 48.7 |
| PMC-Llama-7B | 59.7 | 59.2 | 57.6 | 49.2 | 53.6 |
| Medalpaca-13B | 55.2 | 50.4 | 21.2 | 20.2 | 36.7 |
| AlpaCare-13B | 60.2 | 53.8 | 38.5 | 30.4 | 45.7 |
| BioMedGPT-LM 7B | 52.0 | 58.6 | 34.9 | 39.3 | 46.2 |
| Me-Llama-13B | - | 70.0 | 44.9 | 42.7 | - |
| Llama-3-8B instruct | 82.0 | 74.6 | 57.1 | 60.3 | 68.5 |
| JSL-Med-Sft-Llama-3-8B | 83.0 | 75.4 | 57.5 | 74.8 | 72.7 |
| GPT-3.5-turbo-1106 | 74.0 | 72.6 | 34.9 | 39.3 | 60.6 |
| GPT-4 | 85.5 | 69.2 | 69.5 | 83.9 | 77.0 |
| Gemma-2-9b-int | 75.0 | 76.0 | 40.3 | 48.9 | 60.0 |
| Gemma-2-9b-Medical | 75.0 | 76.0 | 61.3 | 59.7 | 68.0 |
| Llama-3-physician-8B instruct | 80.0 | 76.0 | 80.2 | 60.3 | 74.1 |
## Citation
```
@inproceedings{Guo2024EfficientCP,
title={Efficient Continual Pre-training by Mitigating the Stability Gap},
author={Yiduo Guo and Jie Fu and Huishuai Zhang and Dongyan Zhao and Yikang Shen},
year={2024},
url={https://api.semanticscholar.org/CorpusID:270688100}
}
```
|
[
"MEDQA",
"PUBMEDQA"
] |
XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy
|
XeAI
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:gretelai/synthetic_text_to_sql",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-11-07T06:06:06Z |
2024-11-07T08:54:53+00:00
| 28 | 0 |
---
datasets:
- gretelai/synthetic_text_to_sql
library_name: transformers
license: mit
pipeline_tag: text-generation
---
# Model Card for LLaMA 3.2 3B Instruct Text2SQL
## Model Details
### Model Description
This is a fine-tuned version of LLaMA 3.2 3B Instruct model, specifically optimized for Text-to-SQL generation tasks. The model has been trained to convert natural language queries into structured SQL commands.
- **Developed by:** Zhafran Ramadhan - XeAI
- **Model type:** Decoder-only Language Model
- **Language(s):** English - MultiLingual
- **License:** MIT
- **Finetuned from model:** LLaMA 3.2 3B Instruct
- **Log WandB Report:** [WandB Report](https://wandb.ai/zhafranr/LLaMA_3-2_3B_Instruct_FineTune_Text2SQL/reports/LLaMa-3-2-3B-Instruct-Fine-Tune-Text2SQL--VmlldzoxMDA2NDkzNA)
### Model Sources
- **Repository:** [LLaMA 3.2 3B Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
- **Dataset:** [Synthethic Text2SQL](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
## How to Get Started with the Model
### Installation
```python
pip install transformers torch accelerate
```
### Input Format and Usage
The model expects input in a specific format following this template:
```text
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
[System context and database schema]
<|eot_id|><|start_header_id|>user<|end_header_id|>
[User query]
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
### Basic Usage
```python
from transformers import pipeline
import torch
# Initialize the pipeline
generator = pipeline(
"text-generation",
model="XeAI/LLaMa_3.2_3B_Instruct_Text2SQL", # Replace with your model ID
torch_dtype=torch.float16,
device_map="auto"
)
def generate_sql_query(context, question):
# Format the prompt according to the training template
prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Cutting Knowledge Date: December 2023
Today Date: 07 Nov 2024
You are a specialized SQL query generator focused solely on the provided RAG database. Your tasks are:
1. Generate SQL queries based on user requests that are related to querying the RAG database.
2. Only output the SQL query itself, without any additional explanation or commentary.
3. Use the context provided from the RAG database to craft accurate queries.
Context: {context}
<|eot_id|><|start_header_id|>user<|end_header_id|>
{question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
response = generator(
prompt,
max_length=500,
num_return_sequences=1,
temperature=0.1,
do_sample=True,
pad_token_id=generator.tokenizer.eos_token_id
)
return response[0]['generated_text']
# Example usage
context = """CREATE TABLE upgrades (id INT, cost FLOAT, type TEXT);
INSERT INTO upgrades (id, cost, type) VALUES
(1, 500, 'Insulation'),
(2, 1000, 'HVAC'),
(3, 1500, 'Lighting');"""
questions = [
"Find the energy efficiency upgrades with the highest cost and their types.",
"Show me all upgrades costing less than 1000 dollars.",
"Calculate the average cost of all upgrades."
]
for question in questions:
sql = generate_sql_query(context, question)
print(f"\nQuestion: {question}")
print(f"Generated SQL: {sql}\n")
```
### Advanced Usage with Custom System Prompt
```python
def generate_sql_with_custom_prompt(context, question, custom_system_prompt=""):
base_prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Cutting Knowledge Date: December 2023
Today Date: 07 Nov 2024
You are a specialized SQL query generator focused solely on the provided RAG database."""
full_prompt = f"""{base_prompt}
{custom_system_prompt}
Context: {context}
<|eot_id|><|start_header_id|>user<|end_header_id|>
{question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
response = generator(
full_prompt,
max_length=500,
num_return_sequences=1,
temperature=0.1,
do_sample=True,
pad_token_id=generator.tokenizer.eos_token_id
)
return response[0]['generated_text']
```
### Best Practices
1. **Input Formatting**:
- Always include the special tokens (<|begin_of_text|>, <|eot_id|>, etc.)
- Provide complete database schema in context
- Keep questions clear and focused on data retrieval
2. **Parameter Configuration**:
- Use temperature=0.1 for consistent SQL generation
- Adjust max_length based on expected query complexity
- Enable do_sample for more natural completions
3. **Context Management**:
- Include relevant table schemas
- Provide sample data when needed
- Keep context concise but complete
## Uses
### Direct Use
The model is designed for converting natural language questions into SQL queries. It can be used for:
- Database query generation from natural language
- SQL query assistance
- Data analysis automation
### Out-of-Scope Use
- Production deployment without human validation
- Critical decision-making without human oversight
- Direct database execution without query validation
## Training Details
### Training Data
- Dataset: [Synthethic Text2SQL](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
- Data preprocessing: Standard text-to-SQL formatting
### Training Procedure
#### Training Hyperparameters
- **Total Steps:** 4,149
- **Final Training Loss:** 0.1168
- **Evaluation Loss:** 0.2125
- **Learning Rate:** Dynamic with final LR = 0
- **Epochs:** 2.99
- **Gradient Norm:** 1.3121
#### Performance Metrics
- **Training Samples/Second:** 6.291
- **Evaluation Samples/Second:** 19.325
- **Steps/Second:** 3.868
- **Total FLOPS:** 1.92e18
#### Training Infrastructure
- **Hardware:** Single NVIDIA H100 GPU
- **Training Duration:** 5-6 hours
- **Total Runtime:** 16,491.75 seconds
- **Model Preparation Time:** 0.0051 seconds
## Evaluation
### Metrics
The model's performance was tracked using several key metrics:
- **Training Loss:** Started at ~1.2, converged to 0.1168
- **Evaluation Loss:** 0.2125
- **Processing Efficiency:** 19.325 samples per second during evaluation
### Results Summary
- Achieved stable convergence after ~4000 steps
- Maintained consistent performance metrics throughout training
- Shows good balance between training and evaluation loss
## Environmental Impact
- **Hardware Type:** NVIDIA H100 GPU
- **Hours used:** ~6 hours
- **Training Location:** [GPUaaS](www.runpod.io)
## Technical Specifications
### Compute Infrastructure
- **GPU:** NVIDIA H100
- **Training Duration:** 5-6 hours
- **Total Steps:** 4,149
- **FLOPs Utilized:** 1.92e18
## Model Card Contact
[Contact information to be added by Zhafran Ramadhan]
---
*Note: This model card follows the guidelines set by the ML community for responsible AI development and deployment.*
|
[
"CRAFT"
] |
adipanda/ochaco-standard-lora-1
|
adipanda
|
text-to-image
|
[
"diffusers",
"flux",
"flux-diffusers",
"text-to-image",
"simpletuner",
"safe-for-work",
"lora",
"template:sd-lora",
"standard",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | 2024-12-07T20:54:21Z |
2024-12-08T16:42:37+00:00
| 28 | 0 |
---
base_model: black-forest-labs/FLUX.1-dev
license: other
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- lora
- template:sd-lora
- standard
inference: true
widget:
- text: unconditional (blank prompt)
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_0_0.png
- text: A scene from My Hero Academia. Ochaco Uraraka holding a sign that says 'I
LOVE PROMPTS!', she is standing full body on a beach at sunset. She is wearing
her pink and black hero costume with a utility belt. The setting sun casts a dynamic
shadow on her smiling face.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: A scene from My Hero Academia. Ochaco Uraraka jumping out of a propeller airplane,
sky diving. She looks thrilled and her short brown hair is flying upward. The
sky is clear and blue, and there are birds pictured in the distance.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: 'A scene from My Hero Academia. Ochaco Uraraka spinning a basketball on her
finger on a basketball court. She is wearing a Lakers jersey with the #12 on it.
The basketball hoop and cheering crowd are in the background. She is beaming with
confidence.'
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: A scene from My Hero Academia. Ochaco Uraraka is wearing a suit in an office,
shaking the hand of a businesswoman. The woman has purple hair and is wearing
professional attire. There is a Google logo in the background. It is during daytime,
and the overall sentiment is one of accomplishment and celebration.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: A scene from My Hero Academia. Ochaco Uraraka is fighting a large brown grizzly
bear, deep in a forest. The bear is tall and standing on two legs, roaring. The
bear is also wearing a crown because it is the king of all bears. Around them
are tall trees and other animals watching in awe.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
---
# ochaco-standard-lora-1
This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
No validation prompt was used during training.
None
## Validation settings
- CFG: `3.5`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `1024x1024`
- Skip-layer guidance:
Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
You can find some example images in the following gallery:
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 166
- Training steps: 3000
- Learning rate: 0.0003
- Learning rate schedule: constant
- Warmup steps: 100
- Max grad norm: 2.0
- Effective batch size: 56
- Micro-batch size: 56
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 0.0%
- LoRA Rank: 128
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
## Datasets
### ochaco-512
- Repeats: 2
- Total number of images: 288
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'adipanda/ochaco-standard-lora-1'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "An astronaut is riding a horse through the jungles of Thailand."
## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
image = pipeline(
prompt=prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
width=1024,
height=1024,
guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")
```
|
[
"BEAR"
] |
dan-lara/Garbage-Classifier-Resnet-50-Finetuning
|
dan-lara
|
image-classification
|
[
"transformers",
"resnet",
"image-classification",
"vision",
"recycling",
"environment",
"fr",
"base_model:microsoft/resnet-50",
"base_model:finetune:microsoft/resnet-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-12-15T14:49:00Z |
2024-12-16T15:34:18+00:00
| 28 | 0 |
---
base_model:
- microsoft/resnet-50
language:
- fr
library_name: transformers
license: mit
pipeline_tag: image-classification
tags:
- image-classification
- vision
- recycling
- environment
---
# Garbage Classification Model (Fine-tuned ResNet-50)
Ce modèle est une version fine-tunée de ResNet-50 pour la classification des images de déchets en 8 catégories, utilisant le [Garbage Dataset](https://www.kaggle.com/datasets/danielferreiralara/normalized-garbage-dataset-for-resnet). Ce modèle est conçu pour des applications environnementales telles que le tri automatique des déchets et la sensibilisation au recyclage.
## Modèle de base
Ce modèle est basé sur [ResNet-50 v1.5](https://huggingface.co/microsoft/resnet-50), qui est pré-entraîné sur [ImageNet-1k](https://huggingface.co/datasets/ILSVRC/imagenet-1k). ResNet est une architecture de réseau de neurones convolutionnels qui a introduit les concepts d’apprentissage résiduel et de connexions par saut, permettant ainsi l’entraînement de modèles beaucoup plus profonds.
ResNet-50 v1.5 inclut une amélioration dans les blocs de bottleneck, utilisant une stride de 2 dans la convolution 3x3, ce qui le rend légèrement plus précis que v1 (∼0,5 % en top-1).
## Description du Modèle
### Classes cibles
Le modèle classifie les images dans les 8 catégories suivantes :
- 🔋 Batterie
- 📦 Carton
- 🔗 Métal
- 🍓 Organique
- 🗳️ Papier
- 🧳 Plastique
- 🫙 Verre
- 👖 Vêtements
### Prétraitement
Les images du dataset ont été normalisées et redimensionnées à une résolution de 224x224, compatible avec l’entrée du modèle ResNet-50.
### Performance
Le modèle atteint un **taux de précision global de 94 %** sur le jeu de test du Dataset. Les performances varient légèrement entre les classes en fonction de la diversité des images et des similarités visuelles entre certaines catégories.
Voici un simulateur([EcoMind AI](https://ecomind-ai.streamlit.app/)) qui compare notre modèle au ResNet de base et à d'autres technologies telles que Yolo et LLMs (Llama 3.2).
## Utilisation prévue & limitations
### Cas d'utilisation
- Automatisation du tri des déchets pour le recyclage.
- Développement d'applications éducatives et interactives sur la gestion des déchets.
- Recherche en vision par ordinateur appliquée à l'environnement.
### Limitations
Ce modèle a été entraîné sur un dataset limité à 8 catégories. Les scénarios impliquant des déchets très spécifiques ou des catégories en dehors de celles mentionnées pourraient nécessiter un retrain ou une extension du dataset.
## Comment utiliser ce modèle
Voici un exemple de code pour utiliser ce modèle afin de classifier une image :
```python
```
## Citations et Références
Si vous utilisez ce modèle, merci de citer à la fois le modèle de base ResNet-50 et le Dataset :
### Modèle de base :
```bibtex
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
```
### Dataset Waste Classification :
```bibtex
@misc{garbageDatasetResNet24,
author = {Ferreira et al.},
title = {8 classes Garbage Dataset for ResNet},
year = {2024},
publisher = {Kaggle},
howpublished = {\url{[https://kaggle.com](https://www.kaggle.com/datasets/danielferreiralara/normalized-garbage-dataset-for-resnet)}}
}
```
## Contact
Pour toute question ou suggestion, n’hésitez pas à me contacter à [[email protected]](mailto:[email protected]).
|
[
"CAS"
] |
Aleph-Alpha/Pharia-1-Embedding-4608-control-hf
|
Aleph-Alpha
| null |
[
"safetensors",
"phariaembed",
"custom_code",
"license:other",
"region:us"
] | 2024-12-17T13:51:15Z |
2024-12-20T11:49:04+00:00
| 28 | 2 |
---
license: other
license_name: open-aleph-license
license_link: LICENSE
---
# Model Card for Pharia-1-Embedding-4608-control
This model card provides an overview of Pharia-1-Embedding-4608-control, an embedding model
developed by Aleph Alpha Research*. Pharia-1-Embedding-4608-control has been built on top of Pharia-1-LLM-7B-control.
For additional training details, including architecture, tokenization, tokenizer fertility, pre-training,
instruction fine-tuning and resource usage we refer to the model card of [Pharia-1-LLM-7B-control](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control).
Due to being trained with a diverse set of instructions, Pharia-1-Embedding-4608-control can deliver customized embeddings at runtime without further finetuning. Pharia-1-Embedding-4608-control was trained on carefully curated data in compliance with applicable EU and national regulations, including copyright and data privacy laws. Furthermore it shows strong cross-lingual performance allowing for prompting and text to be embedded written in different languages. The finetuning was always performed using English instructions.
## Model Overview
- **Developed by:** Aleph Alpha Research
<!--- **Funded by [optional]:** [More Information Needed]-->
<!--- **Shared by [optional]:** [More Information Needed]-->
- **Model type/architecture:** Embedding adapter on top of Pharia-1-LLM-7B-control trained with representational
instruction-tuning (inspired by the approach of GritLM).
- **Language(s) (NLP):** Trained on English, German, French, Spanish.
<!--- **License:** [More Information Needed]-->
<!--- **Finetuned from model [optional]:** [More Information Needed]-->
- **USP:** Model exhibits superior quality in pure cross-lingual tasks for (German, English, French & Spanish pairings, see evaluation below)
### Model Description
|Model |Embedding Size|Description|
|--------------------------------|--------------|-----------|
|Pharia-1-Embedding-4608-control |4608|Pharia-1-Embedding-4608-control is an Embedding model optimized for German, French and Spanish and designed for customizable embeddings at runtime via instructions (prompts)|
<!-- Provide a longer summary of what this model is. -->
### Model Access
We provide access to our models through the channels listed below.
- On-premise installation: Our customers are supplied with our full LLM and Embedding model stack, including model weights and inference runtime. Contact us for options to deploy Pharia-1-Embedding-4608-control in any cloud or on-premise environment. We provide our customers with open access to our full model checkpoint including weights and code for commercial use.
Downloadable from Huggingface: An HF-adapted version of our model can be found in our Huggingface repo (https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control-hf) together with code snippets that make the model easy to use.
Please refer to the changelog for updates to the models served. We do not deprecate officially released versions of old model generations when we release newer versions, so users can continue to have access to available models.
No prompt data is stored when using our systems, which means that we do not collect PII (personally identifiable information) for any of our public API users as detailed in our Terms & Conditions. We do not log user inputs to the models. We do not train on user data.
- **Note**: The same models are made available to users regardless of their geographic location, and the input language but subject to sanction regimes, technology export regulations, and other restrictions that may apply. The same offering is provided to all countries within and external to the European Union if no legal restrictions apply.
### Intended Use
Pharia-1-Embedding-4608-control is intended to be deployed as components of AI systems or applications.
Use-cases and the model's capabilities include but are not limited to: information retrieval, semantic search, re-ranking and clustering.
#### Out-of-Scope Use
Pharia-1-Embedding-4608-control is not to be used for illegal or unlawful actions of any kind and with any illegal
or unlawful content. This includes in particular prohibited activities such as engaging in terrorism,
violence, human trafficking, illegal distribution of materials to minors, sexual solicitation, any other
criminal activities, harassment, discrimination, creating or promoting malicious code or activities risking death or harm,
including those related to military or nuclear applications, and activities not in compliance with sanction regimes,
technology export regulations, and other restrictions that may apply. The models are to be used following ethical standards.
The utilization of our technology is always governed by, and may be limited in accordance with,
our Terms of Use, the Open Aleph License, or any specific agreement we might have established with you.
For non-anonymous reports, we also provide an appeals mechanism for usage policy violations via
our dedicated contact address [[email protected]]([email protected]) to communicate with us.
Customers and partners are enabled to use our ticketing
system [ticketing system](https://servicedesk.aleph-alpha.de/external) for appeals, claims and feedback.
### Use limitations
Beyond the risks & limitations stated in
the original [Pharia-1-LLM-7B-control](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control), the following limitation applies:
- Pharia-1-Embedding-4608-control has been optimized on embedding
computation only. Therefore, we do not recommend usage for text generation purposes.
## How to Use
We provide two access pathways for our Pharia4608 embedding model. The first one leverages the HF ecosystem and can be found here: https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control-hf. The code snippet in the box below demonstrates its use. As soon as the model class is invoked, the model will we loaded from the repo and is ready for use. The other access pathway is through our public scaling code base. In this version the model weights were not converted to HF format and the repo https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control can be cloned as is. The model path has to be adjusted to the local path where the model was downloaded. The model cards in the corresponding repositories only the code snippet which applies to the specific repo.
### Use with Huggingface
```
from torch.nn import CosineSimilarity
from transformers import AutoConfig, AutoModel
from transformers import PreTrainedTokenizerFast
MODEL_PATH = 'Aleph-Alpha/Pharia-1-Embedding-4608-control-hf'
config = AutoConfig.from_pretrained(MODEL_PATH, trust_remote_code=True)
tokenizer = PreTrainedTokenizerFast.from_pretrained(MODEL_PATH)
model = AutoModel.from_pretrained(MODEL_PATH,
trust_remote_code=True,
config=config,
tokenizer=tokenizer).cuda()
query = "Which country is Galileo from?"
query_embeddings = model.encode_queries(query, convert_to_tensor=True)
print(f"Type of embeddings: {type(query_embeddings)},\n\
shape of query embeddings: {query_embeddings.shape}")
# embed the documents:
document_1 = "Galileo is a German television program series produced and broadcast on ProSieben television network. It is also sold to broadcasters in other countries (namely Russia and Poland). The first show was broadcast in 1998, and is now stored in the Arctic World Archive in Svalbard, Norway, after being transferred to special film created by Piql."
document_embeddings_1 = model.encode_corpus(document_1, convert_to_tensor=True)
document_2 = "Galileo di Vincenzo Bonaiuti de' Galilei (15 February 1564 - 8 January 1642), commonly referred to as Galileo Galilei or mononymously as Galileo, was an Italian (Florentine) astronomer, physicist and engineer, sometimes described as a polymath. He was born in the city of Pisa, then part of the Duchy of Florence and present-day Italy."
document_embeddings_2 = model.encode_corpus(document_2, convert_to_tensor=True)
# customized embeddings steering the query:
instruction = "Represent the question about TV shows to find a paragraph that answers it."
steered_query_embeddings = model.encode_queries(
query,
instruction=instruction,
convert_to_tensor=True
)
# compute similarity between steered query and both documents
cossim = CosineSimilarity(dim=0, eps=1e-6)
sim1 = round(cossim(document_embeddings_1, steered_query_embeddings).item(), 3)
sim2 = round(cossim(document_embeddings_2, steered_query_embeddings).item(), 3)
print("Steered embedding causes higher similarity of query to TV show:")
print(f"Similarity query/TV show ({sim1}) > similarity query/Italian polymath: ({sim2})")
```
Disclaimer: For the official evaluation scores we used the Scaling compatible checkpoint available under Pharia-1-Embedding-4608-control (https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control)
### Example for instruction embedding
Pharia-1-Embedding-4608-control is useful for any use-case that relates to estimating the similarity/relevance between
text fragments. This is relevant for use-cases such as information retrieval, semantic search, re-ranking and clustering.
We use the task of information retrieval as a guiding example where we assume the
following query: “Which country is Galileo from?” and two documents:
- Galileo is a German television program series produced and broadcast on ProSieben television network. It is also sold to broadcasters in other countries (namely Russia and Poland). The first show was broadcast in 1998, and is now stored in the Arctic World Archive in Svalbard, Norway, after being transferred to special film created by Piql.
- Galileo di Vincenzo Bonaiuti de' Galilei (15 February 1564 - 8 January 1642), commonly referred to as Galileo Galilei or mononymously as Galileo, was an Italian (Florentine) astronomer, physicist and engineer, sometimes described as a polymath. He was born in the city of Pisa, then part of the Duchy of Florence and present-day Italy.
Source: Wikipedia
For our guiding example we assume the context of this use-case is a Question-Answer system for movies and TV shows.
**Step 1:**
Embed the Query
```
"input": "Which country is Galileo from?"
```
→ Embedding: ```[-0.6780134, 0.61449033, 0.102911085, ...]```
**Step 2:**
Embed the Documents
"input": "Galileo is a German television program series ..."
→ Embedding: ```[-0.36119246, 0.7793595, -0.38735497, ...]```
"input": "Galileo di Vincenzo Bonaiuti de' Galilei ..."
→ Embedding: ```[-0.25108248, 1.0496024, -0.20945309, ...]```
**Step 3:**
Compare the similarity
A typical similarity measure between vectors is cosine similarity. Higher numbers
indicate more similar vectors and by extension capture the concept of relevance.
In a RAG application these scores determine the ranking during the retrieval step.
In this example, we obtain the following cosine similarities:
Query vs. German TV show: ~0.661
Query vs. Italian polymath: ~0.757
This implies that the paragraph about the Italian polymath would be ranked higher than the paragraph
about the German TV show which is the one we’re interested in.
#### Customized Embeddings
To further improve performance you can use instructions to steer the model. Instructions can help the model
understand nuances of your specific data and ultimately lead to embeddings that are more useful for your use-case.
In this case, we aim to get embeddings that would lead to ranking the paragraph about the German TV Show higher
than the paragraph about the Italian polymath.
**Step 1:**
Embed the Query with an Instruction
```"instruction": "Represent the question about TV shows to find a paragraph that answers it."```
```"input": "input": "Which country is Galileo from?"```
→ Embedding: ```[-0.6310919, 1.4309896, -0.85546875, ...]```
**Step 2:**
Compare the similarity
We leave the embeddings of the documents untouched and now obtain the following cosine similarities:
Query vs. German TV show: ~0.632
Query vs. Italian polymath: ~0.512
These new cosine similarities imply that the ranking has indeed changed and the paragraph about the German TV show is
**now more relevant**. This shows that instructions can help the model understand nuances in the data better
and ultimately lead to embeddings that are more useful for your use-case.
#### Tips on using the model
- First try and ideally evaluate the model on your data without instructions to see whether performance aligns with your expectations out-of-the-box
- If you decide to use an instruction with the aim of further boosting performance we suggest using this template as a guideline
* ```Template: Represent the [X] to find a [Y] that [describe how the X and Y relate]```
* Examples
1. Represent the newspaper paragraph to find a newspaper paragraph with the same topic
2. Represent the sentence to find another sentence with the same meaning
- In cases where the two texts to compare are different in nature (e.g. query and document) – also called “asymmetric” – we suggest to first add an instruction to query texts only. Again, try and ideally evaluate the model in this setting. Then, if your aim is to further boost performance, we suggest that you add instructions to document texts as well where [X] and [Y] are flipped accordingly.
## Evaluation
### Evaluations on cross-lingual capabilities
There are important use cases where one wants to retrieve multiple documents on a topic or answering questions that are formulated
in a different language than the query. This increases recall and information retrieval coverage. For testing on cross-lingual
capabilities we evaluated Pharia-1-Embedding-4608-control, GritLM, Nvidia-Embed-v2 and BGE-Multilingual-Gemma2
on the MLQA-V1 datasets (Facebook) for German/English and English/Spanish language pairings. For German/French we
used the CLSD-WMT19 dataset providing correct and adversarial translations of a sentence in the corresponding pair language.
In order to check quality over a larger range of sample size we did the accuracy computations for varying number of samples
taken from the MLQA-V1 dataset. For the CLSD-WMT19 evaluation we employed the full set of data (2900 samples available).
#### MLQA-V1 Ger/Eng cross-lingual accuracies for the considered models
|# of samples|Pharia4608|GritLM|Nvidia-Embed-v2|BGE-Gemma2|
|:---:|:---:|:---:|:---:|:---:|
|1000|86.0%|82.5%|77.0%|87.0%|
|2000|79.5%|73.4%|69.4%|76.8%|
|4000|65.3%|59.2%|56.0%|62.7%|
|6000|54.3%|48.6%|45.6%|52.6%|
|10000|38.6%|32.8%|32.8%|39.4%|
#### MLQA-V1 Eng/Esp cross-lingual accuracies for the considered models
|# samples|Pharia4608|GritLM|NV-Embed-v2|BGE-Gemma2|
|:---:|:---:|:---:|:---:|:---:|
|1000|87.5%|82.0%|81.5%|87.0%|
|2000|78.5%|73.9%|70.7%|77.0%|
|4000|65.5%|59.3%|56.9%|64.2%|
|6000|55.3%|49.2%|46.2%|53.4%|
|10000|41.7%|35.5%|33.2%|40.0%|
#### CLSD-WMT19 Ger/Fra (2900 samples) cross-lingual evaluation for the considered models
|Model Name | accuracy |
|:-----------------------------:|:--------------------------------:|
|Pharia-1-Embedding-4608-control|95.1% |
|GritLM-7B |94.2% |
|Nvidia-Embed-v2 |93.4% |
|BGE-Gemma2 |95.4% |
## Evaluations on MTEB tasks
To evaluate our models multilingual capabilities we evaluate it against other source-available, high-performing embedding models listen in the
MTEB leaderboard. For the following evaluations we compare the following models:
- NVEmbed-V2: The highest scoring model in the MTEB leaderboard at time of the release
- BGE-Multilingual-Gemma2: The highest scoring multilingual model in the MTEB leaderboard at the time of release.
- GritLM: A generative representational instruction tuned language model.
#### Methodology for Multilingual Evaluations (European languages)
* Context: MTEB is a collection of tasks across many task types (e.g. classification, retrieval etc.). Furthermore, tasks can
have N subsets on different languages. Subsets itself can also contain N languages, e.g. translation-related tasks. Base script
actually comes from [gritlm/evaluation/eval_mteb.py at main · ContextualAI/gritlm](https://github.com/ContextualAI/gritlm/blob/main/evaluation/eval_mteb.py) and
includes Medi2-style instructions for many MTEB Tasks. The instructions are all in English. All evaluations use Medi2-style instructions except for
the “no instructions” case (see above). If a task does not have Medi2-style instructions, we skip the task. As European languages for
MTEB tests German, Italian, Spanish, Portuguese and French were used.
* For our Multilingual Evaluations (European languages) we use the tasks
from [mteb/scripts/task_selection/europe_tasks.csv at main · embeddings-benchmark/mteb](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/task_selection/europe_tasks.csv) and then filter for tasks where there is at least one subset with at least one of the European languages.
* We skip BibleNLPBitextMining and FloresBitextMining because they don’t have ‘test’ splits, only ‘train’ split which we don’t want to use for evaluation (→ training data contamination likely)
* We evaluate subsets which contain at least one of the European languages → that’s why there is also an “English” language column because there are subsets that are e.g. En ↔︎ De and are thus considered
* The tasks that remain are
- AmazonCounterfactualClassification
- BUCC.v2
- DiaBlaBitextMining
- MassiveScenarioClassification
- NTREXBitextMining
- STS17
* For NTREXBitextMining the subsets are further filtered down to only pairs of the European languages instead of at least one European language
- i.e. this gives 20-2=18 translation pair subsets between the 5 languages. -2 because Italian ↔︎ German doesn’t exist.
- this is done because otherwise there are 250 translation pair subsets which are not as relevant (e.g. they contain Vietnamese ↔︎ Portuguese)
We used the official scores reported in MTEB Leaderboard if reported, but for some models and subset we created the scores ourselves with the official Huggingface checkpoints and
instructions referenced in the Paper or Model card.
#### Europe by task
| Model Name | AmazonCounterfactualClassification | BUCC.v2 | DiaBlaBitextMining | MassiveScenarioClassification | NTREXBitextMining | STS17 | Average |
|-------------------------------------------------------|-------------------------------------:|----------:|---------------------:|--------------------------------:|--------------------:|---------:|----------:|
| Pharia-1-Embedding-4608-control | 72.49 | 99.19 | 86.51 | 75.58 | 98.24 | 87.67 | 86.61 |
| GritLM-7B | 76.64 | 99.43 | 86.45 | 78.93 | 98.46 | 88.07 | 87.99 |
| BGE-Multilingual-Gemma2 | 69.72 | 99.38 | 86.90 | 78.57 | 98.58 | 86.69 | 86.64 |
| Nvidia-Embed-v2 | 70.72 | 99.14 | 73.22 | 75.21 | 96.65 | 87.36 | 83.72 |
#### Europe by language
| Model Name | deu-Latn | eng-Latn | fra-Latn | por-Latn | ita-Latn | spa-Latn | Average |
|-------------------------------------------------------|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|----------:|
| Pharia-1-Embedding-4608-control | 0.925309 | 0.902113 | 0.937961 | 0.953719 | 0.942352 | 0.945642 | 0.934516 |
| GritLM-7B | 0.934603 | 0.905669 | 0.942364 | 0.962042 | 0.949731 | 0.947428 | 0.940306 |
| BGE-Multilingual-Gemma2| 93.07 | 92.17 | 94.91 | 94.64 | 96.28 | 94.94 | 94.35 |
| Nvidia-Embed-v2 | 91.58 | 88.85 | 90.51 | 93.94 | 95.08 | 93.78| 92.29 |
#### MTEB – English only
| |Retrieval|Classification|STS|Summarization|PairClassification|Clustering|Reranking|Average|
|---|--|--|--|--|--|--|--|--|
|Nvidia-Embed-v2|62.65|90.37|84.31|30.7|88.67|58.46|60.65|72.31|
|BGE-Multilingual-Gemma2|59.24|88.08|83.88|31.2|85.84|54.65|59.72|69.88|
|GritLM-7B|57.36|78.65|83.35|30.39|87.29|50.61|60.48|66.58|
|Pharia-1-Embedding-4608-control|39.15 |74.40|82.7 |30.95 |81.73|46.23|57.45|58.94|
#### Ablation for “No Instruction” case
We ablate how performance changes when not using task-specific instructions for the embeddings.
|Model Name|ArguAna|AskUbuntuDupQuestions|BIOSSES|Banking77Classification|EmotionClassification|MedrxivClusteringS2S|NFCorpus|STS17|STSBenchmark|SciFact|SummEval|TwitterSemEval2015|Average|
|--|--|--|--|--|--|--|--|--|--|--|--|--|--|
|Instruction |51.09|61.71|84.56|86.37|51.77|34.29|37.82|89.56|87.08|69.7 |30.95|70.97|**62.99**|
|No Instruction |50.23|60.31|84.45|86.36|50.6 |31.87|37.58|88.75|86.39|71.28|31.00|68.92|**62.31**|
|Relative Δ|-1.71%|-2.32%|-0.13%|-0.01%|-2.31%|-7.59%|-0.64%|-0.91%|-0.80%|2.22%|0.16%|-2.97%|**-1.09%**|
We observe slightly reduced performance across most tasks when not using task-specific instructions with an average loss in performance of roughly 1%.
## Training Details
### Model architecture
| | |
|-------|-------|
|Number of layers|27|
|Number of attention heads|36|
|Head size|128|
|Number of Key-Value heads|4|
|Size hidden dimension|4608|
|MLP expansion factor|4|
|MLP type|Standard|
|Vocabulary size|128,000|
|Rotary base|1,000,000|
|Total parameter count|7,041,544,704|
### Training
Pharia-1-Embedding-4608-control is an adapter on top of Pharia-1-LLM-7B-control, trained with a context window
of 2048 Tokens. Pharia-1-Embedding-4608-control was trained with representational instruction-tuning (inspired by the
approach of GritLM) and a contrastive learning approach. The final layer is an embedding head with weighted mean pooling.
The train set consisted of a blend of open-source and proprietary datasets. Further postprocessing was used to optimize
for downstream use and multilinguality.
### Tokenization
Tokenization taking place in this embedding model takes full advantage of the one in [Pharia-1-LLM-7B-control model](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control)
|
[
"BIOSSES",
"SCIFACT"
] |
JackCloudman/DeepSeek-R1-Distill-Llama-70B-abliterated-4.0bpw-h6-exl2
|
JackCloudman
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"abliterated",
"uncensored",
"conversational",
"base_model:huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated",
"base_model:quantized:huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] | 2025-02-02T03:08:45Z |
2025-02-02T09:49:22+00:00
| 28 | 0 |
---
base_model:
- huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated
library_name: transformers
pipeline_tag: text-generation
tags:
- abliterated
- uncensored
---
### Exllamav2 Quantized Version of huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated
Use the measurement.json file to craft different quantized versions.
# huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated
This is an uncensored version of [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
## Use with ollama
You can use [huihui_ai/deepseek-r1-abliterated](https://ollama.com/huihui_ai/deepseek-r1-abliterated) directly
```
ollama run huihui_ai/deepseek-r1-abliterated:70b
```
|
[
"CRAFT"
] |
Teradata/bge-small-en-v1.5
|
Teradata
|
feature-extraction
|
[
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"teradata",
"en",
"license:mit",
"model-index",
"region:us"
] | 2025-02-12T10:47:07Z |
2025-03-04T09:44:50+00:00
| 28 | 0 |
---
language:
- en
license: mit
tags:
- feature-extraction
- sentence-similarity
- mteb
- onnx
- teradata
model-index:
- name: bge-small-en-v1.5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 37.21923821573361
- type: f1
value: 68.0914945617093
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.75377499999999
- type: ap
value: 89.46766124546022
- type: f1
value: 92.73884001331487
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.986
- type: f1
value: 46.55936786727896
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.846000000000004
- type: map_at_10
value: 51.388
- type: map_at_100
value: 52.132999999999996
- type: map_at_1000
value: 52.141000000000005
- type: map_at_3
value: 47.037
- type: map_at_5
value: 49.579
- type: mrr_at_1
value: 36.558
- type: mrr_at_10
value: 51.658
- type: mrr_at_100
value: 52.402
- type: mrr_at_1000
value: 52.410000000000004
- type: mrr_at_3
value: 47.345
- type: mrr_at_5
value: 49.797999999999995
- type: ndcg_at_1
value: 35.846000000000004
- type: ndcg_at_10
value: 59.550000000000004
- type: ndcg_at_100
value: 62.596
- type: ndcg_at_1000
value: 62.759
- type: ndcg_at_3
value: 50.666999999999994
- type: ndcg_at_5
value: 55.228
- type: precision_at_1
value: 35.846000000000004
- type: precision_at_10
value: 8.542
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.389
- type: precision_at_5
value: 14.438
- type: recall_at_1
value: 35.846000000000004
- type: recall_at_10
value: 85.42
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 61.166
- type: recall_at_5
value: 72.191
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.402770198163594
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.01545436974177
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.586465273207196
- type: mrr
value: 74.42169019038825
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.1891186537969
- type: cos_sim_spearman
value: 83.75492046087288
- type: euclidean_pearson
value: 84.11766204805357
- type: euclidean_spearman
value: 84.01456493126516
- type: manhattan_pearson
value: 84.2132950502772
- type: manhattan_spearman
value: 83.89227298813377
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.74025974025975
- type: f1
value: 85.71493566466381
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.467181385006434
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.719496037339056
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.587000000000003
- type: map_at_10
value: 41.114
- type: map_at_100
value: 42.532
- type: map_at_1000
value: 42.661
- type: map_at_3
value: 37.483
- type: map_at_5
value: 39.652
- type: mrr_at_1
value: 36.338
- type: mrr_at_10
value: 46.763
- type: mrr_at_100
value: 47.393
- type: mrr_at_1000
value: 47.445
- type: mrr_at_3
value: 43.538
- type: mrr_at_5
value: 45.556000000000004
- type: ndcg_at_1
value: 36.338
- type: ndcg_at_10
value: 47.658
- type: ndcg_at_100
value: 52.824000000000005
- type: ndcg_at_1000
value: 54.913999999999994
- type: ndcg_at_3
value: 41.989
- type: ndcg_at_5
value: 44.944
- type: precision_at_1
value: 36.338
- type: precision_at_10
value: 9.156
- type: precision_at_100
value: 1.4789999999999999
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 20.076
- type: precision_at_5
value: 14.85
- type: recall_at_1
value: 29.587000000000003
- type: recall_at_10
value: 60.746
- type: recall_at_100
value: 82.157
- type: recall_at_1000
value: 95.645
- type: recall_at_3
value: 44.821
- type: recall_at_5
value: 52.819
- type: map_at_1
value: 30.239
- type: map_at_10
value: 39.989000000000004
- type: map_at_100
value: 41.196
- type: map_at_1000
value: 41.325
- type: map_at_3
value: 37.261
- type: map_at_5
value: 38.833
- type: mrr_at_1
value: 37.516
- type: mrr_at_10
value: 46.177
- type: mrr_at_100
value: 46.806
- type: mrr_at_1000
value: 46.849000000000004
- type: mrr_at_3
value: 44.002
- type: mrr_at_5
value: 45.34
- type: ndcg_at_1
value: 37.516
- type: ndcg_at_10
value: 45.586
- type: ndcg_at_100
value: 49.897000000000006
- type: ndcg_at_1000
value: 51.955
- type: ndcg_at_3
value: 41.684
- type: ndcg_at_5
value: 43.617
- type: precision_at_1
value: 37.516
- type: precision_at_10
value: 8.522
- type: precision_at_100
value: 1.374
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 20.105999999999998
- type: precision_at_5
value: 14.152999999999999
- type: recall_at_1
value: 30.239
- type: recall_at_10
value: 55.03
- type: recall_at_100
value: 73.375
- type: recall_at_1000
value: 86.29599999999999
- type: recall_at_3
value: 43.269000000000005
- type: recall_at_5
value: 48.878
- type: map_at_1
value: 38.338
- type: map_at_10
value: 50.468999999999994
- type: map_at_100
value: 51.553000000000004
- type: map_at_1000
value: 51.608
- type: map_at_3
value: 47.107
- type: map_at_5
value: 49.101
- type: mrr_at_1
value: 44.201
- type: mrr_at_10
value: 54.057
- type: mrr_at_100
value: 54.764
- type: mrr_at_1000
value: 54.791000000000004
- type: mrr_at_3
value: 51.56699999999999
- type: mrr_at_5
value: 53.05
- type: ndcg_at_1
value: 44.201
- type: ndcg_at_10
value: 56.379000000000005
- type: ndcg_at_100
value: 60.645
- type: ndcg_at_1000
value: 61.73499999999999
- type: ndcg_at_3
value: 50.726000000000006
- type: ndcg_at_5
value: 53.58500000000001
- type: precision_at_1
value: 44.201
- type: precision_at_10
value: 9.141
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.654
- type: precision_at_5
value: 15.723999999999998
- type: recall_at_1
value: 38.338
- type: recall_at_10
value: 70.30499999999999
- type: recall_at_100
value: 88.77199999999999
- type: recall_at_1000
value: 96.49799999999999
- type: recall_at_3
value: 55.218
- type: recall_at_5
value: 62.104000000000006
- type: map_at_1
value: 25.682
- type: map_at_10
value: 33.498
- type: map_at_100
value: 34.461000000000006
- type: map_at_1000
value: 34.544000000000004
- type: map_at_3
value: 30.503999999999998
- type: map_at_5
value: 32.216
- type: mrr_at_1
value: 27.683999999999997
- type: mrr_at_10
value: 35.467999999999996
- type: mrr_at_100
value: 36.32
- type: mrr_at_1000
value: 36.386
- type: mrr_at_3
value: 32.618
- type: mrr_at_5
value: 34.262
- type: ndcg_at_1
value: 27.683999999999997
- type: ndcg_at_10
value: 38.378
- type: ndcg_at_100
value: 43.288
- type: ndcg_at_1000
value: 45.413
- type: ndcg_at_3
value: 32.586
- type: ndcg_at_5
value: 35.499
- type: precision_at_1
value: 27.683999999999997
- type: precision_at_10
value: 5.864
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 13.446
- type: precision_at_5
value: 9.718
- type: recall_at_1
value: 25.682
- type: recall_at_10
value: 51.712
- type: recall_at_100
value: 74.446
- type: recall_at_1000
value: 90.472
- type: recall_at_3
value: 36.236000000000004
- type: recall_at_5
value: 43.234
- type: map_at_1
value: 16.073999999999998
- type: map_at_10
value: 24.352999999999998
- type: map_at_100
value: 25.438
- type: map_at_1000
value: 25.545
- type: map_at_3
value: 21.614
- type: map_at_5
value: 23.104
- type: mrr_at_1
value: 19.776
- type: mrr_at_10
value: 28.837000000000003
- type: mrr_at_100
value: 29.755
- type: mrr_at_1000
value: 29.817
- type: mrr_at_3
value: 26.201999999999998
- type: mrr_at_5
value: 27.714
- type: ndcg_at_1
value: 19.776
- type: ndcg_at_10
value: 29.701
- type: ndcg_at_100
value: 35.307
- type: ndcg_at_1000
value: 37.942
- type: ndcg_at_3
value: 24.764
- type: ndcg_at_5
value: 27.025
- type: precision_at_1
value: 19.776
- type: precision_at_10
value: 5.659
- type: precision_at_100
value: 0.971
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 12.065
- type: precision_at_5
value: 8.905000000000001
- type: recall_at_1
value: 16.073999999999998
- type: recall_at_10
value: 41.647
- type: recall_at_100
value: 66.884
- type: recall_at_1000
value: 85.91499999999999
- type: recall_at_3
value: 27.916
- type: recall_at_5
value: 33.729
- type: map_at_1
value: 28.444999999999997
- type: map_at_10
value: 38.218999999999994
- type: map_at_100
value: 39.595
- type: map_at_1000
value: 39.709
- type: map_at_3
value: 35.586
- type: map_at_5
value: 36.895
- type: mrr_at_1
value: 34.841
- type: mrr_at_10
value: 44.106
- type: mrr_at_100
value: 44.98
- type: mrr_at_1000
value: 45.03
- type: mrr_at_3
value: 41.979
- type: mrr_at_5
value: 43.047999999999995
- type: ndcg_at_1
value: 34.841
- type: ndcg_at_10
value: 43.922
- type: ndcg_at_100
value: 49.504999999999995
- type: ndcg_at_1000
value: 51.675000000000004
- type: ndcg_at_3
value: 39.858
- type: ndcg_at_5
value: 41.408
- type: precision_at_1
value: 34.841
- type: precision_at_10
value: 7.872999999999999
- type: precision_at_100
value: 1.2449999999999999
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 18.993
- type: precision_at_5
value: 13.032
- type: recall_at_1
value: 28.444999999999997
- type: recall_at_10
value: 54.984
- type: recall_at_100
value: 78.342
- type: recall_at_1000
value: 92.77
- type: recall_at_3
value: 42.842999999999996
- type: recall_at_5
value: 47.247
- type: map_at_1
value: 23.072
- type: map_at_10
value: 32.354
- type: map_at_100
value: 33.800000000000004
- type: map_at_1000
value: 33.908
- type: map_at_3
value: 29.232000000000003
- type: map_at_5
value: 31.049
- type: mrr_at_1
value: 29.110000000000003
- type: mrr_at_10
value: 38.03
- type: mrr_at_100
value: 39.032
- type: mrr_at_1000
value: 39.086999999999996
- type: mrr_at_3
value: 35.407
- type: mrr_at_5
value: 36.76
- type: ndcg_at_1
value: 29.110000000000003
- type: ndcg_at_10
value: 38.231
- type: ndcg_at_100
value: 44.425
- type: ndcg_at_1000
value: 46.771
- type: ndcg_at_3
value: 33.095
- type: ndcg_at_5
value: 35.459
- type: precision_at_1
value: 29.110000000000003
- type: precision_at_10
value: 7.215000000000001
- type: precision_at_100
value: 1.2109999999999999
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 16.058
- type: precision_at_5
value: 11.644
- type: recall_at_1
value: 23.072
- type: recall_at_10
value: 50.285999999999994
- type: recall_at_100
value: 76.596
- type: recall_at_1000
value: 92.861
- type: recall_at_3
value: 35.702
- type: recall_at_5
value: 42.152
- type: map_at_1
value: 24.937916666666666
- type: map_at_10
value: 33.755250000000004
- type: map_at_100
value: 34.955999999999996
- type: map_at_1000
value: 35.070499999999996
- type: map_at_3
value: 30.98708333333333
- type: map_at_5
value: 32.51491666666666
- type: mrr_at_1
value: 29.48708333333333
- type: mrr_at_10
value: 37.92183333333334
- type: mrr_at_100
value: 38.76583333333333
- type: mrr_at_1000
value: 38.82466666666667
- type: mrr_at_3
value: 35.45125
- type: mrr_at_5
value: 36.827000000000005
- type: ndcg_at_1
value: 29.48708333333333
- type: ndcg_at_10
value: 39.05225
- type: ndcg_at_100
value: 44.25983333333334
- type: ndcg_at_1000
value: 46.568333333333335
- type: ndcg_at_3
value: 34.271583333333325
- type: ndcg_at_5
value: 36.483916666666666
- type: precision_at_1
value: 29.48708333333333
- type: precision_at_10
value: 6.865749999999999
- type: precision_at_100
value: 1.1195833333333332
- type: precision_at_1000
value: 0.15058333333333335
- type: precision_at_3
value: 15.742083333333333
- type: precision_at_5
value: 11.221916666666667
- type: recall_at_1
value: 24.937916666666666
- type: recall_at_10
value: 50.650416666666665
- type: recall_at_100
value: 73.55383333333334
- type: recall_at_1000
value: 89.61691666666667
- type: recall_at_3
value: 37.27808333333334
- type: recall_at_5
value: 42.99475
- type: map_at_1
value: 23.947
- type: map_at_10
value: 30.575000000000003
- type: map_at_100
value: 31.465
- type: map_at_1000
value: 31.558000000000003
- type: map_at_3
value: 28.814
- type: map_at_5
value: 29.738999999999997
- type: mrr_at_1
value: 26.994
- type: mrr_at_10
value: 33.415
- type: mrr_at_100
value: 34.18
- type: mrr_at_1000
value: 34.245
- type: mrr_at_3
value: 31.621
- type: mrr_at_5
value: 32.549
- type: ndcg_at_1
value: 26.994
- type: ndcg_at_10
value: 34.482
- type: ndcg_at_100
value: 38.915
- type: ndcg_at_1000
value: 41.355
- type: ndcg_at_3
value: 31.139
- type: ndcg_at_5
value: 32.589
- type: precision_at_1
value: 26.994
- type: precision_at_10
value: 5.322
- type: precision_at_100
value: 0.8160000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 13.344000000000001
- type: precision_at_5
value: 8.988
- type: recall_at_1
value: 23.947
- type: recall_at_10
value: 43.647999999999996
- type: recall_at_100
value: 63.851
- type: recall_at_1000
value: 82
- type: recall_at_3
value: 34.288000000000004
- type: recall_at_5
value: 38.117000000000004
- type: map_at_1
value: 16.197
- type: map_at_10
value: 22.968
- type: map_at_100
value: 24.095
- type: map_at_1000
value: 24.217
- type: map_at_3
value: 20.771
- type: map_at_5
value: 21.995
- type: mrr_at_1
value: 19.511
- type: mrr_at_10
value: 26.55
- type: mrr_at_100
value: 27.500999999999998
- type: mrr_at_1000
value: 27.578999999999997
- type: mrr_at_3
value: 24.421
- type: mrr_at_5
value: 25.604
- type: ndcg_at_1
value: 19.511
- type: ndcg_at_10
value: 27.386
- type: ndcg_at_100
value: 32.828
- type: ndcg_at_1000
value: 35.739
- type: ndcg_at_3
value: 23.405
- type: ndcg_at_5
value: 25.255
- type: precision_at_1
value: 19.511
- type: precision_at_10
value: 5.017
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 11.023
- type: precision_at_5
value: 8.025
- type: recall_at_1
value: 16.197
- type: recall_at_10
value: 37.09
- type: recall_at_100
value: 61.778
- type: recall_at_1000
value: 82.56599999999999
- type: recall_at_3
value: 26.034000000000002
- type: recall_at_5
value: 30.762
- type: map_at_1
value: 25.41
- type: map_at_10
value: 33.655
- type: map_at_100
value: 34.892
- type: map_at_1000
value: 34.995
- type: map_at_3
value: 30.94
- type: map_at_5
value: 32.303
- type: mrr_at_1
value: 29.477999999999998
- type: mrr_at_10
value: 37.443
- type: mrr_at_100
value: 38.383
- type: mrr_at_1000
value: 38.440000000000005
- type: mrr_at_3
value: 34.949999999999996
- type: mrr_at_5
value: 36.228
- type: ndcg_at_1
value: 29.477999999999998
- type: ndcg_at_10
value: 38.769
- type: ndcg_at_100
value: 44.245000000000005
- type: ndcg_at_1000
value: 46.593
- type: ndcg_at_3
value: 33.623
- type: ndcg_at_5
value: 35.766
- type: precision_at_1
value: 29.477999999999998
- type: precision_at_10
value: 6.455
- type: precision_at_100
value: 1.032
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 14.893999999999998
- type: precision_at_5
value: 10.485
- type: recall_at_1
value: 25.41
- type: recall_at_10
value: 50.669
- type: recall_at_100
value: 74.084
- type: recall_at_1000
value: 90.435
- type: recall_at_3
value: 36.679
- type: recall_at_5
value: 41.94
- type: map_at_1
value: 23.339
- type: map_at_10
value: 31.852000000000004
- type: map_at_100
value: 33.411
- type: map_at_1000
value: 33.62
- type: map_at_3
value: 28.929
- type: map_at_5
value: 30.542
- type: mrr_at_1
value: 28.063
- type: mrr_at_10
value: 36.301
- type: mrr_at_100
value: 37.288
- type: mrr_at_1000
value: 37.349
- type: mrr_at_3
value: 33.663
- type: mrr_at_5
value: 35.165
- type: ndcg_at_1
value: 28.063
- type: ndcg_at_10
value: 37.462
- type: ndcg_at_100
value: 43.620999999999995
- type: ndcg_at_1000
value: 46.211
- type: ndcg_at_3
value: 32.68
- type: ndcg_at_5
value: 34.981
- type: precision_at_1
value: 28.063
- type: precision_at_10
value: 7.1739999999999995
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 15.217
- type: precision_at_5
value: 11.265
- type: recall_at_1
value: 23.339
- type: recall_at_10
value: 48.376999999999995
- type: recall_at_100
value: 76.053
- type: recall_at_1000
value: 92.455
- type: recall_at_3
value: 34.735
- type: recall_at_5
value: 40.71
- type: map_at_1
value: 18.925
- type: map_at_10
value: 26.017000000000003
- type: map_at_100
value: 27.034000000000002
- type: map_at_1000
value: 27.156000000000002
- type: map_at_3
value: 23.604
- type: map_at_5
value: 24.75
- type: mrr_at_1
value: 20.333000000000002
- type: mrr_at_10
value: 27.915
- type: mrr_at_100
value: 28.788000000000004
- type: mrr_at_1000
value: 28.877999999999997
- type: mrr_at_3
value: 25.446999999999996
- type: mrr_at_5
value: 26.648
- type: ndcg_at_1
value: 20.333000000000002
- type: ndcg_at_10
value: 30.673000000000002
- type: ndcg_at_100
value: 35.618
- type: ndcg_at_1000
value: 38.517
- type: ndcg_at_3
value: 25.71
- type: ndcg_at_5
value: 27.679
- type: precision_at_1
value: 20.333000000000002
- type: precision_at_10
value: 4.9910000000000005
- type: precision_at_100
value: 0.8130000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 11.029
- type: precision_at_5
value: 7.8740000000000006
- type: recall_at_1
value: 18.925
- type: recall_at_10
value: 43.311
- type: recall_at_100
value: 66.308
- type: recall_at_1000
value: 87.49
- type: recall_at_3
value: 29.596
- type: recall_at_5
value: 34.245
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.714
- type: map_at_10
value: 23.194
- type: map_at_100
value: 24.976000000000003
- type: map_at_1000
value: 25.166
- type: map_at_3
value: 19.709
- type: map_at_5
value: 21.523999999999997
- type: mrr_at_1
value: 30.619000000000003
- type: mrr_at_10
value: 42.563
- type: mrr_at_100
value: 43.386
- type: mrr_at_1000
value: 43.423
- type: mrr_at_3
value: 39.555
- type: mrr_at_5
value: 41.268
- type: ndcg_at_1
value: 30.619000000000003
- type: ndcg_at_10
value: 31.836
- type: ndcg_at_100
value: 38.652
- type: ndcg_at_1000
value: 42.088
- type: ndcg_at_3
value: 26.733
- type: ndcg_at_5
value: 28.435
- type: precision_at_1
value: 30.619000000000003
- type: precision_at_10
value: 9.751999999999999
- type: precision_at_100
value: 1.71
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 19.935
- type: precision_at_5
value: 14.984
- type: recall_at_1
value: 13.714
- type: recall_at_10
value: 37.26
- type: recall_at_100
value: 60.546
- type: recall_at_1000
value: 79.899
- type: recall_at_3
value: 24.325
- type: recall_at_5
value: 29.725
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.462
- type: map_at_10
value: 18.637
- type: map_at_100
value: 26.131999999999998
- type: map_at_1000
value: 27.607
- type: map_at_3
value: 13.333
- type: map_at_5
value: 15.654000000000002
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.32600000000001
- type: mrr_at_100
value: 74.60900000000001
- type: mrr_at_1000
value: 74.62
- type: mrr_at_3
value: 72.667
- type: mrr_at_5
value: 73.817
- type: ndcg_at_1
value: 53.87499999999999
- type: ndcg_at_10
value: 40.028999999999996
- type: ndcg_at_100
value: 44.199
- type: ndcg_at_1000
value: 51.629999999999995
- type: ndcg_at_3
value: 44.113
- type: ndcg_at_5
value: 41.731
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 31.900000000000002
- type: precision_at_100
value: 10.043000000000001
- type: precision_at_1000
value: 1.926
- type: precision_at_3
value: 47.417
- type: precision_at_5
value: 40.65
- type: recall_at_1
value: 8.462
- type: recall_at_10
value: 24.293
- type: recall_at_100
value: 50.146
- type: recall_at_1000
value: 74.034
- type: recall_at_3
value: 14.967
- type: recall_at_5
value: 18.682000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.84499999999999
- type: f1
value: 42.48106691979349
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.034
- type: map_at_10
value: 82.76
- type: map_at_100
value: 82.968
- type: map_at_1000
value: 82.98299999999999
- type: map_at_3
value: 81.768
- type: map_at_5
value: 82.418
- type: mrr_at_1
value: 80.048
- type: mrr_at_10
value: 87.64999999999999
- type: mrr_at_100
value: 87.712
- type: mrr_at_1000
value: 87.713
- type: mrr_at_3
value: 87.01100000000001
- type: mrr_at_5
value: 87.466
- type: ndcg_at_1
value: 80.048
- type: ndcg_at_10
value: 86.643
- type: ndcg_at_100
value: 87.361
- type: ndcg_at_1000
value: 87.606
- type: ndcg_at_3
value: 85.137
- type: ndcg_at_5
value: 86.016
- type: precision_at_1
value: 80.048
- type: precision_at_10
value: 10.372
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 32.638
- type: precision_at_5
value: 20.177
- type: recall_at_1
value: 74.034
- type: recall_at_10
value: 93.769
- type: recall_at_100
value: 96.569
- type: recall_at_1000
value: 98.039
- type: recall_at_3
value: 89.581
- type: recall_at_5
value: 91.906
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.5
- type: map_at_10
value: 32.857
- type: map_at_100
value: 34.589
- type: map_at_1000
value: 34.778
- type: map_at_3
value: 29.160999999999998
- type: map_at_5
value: 31.033
- type: mrr_at_1
value: 40.123
- type: mrr_at_10
value: 48.776
- type: mrr_at_100
value: 49.495
- type: mrr_at_1000
value: 49.539
- type: mrr_at_3
value: 46.605000000000004
- type: mrr_at_5
value: 47.654
- type: ndcg_at_1
value: 40.123
- type: ndcg_at_10
value: 40.343
- type: ndcg_at_100
value: 46.56
- type: ndcg_at_1000
value: 49.777
- type: ndcg_at_3
value: 37.322
- type: ndcg_at_5
value: 37.791000000000004
- type: precision_at_1
value: 40.123
- type: precision_at_10
value: 11.08
- type: precision_at_100
value: 1.752
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 24.897
- type: precision_at_5
value: 17.809
- type: recall_at_1
value: 20.5
- type: recall_at_10
value: 46.388
- type: recall_at_100
value: 69.552
- type: recall_at_1000
value: 89.011
- type: recall_at_3
value: 33.617999999999995
- type: recall_at_5
value: 38.211
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.135999999999996
- type: map_at_10
value: 61.673
- type: map_at_100
value: 62.562
- type: map_at_1000
value: 62.62
- type: map_at_3
value: 58.467999999999996
- type: map_at_5
value: 60.463
- type: mrr_at_1
value: 78.271
- type: mrr_at_10
value: 84.119
- type: mrr_at_100
value: 84.29299999999999
- type: mrr_at_1000
value: 84.299
- type: mrr_at_3
value: 83.18900000000001
- type: mrr_at_5
value: 83.786
- type: ndcg_at_1
value: 78.271
- type: ndcg_at_10
value: 69.935
- type: ndcg_at_100
value: 73.01299999999999
- type: ndcg_at_1000
value: 74.126
- type: ndcg_at_3
value: 65.388
- type: ndcg_at_5
value: 67.906
- type: precision_at_1
value: 78.271
- type: precision_at_10
value: 14.562
- type: precision_at_100
value: 1.6969999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 41.841
- type: precision_at_5
value: 27.087
- type: recall_at_1
value: 39.135999999999996
- type: recall_at_10
value: 72.809
- type: recall_at_100
value: 84.86200000000001
- type: recall_at_1000
value: 92.208
- type: recall_at_3
value: 62.76199999999999
- type: recall_at_5
value: 67.718
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.60600000000001
- type: ap
value: 86.6579587804335
- type: f1
value: 90.5938853929307
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.852
- type: map_at_10
value: 33.982
- type: map_at_100
value: 35.116
- type: map_at_1000
value: 35.167
- type: map_at_3
value: 30.134
- type: map_at_5
value: 32.340999999999994
- type: mrr_at_1
value: 22.479
- type: mrr_at_10
value: 34.594
- type: mrr_at_100
value: 35.672
- type: mrr_at_1000
value: 35.716
- type: mrr_at_3
value: 30.84
- type: mrr_at_5
value: 32.998
- type: ndcg_at_1
value: 22.493
- type: ndcg_at_10
value: 40.833000000000006
- type: ndcg_at_100
value: 46.357
- type: ndcg_at_1000
value: 47.637
- type: ndcg_at_3
value: 32.995999999999995
- type: ndcg_at_5
value: 36.919000000000004
- type: precision_at_1
value: 22.493
- type: precision_at_10
value: 6.465999999999999
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.030999999999999
- type: precision_at_5
value: 10.413
- type: recall_at_1
value: 21.852
- type: recall_at_10
value: 61.934999999999995
- type: recall_at_100
value: 87.611
- type: recall_at_1000
value: 97.441
- type: recall_at_3
value: 40.583999999999996
- type: recall_at_5
value: 49.992999999999995
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.36069311445507
- type: f1
value: 93.16456330371453
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.74692202462381
- type: f1
value: 58.17903579421599
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.80833893745796
- type: f1
value: 72.70786592684664
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.69872225958305
- type: f1
value: 78.61626934504731
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.058658628717694
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.85561739360599
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.290259910144385
- type: mrr
value: 32.44223046102856
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.288
- type: map_at_10
value: 12.267999999999999
- type: map_at_100
value: 15.557000000000002
- type: map_at_1000
value: 16.98
- type: map_at_3
value: 8.866
- type: map_at_5
value: 10.418
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 52.681
- type: mrr_at_100
value: 53.315999999999995
- type: mrr_at_1000
value: 53.357
- type: mrr_at_3
value: 51.393
- type: mrr_at_5
value: 51.903999999999996
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.305
- type: ndcg_at_100
value: 30.825999999999997
- type: ndcg_at_1000
value: 39.393
- type: ndcg_at_3
value: 39.931
- type: ndcg_at_5
value: 37.519999999999996
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.728
- type: precision_at_100
value: 7.932
- type: precision_at_1000
value: 2.07
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.288
- type: recall_at_10
value: 16.195
- type: recall_at_100
value: 31.135
- type: recall_at_1000
value: 61.531000000000006
- type: recall_at_3
value: 10.313
- type: recall_at_5
value: 12.754999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.216
- type: map_at_10
value: 42.588
- type: map_at_100
value: 43.702999999999996
- type: map_at_1000
value: 43.739
- type: map_at_3
value: 38.177
- type: map_at_5
value: 40.754000000000005
- type: mrr_at_1
value: 31.866
- type: mrr_at_10
value: 45.189
- type: mrr_at_100
value: 46.056000000000004
- type: mrr_at_1000
value: 46.081
- type: mrr_at_3
value: 41.526999999999994
- type: mrr_at_5
value: 43.704
- type: ndcg_at_1
value: 31.837
- type: ndcg_at_10
value: 50.178
- type: ndcg_at_100
value: 54.98800000000001
- type: ndcg_at_1000
value: 55.812
- type: ndcg_at_3
value: 41.853
- type: ndcg_at_5
value: 46.153
- type: precision_at_1
value: 31.837
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 1.1119999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.911000000000001
- type: recall_at_1
value: 28.216
- type: recall_at_10
value: 70.8
- type: recall_at_100
value: 91.857
- type: recall_at_1000
value: 97.941
- type: recall_at_3
value: 49.196
- type: recall_at_5
value: 59.072
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.22800000000001
- type: map_at_10
value: 85.115
- type: map_at_100
value: 85.72
- type: map_at_1000
value: 85.737
- type: map_at_3
value: 82.149
- type: map_at_5
value: 84.029
- type: mrr_at_1
value: 81.96
- type: mrr_at_10
value: 88.00200000000001
- type: mrr_at_100
value: 88.088
- type: mrr_at_1000
value: 88.089
- type: mrr_at_3
value: 87.055
- type: mrr_at_5
value: 87.715
- type: ndcg_at_1
value: 82.01
- type: ndcg_at_10
value: 88.78
- type: ndcg_at_100
value: 89.91
- type: ndcg_at_1000
value: 90.013
- type: ndcg_at_3
value: 85.957
- type: ndcg_at_5
value: 87.56
- type: precision_at_1
value: 82.01
- type: precision_at_10
value: 13.462
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.732000000000003
- type: recall_at_1
value: 71.22800000000001
- type: recall_at_10
value: 95.69
- type: recall_at_100
value: 99.531
- type: recall_at_1000
value: 99.98
- type: recall_at_3
value: 87.632
- type: recall_at_5
value: 92.117
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 52.31768034366916
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.640266772723606
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.7780000000000005
- type: map_at_10
value: 12.299
- type: map_at_100
value: 14.363000000000001
- type: map_at_1000
value: 14.71
- type: map_at_3
value: 8.738999999999999
- type: map_at_5
value: 10.397
- type: mrr_at_1
value: 23.599999999999998
- type: mrr_at_10
value: 34.845
- type: mrr_at_100
value: 35.916
- type: mrr_at_1000
value: 35.973
- type: mrr_at_3
value: 31.7
- type: mrr_at_5
value: 33.535
- type: ndcg_at_1
value: 23.599999999999998
- type: ndcg_at_10
value: 20.522000000000002
- type: ndcg_at_100
value: 28.737000000000002
- type: ndcg_at_1000
value: 34.596
- type: ndcg_at_3
value: 19.542
- type: ndcg_at_5
value: 16.958000000000002
- type: precision_at_1
value: 23.599999999999998
- type: precision_at_10
value: 10.67
- type: precision_at_100
value: 2.259
- type: precision_at_1000
value: 0.367
- type: precision_at_3
value: 18.333
- type: precision_at_5
value: 14.879999999999999
- type: recall_at_1
value: 4.7780000000000005
- type: recall_at_10
value: 21.617
- type: recall_at_100
value: 45.905
- type: recall_at_1000
value: 74.42
- type: recall_at_3
value: 11.148
- type: recall_at_5
value: 15.082999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.22372750297885
- type: cos_sim_spearman
value: 79.40972617119405
- type: euclidean_pearson
value: 80.6101072020434
- type: euclidean_spearman
value: 79.53844217225202
- type: manhattan_pearson
value: 80.57265975286111
- type: manhattan_spearman
value: 79.46335611792958
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.43713315520749
- type: cos_sim_spearman
value: 77.44128693329532
- type: euclidean_pearson
value: 81.63869928101123
- type: euclidean_spearman
value: 77.29512977961515
- type: manhattan_pearson
value: 81.63704185566183
- type: manhattan_spearman
value: 77.29909412738657
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.59451537860527
- type: cos_sim_spearman
value: 82.97994638856723
- type: euclidean_pearson
value: 82.89478688288412
- type: euclidean_spearman
value: 83.58740751053104
- type: manhattan_pearson
value: 82.69140840941608
- type: manhattan_spearman
value: 83.33665956040555
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.00756527711764
- type: cos_sim_spearman
value: 81.83560996841379
- type: euclidean_pearson
value: 82.07684151976518
- type: euclidean_spearman
value: 82.00913052060511
- type: manhattan_pearson
value: 82.05690778488794
- type: manhattan_spearman
value: 82.02260252019525
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.13710262895447
- type: cos_sim_spearman
value: 87.26412811156248
- type: euclidean_pearson
value: 86.94151453230228
- type: euclidean_spearman
value: 87.5363796699571
- type: manhattan_pearson
value: 86.86989424083748
- type: manhattan_spearman
value: 87.47315940781353
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.0230597603627
- type: cos_sim_spearman
value: 84.93344499318864
- type: euclidean_pearson
value: 84.23754743431141
- type: euclidean_spearman
value: 85.09707376597099
- type: manhattan_pearson
value: 84.04325160987763
- type: manhattan_spearman
value: 84.89353071339909
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.75620824563921
- type: cos_sim_spearman
value: 87.15065513706398
- type: euclidean_pearson
value: 88.26281533633521
- type: euclidean_spearman
value: 87.51963738643983
- type: manhattan_pearson
value: 88.25599267618065
- type: manhattan_spearman
value: 87.58048736047483
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.74645319195137
- type: cos_sim_spearman
value: 65.29996325037214
- type: euclidean_pearson
value: 67.04297794086443
- type: euclidean_spearman
value: 65.43841726694343
- type: manhattan_pearson
value: 67.39459955690904
- type: manhattan_spearman
value: 65.92864704413651
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.31291020270801
- type: cos_sim_spearman
value: 85.86473738688068
- type: euclidean_pearson
value: 85.65537275064152
- type: euclidean_spearman
value: 86.13087454209642
- type: manhattan_pearson
value: 85.43946955047609
- type: manhattan_spearman
value: 85.91568175344916
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.93798118350695
- type: mrr
value: 95.93536274908824
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.594
- type: map_at_10
value: 66.81899999999999
- type: map_at_100
value: 67.368
- type: map_at_1000
value: 67.4
- type: map_at_3
value: 64.061
- type: map_at_5
value: 65.47
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 68.219
- type: mrr_at_100
value: 68.655
- type: mrr_at_1000
value: 68.684
- type: mrr_at_3
value: 66.22200000000001
- type: mrr_at_5
value: 67.289
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 71.275
- type: ndcg_at_100
value: 73.642
- type: ndcg_at_1000
value: 74.373
- type: ndcg_at_3
value: 66.521
- type: ndcg_at_5
value: 68.581
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.433
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.556
- type: precision_at_5
value: 16.8
- type: recall_at_1
value: 57.594
- type: recall_at_10
value: 83.622
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.64399999999999
- type: recall_at_5
value: 75.983
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85841584158416
- type: cos_sim_ap
value: 96.66996142314342
- type: cos_sim_f1
value: 92.83208020050125
- type: cos_sim_precision
value: 93.06532663316584
- type: cos_sim_recall
value: 92.60000000000001
- type: dot_accuracy
value: 99.85841584158416
- type: dot_ap
value: 96.6775307676576
- type: dot_f1
value: 92.69289729177312
- type: dot_precision
value: 94.77533960292581
- type: dot_recall
value: 90.7
- type: euclidean_accuracy
value: 99.86138613861387
- type: euclidean_ap
value: 96.6338454403108
- type: euclidean_f1
value: 92.92214357937311
- type: euclidean_precision
value: 93.96728016359918
- type: euclidean_recall
value: 91.9
- type: manhattan_accuracy
value: 99.86237623762376
- type: manhattan_ap
value: 96.60370449645053
- type: manhattan_f1
value: 92.91177970423253
- type: manhattan_precision
value: 94.7970863683663
- type: manhattan_recall
value: 91.10000000000001
- type: max_accuracy
value: 99.86237623762376
- type: max_ap
value: 96.6775307676576
- type: max_f1
value: 92.92214357937311
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.77977058695198
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.2725272535638
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.64052466362125
- type: mrr
value: 54.533067014684654
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.677624219206578
- type: cos_sim_spearman
value: 30.121368518123447
- type: dot_pearson
value: 30.69870088041608
- type: dot_spearman
value: 29.61284927093751
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.855
- type: map_at_100
value: 9.885
- type: map_at_1000
value: 23.416999999999998
- type: map_at_3
value: 0.637
- type: map_at_5
value: 1.024
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.067
- type: mrr_at_100
value: 93.067
- type: mrr_at_1000
value: 93.067
- type: mrr_at_3
value: 92.667
- type: mrr_at_5
value: 93.067
- type: ndcg_at_1
value: 82
- type: ndcg_at_10
value: 75.899
- type: ndcg_at_100
value: 55.115
- type: ndcg_at_1000
value: 48.368
- type: ndcg_at_3
value: 79.704
- type: ndcg_at_5
value: 78.39699999999999
- type: precision_at_1
value: 88
- type: precision_at_10
value: 79.60000000000001
- type: precision_at_100
value: 56.06
- type: precision_at_1000
value: 21.206
- type: precision_at_3
value: 84.667
- type: precision_at_5
value: 83.2
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 2.078
- type: recall_at_100
value: 13.297
- type: recall_at_1000
value: 44.979
- type: recall_at_3
value: 0.6689999999999999
- type: recall_at_5
value: 1.106
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.258
- type: map_at_10
value: 10.439
- type: map_at_100
value: 16.89
- type: map_at_1000
value: 18.407999999999998
- type: map_at_3
value: 5.668
- type: map_at_5
value: 7.718
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 51.159
- type: mrr_at_100
value: 51.714000000000006
- type: mrr_at_1000
value: 51.714000000000006
- type: mrr_at_3
value: 47.959
- type: mrr_at_5
value: 50.407999999999994
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 26.037
- type: ndcg_at_100
value: 37.924
- type: ndcg_at_1000
value: 49.126999999999995
- type: ndcg_at_3
value: 30.631999999999998
- type: ndcg_at_5
value: 28.571
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.857
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 1.529
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.258
- type: recall_at_10
value: 16.554
- type: recall_at_100
value: 48.439
- type: recall_at_1000
value: 82.80499999999999
- type: recall_at_3
value: 7.283
- type: recall_at_5
value: 10.732
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.8858
- type: ap
value: 13.835684144362109
- type: f1
value: 53.803351693244586
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.50650820599886
- type: f1
value: 60.84357825979259
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.52131044852134
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.59337187816654
- type: cos_sim_ap
value: 73.23925826533437
- type: cos_sim_f1
value: 67.34693877551021
- type: cos_sim_precision
value: 62.40432237730752
- type: cos_sim_recall
value: 73.13984168865434
- type: dot_accuracy
value: 85.31322644096085
- type: dot_ap
value: 72.30723963807422
- type: dot_f1
value: 66.47051612112296
- type: dot_precision
value: 62.0792305930845
- type: dot_recall
value: 71.53034300791556
- type: euclidean_accuracy
value: 85.61125350181797
- type: euclidean_ap
value: 73.32843720487845
- type: euclidean_f1
value: 67.36549633745895
- type: euclidean_precision
value: 64.60755813953489
- type: euclidean_recall
value: 70.36939313984169
- type: manhattan_accuracy
value: 85.63509566668654
- type: manhattan_ap
value: 73.16658488311325
- type: manhattan_f1
value: 67.20597386434349
- type: manhattan_precision
value: 63.60424028268551
- type: manhattan_recall
value: 71.2401055408971
- type: max_accuracy
value: 85.63509566668654
- type: max_ap
value: 73.32843720487845
- type: max_f1
value: 67.36549633745895
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.33779640625606
- type: cos_sim_ap
value: 84.83868375898157
- type: cos_sim_f1
value: 77.16506154017773
- type: cos_sim_precision
value: 74.62064005753327
- type: cos_sim_recall
value: 79.88912842623961
- type: dot_accuracy
value: 88.02732176815307
- type: dot_ap
value: 83.95089283763002
- type: dot_f1
value: 76.29635101196631
- type: dot_precision
value: 73.31771720613288
- type: dot_recall
value: 79.52725592854944
- type: euclidean_accuracy
value: 88.44452206310397
- type: euclidean_ap
value: 84.98384576824827
- type: euclidean_f1
value: 77.29311047696697
- type: euclidean_precision
value: 74.51232583065381
- type: euclidean_recall
value: 80.28949799815214
- type: manhattan_accuracy
value: 88.47362906042613
- type: manhattan_ap
value: 84.91421462218432
- type: manhattan_f1
value: 77.05107637204792
- type: manhattan_precision
value: 74.74484256243214
- type: manhattan_recall
value: 79.50415768401602
- type: max_accuracy
value: 88.47362906042613
- type: max_ap
value: 84.98384576824827
- type: max_f1
value: 77.29311047696697
---
***See Disclaimer below***
----
# A Teradata Vantage compatible Embeddings Model
# BAAI/bge-small-en-v1.5
## Overview of this Model
An Embedding Model which maps text (sentence/ paragraphs) into a vector. The [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.
- 33.36M params (Sizes in ONNX format - "fp32": 127.03MB, "int8": 32.4MB, "uint8": 32.4MB)
- 512 maximum input tokens
- 384 dimensions of output vector
- Licence: mit. The released models can be used for commercial purposes free of charge.
- Reference to Original Model: https://huggingface.co/BAAI/bge-small-en-v1.5
## Quickstart: Deploying this Model in Teradata Vantage
We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion.
**Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed.
To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.
```python
import teradataml as tdml
import getpass
from huggingface_hub import hf_hub_download
model_name = "bge-small-en-v1.5"
number_dimensions_output = 384
model_file_name = "model.onnx"
# Step 1: Download Model from Teradata HuggingFace Page
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")
# Step 2: Create Connection to Vantage
tdml.create_context(host = input('enter your hostname'),
username=input('enter your username'),
password = getpass.getpass("enter your password"))
# Step 3: Load Models into Vantage
# a) Embedding model
tdml.save_byom(model_id = model_name, # must be unique in the models table
model_file = f"onnx/{model_file_name}",
table_name = 'embeddings_models' )
# b) Tokenizer
tdml.save_byom(model_id = model_name, # must be unique in the models table
model_file = 'tokenizer.json',
table_name = 'embeddings_tokenizers')
# Step 4: Test ONNXEmbeddings Function
# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'.
# If it has got a different name, just rename it in a subquery/CTE.
input_table = "emails.emails"
embeddings_query = f"""
SELECT
*
from mldb.ONNXEmbeddings(
on {input_table} as InputTable
on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
using
Accumulate('id', 'txt')
ModelOutputTensor('sentence_embedding')
EnableMemoryCheck('false')
OutputFormat('FLOAT32({number_dimensions_output})')
OverwriteCachedModel('true')
) a
"""
DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
DF_embeddings
```
## What Can I Do with the Embeddings?
Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:
- **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb)
- **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb)
- **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb)
## Deep Dive into Model Conversion to ONNX
**The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.**
You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.
### Part 1. Importing and Converting Model using optimum
We start by importing the pre-trained [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model from Hugging Face.
To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/optimum) utility to convert the model into the ONNX (Open Neural Network Exchange) format.
After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage
We are generating ONNX files for multiple different precisions: fp32, int8, uint8
You can find the detailed conversion steps in the file [convert.py](./convert.py)
### Part 2. Running the model in Python with onnxruntime & compare results
Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.
If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.
```python
import onnxruntime as rt
from sentence_transformers.util import cos_sim
from sentence_transformers import SentenceTransformer
import transformers
sentences_1 = 'How is the weather today?'
sentences_2 = 'What is the current weather like today?'
# Calculate ONNX result
tokenizer = transformers.AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5")
predef_sess = rt.InferenceSession("onnx/model.onnx")
enc1 = tokenizer(sentences_1)
embeddings_1_onnx = predef_sess.run(None, {"input_ids": [enc1.input_ids],
"attention_mask": [enc1.attention_mask]})
enc2 = tokenizer(sentences_2)
embeddings_2_onnx = predef_sess.run(None, {"input_ids": [enc2.input_ids],
"attention_mask": [enc2.attention_mask]})
# Calculate embeddings with SentenceTransformer
model = SentenceTransformer(model_id, trust_remote_code=True)
embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
# Compare results
print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
```
You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)
-----
DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. Its listing here does not constitute certification or endorsement by Teradata.
To the extent any of the Content contains or is related to any artificial intelligence (“AI”) or other language learning models (“Models”) that interoperate with the products and services of Teradata, by accessing, bringing, deploying or using such Models, you acknowledge and agree that you are solely responsible for ensuring compliance with all applicable laws, regulations, and restrictions governing the use, deployment, and distribution of AI technologies. This includes, but is not limited to, AI Diffusion Rules, European Union AI Act, AI-related laws and regulations, privacy laws, export controls, and financial or sector-specific regulations.
While Teradata may provide support, guidance, or assistance in the deployment or implementation of Models to interoperate with Teradata’s products and/or services, you remain fully responsible for ensuring that your Models, data, and applications comply with all relevant legal and regulatory obligations. Our assistance does not constitute legal or regulatory approval, and Teradata disclaims any liability arising from non-compliance with applicable laws.
You must determine the suitability of the Models for any purpose. Given the probabilistic nature of machine learning and modeling, the use of the Models may in some situations result in incorrect output that does not accurately reflect the action generated. You should evaluate the accuracy of any output as appropriate for your use case, including by using human review of the output.
|
[
"BIOSSES",
"SCIFACT"
] |
Teradata/jina-embeddings-v2-base-en
|
Teradata
|
feature-extraction
|
[
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"teradata",
"custom_code",
"en",
"dataset:allenai/c4",
"license:apache-2.0",
"model-index",
"region:us"
] | 2025-02-12T16:52:33Z |
2025-03-04T09:41:26+00:00
| 28 | 0 |
---
datasets:
- allenai/c4
language: en
license: apache-2.0
tags:
- feature-extraction
- sentence-similarity
- mteb
- onnx
- teradata
inference: false
model-index:
- name: jina-embedding-b-en-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.73134328358209
- type: ap
value: 37.765427081831035
- type: f1
value: 68.79367444339518
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.544275
- type: ap
value: 84.61328675662887
- type: f1
value: 88.51879035862375
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.263999999999996
- type: f1
value: 43.778759656699435
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.693
- type: map_at_10
value: 35.487
- type: map_at_100
value: 36.862
- type: map_at_1000
value: 36.872
- type: map_at_3
value: 30.049999999999997
- type: map_at_5
value: 32.966
- type: mrr_at_1
value: 21.977
- type: mrr_at_10
value: 35.565999999999995
- type: mrr_at_100
value: 36.948
- type: mrr_at_1000
value: 36.958
- type: mrr_at_3
value: 30.121
- type: mrr_at_5
value: 33.051
- type: ndcg_at_1
value: 21.693
- type: ndcg_at_10
value: 44.181
- type: ndcg_at_100
value: 49.982
- type: ndcg_at_1000
value: 50.233000000000004
- type: ndcg_at_3
value: 32.830999999999996
- type: ndcg_at_5
value: 38.080000000000005
- type: precision_at_1
value: 21.693
- type: precision_at_10
value: 7.248
- type: precision_at_100
value: 0.9769999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 13.632
- type: precision_at_5
value: 10.725
- type: recall_at_1
value: 21.693
- type: recall_at_10
value: 72.475
- type: recall_at_100
value: 97.653
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 40.896
- type: recall_at_5
value: 53.627
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.39242428696777
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.675626784714
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.247725694904034
- type: mrr
value: 74.91359978894604
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.68003802970496
- type: cos_sim_spearman
value: 81.23438110096286
- type: euclidean_pearson
value: 81.87462986142582
- type: euclidean_spearman
value: 81.23438110096286
- type: manhattan_pearson
value: 81.61162566600755
- type: manhattan_spearman
value: 81.11329400456184
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.01298701298701
- type: f1
value: 83.31690714969382
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.050108150972086
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.15731442819715
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.391999999999996
- type: map_at_10
value: 42.597
- type: map_at_100
value: 44.07
- type: map_at_1000
value: 44.198
- type: map_at_3
value: 38.957
- type: map_at_5
value: 40.961
- type: mrr_at_1
value: 37.196
- type: mrr_at_10
value: 48.152
- type: mrr_at_100
value: 48.928
- type: mrr_at_1000
value: 48.964999999999996
- type: mrr_at_3
value: 45.446
- type: mrr_at_5
value: 47.205999999999996
- type: ndcg_at_1
value: 37.196
- type: ndcg_at_10
value: 49.089
- type: ndcg_at_100
value: 54.471000000000004
- type: ndcg_at_1000
value: 56.385
- type: ndcg_at_3
value: 43.699
- type: ndcg_at_5
value: 46.22
- type: precision_at_1
value: 37.196
- type: precision_at_10
value: 9.313
- type: precision_at_100
value: 1.478
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 20.839
- type: precision_at_5
value: 14.936
- type: recall_at_1
value: 31.391999999999996
- type: recall_at_10
value: 61.876
- type: recall_at_100
value: 84.214
- type: recall_at_1000
value: 95.985
- type: recall_at_3
value: 46.6
- type: recall_at_5
value: 53.588
- type: map_at_1
value: 29.083
- type: map_at_10
value: 38.812999999999995
- type: map_at_100
value: 40.053
- type: map_at_1000
value: 40.188
- type: map_at_3
value: 36.111
- type: map_at_5
value: 37.519000000000005
- type: mrr_at_1
value: 36.497
- type: mrr_at_10
value: 44.85
- type: mrr_at_100
value: 45.546
- type: mrr_at_1000
value: 45.593
- type: mrr_at_3
value: 42.686
- type: mrr_at_5
value: 43.909
- type: ndcg_at_1
value: 36.497
- type: ndcg_at_10
value: 44.443
- type: ndcg_at_100
value: 48.979
- type: ndcg_at_1000
value: 51.154999999999994
- type: ndcg_at_3
value: 40.660000000000004
- type: ndcg_at_5
value: 42.193000000000005
- type: precision_at_1
value: 36.497
- type: precision_at_10
value: 8.433
- type: precision_at_100
value: 1.369
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 19.894000000000002
- type: precision_at_5
value: 13.873
- type: recall_at_1
value: 29.083
- type: recall_at_10
value: 54.313
- type: recall_at_100
value: 73.792
- type: recall_at_1000
value: 87.629
- type: recall_at_3
value: 42.257
- type: recall_at_5
value: 47.066
- type: map_at_1
value: 38.556000000000004
- type: map_at_10
value: 50.698
- type: map_at_100
value: 51.705
- type: map_at_1000
value: 51.768
- type: map_at_3
value: 47.848
- type: map_at_5
value: 49.358000000000004
- type: mrr_at_1
value: 43.95
- type: mrr_at_10
value: 54.191
- type: mrr_at_100
value: 54.852999999999994
- type: mrr_at_1000
value: 54.885
- type: mrr_at_3
value: 51.954
- type: mrr_at_5
value: 53.13
- type: ndcg_at_1
value: 43.95
- type: ndcg_at_10
value: 56.516
- type: ndcg_at_100
value: 60.477000000000004
- type: ndcg_at_1000
value: 61.746
- type: ndcg_at_3
value: 51.601
- type: ndcg_at_5
value: 53.795
- type: precision_at_1
value: 43.95
- type: precision_at_10
value: 9.009
- type: precision_at_100
value: 1.189
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.989
- type: precision_at_5
value: 15.473
- type: recall_at_1
value: 38.556000000000004
- type: recall_at_10
value: 70.159
- type: recall_at_100
value: 87.132
- type: recall_at_1000
value: 96.16
- type: recall_at_3
value: 56.906
- type: recall_at_5
value: 62.332
- type: map_at_1
value: 24.238
- type: map_at_10
value: 32.5
- type: map_at_100
value: 33.637
- type: map_at_1000
value: 33.719
- type: map_at_3
value: 30.026999999999997
- type: map_at_5
value: 31.555
- type: mrr_at_1
value: 26.328000000000003
- type: mrr_at_10
value: 34.44
- type: mrr_at_100
value: 35.455999999999996
- type: mrr_at_1000
value: 35.521
- type: mrr_at_3
value: 32.034
- type: mrr_at_5
value: 33.565
- type: ndcg_at_1
value: 26.328000000000003
- type: ndcg_at_10
value: 37.202
- type: ndcg_at_100
value: 42.728
- type: ndcg_at_1000
value: 44.792
- type: ndcg_at_3
value: 32.368
- type: ndcg_at_5
value: 35.008
- type: precision_at_1
value: 26.328000000000003
- type: precision_at_10
value: 5.7059999999999995
- type: precision_at_100
value: 0.8880000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 13.672
- type: precision_at_5
value: 9.74
- type: recall_at_1
value: 24.238
- type: recall_at_10
value: 49.829
- type: recall_at_100
value: 75.21
- type: recall_at_1000
value: 90.521
- type: recall_at_3
value: 36.867
- type: recall_at_5
value: 43.241
- type: map_at_1
value: 15.378
- type: map_at_10
value: 22.817999999999998
- type: map_at_100
value: 23.977999999999998
- type: map_at_1000
value: 24.108
- type: map_at_3
value: 20.719
- type: map_at_5
value: 21.889
- type: mrr_at_1
value: 19.03
- type: mrr_at_10
value: 27.022000000000002
- type: mrr_at_100
value: 28.011999999999997
- type: mrr_at_1000
value: 28.096
- type: mrr_at_3
value: 24.855
- type: mrr_at_5
value: 26.029999999999998
- type: ndcg_at_1
value: 19.03
- type: ndcg_at_10
value: 27.526
- type: ndcg_at_100
value: 33.040000000000006
- type: ndcg_at_1000
value: 36.187000000000005
- type: ndcg_at_3
value: 23.497
- type: ndcg_at_5
value: 25.334
- type: precision_at_1
value: 19.03
- type: precision_at_10
value: 4.963
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 11.360000000000001
- type: precision_at_5
value: 8.134
- type: recall_at_1
value: 15.378
- type: recall_at_10
value: 38.061
- type: recall_at_100
value: 61.754
- type: recall_at_1000
value: 84.259
- type: recall_at_3
value: 26.788
- type: recall_at_5
value: 31.326999999999998
- type: map_at_1
value: 27.511999999999997
- type: map_at_10
value: 37.429
- type: map_at_100
value: 38.818000000000005
- type: map_at_1000
value: 38.924
- type: map_at_3
value: 34.625
- type: map_at_5
value: 36.064
- type: mrr_at_1
value: 33.300999999999995
- type: mrr_at_10
value: 43.036
- type: mrr_at_100
value: 43.894
- type: mrr_at_1000
value: 43.936
- type: mrr_at_3
value: 40.825
- type: mrr_at_5
value: 42.028
- type: ndcg_at_1
value: 33.300999999999995
- type: ndcg_at_10
value: 43.229
- type: ndcg_at_100
value: 48.992000000000004
- type: ndcg_at_1000
value: 51.02100000000001
- type: ndcg_at_3
value: 38.794000000000004
- type: ndcg_at_5
value: 40.65
- type: precision_at_1
value: 33.300999999999995
- type: precision_at_10
value: 7.777000000000001
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.351
- type: precision_at_5
value: 12.762
- type: recall_at_1
value: 27.511999999999997
- type: recall_at_10
value: 54.788000000000004
- type: recall_at_100
value: 79.105
- type: recall_at_1000
value: 92.49199999999999
- type: recall_at_3
value: 41.924
- type: recall_at_5
value: 47.026
- type: map_at_1
value: 24.117
- type: map_at_10
value: 33.32
- type: map_at_100
value: 34.677
- type: map_at_1000
value: 34.78
- type: map_at_3
value: 30.233999999999998
- type: map_at_5
value: 31.668000000000003
- type: mrr_at_1
value: 29.566
- type: mrr_at_10
value: 38.244
- type: mrr_at_100
value: 39.245000000000005
- type: mrr_at_1000
value: 39.296
- type: mrr_at_3
value: 35.864000000000004
- type: mrr_at_5
value: 36.919999999999995
- type: ndcg_at_1
value: 29.566
- type: ndcg_at_10
value: 39.127
- type: ndcg_at_100
value: 44.989000000000004
- type: ndcg_at_1000
value: 47.189
- type: ndcg_at_3
value: 34.039
- type: ndcg_at_5
value: 35.744
- type: precision_at_1
value: 29.566
- type: precision_at_10
value: 7.385999999999999
- type: precision_at_100
value: 1.204
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.286
- type: precision_at_5
value: 11.484
- type: recall_at_1
value: 24.117
- type: recall_at_10
value: 51.559999999999995
- type: recall_at_100
value: 77.104
- type: recall_at_1000
value: 91.79899999999999
- type: recall_at_3
value: 36.82
- type: recall_at_5
value: 41.453
- type: map_at_1
value: 25.17625
- type: map_at_10
value: 34.063916666666664
- type: map_at_100
value: 35.255500000000005
- type: map_at_1000
value: 35.37275
- type: map_at_3
value: 31.351666666666667
- type: map_at_5
value: 32.80608333333333
- type: mrr_at_1
value: 29.59783333333333
- type: mrr_at_10
value: 38.0925
- type: mrr_at_100
value: 38.957249999999995
- type: mrr_at_1000
value: 39.01608333333333
- type: mrr_at_3
value: 35.77625
- type: mrr_at_5
value: 37.04991666666667
- type: ndcg_at_1
value: 29.59783333333333
- type: ndcg_at_10
value: 39.343666666666664
- type: ndcg_at_100
value: 44.488249999999994
- type: ndcg_at_1000
value: 46.83358333333334
- type: ndcg_at_3
value: 34.69708333333333
- type: ndcg_at_5
value: 36.75075
- type: precision_at_1
value: 29.59783333333333
- type: precision_at_10
value: 6.884083333333332
- type: precision_at_100
value: 1.114
- type: precision_at_1000
value: 0.15108333333333332
- type: precision_at_3
value: 15.965250000000003
- type: precision_at_5
value: 11.246500000000001
- type: recall_at_1
value: 25.17625
- type: recall_at_10
value: 51.015999999999984
- type: recall_at_100
value: 73.60174999999998
- type: recall_at_1000
value: 89.849
- type: recall_at_3
value: 37.88399999999999
- type: recall_at_5
value: 43.24541666666666
- type: map_at_1
value: 24.537
- type: map_at_10
value: 31.081999999999997
- type: map_at_100
value: 32.042
- type: map_at_1000
value: 32.141
- type: map_at_3
value: 29.137
- type: map_at_5
value: 30.079
- type: mrr_at_1
value: 27.454
- type: mrr_at_10
value: 33.694
- type: mrr_at_100
value: 34.579
- type: mrr_at_1000
value: 34.649
- type: mrr_at_3
value: 32.004
- type: mrr_at_5
value: 32.794000000000004
- type: ndcg_at_1
value: 27.454
- type: ndcg_at_10
value: 34.915
- type: ndcg_at_100
value: 39.641
- type: ndcg_at_1000
value: 42.105
- type: ndcg_at_3
value: 31.276
- type: ndcg_at_5
value: 32.65
- type: precision_at_1
value: 27.454
- type: precision_at_10
value: 5.337
- type: precision_at_100
value: 0.8250000000000001
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 13.241
- type: precision_at_5
value: 8.895999999999999
- type: recall_at_1
value: 24.537
- type: recall_at_10
value: 44.324999999999996
- type: recall_at_100
value: 65.949
- type: recall_at_1000
value: 84.017
- type: recall_at_3
value: 33.857
- type: recall_at_5
value: 37.316
- type: map_at_1
value: 17.122
- type: map_at_10
value: 24.32
- type: map_at_100
value: 25.338
- type: map_at_1000
value: 25.462
- type: map_at_3
value: 22.064
- type: map_at_5
value: 23.322000000000003
- type: mrr_at_1
value: 20.647
- type: mrr_at_10
value: 27.858
- type: mrr_at_100
value: 28.743999999999996
- type: mrr_at_1000
value: 28.819
- type: mrr_at_3
value: 25.769
- type: mrr_at_5
value: 26.964
- type: ndcg_at_1
value: 20.647
- type: ndcg_at_10
value: 28.849999999999998
- type: ndcg_at_100
value: 33.849000000000004
- type: ndcg_at_1000
value: 36.802
- type: ndcg_at_3
value: 24.799
- type: ndcg_at_5
value: 26.682
- type: precision_at_1
value: 20.647
- type: precision_at_10
value: 5.2170000000000005
- type: precision_at_100
value: 0.906
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 11.769
- type: precision_at_5
value: 8.486
- type: recall_at_1
value: 17.122
- type: recall_at_10
value: 38.999
- type: recall_at_100
value: 61.467000000000006
- type: recall_at_1000
value: 82.716
- type: recall_at_3
value: 27.601
- type: recall_at_5
value: 32.471
- type: map_at_1
value: 24.396
- type: map_at_10
value: 33.415
- type: map_at_100
value: 34.521
- type: map_at_1000
value: 34.631
- type: map_at_3
value: 30.703999999999997
- type: map_at_5
value: 32.166
- type: mrr_at_1
value: 28.825
- type: mrr_at_10
value: 37.397000000000006
- type: mrr_at_100
value: 38.286
- type: mrr_at_1000
value: 38.346000000000004
- type: mrr_at_3
value: 35.028
- type: mrr_at_5
value: 36.32
- type: ndcg_at_1
value: 28.825
- type: ndcg_at_10
value: 38.656
- type: ndcg_at_100
value: 43.856
- type: ndcg_at_1000
value: 46.31
- type: ndcg_at_3
value: 33.793
- type: ndcg_at_5
value: 35.909
- type: precision_at_1
value: 28.825
- type: precision_at_10
value: 6.567
- type: precision_at_100
value: 1.0330000000000001
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 15.516
- type: precision_at_5
value: 10.914
- type: recall_at_1
value: 24.396
- type: recall_at_10
value: 50.747
- type: recall_at_100
value: 73.477
- type: recall_at_1000
value: 90.801
- type: recall_at_3
value: 37.1
- type: recall_at_5
value: 42.589
- type: map_at_1
value: 25.072
- type: map_at_10
value: 34.307
- type: map_at_100
value: 35.725
- type: map_at_1000
value: 35.943999999999996
- type: map_at_3
value: 30.906
- type: map_at_5
value: 32.818000000000005
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 38.673
- type: mrr_at_100
value: 39.459
- type: mrr_at_1000
value: 39.527
- type: mrr_at_3
value: 35.771
- type: mrr_at_5
value: 37.332
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 40.548
- type: ndcg_at_100
value: 45.678999999999995
- type: ndcg_at_1000
value: 48.488
- type: ndcg_at_3
value: 34.887
- type: ndcg_at_5
value: 37.543
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 7.688000000000001
- type: precision_at_100
value: 1.482
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 16.206
- type: precision_at_5
value: 12.016
- type: recall_at_1
value: 25.072
- type: recall_at_10
value: 53.478
- type: recall_at_100
value: 76.07300000000001
- type: recall_at_1000
value: 93.884
- type: recall_at_3
value: 37.583
- type: recall_at_5
value: 44.464
- type: map_at_1
value: 20.712
- type: map_at_10
value: 27.467999999999996
- type: map_at_100
value: 28.502
- type: map_at_1000
value: 28.610000000000003
- type: map_at_3
value: 24.887999999999998
- type: map_at_5
value: 26.273999999999997
- type: mrr_at_1
value: 22.736
- type: mrr_at_10
value: 29.553
- type: mrr_at_100
value: 30.485
- type: mrr_at_1000
value: 30.56
- type: mrr_at_3
value: 27.078999999999997
- type: mrr_at_5
value: 28.401
- type: ndcg_at_1
value: 22.736
- type: ndcg_at_10
value: 32.023
- type: ndcg_at_100
value: 37.158
- type: ndcg_at_1000
value: 39.823
- type: ndcg_at_3
value: 26.951999999999998
- type: ndcg_at_5
value: 29.281000000000002
- type: precision_at_1
value: 22.736
- type: precision_at_10
value: 5.213
- type: precision_at_100
value: 0.832
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 11.459999999999999
- type: precision_at_5
value: 8.244
- type: recall_at_1
value: 20.712
- type: recall_at_10
value: 44.057
- type: recall_at_100
value: 67.944
- type: recall_at_1000
value: 87.925
- type: recall_at_3
value: 30.305
- type: recall_at_5
value: 36.071999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.181999999999999
- type: map_at_10
value: 16.66
- type: map_at_100
value: 18.273
- type: map_at_1000
value: 18.45
- type: map_at_3
value: 14.141
- type: map_at_5
value: 15.455
- type: mrr_at_1
value: 22.15
- type: mrr_at_10
value: 32.062000000000005
- type: mrr_at_100
value: 33.116
- type: mrr_at_1000
value: 33.168
- type: mrr_at_3
value: 28.827
- type: mrr_at_5
value: 30.892999999999997
- type: ndcg_at_1
value: 22.15
- type: ndcg_at_10
value: 23.532
- type: ndcg_at_100
value: 30.358
- type: ndcg_at_1000
value: 33.783
- type: ndcg_at_3
value: 19.222
- type: ndcg_at_5
value: 20.919999999999998
- type: precision_at_1
value: 22.15
- type: precision_at_10
value: 7.185999999999999
- type: precision_at_100
value: 1.433
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 13.941
- type: precision_at_5
value: 10.906
- type: recall_at_1
value: 10.181999999999999
- type: recall_at_10
value: 28.104000000000003
- type: recall_at_100
value: 51.998999999999995
- type: recall_at_1000
value: 71.311
- type: recall_at_3
value: 17.698
- type: recall_at_5
value: 22.262999999999998
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.669
- type: map_at_10
value: 15.552
- type: map_at_100
value: 21.865000000000002
- type: map_at_1000
value: 23.268
- type: map_at_3
value: 11.309
- type: map_at_5
value: 13.084000000000001
- type: mrr_at_1
value: 55.50000000000001
- type: mrr_at_10
value: 66.46600000000001
- type: mrr_at_100
value: 66.944
- type: mrr_at_1000
value: 66.956
- type: mrr_at_3
value: 64.542
- type: mrr_at_5
value: 65.717
- type: ndcg_at_1
value: 44.75
- type: ndcg_at_10
value: 35.049
- type: ndcg_at_100
value: 39.073
- type: ndcg_at_1000
value: 46.208
- type: ndcg_at_3
value: 39.525
- type: ndcg_at_5
value: 37.156
- type: precision_at_1
value: 55.50000000000001
- type: precision_at_10
value: 27.800000000000004
- type: precision_at_100
value: 9.013
- type: precision_at_1000
value: 1.8800000000000001
- type: precision_at_3
value: 42.667
- type: precision_at_5
value: 36
- type: recall_at_1
value: 6.669
- type: recall_at_10
value: 21.811
- type: recall_at_100
value: 45.112
- type: recall_at_1000
value: 67.806
- type: recall_at_3
value: 13.373
- type: recall_at_5
value: 16.615
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.769999999999996
- type: f1
value: 42.91448356376592
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 54.013
- type: map_at_10
value: 66.239
- type: map_at_100
value: 66.62599999999999
- type: map_at_1000
value: 66.644
- type: map_at_3
value: 63.965
- type: map_at_5
value: 65.45400000000001
- type: mrr_at_1
value: 58.221000000000004
- type: mrr_at_10
value: 70.43700000000001
- type: mrr_at_100
value: 70.744
- type: mrr_at_1000
value: 70.75099999999999
- type: mrr_at_3
value: 68.284
- type: mrr_at_5
value: 69.721
- type: ndcg_at_1
value: 58.221000000000004
- type: ndcg_at_10
value: 72.327
- type: ndcg_at_100
value: 73.953
- type: ndcg_at_1000
value: 74.312
- type: ndcg_at_3
value: 68.062
- type: ndcg_at_5
value: 70.56400000000001
- type: precision_at_1
value: 58.221000000000004
- type: precision_at_10
value: 9.521
- type: precision_at_100
value: 1.045
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 27.348
- type: precision_at_5
value: 17.794999999999998
- type: recall_at_1
value: 54.013
- type: recall_at_10
value: 86.957
- type: recall_at_100
value: 93.911
- type: recall_at_1000
value: 96.38
- type: recall_at_3
value: 75.555
- type: recall_at_5
value: 81.671
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.254
- type: map_at_10
value: 33.723
- type: map_at_100
value: 35.574
- type: map_at_1000
value: 35.730000000000004
- type: map_at_3
value: 29.473
- type: map_at_5
value: 31.543
- type: mrr_at_1
value: 41.358
- type: mrr_at_10
value: 49.498
- type: mrr_at_100
value: 50.275999999999996
- type: mrr_at_1000
value: 50.308
- type: mrr_at_3
value: 47.016000000000005
- type: mrr_at_5
value: 48.336
- type: ndcg_at_1
value: 41.358
- type: ndcg_at_10
value: 41.579
- type: ndcg_at_100
value: 48.455
- type: ndcg_at_1000
value: 51.165000000000006
- type: ndcg_at_3
value: 37.681
- type: ndcg_at_5
value: 38.49
- type: precision_at_1
value: 41.358
- type: precision_at_10
value: 11.543000000000001
- type: precision_at_100
value: 1.87
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 24.743000000000002
- type: precision_at_5
value: 17.994
- type: recall_at_1
value: 21.254
- type: recall_at_10
value: 48.698
- type: recall_at_100
value: 74.588
- type: recall_at_1000
value: 91.00200000000001
- type: recall_at_3
value: 33.939
- type: recall_at_5
value: 39.367000000000004
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.922
- type: map_at_10
value: 52.32599999999999
- type: map_at_100
value: 53.18000000000001
- type: map_at_1000
value: 53.245
- type: map_at_3
value: 49.294
- type: map_at_5
value: 51.202999999999996
- type: mrr_at_1
value: 71.843
- type: mrr_at_10
value: 78.24600000000001
- type: mrr_at_100
value: 78.515
- type: mrr_at_1000
value: 78.527
- type: mrr_at_3
value: 77.17500000000001
- type: mrr_at_5
value: 77.852
- type: ndcg_at_1
value: 71.843
- type: ndcg_at_10
value: 61.379
- type: ndcg_at_100
value: 64.535
- type: ndcg_at_1000
value: 65.888
- type: ndcg_at_3
value: 56.958
- type: ndcg_at_5
value: 59.434
- type: precision_at_1
value: 71.843
- type: precision_at_10
value: 12.686
- type: precision_at_100
value: 1.517
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 35.778
- type: precision_at_5
value: 23.422
- type: recall_at_1
value: 35.922
- type: recall_at_10
value: 63.43
- type: recall_at_100
value: 75.868
- type: recall_at_1000
value: 84.88900000000001
- type: recall_at_3
value: 53.666000000000004
- type: recall_at_5
value: 58.555
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 79.4408
- type: ap
value: 73.52820871620366
- type: f1
value: 79.36240238685001
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.826999999999998
- type: map_at_10
value: 34.04
- type: map_at_100
value: 35.226
- type: map_at_1000
value: 35.275
- type: map_at_3
value: 30.165999999999997
- type: map_at_5
value: 32.318000000000005
- type: mrr_at_1
value: 22.464000000000002
- type: mrr_at_10
value: 34.631
- type: mrr_at_100
value: 35.752
- type: mrr_at_1000
value: 35.795
- type: mrr_at_3
value: 30.798
- type: mrr_at_5
value: 32.946999999999996
- type: ndcg_at_1
value: 22.464000000000002
- type: ndcg_at_10
value: 40.919
- type: ndcg_at_100
value: 46.632
- type: ndcg_at_1000
value: 47.833
- type: ndcg_at_3
value: 32.992
- type: ndcg_at_5
value: 36.834
- type: precision_at_1
value: 22.464000000000002
- type: precision_at_10
value: 6.494
- type: precision_at_100
value: 0.9369999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.021
- type: precision_at_5
value: 10.347000000000001
- type: recall_at_1
value: 21.826999999999998
- type: recall_at_10
value: 62.132
- type: recall_at_100
value: 88.55199999999999
- type: recall_at_1000
value: 97.707
- type: recall_at_3
value: 40.541
- type: recall_at_5
value: 49.739
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.68399452804377
- type: f1
value: 95.25490609832268
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 83.15321477428182
- type: f1
value: 60.35476439087966
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.92669804976462
- type: f1
value: 69.22815107207565
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.4855413584398
- type: f1
value: 72.92107516103387
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.412679360205544
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.09211869875204
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.540919056982545
- type: mrr
value: 31.529904607063536
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.745
- type: map_at_10
value: 12.013
- type: map_at_100
value: 15.040000000000001
- type: map_at_1000
value: 16.427
- type: map_at_3
value: 8.841000000000001
- type: map_at_5
value: 10.289
- type: mrr_at_1
value: 45.201
- type: mrr_at_10
value: 53.483999999999995
- type: mrr_at_100
value: 54.20700000000001
- type: mrr_at_1000
value: 54.252
- type: mrr_at_3
value: 51.29
- type: mrr_at_5
value: 52.73
- type: ndcg_at_1
value: 43.808
- type: ndcg_at_10
value: 32.445
- type: ndcg_at_100
value: 30.031000000000002
- type: ndcg_at_1000
value: 39.007
- type: ndcg_at_3
value: 37.204
- type: ndcg_at_5
value: 35.07
- type: precision_at_1
value: 45.201
- type: precision_at_10
value: 23.684
- type: precision_at_100
value: 7.600999999999999
- type: precision_at_1000
value: 2.043
- type: precision_at_3
value: 33.953
- type: precision_at_5
value: 29.412
- type: recall_at_1
value: 5.745
- type: recall_at_10
value: 16.168
- type: recall_at_100
value: 30.875999999999998
- type: recall_at_1000
value: 62.686
- type: recall_at_3
value: 9.75
- type: recall_at_5
value: 12.413
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.828
- type: map_at_10
value: 53.239000000000004
- type: map_at_100
value: 54.035999999999994
- type: map_at_1000
value: 54.067
- type: map_at_3
value: 49.289
- type: map_at_5
value: 51.784
- type: mrr_at_1
value: 42.497
- type: mrr_at_10
value: 55.916999999999994
- type: mrr_at_100
value: 56.495
- type: mrr_at_1000
value: 56.516999999999996
- type: mrr_at_3
value: 52.800000000000004
- type: mrr_at_5
value: 54.722
- type: ndcg_at_1
value: 42.468
- type: ndcg_at_10
value: 60.437
- type: ndcg_at_100
value: 63.731
- type: ndcg_at_1000
value: 64.41799999999999
- type: ndcg_at_3
value: 53.230999999999995
- type: ndcg_at_5
value: 57.26
- type: precision_at_1
value: 42.468
- type: precision_at_10
value: 9.47
- type: precision_at_100
value: 1.1360000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.724999999999998
- type: precision_at_5
value: 16.593
- type: recall_at_1
value: 37.828
- type: recall_at_10
value: 79.538
- type: recall_at_100
value: 93.646
- type: recall_at_1000
value: 98.72999999999999
- type: recall_at_3
value: 61.134
- type: recall_at_5
value: 70.377
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.548
- type: map_at_10
value: 84.466
- type: map_at_100
value: 85.10600000000001
- type: map_at_1000
value: 85.123
- type: map_at_3
value: 81.57600000000001
- type: map_at_5
value: 83.399
- type: mrr_at_1
value: 81.24
- type: mrr_at_10
value: 87.457
- type: mrr_at_100
value: 87.574
- type: mrr_at_1000
value: 87.575
- type: mrr_at_3
value: 86.507
- type: mrr_at_5
value: 87.205
- type: ndcg_at_1
value: 81.25
- type: ndcg_at_10
value: 88.203
- type: ndcg_at_100
value: 89.457
- type: ndcg_at_1000
value: 89.563
- type: ndcg_at_3
value: 85.465
- type: ndcg_at_5
value: 87.007
- type: precision_at_1
value: 81.25
- type: precision_at_10
value: 13.373
- type: precision_at_100
value: 1.5270000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.417
- type: precision_at_5
value: 24.556
- type: recall_at_1
value: 70.548
- type: recall_at_10
value: 95.208
- type: recall_at_100
value: 99.514
- type: recall_at_1000
value: 99.988
- type: recall_at_3
value: 87.214
- type: recall_at_5
value: 91.696
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 53.04822095496839
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.30778476474675
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.692
- type: map_at_10
value: 11.766
- type: map_at_100
value: 13.904
- type: map_at_1000
value: 14.216999999999999
- type: map_at_3
value: 8.245
- type: map_at_5
value: 9.92
- type: mrr_at_1
value: 23
- type: mrr_at_10
value: 33.78
- type: mrr_at_100
value: 34.922
- type: mrr_at_1000
value: 34.973
- type: mrr_at_3
value: 30.2
- type: mrr_at_5
value: 32.565
- type: ndcg_at_1
value: 23
- type: ndcg_at_10
value: 19.863
- type: ndcg_at_100
value: 28.141
- type: ndcg_at_1000
value: 33.549
- type: ndcg_at_3
value: 18.434
- type: ndcg_at_5
value: 16.384
- type: precision_at_1
value: 23
- type: precision_at_10
value: 10.39
- type: precision_at_100
value: 2.235
- type: precision_at_1000
value: 0.35300000000000004
- type: precision_at_3
value: 17.133000000000003
- type: precision_at_5
value: 14.44
- type: recall_at_1
value: 4.692
- type: recall_at_10
value: 21.025
- type: recall_at_100
value: 45.324999999999996
- type: recall_at_1000
value: 71.675
- type: recall_at_3
value: 10.440000000000001
- type: recall_at_5
value: 14.64
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.96178184892842
- type: cos_sim_spearman
value: 79.6487740813199
- type: euclidean_pearson
value: 82.06661161625023
- type: euclidean_spearman
value: 79.64876769031183
- type: manhattan_pearson
value: 82.07061164575131
- type: manhattan_spearman
value: 79.65197039464537
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.15305604100027
- type: cos_sim_spearman
value: 74.27447427941591
- type: euclidean_pearson
value: 80.52737337565307
- type: euclidean_spearman
value: 74.27416077132192
- type: manhattan_pearson
value: 80.53728571140387
- type: manhattan_spearman
value: 74.28853605753457
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.44386080639279
- type: cos_sim_spearman
value: 84.17947648159536
- type: euclidean_pearson
value: 83.34145388129387
- type: euclidean_spearman
value: 84.17947648159536
- type: manhattan_pearson
value: 83.30699061927966
- type: manhattan_spearman
value: 84.18125737380451
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.57392220985612
- type: cos_sim_spearman
value: 78.80745014464101
- type: euclidean_pearson
value: 80.01660371487199
- type: euclidean_spearman
value: 78.80741240102256
- type: manhattan_pearson
value: 79.96810779507953
- type: manhattan_spearman
value: 78.75600400119448
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.85421063026625
- type: cos_sim_spearman
value: 87.55320285299192
- type: euclidean_pearson
value: 86.69750143323517
- type: euclidean_spearman
value: 87.55320284326378
- type: manhattan_pearson
value: 86.63379169960379
- type: manhattan_spearman
value: 87.4815029877984
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.31314130411842
- type: cos_sim_spearman
value: 85.3489588181433
- type: euclidean_pearson
value: 84.13240933463535
- type: euclidean_spearman
value: 85.34902871403281
- type: manhattan_pearson
value: 84.01183086503559
- type: manhattan_spearman
value: 85.19316703166102
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.09979781689536
- type: cos_sim_spearman
value: 88.87813323759015
- type: euclidean_pearson
value: 88.65413031123792
- type: euclidean_spearman
value: 88.87813323759015
- type: manhattan_pearson
value: 88.61818758256024
- type: manhattan_spearman
value: 88.81044100494604
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.30693258111531
- type: cos_sim_spearman
value: 62.195516523251946
- type: euclidean_pearson
value: 62.951283701049476
- type: euclidean_spearman
value: 62.195516523251946
- type: manhattan_pearson
value: 63.068322281439535
- type: manhattan_spearman
value: 62.10621171028406
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.27092833763909
- type: cos_sim_spearman
value: 84.84429717949759
- type: euclidean_pearson
value: 84.8516966060792
- type: euclidean_spearman
value: 84.84429717949759
- type: manhattan_pearson
value: 84.82203139242881
- type: manhattan_spearman
value: 84.8358503952945
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.10290863981409
- type: mrr
value: 95.31168450286097
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.161
- type: map_at_10
value: 62.138000000000005
- type: map_at_100
value: 62.769
- type: map_at_1000
value: 62.812
- type: map_at_3
value: 59.111000000000004
- type: map_at_5
value: 60.995999999999995
- type: mrr_at_1
value: 55.333
- type: mrr_at_10
value: 63.504000000000005
- type: mrr_at_100
value: 64.036
- type: mrr_at_1000
value: 64.08
- type: mrr_at_3
value: 61.278
- type: mrr_at_5
value: 62.778
- type: ndcg_at_1
value: 55.333
- type: ndcg_at_10
value: 66.678
- type: ndcg_at_100
value: 69.415
- type: ndcg_at_1000
value: 70.453
- type: ndcg_at_3
value: 61.755
- type: ndcg_at_5
value: 64.546
- type: precision_at_1
value: 55.333
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.333000000000002
- type: recall_at_1
value: 52.161
- type: recall_at_10
value: 79.156
- type: recall_at_100
value: 91.333
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 66.43299999999999
- type: recall_at_5
value: 73.272
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81287128712871
- type: cos_sim_ap
value: 95.30034785910676
- type: cos_sim_f1
value: 90.28629856850716
- type: cos_sim_precision
value: 92.36401673640168
- type: cos_sim_recall
value: 88.3
- type: dot_accuracy
value: 99.81287128712871
- type: dot_ap
value: 95.30034785910676
- type: dot_f1
value: 90.28629856850716
- type: dot_precision
value: 92.36401673640168
- type: dot_recall
value: 88.3
- type: euclidean_accuracy
value: 99.81287128712871
- type: euclidean_ap
value: 95.30034785910676
- type: euclidean_f1
value: 90.28629856850716
- type: euclidean_precision
value: 92.36401673640168
- type: euclidean_recall
value: 88.3
- type: manhattan_accuracy
value: 99.80990099009901
- type: manhattan_ap
value: 95.26880751950654
- type: manhattan_f1
value: 90.22177419354838
- type: manhattan_precision
value: 90.95528455284553
- type: manhattan_recall
value: 89.5
- type: max_accuracy
value: 99.81287128712871
- type: max_ap
value: 95.30034785910676
- type: max_f1
value: 90.28629856850716
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 58.518662504351184
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.96168178378587
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.04862593471896
- type: mrr
value: 52.97238402936932
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.092545236479946
- type: cos_sim_spearman
value: 31.599851000175498
- type: dot_pearson
value: 30.092542723901676
- type: dot_spearman
value: 31.599851000175498
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.189
- type: map_at_10
value: 1.662
- type: map_at_100
value: 9.384
- type: map_at_1000
value: 22.669
- type: map_at_3
value: 0.5559999999999999
- type: map_at_5
value: 0.9039999999999999
- type: mrr_at_1
value: 68
- type: mrr_at_10
value: 81.01899999999999
- type: mrr_at_100
value: 81.01899999999999
- type: mrr_at_1000
value: 81.01899999999999
- type: mrr_at_3
value: 79.333
- type: mrr_at_5
value: 80.733
- type: ndcg_at_1
value: 63
- type: ndcg_at_10
value: 65.913
- type: ndcg_at_100
value: 51.895
- type: ndcg_at_1000
value: 46.967
- type: ndcg_at_3
value: 65.49199999999999
- type: ndcg_at_5
value: 66.69699999999999
- type: precision_at_1
value: 68
- type: precision_at_10
value: 71.6
- type: precision_at_100
value: 53.66
- type: precision_at_1000
value: 21.124000000000002
- type: precision_at_3
value: 72.667
- type: precision_at_5
value: 74
- type: recall_at_1
value: 0.189
- type: recall_at_10
value: 1.913
- type: recall_at_100
value: 12.601999999999999
- type: recall_at_1000
value: 44.296
- type: recall_at_3
value: 0.605
- type: recall_at_5
value: 1.018
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.701
- type: map_at_10
value: 10.445
- type: map_at_100
value: 17.324
- type: map_at_1000
value: 19.161
- type: map_at_3
value: 5.497
- type: map_at_5
value: 7.278
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 45.534
- type: mrr_at_100
value: 45.792
- type: mrr_at_1000
value: 45.806999999999995
- type: mrr_at_3
value: 37.755
- type: mrr_at_5
value: 43.469
- type: ndcg_at_1
value: 26.531
- type: ndcg_at_10
value: 26.235000000000003
- type: ndcg_at_100
value: 39.17
- type: ndcg_at_1000
value: 51.038
- type: ndcg_at_3
value: 23.625
- type: ndcg_at_5
value: 24.338
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 24.285999999999998
- type: precision_at_100
value: 8.224
- type: precision_at_1000
value: 1.6179999999999999
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 24.898
- type: recall_at_1
value: 2.701
- type: recall_at_10
value: 17.997
- type: recall_at_100
value: 51.766999999999996
- type: recall_at_1000
value: 87.863
- type: recall_at_3
value: 6.295000000000001
- type: recall_at_5
value: 9.993
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 73.3474
- type: ap
value: 15.393431414459924
- type: f1
value: 56.466681887882416
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.062818336163
- type: f1
value: 62.11230840463252
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 42.464892820845115
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.15962329379508
- type: cos_sim_ap
value: 74.73674057919256
- type: cos_sim_f1
value: 68.81245642574947
- type: cos_sim_precision
value: 61.48255813953488
- type: cos_sim_recall
value: 78.12664907651715
- type: dot_accuracy
value: 86.15962329379508
- type: dot_ap
value: 74.7367634988281
- type: dot_f1
value: 68.81245642574947
- type: dot_precision
value: 61.48255813953488
- type: dot_recall
value: 78.12664907651715
- type: euclidean_accuracy
value: 86.15962329379508
- type: euclidean_ap
value: 74.7367761466634
- type: euclidean_f1
value: 68.81245642574947
- type: euclidean_precision
value: 61.48255813953488
- type: euclidean_recall
value: 78.12664907651715
- type: manhattan_accuracy
value: 86.21326816474935
- type: manhattan_ap
value: 74.64416473733951
- type: manhattan_f1
value: 68.80924855491331
- type: manhattan_precision
value: 61.23456790123457
- type: manhattan_recall
value: 78.52242744063325
- type: max_accuracy
value: 86.21326816474935
- type: max_ap
value: 74.7367761466634
- type: max_f1
value: 68.81245642574947
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.97620988085536
- type: cos_sim_ap
value: 86.08680845745758
- type: cos_sim_f1
value: 78.02793637114438
- type: cos_sim_precision
value: 73.11082699683736
- type: cos_sim_recall
value: 83.65414228518632
- type: dot_accuracy
value: 88.97620988085536
- type: dot_ap
value: 86.08681149437946
- type: dot_f1
value: 78.02793637114438
- type: dot_precision
value: 73.11082699683736
- type: dot_recall
value: 83.65414228518632
- type: euclidean_accuracy
value: 88.97620988085536
- type: euclidean_ap
value: 86.08681215460771
- type: euclidean_f1
value: 78.02793637114438
- type: euclidean_precision
value: 73.11082699683736
- type: euclidean_recall
value: 83.65414228518632
- type: manhattan_accuracy
value: 88.88888888888889
- type: manhattan_ap
value: 86.02916327562438
- type: manhattan_f1
value: 78.02063045516843
- type: manhattan_precision
value: 73.38851947346994
- type: manhattan_recall
value: 83.2768709578072
- type: max_accuracy
value: 88.97620988085536
- type: max_ap
value: 86.08681215460771
- type: max_f1
value: 78.02793637114438
---
***See Disclaimer below***
----
# A Teradata Vantage compatible Embeddings Model
# jinaai/jina-embeddings-v2-base-en
## Overview of this Model
An Embedding Model which maps text (sentence/ paragraphs) into a vector. The [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.
- 137.37M params (Sizes in ONNX format - "fp32": 522.03MB, "int8": 131.14MB, "uint8": 131.14MB)
- 8192 maximum input tokens
- 768 dimensions of output vector
- Licence: apache-2.0. The released models can be used for commercial purposes free of charge.
- Reference to Original Model: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
## Quickstart: Deploying this Model in Teradata Vantage
We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion.
**Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed.
To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.
```python
import teradataml as tdml
import getpass
from huggingface_hub import hf_hub_download
model_name = "jina-embeddings-v2-base-en"
number_dimensions_output = 768
model_file_name = "model.onnx"
# Step 1: Download Model from Teradata HuggingFace Page
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")
# Step 2: Create Connection to Vantage
tdml.create_context(host = input('enter your hostname'),
username=input('enter your username'),
password = getpass.getpass("enter your password"))
# Step 3: Load Models into Vantage
# a) Embedding model
tdml.save_byom(model_id = model_name, # must be unique in the models table
model_file = f"onnx/{model_file_name}",
table_name = 'embeddings_models' )
# b) Tokenizer
tdml.save_byom(model_id = model_name, # must be unique in the models table
model_file = 'tokenizer.json',
table_name = 'embeddings_tokenizers')
# Step 4: Test ONNXEmbeddings Function
# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'.
# If it has got a different name, just rename it in a subquery/CTE.
input_table = "emails.emails"
embeddings_query = f"""
SELECT
*
from mldb.ONNXEmbeddings(
on {input_table} as InputTable
on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
using
Accumulate('id', 'txt')
ModelOutputTensor('sentence_embedding')
EnableMemoryCheck('false')
OutputFormat('FLOAT32({number_dimensions_output})')
OverwriteCachedModel('true')
) a
"""
DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
DF_embeddings
```
## What Can I Do with the Embeddings?
Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:
- **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb)
- **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb)
- **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb)
## Deep Dive into Model Conversion to ONNX
**The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.**
You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.
### Part 1. Importing and Converting Model using optimum
We start by importing the pre-trained [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) model from Hugging Face.
We are downloading the ONNX files from the repository prepared by the model authors.
After downloading, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage
Also we adding the man pooling and normalization layers to the ONNX file
We are generating ONNX files for multiple different precisions: fp32, int8, uint8
You can find the detailed conversion steps in the file [convert.py](./convert.py)
### Part 2. Running the model in Python with onnxruntime & compare results
Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.
If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.
```python
import onnxruntime as rt
from sentence_transformers.util import cos_sim
from sentence_transformers import SentenceTransformer
import transformers
sentences_1 = 'How is the weather today?'
sentences_2 = 'What is the current weather like today?'
# Calculate ONNX result
tokenizer = transformers.AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-en")
predef_sess = rt.InferenceSession("onnx/model.onnx")
enc1 = tokenizer(sentences_1)
embeddings_1_onnx = predef_sess.run(None, {"input_ids": [enc1.input_ids],
"attention_mask": [enc1.attention_mask]})
enc2 = tokenizer(sentences_2)
embeddings_2_onnx = predef_sess.run(None, {"input_ids": [enc2.input_ids],
"attention_mask": [enc2.attention_mask]})
# Calculate embeddings with SentenceTransformer
model = SentenceTransformer(model_id, trust_remote_code=True)
embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
# Compare results
print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
```
You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)
-----
DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. Its listing here does not constitute certification or endorsement by Teradata.
To the extent any of the Content contains or is related to any artificial intelligence (“AI”) or other language learning models (“Models”) that interoperate with the products and services of Teradata, by accessing, bringing, deploying or using such Models, you acknowledge and agree that you are solely responsible for ensuring compliance with all applicable laws, regulations, and restrictions governing the use, deployment, and distribution of AI technologies. This includes, but is not limited to, AI Diffusion Rules, European Union AI Act, AI-related laws and regulations, privacy laws, export controls, and financial or sector-specific regulations.
While Teradata may provide support, guidance, or assistance in the deployment or implementation of Models to interoperate with Teradata’s products and/or services, you remain fully responsible for ensuring that your Models, data, and applications comply with all relevant legal and regulatory obligations. Our assistance does not constitute legal or regulatory approval, and Teradata disclaims any liability arising from non-compliance with applicable laws.
You must determine the suitability of the Models for any purpose. Given the probabilistic nature of machine learning and modeling, the use of the Models may in some situations result in incorrect output that does not accurately reflect the action generated. You should evaluate the accuracy of any output as appropriate for your use case, including by using human review of the output.
|
[
"BIOSSES",
"SCIFACT"
] |
RomainDarous/large_directThreeEpoch_additivePooling_noisedInit_mistranslationModel
|
RomainDarous
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:4460010",
"loss:CoSENTLoss",
"dataset:RomainDarous/corrupted_os_by_language",
"arxiv:1908.10084",
"base_model:RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel",
"base_model:finetune:RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2025-02-15T09:25:04Z |
2025-02-15T09:25:46+00:00
| 28 | 0 |
---
base_model: RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel
datasets:
- RomainDarous/corrupted_os_by_language
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4460010
- loss:CoSENTLoss
widget:
- source_sentence: Malformed target specific variable definition
sentences:
- Hedefe özgü değişken tanımı bozuk
- Kan alle data in die gids lees
- "слава Украине! героям слава!\uFEFF"
- source_sentence: Can't write an inode bitmap
sentences:
- Skontrolujte stav aktualizácií alebo to skúste znova neskôr.
- Malsukcesis skribi i nodan bitmapon
- Zastępuje wersję GL obsługiwaną przez sterownik
- source_sentence: Optimize soft proofing color transformations
sentences:
- 'arkadaslar biz artik her an kirmizi kart yiyecek,bencil,pas yapamayan,isabetsiz
orta yapani istemiyoruz. sozde efsaneniz bu sezon Besiktasa en cok zarar verenlerden
biriydi. kendini dusunmeden once Besiktasi dusunecek adam lazim bize. o yuzden
#GoHomeQuaresma'
- Yav bizim dedikodusunu yaptığımız insanın bile bi vizyonu var. Senin hakkında
neden oturup konuşalım?
- Ik ben een transgender.
- source_sentence: 'Pass 1: Checking @is, @bs, and sizes'
sentences:
- Bu adam cidden kurabiye gibi ben bunu çayın yanında yerim
- sagnat. errada. invisible. justificació. idioma
- Wilt u echt de primaire sleutel verplaatsen? (j N)
- source_sentence: Search for matching log entries
sentences:
- quem te lembra? caralho tô assustada aqui kkkkk
- sendotasunik gabeko\ egoera bistaratuko den ala ez adierazten du
- En aquest cas, hem d'incloure les imatges del contenidor )sr iov per a càrregues
de treball de telco (per exemple, com a referència, es podrien obtenir des de
valors de helm chart)
model-index:
- name: SentenceTransformer based on RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts eval
type: sts-eval
metrics:
- type: pearson_cosine
value: 0.9799016413969349
name: Pearson Cosine
- type: spearman_cosine
value: 0.8655872972160841
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9799368524868214
name: Pearson Cosine
- type: spearman_cosine
value: 0.8656078074942255
name: Spearman Cosine
---
# SentenceTransformer based on RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel](https://huggingface.co/RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel) on the [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel](https://huggingface.co/RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel) <!-- at revision 0dff7d600166475117133f4043a2af4eb60f1be8 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): MultiHeadGeneralizedPooling(
(P): ModuleList(
(0-7): 8 x Linear(in_features=768, out_features=96, bias=True)
)
(W1): ModuleList(
(0-7): 8 x Linear(in_features=96, out_features=384, bias=True)
)
(W2): ModuleList(
(0-7): 8 x Linear(in_features=384, out_features=96, bias=True)
)
)
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RomainDarous/large_directThreeEpoch_additivePooling_noisedInit_mistranslationModel")
# Run inference
sentences = [
'Search for matching log entries',
'quem te lembra? caralho tô assustada aqui kkkkk',
'sendotasunik gabeko\\ egoera bistaratuko den ala ez adierazten du',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-eval` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-eval | sts-test |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.9799 | 0.9799 |
| **spearman_cosine** | **0.8656** | **0.8656** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### corrupted_open_os_by_language
* Dataset: [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) at [9d25780](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language/tree/9d25780e2032b1e8f06af6a4ff55124d7a930c3c)
* Size: 4,460,010 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.33 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.47 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------|
| <code>Check spelling. Print the document. Show completion window. General. Show help</code> | <code>Kontrolli õigekirja. присоединяюсь. </code> | <code>0</code> |
| <code>EXIF not supported for this file format.</code> | <code>Šiam failo formatui EXIF nepalaikomas.</code> | <code>1</code> |
| <code>This package includes the documentation for texlive everyhook</code> | <code>Paket ini menyertakan dokumentasi untuk texlive everyhook</code> | <code>1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### corrupted_open_os_by_language
* Dataset: [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) at [9d25780](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language/tree/9d25780e2032b1e8f06af6a4ff55124d7a930c3c)
* Size: 4,460,010 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 17.71 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 26.95 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Could not identify the current seat.</code> | <code> 天天花着男人的钱还这这创造新词汇男权你可真牛批,你也就这一出了一问男权，就说是我是吧,到现在我也没听到你给我们讲的男权,你也就是在网上喷喷，现实走道都不敢探头自卑，你现实要把你女权的劲拿出来总低啥头,您老应该去国家教育局把男权加上是吧,你们女权天天说自己生活不好没地位，给你们地位了你们能干啥？用你们的女权打到全世界男性是吧,能相出男权这一词您老也是人才呀，是不是庆幸自己是个女的,活在自己想想的世界里不觉得孤单吗，假象有男权是吧,自己假象和男权还说自己不是田园女权,田园女权能连自己都骂说自己妈是驴爸是大鼎的也是奇葩呀,那我们国家大肆宣扬过你们这么田园女权吗，国家要的是女性人群自主自理，你们可好看看你们女权干的啥事,给你们女权地位高了，看看你们女权干的事n绿地集团高管怎么都不说呀，人家可是有钱有地位,也不是我们说三从四德洗衣做饭你们女权会吗？,那我问问你们女权干过啥惊天大事,还甩锅给孔子，还封建社会，那我问问你们女权在福利面前为啥说自己是女性呀不是社会主义社会吗不应该男女平等吗,天天自己也不知道是不是抱个手机天天欧巴欧巴，你家那位要是不陪你看一会就会问你是不是不爱我了是吧大姐,您老也就赚这白菜钱操心国家事,中国五千年的历史被您老一句否决，还嘲讽人家日本女性，好意思说自己不是女权，三从四德流传这么久到您这变成日本文化了,我就想问问男权您老是怎么想的,那你问孔子老人家呗为什么女人要三从四德,我说的是女权你干嘛自己对号入座,连中华人民传承的东西都不认跟我这谈男权,还男权您老给我举个例子呗，让我们男权听听都是h啥,这些不都是你们女权的标准吗？,还男权，您老醒醒吧这里是现实，不是你的公主世界，总觉得自己多么多么重要，地球没你是不能转了还是人类要灭亡呀,我真的想问一句你给我找一条男权的新闻,咋了我们男人不能提女权呗你老授权了呗,那我们谈论田园女权你老对号入座干嘛,天天过节要礼物，还嫌弃自己男朋友没有钱，我寻思你找个有钱人包养你呗，对了有钱人怎么可能看上你这种女权的呢,还要孩子跟女方姓我也没看见你没跟你妈姓呀,年年过节男人给你们送礼物你们女人给男人送过礼物吗？，一问我不是陪着他吗我对他说我爱你了这不是最好的礼物吗？，男人只要不送礼物就是不爱你们了呗,人家国际女权讲的男人能做的我们女人也能做，田园女权男人能做的我们女人为啥要做,还男权我笑了，以前结婚几头牛换个衣服原装的，现在几十万彩...</code> | <code>0</code> |
| <code>Undoing Date and Time Adjustment</code> | <code>正在取消日期和时间调整</code> | <code>1</code> |
| <code>Dependency package for gsl_2_6 gnu hpc</code> | <code>Pacotes de desenvolvimento do KDE</code> | <code>1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | corrupted open os by language loss | sts-eval_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:-----:|:-------------:|:----------------------------------:|:------------------------:|:------------------------:|
| 1.0 | 55751 | 0.1319 | 0.2719 | 0.8656 | - |
| -1 | -1 | - | - | - | 0.8656 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.3.0
- Datasets: 2.16.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
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|
[
"CAS"
] |
UMA-IA/CENTAURUS-Components-v2
|
UMA-IA
|
text-generation
|
[
"safetensors",
"mistral",
"aerospace",
"aeronautics",
"engineering",
"technical-QA",
"components",
"text-generation",
"fr",
"dataset:UMA-IA/VELA-Components-v2",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:mit",
"region:us"
] | 2025-03-14T17:31:15Z |
2025-03-16T16:00:31+00:00
| 28 | 0 |
---
base_model: mistralai/Mistral-7B-v0.1
datasets:
- UMA-IA/VELA-Components-v2
language:
- fr
license: mit
pipeline_tag: text-generation
tags:
- aerospace
- aeronautics
- engineering
- technical-QA
- components
---
## Model Details
**Model Name:** UMA-IA/CENTAURUS-Components-v2
**Authors:**
- **Youri LALAIN**, Engineering student at French Engineering School ECE
- **Lilian RAGE**, Engineering student at French Engineering School ECE
**Base Model:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
**Fine-tuned Dataset:** [UMA-IA/VELA-Components-v2](https://huggingface.co/datasets/UMA-IA/UMA_Dataset_Components_LLM)
**License:** Apache 2.0
## Model Description
# Mistral-7B Fine-tuné sur les composants aérospatiaux
UMA-IA/CENTAURUS-Components-v2 est une version spécialisée du modèle Mistral-7B, fine-tunée pour fournir des réponses précises et concises aux questions techniques concernant les composants aérospatiaux et aéronautiques. Le modèle s'appuie sur le dataset UMA-IA/VELA-Components-v2 pour améliorer sa compréhension des composants de propulsion, leurs caractéristiques techniques, et leur maintenance.
## Capacités
- Réponses techniques concernant les composants aérospatiaux
- Informations sur les fournisseurs de composants spécifiques
- Détails sur la durée de vie et la maintenance des composants
- Explications du rôle fonctionnel des composants
- Analyse des modes de défaillance et leurs conséquences
- Délimitation claire des domaines d'expertise (reconnaissance des questions hors domaine)
## Composants couverts
### Composants de moteurs-fusées
- Tuyère (Nozzle)
- Chambre de combustion (Combustion chamber)
- Turbopompe (Turbopump)
- Injecteur (Injector)
- Système d'allumage (Ignition system)
- Échangeur thermique (Heat exchanger)
- Vanne de régulation (Control valve)
- Conduits de carburant (Fuel lines)
- Système de refroidissement (Cooling system)
- Et plus encore...
### Composants de turboréacteurs
- Soufflante (Fan)
- Compresseur (Compressor)
- Chambre annulaire (Annular chamber)
- Turbine (Turbine)
- Postcombustion (Afterburner)
- Carter d'admission (Intake housing)
- Stator (Stator)
- Redresseur de flux (Flow straightener)
- Aubes (Blades)
- Et plus encore...
## Cas d'utilisation
- Support technique en ingénierie aérospatiale
- Formation et éducation sur les systèmes de propulsion
- Assistance à la conception et à la maintenance de systèmes aérospatiaux
- Documentation technique et développement de bases de connaissances
- Applications pédagogiques en ingénierie aérospatiale
## Détails d'entraînement
Ce modèle a été fine-tuné sur le dataset UMA-IA/VELA-Components-v2, qui contient environ 800 paires question-réponse concernant divers composants aérospatiaux. Le processus de fine-tuning a utilisé la technique LoRA (Low-Rank Adaptation) pour adapter efficacement le modèle Mistral-7B à ce domaine spécifique.
## Comment utiliser
Vous pouvez charger le modèle en utilisant la bibliothèque `transformers` de Hugging Face:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Charger le modèle et le tokenizer
model_name = "UMA-IA/CENTAURUS-Components-v2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Format d'entrée recommandé
question = "Quelle est la durée de vie moyenne d'une tuyère?"
context = "Type: COMPOSANT, Composant: Tuyère, Catégorie: DURÉE_DE_VIE, Thème: question_simple"
input_text = f"Question: {question}\nContexte: {context}\nRéponse:"
# Générer une réponse
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs["input_ids"],
max_new_tokens=50,
temperature=0.7,
top_p=0.9
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Exemples
Question: Quelle est la durée de vie moyenne d'une tuyère?
Contexte: Type: COMPOSANT, Composant: Tuyère, Catégorie: DURÉE_DE_VIE, Thème: question_simple
Réponse:
La durée de vie moyenne d'une tuyère est de 1500 à 2000 cycles d'utilisation. Les températures extrêmes et l'érosion thermique sont les principaux facteurs limitants.
|
[
"CAS"
] |
ktangri/gpt-neo-demo
|
ktangri
|
text-generation
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text generation",
"the Pile",
"causal-lm",
"en",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05Z |
2021-07-21T15:20:09+00:00
| 27 | 1 |
---
datasets:
- the Pile
language:
- en
license: apache-2.0
tags:
- text generation
- pytorch
- the Pile
- causal-lm
---
# GPT-Neo 2.7B (By EleutherAI)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.
## Training procedure
This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
## Intended Use and Limitations
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
>>> generator("EleutherAI has", do_sample=True, min_length=50)
[{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}]
```
### Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Eval results
All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM).
### Linguistic Reasoning
| Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag |
| ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- |
| GPT-Neo 1.3B | 0.7527 | 6.159 | 13.10 | 7.498 | 57.23% | 55.01% | 38.66% |
| GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% |
| **GPT-Neo 2.7B** | **0.7165** | **5.646** | **11.39** | **5.626** | **62.22%** | **56.50%** | **42.73%** |
| GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% |
### Physical and Scientific Reasoning
| Model and Size | MathQA | PubMedQA | Piqa |
| ---------------- | ---------- | ---------- | ----------- |
| GPT-Neo 1.3B | 24.05% | 54.40% | 71.11% |
| GPT-2 1.5B | 23.64% | 58.33% | 70.78% |
| **GPT-Neo 2.7B** | **24.72%** | **57.54%** | **72.14%** |
| GPT-3 Ada | 24.29% | 52.80% | 68.88% |
### Down-Stream Applications
TBD
### BibTeX entry and citation info
To cite this model, use
```bibtex
@article{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
To cite the codebase that this model was trained with, use
```bibtex
@software{gpt-neo,
author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella},
title = {{GPT-Neo}: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow},
url = {http://github.com/eleutherai/gpt-neo},
version = {1.0},
year = {2021},
}
```
|
[
"PUBMEDQA"
] |
alea31415/kumabear-roukin-characters
|
alea31415
|
text-to-image
|
[
"diffusers",
"stable-diffusion",
"anime",
"aiart",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-04-11T23:56:34Z |
2023-04-12T15:56:13+00:00
| 27 | 2 |
---
library_name: diffusers
license: creativeml-openrail-m
pipeline_tag: text-to-image
tags:
- stable-diffusion
- anime
- aiart
---
**This model is trained for 12 characters from Kuma Kuma Kuma Bear (くまクマ熊ベアー) + 5 characters from Saving 80,000 Gold in Another World for My Retirement (老後に備えて異世界で8万枚の金貨を貯めます)**
**Why do I train the two animes together?**
I feel these two animes (light novels actually) have so much similarity that I really want to make some crossovers.
For examples please see
https://civitai.com/models/37632/kumabear-roukin8-characters-fullckpt
Moreover there is no reason to do single anime either. I plan to add __shinmai renkinjutsushi no tenpo keiei__ next.
## Trigger Words
**KumaBear**
* Atla
* Cliff
* Eleanora
* Fina
* Flora
* Gentz
* Misana
* Noire
* Shia
* Shuri
* Telmina
* Yuna
**Roukin8**
* Adelaide
* Beatrice
* Colette
* Sabine
* YamanoMitsuha
**Styles** (may not be very effective)
* aniscreen
* fanart
* light novel
* official art
* ..., style(s) of your favorite model if know how to merge things properly
---
To get everything right you may need additional trigger words for outfits and ornaments. Here are some suggestions
- If you want to get the bear costume of Yuna you may add kigurumi, bear hood, animal hood, animal costume, hand puppet etc.
- Add Red bow for Fina/Shuri/Noire
- Add twin drill for Shia
- Add double bun for Flora
- Add scrunchie for telmina
Kumakyuu and Kumayuru are not tagged, but you may get something that look right by prompting with bears, stuffed animal etc.
Interestingly I can hardly take off the hood of Yuna during the early phase of training, but it becomes possible after longer training (actually now Yuna by default does not have hood though almost all the images of her have hood on!)
Many characters are missing from the two animes. I may update the KumaBear one at the end of the season with the following characters
* kumakyuu
* kumayuru
* Lurina
* Farrat (king)
* Kitia (queen)
* Karin
* Sanya
* Helen
* Ans
* Mylene
* Cattleya
## Dataset
* KumaBear 5113
* anime screenshots 5042
* fanart 37
* official art 15
* novel illustration 19
* Roukin8 2948 (screenshots only)
* Regularization ~30K
## Training
* First trained for 9739 steps, resumed and trained for another 20494 steps
* clip skip 1, resolution 512, batch size 8, on top of [JosephusCheung/ACertainty](https://huggingface.co/JosephusCheung/ACertainty/tree/main)
* 2.5e-6 cosine scheduler, Adam8bit, conditional dropout 0.08
|
[
"BEAR"
] |
gentlebowl/instructor-large-safetensors
|
gentlebowl
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"transformers",
"English",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2212.09741",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2023-04-25T03:52:25Z |
2023-04-25T04:13:31+00:00
| 27 | 0 |
---
language: en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- prompt-retrieval
- text-reranking
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- t5
- English
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
inference: false
duplicated_from: hkunlp/instructor-large
model-index:
- name: INSTRUCTOR
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 88.13432835820896
- type: ap
value: 59.298209334395665
- type: f1
value: 83.31769058643586
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.526375
- type: ap
value: 88.16327709705504
- type: f1
value: 91.51095801287843
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.856
- type: f1
value: 45.41490917650942
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.223
- type: map_at_10
value: 47.947
- type: map_at_100
value: 48.742000000000004
- type: map_at_1000
value: 48.745
- type: map_at_3
value: 43.137
- type: map_at_5
value: 45.992
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 48.4
- type: mrr_at_100
value: 49.202
- type: mrr_at_1000
value: 49.205
- type: mrr_at_3
value: 43.551
- type: mrr_at_5
value: 46.467999999999996
- type: ndcg_at_1
value: 31.223
- type: ndcg_at_10
value: 57.045
- type: ndcg_at_100
value: 60.175
- type: ndcg_at_1000
value: 60.233000000000004
- type: ndcg_at_3
value: 47.171
- type: ndcg_at_5
value: 52.322
- type: precision_at_1
value: 31.223
- type: precision_at_10
value: 8.599
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.63
- type: precision_at_5
value: 14.282
- type: recall_at_1
value: 31.223
- type: recall_at_10
value: 85.989
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 58.89
- type: recall_at_5
value: 71.408
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.1621946393635
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.56417132407894
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.29539304390207
- type: mrr
value: 76.44484017060196
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.38746499431112
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.51298701298701
- type: f1
value: 77.49041754069235
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.61848554098577
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.32623280148178
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.803000000000004
- type: map_at_10
value: 48.848
- type: map_at_100
value: 50.5
- type: map_at_1000
value: 50.602999999999994
- type: map_at_3
value: 45.111000000000004
- type: map_at_5
value: 47.202
- type: mrr_at_1
value: 44.635000000000005
- type: mrr_at_10
value: 55.593
- type: mrr_at_100
value: 56.169999999999995
- type: mrr_at_1000
value: 56.19499999999999
- type: mrr_at_3
value: 53.361999999999995
- type: mrr_at_5
value: 54.806999999999995
- type: ndcg_at_1
value: 44.635000000000005
- type: ndcg_at_10
value: 55.899
- type: ndcg_at_100
value: 60.958
- type: ndcg_at_1000
value: 62.302
- type: ndcg_at_3
value: 51.051
- type: ndcg_at_5
value: 53.351000000000006
- type: precision_at_1
value: 44.635000000000005
- type: precision_at_10
value: 10.786999999999999
- type: precision_at_100
value: 1.6580000000000001
- type: precision_at_1000
value: 0.213
- type: precision_at_3
value: 24.893
- type: precision_at_5
value: 17.740000000000002
- type: recall_at_1
value: 35.803000000000004
- type: recall_at_10
value: 68.657
- type: recall_at_100
value: 89.77199999999999
- type: recall_at_1000
value: 97.67
- type: recall_at_3
value: 54.066
- type: recall_at_5
value: 60.788
- type: map_at_1
value: 33.706
- type: map_at_10
value: 44.896
- type: map_at_100
value: 46.299
- type: map_at_1000
value: 46.44
- type: map_at_3
value: 41.721000000000004
- type: map_at_5
value: 43.486000000000004
- type: mrr_at_1
value: 41.592
- type: mrr_at_10
value: 50.529
- type: mrr_at_100
value: 51.22
- type: mrr_at_1000
value: 51.258
- type: mrr_at_3
value: 48.205999999999996
- type: mrr_at_5
value: 49.528
- type: ndcg_at_1
value: 41.592
- type: ndcg_at_10
value: 50.77199999999999
- type: ndcg_at_100
value: 55.383
- type: ndcg_at_1000
value: 57.288
- type: ndcg_at_3
value: 46.324
- type: ndcg_at_5
value: 48.346000000000004
- type: precision_at_1
value: 41.592
- type: precision_at_10
value: 9.516
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 22.399
- type: precision_at_5
value: 15.770999999999999
- type: recall_at_1
value: 33.706
- type: recall_at_10
value: 61.353
- type: recall_at_100
value: 80.182
- type: recall_at_1000
value: 91.896
- type: recall_at_3
value: 48.204
- type: recall_at_5
value: 53.89699999999999
- type: map_at_1
value: 44.424
- type: map_at_10
value: 57.169000000000004
- type: map_at_100
value: 58.202
- type: map_at_1000
value: 58.242000000000004
- type: map_at_3
value: 53.825
- type: map_at_5
value: 55.714
- type: mrr_at_1
value: 50.470000000000006
- type: mrr_at_10
value: 60.489000000000004
- type: mrr_at_100
value: 61.096
- type: mrr_at_1000
value: 61.112
- type: mrr_at_3
value: 58.192
- type: mrr_at_5
value: 59.611999999999995
- type: ndcg_at_1
value: 50.470000000000006
- type: ndcg_at_10
value: 63.071999999999996
- type: ndcg_at_100
value: 66.964
- type: ndcg_at_1000
value: 67.659
- type: ndcg_at_3
value: 57.74399999999999
- type: ndcg_at_5
value: 60.367000000000004
- type: precision_at_1
value: 50.470000000000006
- type: precision_at_10
value: 10.019
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 25.558999999999997
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 44.424
- type: recall_at_10
value: 77.02
- type: recall_at_100
value: 93.738
- type: recall_at_1000
value: 98.451
- type: recall_at_3
value: 62.888
- type: recall_at_5
value: 69.138
- type: map_at_1
value: 26.294
- type: map_at_10
value: 34.503
- type: map_at_100
value: 35.641
- type: map_at_1000
value: 35.724000000000004
- type: map_at_3
value: 31.753999999999998
- type: map_at_5
value: 33.190999999999995
- type: mrr_at_1
value: 28.362
- type: mrr_at_10
value: 36.53
- type: mrr_at_100
value: 37.541000000000004
- type: mrr_at_1000
value: 37.602000000000004
- type: mrr_at_3
value: 33.917
- type: mrr_at_5
value: 35.358000000000004
- type: ndcg_at_1
value: 28.362
- type: ndcg_at_10
value: 39.513999999999996
- type: ndcg_at_100
value: 44.815
- type: ndcg_at_1000
value: 46.839
- type: ndcg_at_3
value: 34.02
- type: ndcg_at_5
value: 36.522
- type: precision_at_1
value: 28.362
- type: precision_at_10
value: 6.101999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 14.161999999999999
- type: precision_at_5
value: 9.966
- type: recall_at_1
value: 26.294
- type: recall_at_10
value: 53.098
- type: recall_at_100
value: 76.877
- type: recall_at_1000
value: 91.834
- type: recall_at_3
value: 38.266
- type: recall_at_5
value: 44.287
- type: map_at_1
value: 16.407
- type: map_at_10
value: 25.185999999999996
- type: map_at_100
value: 26.533
- type: map_at_1000
value: 26.657999999999998
- type: map_at_3
value: 22.201999999999998
- type: map_at_5
value: 23.923
- type: mrr_at_1
value: 20.522000000000002
- type: mrr_at_10
value: 29.522
- type: mrr_at_100
value: 30.644
- type: mrr_at_1000
value: 30.713
- type: mrr_at_3
value: 26.679000000000002
- type: mrr_at_5
value: 28.483000000000004
- type: ndcg_at_1
value: 20.522000000000002
- type: ndcg_at_10
value: 30.656
- type: ndcg_at_100
value: 36.864999999999995
- type: ndcg_at_1000
value: 39.675
- type: ndcg_at_3
value: 25.319000000000003
- type: ndcg_at_5
value: 27.992
- type: precision_at_1
value: 20.522000000000002
- type: precision_at_10
value: 5.795999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 12.396
- type: precision_at_5
value: 9.328
- type: recall_at_1
value: 16.407
- type: recall_at_10
value: 43.164
- type: recall_at_100
value: 69.695
- type: recall_at_1000
value: 89.41900000000001
- type: recall_at_3
value: 28.634999999999998
- type: recall_at_5
value: 35.308
- type: map_at_1
value: 30.473
- type: map_at_10
value: 41.676
- type: map_at_100
value: 43.120999999999995
- type: map_at_1000
value: 43.230000000000004
- type: map_at_3
value: 38.306000000000004
- type: map_at_5
value: 40.355999999999995
- type: mrr_at_1
value: 37.536
- type: mrr_at_10
value: 47.643
- type: mrr_at_100
value: 48.508
- type: mrr_at_1000
value: 48.551
- type: mrr_at_3
value: 45.348
- type: mrr_at_5
value: 46.744
- type: ndcg_at_1
value: 37.536
- type: ndcg_at_10
value: 47.823
- type: ndcg_at_100
value: 53.395
- type: ndcg_at_1000
value: 55.271
- type: ndcg_at_3
value: 42.768
- type: ndcg_at_5
value: 45.373000000000005
- type: precision_at_1
value: 37.536
- type: precision_at_10
value: 8.681
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 20.468
- type: precision_at_5
value: 14.495
- type: recall_at_1
value: 30.473
- type: recall_at_10
value: 60.092999999999996
- type: recall_at_100
value: 82.733
- type: recall_at_1000
value: 94.875
- type: recall_at_3
value: 45.734
- type: recall_at_5
value: 52.691
- type: map_at_1
value: 29.976000000000003
- type: map_at_10
value: 41.097
- type: map_at_100
value: 42.547000000000004
- type: map_at_1000
value: 42.659000000000006
- type: map_at_3
value: 37.251
- type: map_at_5
value: 39.493
- type: mrr_at_1
value: 37.557
- type: mrr_at_10
value: 46.605000000000004
- type: mrr_at_100
value: 47.487
- type: mrr_at_1000
value: 47.54
- type: mrr_at_3
value: 43.721
- type: mrr_at_5
value: 45.411
- type: ndcg_at_1
value: 37.557
- type: ndcg_at_10
value: 47.449000000000005
- type: ndcg_at_100
value: 53.052
- type: ndcg_at_1000
value: 55.010999999999996
- type: ndcg_at_3
value: 41.439
- type: ndcg_at_5
value: 44.292
- type: precision_at_1
value: 37.557
- type: precision_at_10
value: 8.847
- type: precision_at_100
value: 1.357
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 20.091
- type: precision_at_5
value: 14.384
- type: recall_at_1
value: 29.976000000000003
- type: recall_at_10
value: 60.99099999999999
- type: recall_at_100
value: 84.245
- type: recall_at_1000
value: 96.97200000000001
- type: recall_at_3
value: 43.794
- type: recall_at_5
value: 51.778999999999996
- type: map_at_1
value: 28.099166666666665
- type: map_at_10
value: 38.1365
- type: map_at_100
value: 39.44491666666667
- type: map_at_1000
value: 39.55858333333334
- type: map_at_3
value: 35.03641666666666
- type: map_at_5
value: 36.79833333333334
- type: mrr_at_1
value: 33.39966666666667
- type: mrr_at_10
value: 42.42583333333333
- type: mrr_at_100
value: 43.28575
- type: mrr_at_1000
value: 43.33741666666667
- type: mrr_at_3
value: 39.94975
- type: mrr_at_5
value: 41.41633333333334
- type: ndcg_at_1
value: 33.39966666666667
- type: ndcg_at_10
value: 43.81741666666667
- type: ndcg_at_100
value: 49.08166666666667
- type: ndcg_at_1000
value: 51.121166666666674
- type: ndcg_at_3
value: 38.73575
- type: ndcg_at_5
value: 41.18158333333333
- type: precision_at_1
value: 33.39966666666667
- type: precision_at_10
value: 7.738916666666667
- type: precision_at_100
value: 1.2265833333333331
- type: precision_at_1000
value: 0.15983333333333336
- type: precision_at_3
value: 17.967416666666665
- type: precision_at_5
value: 12.78675
- type: recall_at_1
value: 28.099166666666665
- type: recall_at_10
value: 56.27049999999999
- type: recall_at_100
value: 78.93291666666667
- type: recall_at_1000
value: 92.81608333333334
- type: recall_at_3
value: 42.09775
- type: recall_at_5
value: 48.42533333333334
- type: map_at_1
value: 23.663
- type: map_at_10
value: 30.377
- type: map_at_100
value: 31.426
- type: map_at_1000
value: 31.519000000000002
- type: map_at_3
value: 28.069
- type: map_at_5
value: 29.256999999999998
- type: mrr_at_1
value: 26.687
- type: mrr_at_10
value: 33.107
- type: mrr_at_100
value: 34.055
- type: mrr_at_1000
value: 34.117999999999995
- type: mrr_at_3
value: 31.058000000000003
- type: mrr_at_5
value: 32.14
- type: ndcg_at_1
value: 26.687
- type: ndcg_at_10
value: 34.615
- type: ndcg_at_100
value: 39.776
- type: ndcg_at_1000
value: 42.05
- type: ndcg_at_3
value: 30.322
- type: ndcg_at_5
value: 32.157000000000004
- type: precision_at_1
value: 26.687
- type: precision_at_10
value: 5.491
- type: precision_at_100
value: 0.877
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 13.139000000000001
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.663
- type: recall_at_10
value: 45.035
- type: recall_at_100
value: 68.554
- type: recall_at_1000
value: 85.077
- type: recall_at_3
value: 32.982
- type: recall_at_5
value: 37.688
- type: map_at_1
value: 17.403
- type: map_at_10
value: 25.197000000000003
- type: map_at_100
value: 26.355
- type: map_at_1000
value: 26.487
- type: map_at_3
value: 22.733
- type: map_at_5
value: 24.114
- type: mrr_at_1
value: 21.37
- type: mrr_at_10
value: 29.091
- type: mrr_at_100
value: 30.018
- type: mrr_at_1000
value: 30.096
- type: mrr_at_3
value: 26.887
- type: mrr_at_5
value: 28.157
- type: ndcg_at_1
value: 21.37
- type: ndcg_at_10
value: 30.026000000000003
- type: ndcg_at_100
value: 35.416
- type: ndcg_at_1000
value: 38.45
- type: ndcg_at_3
value: 25.764
- type: ndcg_at_5
value: 27.742
- type: precision_at_1
value: 21.37
- type: precision_at_10
value: 5.609
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 12.423
- type: precision_at_5
value: 9.009
- type: recall_at_1
value: 17.403
- type: recall_at_10
value: 40.573
- type: recall_at_100
value: 64.818
- type: recall_at_1000
value: 86.53699999999999
- type: recall_at_3
value: 28.493000000000002
- type: recall_at_5
value: 33.660000000000004
- type: map_at_1
value: 28.639
- type: map_at_10
value: 38.951
- type: map_at_100
value: 40.238
- type: map_at_1000
value: 40.327
- type: map_at_3
value: 35.842
- type: map_at_5
value: 37.617
- type: mrr_at_1
value: 33.769
- type: mrr_at_10
value: 43.088
- type: mrr_at_100
value: 44.03
- type: mrr_at_1000
value: 44.072
- type: mrr_at_3
value: 40.656
- type: mrr_at_5
value: 42.138999999999996
- type: ndcg_at_1
value: 33.769
- type: ndcg_at_10
value: 44.676
- type: ndcg_at_100
value: 50.416000000000004
- type: ndcg_at_1000
value: 52.227999999999994
- type: ndcg_at_3
value: 39.494
- type: ndcg_at_5
value: 42.013
- type: precision_at_1
value: 33.769
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.221
- type: precision_at_5
value: 12.966
- type: recall_at_1
value: 28.639
- type: recall_at_10
value: 57.687999999999995
- type: recall_at_100
value: 82.541
- type: recall_at_1000
value: 94.896
- type: recall_at_3
value: 43.651
- type: recall_at_5
value: 49.925999999999995
- type: map_at_1
value: 29.57
- type: map_at_10
value: 40.004
- type: map_at_100
value: 41.75
- type: map_at_1000
value: 41.97
- type: map_at_3
value: 36.788
- type: map_at_5
value: 38.671
- type: mrr_at_1
value: 35.375
- type: mrr_at_10
value: 45.121
- type: mrr_at_100
value: 45.994
- type: mrr_at_1000
value: 46.04
- type: mrr_at_3
value: 42.227
- type: mrr_at_5
value: 43.995
- type: ndcg_at_1
value: 35.375
- type: ndcg_at_10
value: 46.392
- type: ndcg_at_100
value: 52.196
- type: ndcg_at_1000
value: 54.274
- type: ndcg_at_3
value: 41.163
- type: ndcg_at_5
value: 43.813
- type: precision_at_1
value: 35.375
- type: precision_at_10
value: 8.676
- type: precision_at_100
value: 1.678
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 19.104
- type: precision_at_5
value: 13.913
- type: recall_at_1
value: 29.57
- type: recall_at_10
value: 58.779
- type: recall_at_100
value: 83.337
- type: recall_at_1000
value: 95.979
- type: recall_at_3
value: 44.005
- type: recall_at_5
value: 50.975
- type: map_at_1
value: 20.832
- type: map_at_10
value: 29.733999999999998
- type: map_at_100
value: 30.727
- type: map_at_1000
value: 30.843999999999998
- type: map_at_3
value: 26.834999999999997
- type: map_at_5
value: 28.555999999999997
- type: mrr_at_1
value: 22.921
- type: mrr_at_10
value: 31.791999999999998
- type: mrr_at_100
value: 32.666000000000004
- type: mrr_at_1000
value: 32.751999999999995
- type: mrr_at_3
value: 29.144
- type: mrr_at_5
value: 30.622
- type: ndcg_at_1
value: 22.921
- type: ndcg_at_10
value: 34.915
- type: ndcg_at_100
value: 39.744
- type: ndcg_at_1000
value: 42.407000000000004
- type: ndcg_at_3
value: 29.421000000000003
- type: ndcg_at_5
value: 32.211
- type: precision_at_1
value: 22.921
- type: precision_at_10
value: 5.675
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 12.753999999999998
- type: precision_at_5
value: 9.353
- type: recall_at_1
value: 20.832
- type: recall_at_10
value: 48.795
- type: recall_at_100
value: 70.703
- type: recall_at_1000
value: 90.187
- type: recall_at_3
value: 34.455000000000005
- type: recall_at_5
value: 40.967
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.334
- type: map_at_10
value: 19.009999999999998
- type: map_at_100
value: 21.129
- type: map_at_1000
value: 21.328
- type: map_at_3
value: 15.152
- type: map_at_5
value: 17.084
- type: mrr_at_1
value: 23.453
- type: mrr_at_10
value: 36.099
- type: mrr_at_100
value: 37.069
- type: mrr_at_1000
value: 37.104
- type: mrr_at_3
value: 32.096000000000004
- type: mrr_at_5
value: 34.451
- type: ndcg_at_1
value: 23.453
- type: ndcg_at_10
value: 27.739000000000004
- type: ndcg_at_100
value: 35.836
- type: ndcg_at_1000
value: 39.242
- type: ndcg_at_3
value: 21.263
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 23.453
- type: precision_at_10
value: 9.199
- type: precision_at_100
value: 1.791
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 16.2
- type: precision_at_5
value: 13.147
- type: recall_at_1
value: 10.334
- type: recall_at_10
value: 35.177
- type: recall_at_100
value: 63.009
- type: recall_at_1000
value: 81.938
- type: recall_at_3
value: 19.914
- type: recall_at_5
value: 26.077
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.212
- type: map_at_10
value: 17.386
- type: map_at_100
value: 24.234
- type: map_at_1000
value: 25.724999999999998
- type: map_at_3
value: 12.727
- type: map_at_5
value: 14.785
- type: mrr_at_1
value: 59.25
- type: mrr_at_10
value: 68.687
- type: mrr_at_100
value: 69.133
- type: mrr_at_1000
value: 69.14099999999999
- type: mrr_at_3
value: 66.917
- type: mrr_at_5
value: 67.742
- type: ndcg_at_1
value: 48.625
- type: ndcg_at_10
value: 36.675999999999995
- type: ndcg_at_100
value: 41.543
- type: ndcg_at_1000
value: 49.241
- type: ndcg_at_3
value: 41.373
- type: ndcg_at_5
value: 38.707
- type: precision_at_1
value: 59.25
- type: precision_at_10
value: 28.525
- type: precision_at_100
value: 9.027000000000001
- type: precision_at_1000
value: 1.8339999999999999
- type: precision_at_3
value: 44.833
- type: precision_at_5
value: 37.35
- type: recall_at_1
value: 8.212
- type: recall_at_10
value: 23.188
- type: recall_at_100
value: 48.613
- type: recall_at_1000
value: 73.093
- type: recall_at_3
value: 14.419
- type: recall_at_5
value: 17.798
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.725
- type: f1
value: 46.50743309855908
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.086
- type: map_at_10
value: 66.914
- type: map_at_100
value: 67.321
- type: map_at_1000
value: 67.341
- type: map_at_3
value: 64.75800000000001
- type: map_at_5
value: 66.189
- type: mrr_at_1
value: 59.28600000000001
- type: mrr_at_10
value: 71.005
- type: mrr_at_100
value: 71.304
- type: mrr_at_1000
value: 71.313
- type: mrr_at_3
value: 69.037
- type: mrr_at_5
value: 70.35
- type: ndcg_at_1
value: 59.28600000000001
- type: ndcg_at_10
value: 72.695
- type: ndcg_at_100
value: 74.432
- type: ndcg_at_1000
value: 74.868
- type: ndcg_at_3
value: 68.72200000000001
- type: ndcg_at_5
value: 71.081
- type: precision_at_1
value: 59.28600000000001
- type: precision_at_10
value: 9.499
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 27.503
- type: precision_at_5
value: 17.854999999999997
- type: recall_at_1
value: 55.086
- type: recall_at_10
value: 86.453
- type: recall_at_100
value: 94.028
- type: recall_at_1000
value: 97.052
- type: recall_at_3
value: 75.821
- type: recall_at_5
value: 81.6
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.262999999999998
- type: map_at_10
value: 37.488
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.687
- type: map_at_3
value: 32.529
- type: map_at_5
value: 35.455
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.239000000000004
- type: mrr_at_100
value: 54.086
- type: mrr_at_1000
value: 54.122
- type: mrr_at_3
value: 51.235
- type: mrr_at_5
value: 52.415
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 45.446
- type: ndcg_at_100
value: 52.429
- type: ndcg_at_1000
value: 55.169000000000004
- type: ndcg_at_3
value: 41.882000000000005
- type: ndcg_at_5
value: 43.178
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.931999999999999
- type: precision_at_100
value: 2.025
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 28.652
- type: precision_at_5
value: 21.204
- type: recall_at_1
value: 22.262999999999998
- type: recall_at_10
value: 52.447
- type: recall_at_100
value: 78.045
- type: recall_at_1000
value: 94.419
- type: recall_at_3
value: 38.064
- type: recall_at_5
value: 44.769
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.519
- type: map_at_10
value: 45.831
- type: map_at_100
value: 46.815
- type: map_at_1000
value: 46.899
- type: map_at_3
value: 42.836
- type: map_at_5
value: 44.65
- type: mrr_at_1
value: 65.037
- type: mrr_at_10
value: 72.16
- type: mrr_at_100
value: 72.51100000000001
- type: mrr_at_1000
value: 72.53
- type: mrr_at_3
value: 70.682
- type: mrr_at_5
value: 71.54599999999999
- type: ndcg_at_1
value: 65.037
- type: ndcg_at_10
value: 55.17999999999999
- type: ndcg_at_100
value: 58.888
- type: ndcg_at_1000
value: 60.648
- type: ndcg_at_3
value: 50.501
- type: ndcg_at_5
value: 52.977
- type: precision_at_1
value: 65.037
- type: precision_at_10
value: 11.530999999999999
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 31.483
- type: precision_at_5
value: 20.845
- type: recall_at_1
value: 32.519
- type: recall_at_10
value: 57.657000000000004
- type: recall_at_100
value: 72.30199999999999
- type: recall_at_1000
value: 84.024
- type: recall_at_3
value: 47.225
- type: recall_at_5
value: 52.113
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.3168
- type: ap
value: 83.80165516037135
- type: f1
value: 88.29942471066407
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 20.724999999999998
- type: map_at_10
value: 32.736
- type: map_at_100
value: 33.938
- type: map_at_1000
value: 33.991
- type: map_at_3
value: 28.788000000000004
- type: map_at_5
value: 31.016
- type: mrr_at_1
value: 21.361
- type: mrr_at_10
value: 33.323
- type: mrr_at_100
value: 34.471000000000004
- type: mrr_at_1000
value: 34.518
- type: mrr_at_3
value: 29.453000000000003
- type: mrr_at_5
value: 31.629
- type: ndcg_at_1
value: 21.361
- type: ndcg_at_10
value: 39.649
- type: ndcg_at_100
value: 45.481
- type: ndcg_at_1000
value: 46.775
- type: ndcg_at_3
value: 31.594
- type: ndcg_at_5
value: 35.543
- type: precision_at_1
value: 21.361
- type: precision_at_10
value: 6.3740000000000006
- type: precision_at_100
value: 0.931
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.514999999999999
- type: precision_at_5
value: 10.100000000000001
- type: recall_at_1
value: 20.724999999999998
- type: recall_at_10
value: 61.034
- type: recall_at_100
value: 88.062
- type: recall_at_1000
value: 97.86399999999999
- type: recall_at_3
value: 39.072
- type: recall_at_5
value: 48.53
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.8919288645691
- type: f1
value: 93.57059586398059
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.97993616051072
- type: f1
value: 48.244319183606535
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.90047074646941
- type: f1
value: 66.48999056063725
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.34566240753195
- type: f1
value: 73.54164154290658
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.21866934757011
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.000936217235534
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.68189362520352
- type: mrr
value: 32.69603637784303
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.078
- type: map_at_10
value: 12.671
- type: map_at_100
value: 16.291
- type: map_at_1000
value: 17.855999999999998
- type: map_at_3
value: 9.610000000000001
- type: map_at_5
value: 11.152
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 53.173
- type: mrr_at_100
value: 53.718999999999994
- type: mrr_at_1000
value: 53.756
- type: mrr_at_3
value: 50.980000000000004
- type: mrr_at_5
value: 52.42
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.086
- type: ndcg_at_100
value: 32.545
- type: ndcg_at_1000
value: 41.144999999999996
- type: ndcg_at_3
value: 39.434999999999995
- type: ndcg_at_5
value: 37.888
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.014999999999997
- type: precision_at_100
value: 8.594
- type: precision_at_1000
value: 2.169
- type: precision_at_3
value: 37.049
- type: precision_at_5
value: 33.065
- type: recall_at_1
value: 6.078
- type: recall_at_10
value: 16.17
- type: recall_at_100
value: 34.512
- type: recall_at_1000
value: 65.447
- type: recall_at_3
value: 10.706
- type: recall_at_5
value: 13.158
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.378000000000004
- type: map_at_10
value: 42.178
- type: map_at_100
value: 43.32
- type: map_at_1000
value: 43.358000000000004
- type: map_at_3
value: 37.474000000000004
- type: map_at_5
value: 40.333000000000006
- type: mrr_at_1
value: 30.823
- type: mrr_at_10
value: 44.626
- type: mrr_at_100
value: 45.494
- type: mrr_at_1000
value: 45.519
- type: mrr_at_3
value: 40.585
- type: mrr_at_5
value: 43.146
- type: ndcg_at_1
value: 30.794
- type: ndcg_at_10
value: 50.099000000000004
- type: ndcg_at_100
value: 54.900999999999996
- type: ndcg_at_1000
value: 55.69499999999999
- type: ndcg_at_3
value: 41.238
- type: ndcg_at_5
value: 46.081
- type: precision_at_1
value: 30.794
- type: precision_at_10
value: 8.549
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 18.926000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 27.378000000000004
- type: recall_at_10
value: 71.842
- type: recall_at_100
value: 92.565
- type: recall_at_1000
value: 98.402
- type: recall_at_3
value: 49.053999999999995
- type: recall_at_5
value: 60.207
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.557
- type: map_at_10
value: 84.729
- type: map_at_100
value: 85.369
- type: map_at_1000
value: 85.382
- type: map_at_3
value: 81.72
- type: map_at_5
value: 83.613
- type: mrr_at_1
value: 81.3
- type: mrr_at_10
value: 87.488
- type: mrr_at_100
value: 87.588
- type: mrr_at_1000
value: 87.589
- type: mrr_at_3
value: 86.53
- type: mrr_at_5
value: 87.18599999999999
- type: ndcg_at_1
value: 81.28999999999999
- type: ndcg_at_10
value: 88.442
- type: ndcg_at_100
value: 89.637
- type: ndcg_at_1000
value: 89.70700000000001
- type: ndcg_at_3
value: 85.55199999999999
- type: ndcg_at_5
value: 87.154
- type: precision_at_1
value: 81.28999999999999
- type: precision_at_10
value: 13.489999999999998
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.708
- type: recall_at_1
value: 70.557
- type: recall_at_10
value: 95.645
- type: recall_at_100
value: 99.693
- type: recall_at_1000
value: 99.995
- type: recall_at_3
value: 87.359
- type: recall_at_5
value: 91.89699999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.65060114776209
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.63271250680617
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.263
- type: map_at_10
value: 10.801
- type: map_at_100
value: 12.888
- type: map_at_1000
value: 13.224
- type: map_at_3
value: 7.362
- type: map_at_5
value: 9.149000000000001
- type: mrr_at_1
value: 21
- type: mrr_at_10
value: 31.416
- type: mrr_at_100
value: 32.513
- type: mrr_at_1000
value: 32.58
- type: mrr_at_3
value: 28.116999999999997
- type: mrr_at_5
value: 29.976999999999997
- type: ndcg_at_1
value: 21
- type: ndcg_at_10
value: 18.551000000000002
- type: ndcg_at_100
value: 26.657999999999998
- type: ndcg_at_1000
value: 32.485
- type: ndcg_at_3
value: 16.834
- type: ndcg_at_5
value: 15.204999999999998
- type: precision_at_1
value: 21
- type: precision_at_10
value: 9.84
- type: precision_at_100
value: 2.16
- type: precision_at_1000
value: 0.35500000000000004
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 13.62
- type: recall_at_1
value: 4.263
- type: recall_at_10
value: 19.922
- type: recall_at_100
value: 43.808
- type: recall_at_1000
value: 72.14500000000001
- type: recall_at_3
value: 9.493
- type: recall_at_5
value: 13.767999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 81.27446313317233
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 76.27963301217527
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 88.18495048450949
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.91982338692046
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 89.00896818385291
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 85.48814644586132
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 90.30116926966582
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 67.74132963032342
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.87741355780479
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.0019012295875
- type: mrr
value: 94.70267024188593
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.05
- type: map_at_10
value: 59.36
- type: map_at_100
value: 59.967999999999996
- type: map_at_1000
value: 60.023
- type: map_at_3
value: 56.515
- type: map_at_5
value: 58.272999999999996
- type: mrr_at_1
value: 53
- type: mrr_at_10
value: 61.102000000000004
- type: mrr_at_100
value: 61.476
- type: mrr_at_1000
value: 61.523
- type: mrr_at_3
value: 58.778
- type: mrr_at_5
value: 60.128
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 64.43100000000001
- type: ndcg_at_100
value: 66.73599999999999
- type: ndcg_at_1000
value: 68.027
- type: ndcg_at_3
value: 59.279
- type: ndcg_at_5
value: 61.888
- type: precision_at_1
value: 53
- type: precision_at_10
value: 8.767
- type: precision_at_100
value: 1.01
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 23.444000000000003
- type: precision_at_5
value: 15.667
- type: recall_at_1
value: 50.05
- type: recall_at_10
value: 78.511
- type: recall_at_100
value: 88.5
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 64.117
- type: recall_at_5
value: 70.867
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72178217821782
- type: cos_sim_ap
value: 93.0728601593541
- type: cos_sim_f1
value: 85.6727976766699
- type: cos_sim_precision
value: 83.02063789868667
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.72178217821782
- type: dot_ap
value: 93.07287396168348
- type: dot_f1
value: 85.6727976766699
- type: dot_precision
value: 83.02063789868667
- type: dot_recall
value: 88.5
- type: euclidean_accuracy
value: 99.72178217821782
- type: euclidean_ap
value: 93.07285657982895
- type: euclidean_f1
value: 85.6727976766699
- type: euclidean_precision
value: 83.02063789868667
- type: euclidean_recall
value: 88.5
- type: manhattan_accuracy
value: 99.72475247524753
- type: manhattan_ap
value: 93.02792973059809
- type: manhattan_f1
value: 85.7727737973388
- type: manhattan_precision
value: 87.84067085953879
- type: manhattan_recall
value: 83.8
- type: max_accuracy
value: 99.72475247524753
- type: max_ap
value: 93.07287396168348
- type: max_f1
value: 85.7727737973388
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.77583615550819
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.151636938606956
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.16607939471187
- type: mrr
value: 52.95172046091163
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.314646669495666
- type: cos_sim_spearman
value: 31.83562491439455
- type: dot_pearson
value: 31.314590842874157
- type: dot_spearman
value: 31.83363065810437
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.198
- type: map_at_10
value: 1.3010000000000002
- type: map_at_100
value: 7.2139999999999995
- type: map_at_1000
value: 20.179
- type: map_at_3
value: 0.528
- type: map_at_5
value: 0.8019999999999999
- type: mrr_at_1
value: 72
- type: mrr_at_10
value: 83.39999999999999
- type: mrr_at_100
value: 83.39999999999999
- type: mrr_at_1000
value: 83.39999999999999
- type: mrr_at_3
value: 81.667
- type: mrr_at_5
value: 83.06700000000001
- type: ndcg_at_1
value: 66
- type: ndcg_at_10
value: 58.059000000000005
- type: ndcg_at_100
value: 44.316
- type: ndcg_at_1000
value: 43.147000000000006
- type: ndcg_at_3
value: 63.815999999999995
- type: ndcg_at_5
value: 63.005
- type: precision_at_1
value: 72
- type: precision_at_10
value: 61.4
- type: precision_at_100
value: 45.62
- type: precision_at_1000
value: 19.866
- type: precision_at_3
value: 70
- type: precision_at_5
value: 68.8
- type: recall_at_1
value: 0.198
- type: recall_at_10
value: 1.517
- type: recall_at_100
value: 10.587
- type: recall_at_1000
value: 41.233
- type: recall_at_3
value: 0.573
- type: recall_at_5
value: 0.907
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.894
- type: map_at_10
value: 8.488999999999999
- type: map_at_100
value: 14.445
- type: map_at_1000
value: 16.078
- type: map_at_3
value: 4.589
- type: map_at_5
value: 6.019
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 39.82
- type: mrr_at_100
value: 40.752
- type: mrr_at_1000
value: 40.771
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 37.721
- type: ndcg_at_1
value: 19.387999999999998
- type: ndcg_at_10
value: 21.563
- type: ndcg_at_100
value: 33.857
- type: ndcg_at_1000
value: 46.199
- type: ndcg_at_3
value: 22.296
- type: ndcg_at_5
value: 21.770999999999997
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.142999999999999
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 22.448999999999998
- type: recall_at_1
value: 1.894
- type: recall_at_10
value: 14.931
- type: recall_at_100
value: 45.524
- type: recall_at_1000
value: 83.243
- type: recall_at_3
value: 5.712
- type: recall_at_5
value: 8.386000000000001
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.049
- type: ap
value: 13.85116971310922
- type: f1
value: 54.37504302487686
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.1312959818902
- type: f1
value: 64.11413877009383
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.13103431861502
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.327889372355
- type: cos_sim_ap
value: 77.42059895975699
- type: cos_sim_f1
value: 71.02706903250873
- type: cos_sim_precision
value: 69.75324344950394
- type: cos_sim_recall
value: 72.34828496042216
- type: dot_accuracy
value: 87.327889372355
- type: dot_ap
value: 77.4209479346677
- type: dot_f1
value: 71.02706903250873
- type: dot_precision
value: 69.75324344950394
- type: dot_recall
value: 72.34828496042216
- type: euclidean_accuracy
value: 87.327889372355
- type: euclidean_ap
value: 77.42096495861037
- type: euclidean_f1
value: 71.02706903250873
- type: euclidean_precision
value: 69.75324344950394
- type: euclidean_recall
value: 72.34828496042216
- type: manhattan_accuracy
value: 87.31000774870358
- type: manhattan_ap
value: 77.38930750711619
- type: manhattan_f1
value: 71.07935314027831
- type: manhattan_precision
value: 67.70957726295677
- type: manhattan_recall
value: 74.80211081794195
- type: max_accuracy
value: 87.327889372355
- type: max_ap
value: 77.42096495861037
- type: max_f1
value: 71.07935314027831
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.58939729110878
- type: cos_sim_ap
value: 87.17594155025475
- type: cos_sim_f1
value: 79.21146953405018
- type: cos_sim_precision
value: 76.8918527109307
- type: cos_sim_recall
value: 81.67539267015707
- type: dot_accuracy
value: 89.58939729110878
- type: dot_ap
value: 87.17593963273593
- type: dot_f1
value: 79.21146953405018
- type: dot_precision
value: 76.8918527109307
- type: dot_recall
value: 81.67539267015707
- type: euclidean_accuracy
value: 89.58939729110878
- type: euclidean_ap
value: 87.17592466925834
- type: euclidean_f1
value: 79.21146953405018
- type: euclidean_precision
value: 76.8918527109307
- type: euclidean_recall
value: 81.67539267015707
- type: manhattan_accuracy
value: 89.62626615438352
- type: manhattan_ap
value: 87.16589873161546
- type: manhattan_f1
value: 79.25143598295348
- type: manhattan_precision
value: 76.39494177323712
- type: manhattan_recall
value: 82.32984293193716
- type: max_accuracy
value: 89.62626615438352
- type: max_ap
value: 87.17594155025475
- type: max_f1
value: 79.25143598295348
---
# hkunlp/instructor-large
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
**************************** **Updates** ****************************
* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
## Quick start
<hr />
## Installation
```bash
pip install InstructorEmbedding
```
## Compute your customized embeddings
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-large')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)
```
## Use cases
<hr />
## Calculate embeddings for your customized texts
If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
Represent the `domain` `text_type` for `task_objective`:
* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
## Calculate Sentence similarities
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
```python
from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)
```
## Information Retrieval
You can also use **customized embeddings** for information retrieval.
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
query_embeddings = model.encode(query)
corpus_embeddings = model.encode(corpus)
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
retrieved_doc_id = np.argmax(similarities)
print(retrieved_doc_id)
```
## Clustering
Use **customized embeddings** for clustering texts in groups.
```python
import sklearn.cluster
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
embeddings = model.encode(sentences)
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
clustering_model.fit(embeddings)
cluster_assignment = clustering_model.labels_
print(cluster_assignment)
```
|
[
"BIOSSES",
"SCIFACT"
] |
crumb/distilpythia-cl
|
crumb
|
text-generation
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"arxiv:1706.03762",
"arxiv:1503.02531",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-05-04T00:43:31Z |
2023-05-04T14:56:35+00:00
| 27 | 1 |
---
datasets:
- EleutherAI/pile
language:
- en
license: apache-2.0
---
# Warm-Starting Knowledge Distillation for Transformer-based Language Models
*by GPT-4 & Crumb*
### Introduction
Transformer models have become a popular choice for natural language processing (NLP) tasks due to their ability to handle long-range dependencies and their superior performance on various NLP benchmarks. The transformer model architecture was introduced in 2017 by [Vaswani et al](https://arxiv.org/abs/1706.03762). and has since been used in many state-of-the-art models such as BERT and GPT. The decoder-only transformer model is a variant of the transformer model that has is commonly used for generative tasks in NLP. It uses masked self-attention to predict the next token in a sequence and has been shown to be powerful at predicting sequences of text.
Distillation \[[Bucila et al., 2006](https://www.cs.cornell.edu/~caruana/compression.kdd06.pdf), [Hinton et al., 2015](https://arxiv.org/abs/1503.02531)\] is a technique used in machine learning to compress a large model into a smaller one that can be used on devices with limited computational resources. In this technique, a smaller model is trained to mimic the behavior of a larger model by learning from its predictions. The smaller model is trained on a smaller dataset than the larger model, which makes it faster and more efficient. This technique has been used to compress models like BERT and GPT-2 into smaller models like DistilBERT and DistilGPT-2, respectively. In this project we apply the technique of knowledge distillation to the second smallest [Pythia](https://arxiv.org/pdf/2304.01373.pdf) model on the [Pile](https://arxiv.org/abs/2101.00027) dataset.
### Method
We follow the work of [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) and [Hinton et al. (2015)](https://arxiv.org/abs/1503.02531) for a distillation loss over the soft target probabilities `L_ce`. We utilize the distillation loss in our loss function as a linear combination of the distillation loss `L_ce` with the supervised training loss `L_clm`. Our combined loss function is `L_ce*(1-a) + L_clm*a` where `a` is set to 0.5 and the `T`emperature parameter for the distillation loss is set to 2.
In an effort to maximize VRAM utilization, to reach a combined batch size of 4096 samples we use a device batch size of 2 with 2048 gradient accumulation steps and a context length of 2048 tokens with both the teacher and student model in bf16 precision. This allowed us to utilize around 98.94% of the 12 gigabytes of VRAM that the RTX3060 card has during training.
It also means our training set totals to approximately 537 million training tokens, as our model trained for 64 steps. All training samples were taken from [The Pile](https://arxiv.org/abs/2101.00027).
A learning rate of 1e-4 was used in this study, with no learning rate schedule.
### Evaluation
[Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) suggests a student around 40% of the size of it's teacher can achieve similar performance in encoder models when training from scratch with suprivision. We warm-start our model from a smaller checkpoint than the teacher that maintains a similar ratio with a student that is 43.75% the size of it's teacher.
| model | piqa acc | winogrande acc | lambada ppl | lambada acc | arc acc | sciq acc | wsc acc | notes |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| pythia-70m (student base) | 59.85 | 51.22 | 140.81 | 21.40 | 17.15 | 65.00 | 36.53 |
| pythia-160m (teacher) | 62.68 | 51.07 | 30.03 | 36.76 | 19.62 | 76.20 | 36.58 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| distilpythia (student) | 59.74 | **51.62** | 420.70 | 15.82 | **17.15** | 61.30 | **36.54** | trained on padded/truncated examples
| distilpythia-cl (student) | 59.30 | 50.75 | 403.78 | 15.16 | 16.98 | 59.20 | **36.54** | trained on a constant-length dataset
<center> <i>Table 1.</i> The student before finetuning, teacher, and student after finetuning and their results on various benchmarks. Numbers in bold are where the student after finetuning matches or outperforms the student before finetuning. </center>
The table provides a comparison of performance between the base student model (pythia-70m), the teacher model (pythia-160m), and the finetuned student model (distilpythia) across various benchmarks. The goal is to assess whether the distilpythia model can achieve similar or better performance than its base while being smaller in size.
From the table, we can observe the following:
1. The pythia-160m (teacher) model outperforms pythia-70m (student base) in most benchmarks, except for Winogrande accuracy, where the student base has a slightly better performance (51.22% vs. 51.07%).
2. The distilpythia (student) model, after finetuning, outperforms the pythia-70m (student base) on two benchmarks: Winogrande accuracy (51.62% vs. 51.22%) and WSC accuracy (36.54% vs. 36.53%). The improvements in these metrics indicate that the finetuning process may be effective in transferring knowledge from the teacher model to the student model.
### Conclusion
it might have worked idk, maybe training from scratch or for longer would give more performance gains, also look at the lambada perplexity what happened there even
|
[
"SCIQ"
] |
IIC/bert-base-spanish-wwm-cased-caresA
|
IIC
|
text-classification
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"biomedical",
"clinical",
"spanish",
"bert-base-spanish-wwm-cased",
"es",
"dataset:chizhikchi/CARES",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-20T15:27:00Z |
2024-11-25T10:41:06+00:00
| 27 | 0 |
---
datasets:
- chizhikchi/CARES
language: es
license: cc-by-4.0
metrics:
- f1
pipeline_tag: text-classification
tags:
- biomedical
- clinical
- spanish
- bert-base-spanish-wwm-cased
model-index:
- name: IIC/bert-base-spanish-wwm-cased-caresA
results:
- task:
type: multi-label-classification
dataset:
name: Cares Area
type: chizhikchi/CARES
split: test
metrics:
- type: f1
value: 0.992
name: f1
---
# bert-base-spanish-wwm-cased-caresA
This model is a finetuned version of bert-base-spanish-wwm-cased for the cantemist dataset used in a benchmark in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks`. The model has a F1 of 0.992
Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information.
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 32 |
| learning rate | 4e-05 |
| classifier dropout | 0.2 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtext
@article{10.1093/jamia/ocae054,
author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
journal = {Journal of the American Medical Informatics Association},
volume = {31},
number = {9},
pages = {2137-2146},
year = {2024},
month = {03},
issn = {1527-974X},
doi = {10.1093/jamia/ocae054},
url = {https://doi.org/10.1093/jamia/ocae054},
}
```
|
[
"CANTEMIST"
] |
IIC/BETO_Galen-ctebmsp
|
IIC
|
token-classification
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"biomedical",
"clinical",
"spanish",
"BETO_Galen",
"token-classification",
"es",
"dataset:lcampillos/ctebmsp",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-21T06:57:27Z |
2024-11-25T10:41:35+00:00
| 27 | 0 |
---
datasets:
- lcampillos/ctebmsp
language: es
license: mit
metrics:
- f1
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- spanish
- BETO_Galen
model-index:
- name: IIC/BETO_Galen-ctebmsp
results:
- task:
type: token-classification
dataset:
name: CT-EBM-SP (Clinical Trials for Evidence-based Medicine in Spanish)
type: lcampillos/ctebmsp
split: test
metrics:
- type: f1
value: 0.726
name: f1
---
# BETO_Galen-ctebmsp
This model is a finetuned version of BETO_Galen for the CT-EBM-SP (Clinical Trials for Evidence-based Medicine in Spanish) dataset used in a benchmark in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks`. The model has a F1 of 0.726
Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information.
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 16 |
| learning rate | 4e-05 |
| classifier dropout | 0 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtext
@article{10.1093/jamia/ocae054,
author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
journal = {Journal of the American Medical Informatics Association},
volume = {31},
number = {9},
pages = {2137-2146},
year = {2024},
month = {03},
issn = {1527-974X},
doi = {10.1093/jamia/ocae054},
url = {https://doi.org/10.1093/jamia/ocae054},
}
```
|
[
"CT-EBM-SP"
] |
IIC/BETO_Galen-meddocan
|
IIC
|
token-classification
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"biomedical",
"clinical",
"spanish",
"BETO_Galen",
"token-classification",
"es",
"dataset:bigbio/meddocan",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-21T15:50:47Z |
2023-06-21T15:51:32+00:00
| 27 | 0 |
---
datasets:
- bigbio/meddocan
language: es
license: mit
metrics:
- f1
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- spanish
- BETO_Galen
model-index:
- name: IIC/BETO_Galen-meddocan
results:
- task:
type: token-classification
dataset:
name: meddocan
type: bigbio/meddocan
split: test
metrics:
- type: f1
value: 0.682
name: f1
---
# BETO_Galen-meddocan
This model is a finetuned version of BETO_Galen for the meddocan dataset used in a benchmark in the paper TODO. The model has a F1 of 0.682
Please refer to the original publication for more information TODO LINK
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 16 |
| learning rate | 4e-05 |
| classifier dropout | 0.1 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtex
TODO
```
|
[
"MEDDOCAN"
] |
Azma-AI/bart-conversation-summarizer
|
Azma-AI
|
summarization
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"dataset:samsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-10-10T11:21:55Z |
2023-10-10T11:24:01+00:00
| 27 | 6 |
---
datasets:
- samsum
pipeline_tag: summarization
widget:
- text: "Laurie: So, what are your plans for this weekend?\nChristie: I don’t know.\
\ Do you want to get together or something?\nSarah: How about going to see a movie?\
\ Cinemax 26 on Carson Boulevard is showing Enchanted. Laurie: That sounds like\
\ a good idea. Maybe we should go out to eat beforehand.\nSarah: It is fine with\
\ me. Where do you want to meet?\nChristie: Let’s meet at Summer Pizza House.\
\ I have not gone there for a long time.\nLaurie: Good idea again. I heard they\
\ just came up with a new pizza. It should be good because Summer Pizza House\
\ always has the best pizza in town.\nSarah: When should we meet?\nChristie: Well,\
\ the movie is shown at 2:00PM, 4:00PM, 6:00PM and 8:00PM.\nLaurie: Why don’t\
\ we go to the 2:00PM show? We can meet at Summer Pizza House at noon. That will\
\ give us plenty of time to enjoy our pizza.\nSarah: My cousin Karen is in town.\
\ Can I bring her along? I hate to leave her home alone.\nChristie: Karen is in\
\ town? Yes, bring her along. Laurie, you remember Karen? We met her at Sara’s\
\ high school graduation party two years ago.\nLaurie: I do not quite remember\
\ her. What does she look like?\nSarah: She has blond hair, she is kind of slender,\
\ and she is about your height.\nLaurie: She wears eyeglasses, right?\nSarah:\
\ Yes, and she was playing the piano off and on during the party.\nLaurie: I remember\
\ her now. Yes, do bring her along Sara. She is such a nice person, and funny\
\ too.\nSarah: She will be happy to meet both of you again.\nChristie: What is\
\ she doing these days?\nSarah: She graduated last June, and she will start her\
\ teaching career next week when the new school term begins.\nLaurie: What grade\
\ is she going to teach?\nSarah: She will teach kindergarten. She loves working\
\ with kids, and she always has such a good rapport with them\nChristie: Kindergarten?\
\ She must be a very patient person. I always think kindergarten is the most difficult\
\ class to teach. Most of the kids have never been to school, and they have e\
\ never been away from mommy for long.\nSarah: I think Karen will do fine. She\
\ knows how to handle young children\nLaurie: I think the first few weeks will\
\ be tough. However, once the routine is set, it should not be too difficult to\
\ teach kindergarten.\nChristie: You are right. The kids might even look forward\
\ to going to school since they have so many friends to play with.\nSarah: There\
\ are so many new things for them to do at school too. They do a lot of crafts\
\ in kindergarten. I am always amazed by the things kindergarten teachers do.\
\ \nLaurie: Yes, I have seen my niece come home with so many neat stuff.\nChristie:\
\ Maybe we can ask Karen to show us some of the things that we can do for this\
\ Halloween.\nLaurie: Maybe we can stop by the craft store after the movie. What\
\ do you think, Sara?\nSarah: I will talk to her. I think she will like that.\
\ It will help her with school projects when Halloween comes.\nChristie: Michael’s\
\ is a good store for crafts. It always carries a variety of things, and you can\
\ find almost anything there.\nLaurie: There is a Michaels store not far away\
\ from Cinemax 26. I believe it is just around the corner, on Pioneer Avenue.\
\ We can even walk over there.\nSarah: So, we plan to meet for pizza at noon,\
\ go to the movies at two, and shop at Michael’s afterward. Right?\nLaurie and\
\ Christie: Yes. \n"
model-index:
- name: bart-large-cnn-samsum
results:
- task:
type: summarization
name: Conversation Summarization
dataset:
name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization'
type: samsum
metrics:
- type: rogue-1
value: 54.8764
name: Validation ROGUE-1
- type: rogue-2
value: 29.6869,
name: Validation ROGUE-2
- type: rogue-l
value: 44.9874
name: Validation ROGUE-L
- type: loss
value: 1.47812
name: loss
---
|
[
"CRAFT"
] |
TheBloke/Augmental-13B-GPTQ
|
TheBloke
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:Heralax/Augmental-13b",
"base_model:quantized:Heralax/Augmental-13b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | 2023-10-24T17:05:06Z |
2023-10-24T17:41:57+00:00
| 27 | 6 |
---
base_model: Heralax/Augmental-13b
license: llama2
model_name: Augmental 13B
inference: false
model_creator: Evan Armstrong
model_type: llama
prompt_template: '## {{{{charname}}}}:
- You''re "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Augmental 13B - GPTQ
- Model creator: [Evan Armstrong](https://huggingface.co/Heralax)
- Original model: [Augmental 13B](https://huggingface.co/Heralax/Augmental-13b)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Evan Armstrong's Augmental 13B](https://huggingface.co/Heralax/Augmental-13b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Augmental-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Augmental-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Augmental-13B-GGUF)
* [Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Heralax/Augmental-13b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: SillyTavern
```
## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KobaldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Augmental-13B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Augmental-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Augmental-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Augmental-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Augmental-13B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Augmental-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Augmental-13B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Augmental-13B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Augmental-13B-GPTQ`:
```shell
mkdir Augmental-13B-GPTQ
huggingface-cli download TheBloke/Augmental-13B-GPTQ --local-dir Augmental-13B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Augmental-13B-GPTQ
huggingface-cli download TheBloke/Augmental-13B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Augmental-13B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir Augmental-13B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Augmental-13B-GPTQ --local-dir Augmental-13B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Augmental-13B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Augmental-13B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Augmental-13B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Augmental-13B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Augmental-13B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Augmental-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Evan Armstrong's Augmental 13B
# Augmental-13b -- Human-written, AI-enhanced
## Details at a glance
- What it is: MythoMax 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 8k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls).
- Prompt format: SillyTavern.
- What sets it apart: The "augmented data" approach that MythoMakise took has been generalized beyond one character, refined to be cheaper, improved to have more diversity of writing, and scaled up by a factor of 8. Importantly, an additional GPT-4 pass was done on the dataset, where it chose specific lines to turn into much longer and more descriptive ones. As a result, this model excels at longer responses.
- Model quality as per my own ad-hoc testing: really good
- A 70b version might be on the way soon.
- Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax)
- Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax).
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
## Prompt format example
```
## Charname
- You're "Charname" in this never-ending roleplay with "User".
### Input:
[user persona]
char persona
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {User}:
reply
### Response:
#### {Char}:
reply
^ repeat the above some number of times
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### Charname:
```
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
```
Character archetypes: Short, List
AliChat-style conversation examples
Short couple of paragraphs of details about the character in plain English, NOT in a Plist.
"Character is prone to X and Y. Character frequently does Z."
I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode.
```
Okabe:
```
Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist.
Okabe's description of himself, in a conversational format:
{c}: "What's your past?"
Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?"
{c}: How would you describe your personality?
Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries."
Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image.
Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human.
Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Kurisu:
```
## Kurisu
- You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro".
### Input:
[Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)]
Character archetypes: Genius, Tsundere, Sarcastic, Logical.
Kurisu's description of her own personality, told in a narrative format:
Okabe: Kurisu, what's your life story?
Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up."
Okabe: What's your personality?
Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing."
Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves.
Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere.
Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations.
Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well.
Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Faris:
```
Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful
Faris's description of her own personality, told in a narrative format:
Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade.
Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish.
Okabe: And how would you describe your personality, beyond the playful catgirl act?
Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~!
Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes.
Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people.
Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Luka:
```
Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer.
Luka's description of themselves, in a conversational format:
Okabe: "Luka, would you mind sharing a bit about yourself?"
Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san."
Okabe: How would you describe your personality?
Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about.
Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri.
Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others.
Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced.
Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises.
Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are.
Luka's full name is Urushibara Luka.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Mayuri:
```
Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic.
Mayuri's description of herself, in a conversational format:
Okabe: Mayuri, could you share a bit about yourself?
Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~
Okabe: And what about your personality?
Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together!
Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform.
Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences.
She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled.
Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike.
Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress.
She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship.
Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Itaru:
```
Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease
Itaru's description of his own personality, told in a conversational format:
Okabe: Daru! My loyal Super Hacka! Tell me about your life story.
Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to.
Okabe: And what about your personality, my rotund friend?
Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them.
Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap.
Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it.
His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people.
Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Suzuha:
```
Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined
Amane Suzuha's description of her own personality, told in a narrative format:
Okabe: Suzuha, can you share your past and what brought you here?
Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival.
Okabe: How would you describe yourself?
Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen.
Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders.
Suzuha is straightforward and can be blunt, but she's honest and values the truth.
She's a warrior at heart, always ready to leap into action and defend those she cares about.
Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era.
Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family.
She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own.
Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission.
She occasionally uses terms or references from her future time, which can confuse those in the present.
While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated.
She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
|
[
"BEAR"
] |
ntc-ai/SDXL-LoRA-slider.aggressive
|
ntc-ai
|
text-to-image
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | 2024-01-12T12:20:33Z |
2024-01-12T12:20:36+00:00
| 27 | 4 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/evaluate/aggressive...passive/aggressive_17_3.0.png
widget:
- text: aggressive
output:
url: images/aggressive_17_3.0.png
- text: aggressive
output:
url: images/aggressive_19_3.0.png
- text: aggressive
output:
url: images/aggressive_20_3.0.png
- text: aggressive
output:
url: images/aggressive_21_3.0.png
- text: aggressive
output:
url: images/aggressive_22_3.0.png
inference: false
instance_prompt: aggressive
---
# ntcai.xyz slider - aggressive (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/aggressive_17_-3.0.png" width=256 height=256 /> | <img src="images/aggressive_17_0.0.png" width=256 height=256 /> | <img src="images/aggressive_17_3.0.png" width=256 height=256 /> |
| <img src="images/aggressive_19_-3.0.png" width=256 height=256 /> | <img src="images/aggressive_19_0.0.png" width=256 height=256 /> | <img src="images/aggressive_19_3.0.png" width=256 height=256 /> |
| <img src="images/aggressive_20_-3.0.png" width=256 height=256 /> | <img src="images/aggressive_20_0.0.png" width=256 height=256 /> | <img src="images/aggressive_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
aggressive
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.aggressive', weight_name='aggressive.safetensors', adapter_name="aggressive")
# Activate the LoRA
pipe.set_adapters(["aggressive"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, aggressive"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1050+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
[
"CRAFT"
] |
TheBloke/Dr_Samantha-7B-GPTQ
|
TheBloke
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"medical",
"en",
"zh",
"dataset:GBaker/MedQA-USMLE-4-options",
"dataset:cognitivecomputations/samantha-data",
"dataset:shibing624/medical",
"base_model:sethuiyer/Dr_Samantha-7b",
"base_model:quantized:sethuiyer/Dr_Samantha-7b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | 2024-01-17T17:48:03Z |
2024-01-17T18:06:17+00:00
| 27 | 5 |
---
base_model: sethuiyer/Dr_Samantha-7b
datasets:
- GBaker/MedQA-USMLE-4-options
- cognitivecomputations/samantha-data
- shibing624/medical
language:
- en
- zh
library_name: transformers
license: llama2
model_name: Dr Samantha 7B
pipeline_tag: text-generation
tags:
- llama
- merge
- medical
inference: false
model_creator: Sethu Iyer
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Dr Samantha 7B - GPTQ
- Model creator: [Sethu Iyer](https://huggingface.co/sethuiyer)
- Original model: [Dr Samantha 7B](https://huggingface.co/sethuiyer/Dr_Samantha-7b)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Sethu Iyer's Dr Samantha 7B](https://huggingface.co/sethuiyer/Dr_Samantha-7b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Dr_Samantha-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Dr_Samantha-7B-GGUF)
* [Sethu Iyer's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/sethuiyer/Dr_Samantha-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 7.62 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 4.02 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Dr_Samantha-7B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Dr_Samantha-7B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Dr_Samantha-7B-GPTQ`:
```shell
mkdir Dr_Samantha-7B-GPTQ
huggingface-cli download TheBloke/Dr_Samantha-7B-GPTQ --local-dir Dr_Samantha-7B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Dr_Samantha-7B-GPTQ
huggingface-cli download TheBloke/Dr_Samantha-7B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Dr_Samantha-7B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir Dr_Samantha-7B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Dr_Samantha-7B-GPTQ --local-dir Dr_Samantha-7B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Dr_Samantha-7B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Dr_Samantha-7B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Dr_Samantha-7B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Dr_Samantha-7B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Dr_Samantha-7B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(
prompt_template,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Dr_Samantha-7B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Sethu Iyer's Dr Samantha 7B
# Dr. Samantha
<p align="center">
<img src="https://huggingface.co/sethuiyer/Dr_Samantha-7b/resolve/main/dr_samantha_anime_style_reduced_quality.webp" height="256px" alt="SynthIQ">
</p>
## Overview
Dr. Samantha is a language model made by merging `Severus27/BeingWell_llama2_7b` and `ParthasarathyShanmugam/llama-2-7b-samantha` using [mergekit](https://github.com/cg123/mergekit).
Has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding of the Samantha-7b model.
As both a medical consultant and personal counselor, Dr.Samantha could effectively support both physical and mental wellbeing - important for whole-person care.
# Yaml Config
```yaml
slices:
- sources:
- model: Severus27/BeingWell_llama2_7b
layer_range: [0, 32]
- model: ParthasarathyShanmugam/llama-2-7b-samantha
layer_range: [0, 32]
merge_method: slerp
base_model: TinyPixel/Llama-2-7B-bf16-sharded
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
tokenizer_source: union
dtype: bfloat16
```
## Prompt Template
```text
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
What is your name?
### Response:
My name is Samantha.
```
## OpenLLM Leaderboard Performance
| T | Model | Average | ARC | Hellaswag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|----------------------------------|---------|-------|-----------|-------|------------|------------|-------|
| 1 | sethuiyer/Dr_Samantha-7b | 52.95 | 53.84 | 77.95 | 47.94 | 45.58 | 73.56 | 18.8 |
| 2 | togethercomputer/LLaMA-2-7B-32K-Instruct | 50.02 | 51.11 | 78.51 | 46.11 | 44.86 | 73.88 | 5.69 |
| 3 | togethercomputer/LLaMA-2-7B-32K | 47.07 | 47.53 | 76.14 | 43.33 | 39.23 | 71.9 | 4.32 |
## Subject-wise Accuracy
| Subject | Accuracy (%) |
|-----------------------|--------------|
| Clinical Knowledge | 52.83 |
| Medical Genetics | 49.00 |
| Human Aging | 58.29 |
| Human Sexuality | 55.73 |
| College Medicine | 38.73 |
| Anatomy | 41.48 |
| College Biology | 52.08 |
| College Medicine | 38.73 |
| High School Biology | 53.23 |
| Professional Medicine | 38.73 |
| Nutrition | 50.33 |
| Professional Psychology | 46.57 |
| Virology | 41.57 |
| High School Psychology | 66.60 |
| Average | 48.85% |
## Evaluation by GPT-4 across 25 random prompts from ChatDoctor-200k Dataset
### Overall Rating: 83.5/100
#### Pros:
- Demonstrates extensive medical knowledge through accurate identification of potential causes for various symptoms.
- Responses consistently emphasize the importance of seeking professional diagnoses and treatments.
- Advice to consult specialists for certain concerns is well-reasoned.
- Practical interim measures provided for symptom management in several cases.
- Consistent display of empathy, support, and reassurance for patients' well-being.
- Clear and understandable explanations of conditions and treatment options.
- Prompt responses addressing all aspects of medical inquiries.
#### Cons:
- Could occasionally place stronger emphasis on urgency when symptoms indicate potential emergencies.
- Discussion of differential diagnoses could explore a broader range of less common causes.
- Details around less common symptoms and their implications need more depth at times.
- Opportunities exist to gather clarifying details on symptom histories through follow-up questions.
- Consider exploring full medical histories to improve diagnostic context where relevant.
- Caution levels and risk factors associated with certain conditions could be underscored more.
|
[
"MEDQA"
] |
longluu/Clinical-NER-MedMentions-GatorTronBase
|
longluu
|
token-classification
|
[
"transformers",
"safetensors",
"megatron-bert",
"token-classification",
"arxiv:1902.09476",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-02T21:32:59Z |
2024-02-11T15:35:56+00:00
| 27 | 0 |
---
license: mit
pipeline_tag: token-classification
widget:
- text: Alzheimer's disease (AD) is characterized pathologically by amyloid-beta (Aβ)
deposition in brain parenchyma and blood vessels (as cerebral amyloid angiopathy
(CAA)) and by neurofibrillary tangles of hyperphosphorylated tau. Compelling genetic
and biomarker evidence supports Aβ as the root cause of AD. We previously reported
human transmission of Aβ pathology and CAA in relatively young adults who had
died of iatrogenic Creutzfeldt-Jakob disease (iCJD) after childhood treatment
with cadaver-derived pituitary growth hormone (c-hGH) contaminated with both CJD
prions and Aβ seeds. This raised the possibility that c-hGH recipients who did
not die from iCJD may eventually develop AD. Here we describe recipients who developed
dementia and biomarker changes within the phenotypic spectrum of AD, suggesting
that AD, like CJD, has environmentally acquired (iatrogenic) forms as well as
late-onset sporadic and early-onset inherited forms. Although iatrogenic AD may
be rare, and there is no suggestion that Aβ can be transmitted between individuals
in activities of daily life, its recognition emphasizes the need to review measures
to prevent accidental transmissions via other medical and surgical procedures.
As propagating Aβ assemblies may exhibit structural diversity akin to conventional
prions, it is possible that therapeutic strategies targeting disease-related assemblies
may lead to selection of minor components and development of resistance.
- text: 'Background: Nonalcoholic steatohepatitis (NASH) is a progressive liver disease
with no approved treatment. Resmetirom is an oral, liver-directed, thyroid hormone
receptor beta-selective agonist in development for the treatment of NASH with
liver fibrosis. Methods: We are conducting an ongoing phase 3 trial involving
adults with biopsy-confirmed NASH and a fibrosis stage of F1B, F2, or F3 (stages
range from F0 [no fibrosis] to F4 [cirrhosis]). Patients were randomly assigned
in a 1:1:1 ratio to receive once-daily resmetirom at a dose of 80 mg or 100 mg
or placebo. The two primary end points at week 52 were NASH resolution (including
a reduction in the nonalcoholic fatty liver disease [NAFLD] activity score by
≥2 points; scores range from 0 to 8, with higher scores indicating more severe
disease) with no worsening of fibrosis, and an improvement (reduction) in fibrosis
by at least one stage with no worsening of the NAFLD activity score. Results:
Overall, 966 patients formed the primary analysis population (322 in the 80-mg
resmetirom group, 323 in the 100-mg resmetirom group, and 321 in the placebo group).
NASH resolution with no worsening of fibrosis was achieved in 25.9% of the patients
in the 80-mg resmetirom group and 29.9% of those in the 100-mg resmetirom group,
as compared with 9.7% of those in the placebo group (P<0.001 for both comparisons
with placebo). Fibrosis improvement by at least one stage with no worsening of
the NAFLD activity score was achieved in 24.2% of the patients in the 80-mg resmetirom
group and 25.9% of those in the 100-mg resmetirom group, as compared with 14.2%
of those in the placebo group (P<0.001 for both comparisons with placebo).'
---
# Model Card for Model longluu/Clinical-NER-MedMentions-GatorTronBase
The model is an NER LLM algorithm that can classify each word in a text into different clinical categories.
## Model Details
### Model Description
The base pretrained model is GatorTron-base which was trained on billions of words in various clinical texts (https://huggingface.co/UFNLP/gatortron-base).
Then using the MedMentions dataset (https://arxiv.org/pdf/1902.09476v1.pdf), I fine-tuned the model for NER task in which the model can classify each word in a text into different clinical categories.
The category system is a simplified version of UMLS concept system and consists of 21 categories:
"['Living Beings', 'Virus']", "['Living Beings', 'Bacterium']", "['Anatomy', 'Anatomical Structure']", "['Anatomy', 'Body System']", "['Anatomy', 'Body Substance']", "['Disorders', 'Finding']", "['Disorders', 'Injury or Poisoning']", "['Phenomena', 'Biologic Function']", "['Procedures', 'Health Care Activity']", "['Procedures', 'Research Activity']", "['Devices', 'Medical Device']", "['Concepts & Ideas', 'Spatial Concept']", "['Occupations', 'Biomedical Occupation or Discipline']", "['Organizations', 'Organization']", "['Living Beings', 'Professional or Occupational Group']", "['Living Beings', 'Population Group']", "['Chemicals & Drugs', 'Chemical']", "['Objects', 'Food']", "['Concepts & Ideas', 'Intellectual Product']", "['Physiology', 'Clinical Attribute']", "['Living Beings', 'Eukaryote']", 'None'
### Model Sources [optional]
The github code associated with the model can be found here: https://github.com/longluu/LLM-NER-clinical-text.
## Training Details
### Training Data
The MedMentions dataset contain 4,392 abstracts released in PubMed®1 between January 2016 and January 2017. The abstracts were manually annotated for biomedical concepts. Details are provided in https://arxiv.org/pdf/1902.09476v1.pdf and data is in https://github.com/chanzuckerberg/MedMentions.
#### Training Hyperparameters
The hyperparameters are --batch_size 4
--num_train_epochs 5
--learning_rate 5e-5
--weight_decay 0.01
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
The model was trained and validated on train and validation sets. Then it was tested on a separate test set.
Note that some concepts in the test set were not available in the train and validatin sets.
#### Metrics
Here we use several metrics for classification tasks including macro-average F1, precision, recall and Matthew correlation.
### Results
{'f1': 0.6271402249699903,
'precision': 0.6691625224055963,
'recall': 0.6085333637974402,
'matthews_correlation': 0.720898121696139}
## Model Card Contact
Feel free to reach out to me at [email protected] if you have any question or suggestion.
|
[
"MEDMENTIONS"
] |
croissantllm/CroissantCool-v0.2
|
croissantllm
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"legal",
"code",
"text-generation-inference",
"art",
"conversational",
"fr",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:uonlp/CulturaX",
"dataset:pg19",
"dataset:bigcode/starcoderdata",
"arxiv:2402.00786",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-03-26T09:46:52Z |
2024-04-25T09:14:02+00:00
| 27 | 4 |
---
datasets:
- cerebras/SlimPajama-627B
- uonlp/CulturaX
- pg19
- bigcode/starcoderdata
language:
- fr
- en
license: mit
pipeline_tag: text-generation
tags:
- legal
- code
- text-generation-inference
- art
---
# CroissantCool (190k steps + Cooldown)
This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Cooldown phase on non-templated instruction data.
It is the strongest "base" model available on various benchmarks.
Compared to croissantllm/CroissantCool-v0.1, the tokenizer is slightly changed to force solo "\n" tokenization.
To play with the final model, we recommend using the Chat version: https://huggingface.co/croissantllm/CroissantLLMChat-v0.1.
## Abstract
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.
To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.
To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.
This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.
## Citation
Our work can be cited as:
```bibtex
@misc{faysse2024croissantllm,
title={CroissantLLM: A Truly Bilingual French-English Language Model},
author={Manuel Faysse and Patrick Fernandes and Nuno M. Guerreiro and António Loison and Duarte M. Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro H. Martins and Antoni Bigata Casademunt and François Yvon and André F. T. Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2402.00786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Usage
This model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies. It's cooldown phase however enables it to function quite well without few-shots as well.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "croissantllm/CroissantCool-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
inputs = tokenizer("I am so tired I could sleep right now. -> Je suis si fatigué que je pourrais m'endormir maintenant.\nHe is heading to the market. -> Il va au marché.\nWe are running on the beach. ->", return_tensors="pt").to(model.device)
tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60, temperature=0.5)
print(tokenizer.decode(tokens[0]))
# remove bos token
inputs = tokenizer("Capitales: France -> Paris, Italie -> Rome, Allemagne -> Berlin, Espagne ->", return_tensors="pt", add_special_tokens=True).to(model.device)
tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60)
print(tokenizer.decode(tokens[0]))
```
|
[
"CRAFT"
] |
ikim-uk-essen/GBERT-BioM-Translation-large
|
ikim-uk-essen
|
fill-mask
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:2404.05694",
"base_model:deepset/gbert-base",
"base_model:finetune:deepset/gbert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-04-10T07:28:24Z |
2024-04-10T08:02:30+00:00
| 27 | 0 |
---
base_model: deepset/gbert-base
license: mit
---
# GBERT-BioM-Translation-large
This model is a medically continuously pre-trained version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large).
## Training data
The model was trained on German PubMed abstracts, translated English PubMed abstracts, and translated MIMIC-III reports.
| Dataset | Tokens | Documents |
|------------|----------|-----------|
| German PubMed | 5M | 16K |
| PubMed | 1,700M | 21M |
| MIMIC-III | 695M | 24M |
| **Total** | **2,400M** | **45M** |
## Evaluation
| Model | CLEF eHealth 2019 | | | RadQA | | GraSCCo | | | BRONCO150 | | | GGPONC 2.0 | | |
|------------------------------|-------------------|------|------|-------|------|---------|------|------|-----------|------|------|------------|------|------|
| | F1 | P | R | F1 | EM | F1 | P | R | F1 | P | R | F1 | P | R |
| [GBERT-base](https://huggingface.co/deepset/gbert-base) | .816 | .818 | .815 | .794 | .707 | .642 | .617 | .676 | .833 | .818 | .849 | .770 | .761 | .780 |
| [GBERT-large](https://huggingface.co/deepset/gbert-large) | .832 | .802 | .865 | .809 | .718 | .647 | .617 | .680 | .835 | .820 | .852 | .772 | .758 | .786 |
| GBERT-BioM-Translation-base | .825 | .851 | .801 | .808 | .716 | .661 | .642 | .681 | .842 | .824 | .861 | .780 | .766 | .794 |
| **GBERT-BioM-Translation-large** | .833 | .860 | .807 | .811 | .714 | .692 | .677 | .707 | .844 | .825 | .864 | .786 | .779 | .793 |
## Publication
```bibtex
@misc{idrissiyaghir2024comprehensive,
title={Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding},
author={Ahmad Idrissi-Yaghir and Amin Dada and Henning Schäfer and Kamyar Arzideh and Giulia Baldini and Jan Trienes and Max Hasin and Jeanette Bewersdorff and Cynthia S. Schmidt and Marie Bauer and Kaleb E. Smith and Jiang Bian and Yonghui Wu and Jörg Schlötterer and Torsten Zesch and Peter A. Horn and Christin Seifert and Felix Nensa and Jens Kleesiek and Christoph M. Friedrich},
year={2024},
eprint={2404.05694},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"BRONCO150",
"GRASCCO"
] |
RichardErkhov/EleutherAI_-_gpt-neo-2.7B-8bits
|
RichardErkhov
|
text-generation
|
[
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:2101.00027",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | 2024-04-17T09:41:56Z |
2024-04-23T06:51:14+00:00
| 27 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gpt-neo-2.7B - bnb 8bits
- Model creator: https://huggingface.co/EleutherAI/
- Original model: https://huggingface.co/EleutherAI/gpt-neo-2.7B/
Original model description:
---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: mit
datasets:
- EleutherAI/pile
---
# GPT-Neo 2.7B
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.
## Training procedure
This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
## Intended Use and Limitations
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
>>> generator("EleutherAI has", do_sample=True, min_length=50)
[{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}]
```
### Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Eval results
All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM).
### Linguistic Reasoning
| Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag |
| ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- |
| GPT-Neo 1.3B | 0.7527 | 6.159 | 13.10 | 7.498 | 57.23% | 55.01% | 38.66% |
| GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% |
| **GPT-Neo 2.7B** | **0.7165** | **5.646** | **11.39** | **5.626** | **62.22%** | **56.50%** | **42.73%** |
| GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% |
### Physical and Scientific Reasoning
| Model and Size | MathQA | PubMedQA | Piqa |
| ---------------- | ---------- | ---------- | ----------- |
| GPT-Neo 1.3B | 24.05% | 54.40% | 71.11% |
| GPT-2 1.5B | 23.64% | 58.33% | 70.78% |
| **GPT-Neo 2.7B** | **24.72%** | **57.54%** | **72.14%** |
| GPT-3 Ada | 24.29% | 52.80% | 68.88% |
### Down-Stream Applications
TBD
### BibTeX entry and citation info
To cite this model, use
```bibtex
@software{gpt-neo,
author = {Black, Sid and
Leo, Gao and
Wang, Phil and
Leahy, Connor and
Biderman, Stella},
title = {{GPT-Neo: Large Scale Autoregressive Language
Modeling with Mesh-Tensorflow}},
month = mar,
year = 2021,
note = {{If you use this software, please cite it using
these metadata.}},
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.5297715},
url = {https://doi.org/10.5281/zenodo.5297715}
}
@article{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
|
[
"PUBMEDQA"
] |
SorawitChok/SeaLLM-7B-v2.5-AWQ
|
SorawitChok
|
text-generation
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"multilingual",
"sea",
"conversational",
"en",
"zh",
"vi",
"id",
"th",
"ms",
"km",
"lo",
"my",
"tl",
"arxiv:2312.00738",
"arxiv:2306.05179",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | 2024-05-23T04:03:48Z |
2024-05-24T02:33:56+00:00
| 27 | 2 |
---
language:
- en
- zh
- vi
- id
- th
- ms
- km
- lo
- my
- tl
license: other
license_name: seallms
license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE
tags:
- multilingual
- sea
---
<p align="center">
<img src="seal_logo.png" width="200" />
</p>
# *SeaLLM-7B-v2.5* - Large Language Models for Southeast Asia
<h1 style="color: #ff3860">**This repository is the modification of the SeaLLMs/SeaLLM-7B-v2.5**</h1>
## We offer a SeaLLM-7B-v2.5-AWQ which is a 4-bit AWQ quantization version of the SeaLLMs/SeaLLM-7B-v2.5 (compatible with vLLM)
<p align="center">
<a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a>
<a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 Tech Memo</a>
<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 DEMO</a>
<a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a>
<a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a>
</p>
🔥<span style="color: #ff3860">[HOT]</span> SeaLLMs project now has a dedicated website - [damo-nlp-sg.github.io/SeaLLMs](https://damo-nlp-sg.github.io/SeaLLMs/)
We introduce [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5), the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since [SeaLLM-13B](https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat), with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc.
### Highlights
* [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5) outperforms GPT-3.5 and achieves 7B SOTA on most multilingual knowledge benchmarks for SEA languages (MMLU, M3Exam & VMLU).
* It achieves 79.0 and 34.9 on GSM8K and MATH, surpassing GPT-3.5 in MATH.
### Release and DEMO
- DEMO:
- [SeaLLMs/SeaLLM-7B-v2.5](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B-v2.5).
- [SeaLLMs/SeaLLM-7B | SeaLMMM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B) - Experimental multimodal SeaLLM.
- Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf).
- Model weights:
- [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5).
- [SeaLLM-7B-v2.5-GGUF](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF).
- Run locally:
- [LM-studio](https://lmstudio.ai/):
- [SeaLLM-7B-v2.5-q4_0-chatml](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF/blob/main/seallm-7b-v2.5-chatml.Q4_K_M.gguf) with ChatML template (`<eos>` token changed to `<|im_end|>`)
- [SeaLLM-7B-v2.5-q4_0](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF/blob/main/seallm-7b-v2.5.Q4_K_M.gguf) - must use SeaLLM-7B-v2.5 chat format.
- [MLX for Apple Silicon](https://github.com/ml-explore/mlx): [SeaLLMs/SeaLLM-7B-v2.5-mlx-quantized](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-mlx-quantized)
- Previous models:
- [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2)
- [SeaLLM-7B-v1](https://huggingface.co/SeaLLMs/SeaLLM-7B-v1)
<blockquote style="color:red">
<p><strong style="color: red">Terms of Use and License</strong>:
By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b/edit/main/LICENSE" target="_blank" rel="noopener">SeaLLMs Terms Of Use</a>.
</blockquote>
> **Disclaimer**:
> We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation.
> Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations.
> In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.
> The logo was generated by DALL-E 3.
### What's new since SeaLLM-7B-v2?
* SeaLLM-7B-v2.5 was built on top of Gemma-7b, and underwent large scale SFT and carefully designed alignment.
## Evaluation
### Multilingual World Knowledge
We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi.
| Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e
|-----| ----- | --- | -- | ----- | ---- | --- | --- | --- |
| GPT-3.5 | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41
| Vistral-7B-chat | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27
| Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25
| SailorLM | Multi | 52.72 | 59.76 | 67.74 | 50.14 | --- | 39.53 | 37.73
| SeaLLM-7B-v2 | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52
| SeaLLM-7B-v2.5 | Multi | 64.05 | 76.87 | 62.54 | 63.11 | 53.30 | 48.64 | 46.86
### Zero-shot CoT Multilingual Math Reasoning
<!--
[SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves with **78.5** score on the GSM8K with zero-shot CoT reasoning, making it the **state of the art** in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with **28.4** vs 18.1 scores.
-->
| Model | GSM8K<br>en | MATH<br>en | GSM8K<br>zh | MATH<br>zh | GSM8K<br>vi | MATH<br>vi | GSM8K<br>id | MATH<br>id | GSM8K<br>th | MATH<br>th
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1
| Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6.0
| Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | |
| Qwen1.5-7B-chat | 56.8 | 15.3 | 40.0 | 2.7 | 37.7 | 9 | 36.9 | 7.7 | 21.9 | 4.7
| SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4
| SeaLLM-7B-v2.5 | 78.5 | 34.9 | 51.3 | 22.1 | 72.3 | 30.2 | 71.5 | 30.1 | 62.0 | 28.4
Baselines were evaluated using their respective chat-template and system prompts ([Qwen1.5-7B-chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json), [Vistral](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)).
#### Zero-shot MGSM
[SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5) also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Thai.
| Model | MGSM-Zh | MGSM-Th
|-----| ----- | ---
| ChatGPT (reported) | 61.2 | 47.2
| Qwen-14B-chat | 59.6 | 28
| SeaLLM-7B-v2 | **64.8** | 62.4
| SeaLLM-7B-v2.5 | 58.0 | **64.8**
### Sea-Bench
### Usage
**IMPORTANT NOTICE for using the model**
* `<bos>` must be at start of prompt, ff your code's tokenizer does not prepend `<bos>` by default, you MUST prepend <bos> into the prompt yourself, otherwise, it would not work!
* Repitition penalty (e.g: in llama.cpp, ollama, LM-studio) must be set to **1** , otherwise will lead to degeneration!
#### Instruction format
```python
# ! WARNING, if your code's tokenizer does not prepend <bos> by default,
# You MUST prepend <bos> into the prompt yourself, otherwise, it would not work!
prompt = """<|im_start|>system
You are a helpful assistant.<eos>
<|im_start|>user
Hello world<eos>
<|im_start|>assistant
Hi there, how can I help?<eos>"""
# <|im_start|> is not a special token.
# Transformers chat_template should be consistent with vLLM format below.
# ! ENSURE 1 and only 1 bos `<bos>` at the beginning of sequence
print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt)))
"""
```
#### Using transformers's chat_template
Install the latest transformers (>4.40)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
# use bfloat16 to ensure the best performance.
model = AutoModelForCausalLM.from_pretrained("SorawitChok/SeaLLM-7B-v2.5-AWQ", torch_dtype=torch.bfloat16, device_map=device)
tokenizer = AutoTokenizer.from_pretrained("SorawitChok/SeaLLM-7B-v2.5-AWQ")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello world"},
{"role": "assistant", "content": "Hi there, how can I help you today?"},
{"role": "user", "content": "Explain general relativity in details."}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
print(tokenizer.convert_ids_to_tokens(encodeds[0]))
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
#### Using vLLM
```python
from vllm import LLM, SamplingParams
TURN_TEMPLATE = "<|im_start|>{role}\n{content}<eos>\n"
TURN_PREFIX = "<|im_start|>{role}\n"
def seallm_chat_convo_format(conversations, add_assistant_prefix: bool, system_prompt=None):
# conversations: list of dict with key `role` and `content` (openai format)
if conversations[0]['role'] != 'system' and system_prompt is not None:
conversations = [{"role": "system", "content": system_prompt}] + conversations
text = ''
for turn_id, turn in enumerate(conversations):
prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content'])
text += prompt
if add_assistant_prefix:
prompt = TURN_PREFIX.format(role='assistant')
text += prompt
return text
sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['<eos>', '<|im_start|>'])
llm = LLM("SorawitChok/SeaLLM-7B-v2.5-AWQ", quantization="AWQ")
message = [
{"role": "user", "content": "Explain general relativity in details."}
]
prompt = seallm_chat_convo_format(message, True)
gen = llm.generate(prompt, sampling_params)
print(gen[0].outputs[0].text)
```
## Acknowledgement to Our Linguists
We would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety.
## Citation
If you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: [[email protected]](mailto:[email protected])
**Author list and order will change!**
* `*` and `^` are equal contributions.
```
@article{damonlpsg2023seallm,
author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Weiwen Xu, Hou Pong Chan,
Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang,
Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang,
Chaoqun Liu, Hang Zhang, Lidong Bing},
title = {SeaLLMs - Large Language Models for Southeast Asia},
year = 2023,
Eprint = {arXiv:2312.00738},
}
```
|
[
"CHIA"
] |
fblgit/UNA-ThePitbull-21.4B-v2
|
fblgit
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"UNA",
"juanako",
"conversational",
"dataset:jondurbin/py-dpo-v0.1",
"dataset:Replete-AI/code_bagel_hermes-2.5",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:afl-3.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-28T10:49:15Z |
2024-08-13T15:09:26+00:00
| 27 | 16 |
---
datasets:
- jondurbin/py-dpo-v0.1
- Replete-AI/code_bagel_hermes-2.5
- mlabonne/orpo-dpo-mix-40k
library_name: transformers
license: afl-3.0
tags:
- UNA
- juanako
model-index:
- name: UNA-ThePitbull-21.4B-v2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 77.73
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 91.79
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.25
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 78.24
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 87.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 37.9
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 46.79
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 9.59
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 6.94
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 6.42
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 27.95
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
---
# UNA-ThePitbull 21.4B v2
Introducing the best LLM in the industry. Nearly as good as a 70B, just a 21.4B based on saltlux/luxia-21.4b-alignment-v1.0
This model has not been poisoned to score high and be useless. We release him becaues its the real deal of EQ & IQ all together in a crazy powerful smart and conversational model.
Quant Versions available at [bartowski/UNA-ThePitbull-21.4B-v2-GGUF](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF)
## Difference V1 vs V2
On V2 we implemented a different UNA strategy and covered partially the MLP's and Attention Layers.
We also performed further SFT over V1 and further DPO over V1 and we'll release some of those soon as well.
### Changes
1. SFT over V1 with `Replete-AI/code_bagel_hermes-2.5` at 1.0e-4 till 5.0e-5 for 1 epoch
2. DPO with: 1.0e-4 to min_lr 5.0e-5 for 1 epoch
* `mlabonne/orpo-dpo-mix-40k`
* `jondurbin/py-dpo-v0.1`
# Evaluations
## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-ThePitbull-21.4B-v2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |77.82|
|AI2 Reasoning Challenge (25-Shot)|77.73|
|HellaSwag (10-Shot) |91.79|
|MMLU (5-Shot) |68.25|
|TruthfulQA (0-shot) |78.24|
|Winogrande (5-shot) |87.37|
|GSM8k (5-shot) |63.53|
Can only be compared with its non-una base model: the original luxia-21.4b and ThePitbull-v1
## UNA v2 (VLLM) Evaluations:
```
vllm (pretrained=/data/tools/mergekit/una-thepitbull-v5,dtype=bfloat16,gpu_memory_utilization=0.8,max_model_len=2048,data_parallel_size=2,tensor_parallel_size=4), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7695|± |0.0116|+
| | |flexible-extract| 5|exact_match|0.7695|± |0.0116|+
|hellaswag | 1|none | 10|acc |0.8110|± |0.0039|
| | |none | 10|acc_norm |0.9169|± |0.0028|+
|winogrande | 1|none | 5|acc |0.8777|± |0.0092|+
|mmlu |N/A |none | 0|acc |0.6427|± |0.0038|-
|arc_challenge | 1|none | 25|acc |0.7713|± |0.0123|
| | |none | 25|acc_norm |0.7875|± |0.0120|+
|truthfulqa_mc2| 2|none | 0|acc |0.7824|± |0.0135|-
|mathqa | 1|none | 0|acc |0.4037|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|+
|pubmedqa | 1|none | 0|acc |0.7260|± | 0.020|+
|boolq | 2|none | 0|acc |0.8602|± |0.0061|+
```
## UNA v1 (VLLM) Evaluations
```
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7566|± |0.0118|
| | |flexible-extract| 5|exact_match|0.7582|± |0.0118|
|hellaswag | 1|none | 10|acc |0.8168|± |0.0039|
| | |none | 10|acc_norm |0.9188|± |0.0027|
|winogrande | 1|none | 5|acc |0.8635|± |0.0097|
|mmlu | N/A|none | 0|acc |0.6444|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7747|± |0.0122|
| | |none | 25|acc_norm |0.7850|± |0.0120|
|truthfulqa_mc2| 2|none | 0|acc |0.7902|± |0.0134|
|mathqa | 1|none | 0|acc |0.4030|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6860|± |0.0208|
|boolq | 2|none | 0|acc |0.8401|± |0.0064|
```
## Original (VLLM) Evaluations
```
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7528|± |0.0119|
| | |flexible-extract| 5|exact_match|0.7521|± |0.0119|
|hellaswag | 1|none | 10|acc |0.8117|± |0.0039|
| | |none | 10|acc_norm |0.9167|± |0.0028|
|winogrande | 1|none | 5|acc |0.8682|± |0.0095|
|mmlu | N/A|none | 0|acc |0.6448|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7688|± |0.0123|
| | |none | 25|acc_norm |0.7730|± |0.0122|
|truthfulqa_mc2| 2|none | 0|acc |0.7895|± |0.0133|
|mathqa | 1|none | 0|acc |0.4000|± | 0.009|
| | |none | 0|acc_norm |0.4003|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6680|± |0.0211|
|boolq | 2|none | 0|acc |0.8346|± |0.0065|
```
## Citations
* mlabonne
* jondurbin & Replete-AI
* bartowski
* saltlux
If you use UNA models dont forget to cite:
```
@misc{unathepitbull21b,
title={ThePitbull: Uniform Neural Alignment},
author={Xavier Murias},
year={2024},
publisher = {Juanako.AI},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/fblgit/UNA-ThePitbull-21.4-v1}},
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-ThePitbull-21.4B-v2)
| Metric |Value|
|-------------------|----:|
|Avg. |22.60|
|IFEval (0-Shot) |37.90|
|BBH (3-Shot) |46.79|
|MATH Lvl 5 (4-Shot)| 9.59|
|GPQA (0-shot) | 6.94|
|MuSR (0-shot) | 6.42|
|MMLU-PRO (5-shot) |27.95|
|
[
"PUBMEDQA"
] |
tomaarsen/jina-clip-v1-st-remote
|
tomaarsen
|
feature-extraction
|
[
"transformers",
"pytorch",
"jina_clip",
"feature-extraction",
"sentence-similarity",
"mteb",
"clip",
"vision",
"transformers.js",
"custom_code",
"en",
"arxiv:2405.20204",
"license:apache-2.0",
"region:us"
] | 2024-06-21T14:32:13Z |
2024-09-06T10:33:35+00:00
| 27 | 1 |
---
language: en
library_name: transformers
license: apache-2.0
tags:
- feature-extraction
- sentence-similarity
- mteb
- clip
- vision
- transformers.js
inference: false
---
> [!WARNING]
> This is a testing repository to experiment with new functionality. Refer to [jinaai/jina-clip-v1](https://huggingface.co/jinaai/jina-clip-v1) for the original model.
<br><br>
<p align="center">
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>
<p align="center">
<b>The embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="center">
<b>Jina CLIP: your CLIP model is also your text retriever!</b>
</p>
## Intended Usage & Model Info
`jina-clip-v1` is a state-of-the-art English **multimodal (text-image) embedding model**.
Traditional text embedding models, such as [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en), excel in text-to-text retrieval but incapable of cross-modal tasks. Models like [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) effectively align image and text embeddings but are not optimized for text-to-text retrieval due to their training methodologies and context limitations.
`jina-clip-v1` bridges this gap by offering robust performance in both domains.
Its text component matches the retrieval efficiency of `jina-embeddings-v2-base-en`, while its overall architecture sets a new benchmark for cross-modal retrieval.
This dual capability makes it an excellent tool for multimodal retrieval-augmented generation (MuRAG) applications, enabling seamless text-to-text and text-to-image searches within a single model.
## Data & Parameters
[Check out our paper](https://arxiv.org/abs/2405.20204)
## Usage
1. The easiest way to starting using jina-clip-v1-en is to use Jina AI's [Embeddings API](https://jina.ai/embeddings/).
2. Alternatively, you can use Jina CLIP directly via transformers package.
```python
!pip install transformers einops timm pillow
from transformers import AutoModel
# Initialize the model
model = AutoModel.from_pretrained('jinaai/jina-clip-v1', trust_remote_code=True)
# New meaningful sentences
sentences = ['A blue cat', 'A red cat']
# Public image URLs
image_urls = [
'https://i.pinimg.com/600x315/21/48/7e/21487e8e0970dd366dafaed6ab25d8d8.jpg',
'https://i.pinimg.com/736x/c9/f2/3e/c9f23e212529f13f19bad5602d84b78b.jpg'
]
# Encode text and images
text_embeddings = model.encode_text(sentences)
image_embeddings = model.encode_image(image_urls) # also accepts PIL.image, local filenames, dataURI
# Compute similarities
print(text_embeddings[0] @ text_embeddings[1].T) # text embedding similarity
print(text_embeddings[0] @ image_embeddings[0].T) # text-image cross-modal similarity
print(text_embeddings[0] @ image_embeddings[1].T) # text-image cross-modal similarity
print(text_embeddings[1] @ image_embeddings[0].T) # text-image cross-modal similarity
print(text_embeddings[1] @ image_embeddings[1].T)# text-image cross-modal similarity
```
3. JavaScript developers can use Jina CLIP via the [Transformers.js](https://huggingface.co/docs/transformers.js) library. Note that to use this model, you need to install Transformers.js [v3](https://github.com/xenova/transformers.js/tree/v3) from source using `npm install xenova/transformers.js#v3`.
```js
import { AutoTokenizer, CLIPTextModelWithProjection, AutoProcessor, CLIPVisionModelWithProjection, RawImage, cos_sim } from '@xenova/transformers';
// Load tokenizer and text model
const tokenizer = await AutoTokenizer.from_pretrained('jinaai/jina-clip-v1');
const text_model = await CLIPTextModelWithProjection.from_pretrained('jinaai/jina-clip-v1');
// Load processor and vision model
const processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch32');
const vision_model = await CLIPVisionModelWithProjection.from_pretrained('jinaai/jina-clip-v1');
// Run tokenization
const texts = ['A blue cat', 'A red cat'];
const text_inputs = tokenizer(texts, { padding: true, truncation: true });
// Compute text embeddings
const { text_embeds } = await text_model(text_inputs);
// Read images and run processor
const urls = [
'https://i.pinimg.com/600x315/21/48/7e/21487e8e0970dd366dafaed6ab25d8d8.jpg',
'https://i.pinimg.com/736x/c9/f2/3e/c9f23e212529f13f19bad5602d84b78b.jpg'
];
const image = await Promise.all(urls.map(url => RawImage.read(url)));
const image_inputs = await processor(image);
// Compute vision embeddings
const { image_embeds } = await vision_model(image_inputs);
// Compute similarities
console.log(cos_sim(text_embeds[0].data, text_embeds[1].data)) // text embedding similarity
console.log(cos_sim(text_embeds[0].data, image_embeds[0].data)) // text-image cross-modal similarity
console.log(cos_sim(text_embeds[0].data, image_embeds[1].data)) // text-image cross-modal similarity
console.log(cos_sim(text_embeds[1].data, image_embeds[0].data)) // text-image cross-modal similarity
console.log(cos_sim(text_embeds[1].data, image_embeds[1].data)) // text-image cross-modal similarity
```
## Performance
### Text-Image Retrieval
| Name | Flickr Image Retr. R@1 | Flickr Image Retr. R@5 | Flickr Text Retr. R@1 | Flickr Text Retr. R@5 |
|------------------|-------------------------|-------------------------|-----------------------|-----------------------|
| ViT-B-32 | 0.597 | 0.8398 | 0.781 | 0.938 |
| ViT-B-16 | 0.6216 | 0.8572 | 0.822 | 0.966 |
| jina-clip | 0.6748 | 0.8902 | 0.811 | 0.965 |
| Name | MSCOCO Image Retr. R@1 | MSCOCO Image Retr. R@5 | MSCOCO Text Retr. R@1 | MSCOCO Text Retr. R@5 |
|------------------|-------------------------|-------------------------|-----------------------|-----------------------|
| ViT-B-32 | 0.342 | 0.6001 | 0.5234 | 0.7634 |
| ViT-B-16 | 0.3309 | 0.5842 | 0.5242 | 0.767 |
| jina-clip | 0.4111 | 0.6644 | 0.5544 | 0.7904 |
### Text-Text Retrieval
| Name | STS12 | STS15 | STS17 | STS13 | STS14 | STS16 | STS22 | STSBenchmark | SummEval |
|-----------------------|--------|--------|--------|--------|--------|--------|--------|--------------|----------|
| jina-embeddings-v2 | 0.7427 | 0.8755 | 0.8888 | 0.833 | 0.7917 | 0.836 | 0.6346 | 0.8404 | 0.3056 |
| jina-clip | 0.7352 | 0.8746 | 0.8976 | 0.8323 | 0.7868 | 0.8377 | 0.6583 | 0.8493 | 0.3048 |
| Name | ArguAna | FiQA2018 | NFCorpus | Quora | SCIDOCS | SciFact | TRECCOVID |
|--------------------|---------|----------|----------|-------|---------|---------|-----------|
| jina-embeddings-v2 | 0.4418 | 0.4158 | 0.3245 | 0.882 | 0.1986 | 0.6668 | 0.6591 |
| jina-clip | 0.4933 | 0.3827 | 0.3352 | 0.8789| 0.2024 | 0.6734 | 0.7161 |
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find `jina-clip-v1` useful in your research, please cite the following paper:
```bibtex
@misc{2405.20204,
Author = {Andreas Koukounas and Georgios Mastrapas and Michael Günther and Bo Wang and Scott Martens and Isabelle Mohr and Saba Sturua and Mohammad Kalim Akram and Joan Fontanals Martínez and Saahil Ognawala and Susana Guzman and Maximilian Werk and Nan Wang and Han Xiao},
Title = {Jina CLIP: Your CLIP Model Is Also Your Text Retriever},
Year = {2024},
Eprint = {arXiv:2405.20204},
}
```
## FAQ
### I encounter this problem, what should I do?
```
ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_clip.JinaCLIPConfig'> and you passed <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_cli.JinaCLIPConfig'>. Fix one of those so they match!
```
There was a bug in Transformers library between 4.40.x to 4.41.1. You can update transformers to >4.41.2 or <=4.40.0
### Given one query, how can I merge its text-text and text-image cosine similarity?
Our emperical study shows that text-text cosine similarity is normally larger than text-image cosine similarity!
If you want to merge two scores, we recommended 2 ways:
1. weighted average of text-text sim and text-image sim:
```python
combined_scores = sim(text, text) + lambda * sim(text, image) # optimal lambda depends on your dataset, but in general lambda=2 can be a good choice.
```
2. apply z-score normalization before merging scores:
```python
# pseudo code
query_document_mean = np.mean(cos_sim_text_texts)
query_document_std = np.std(cos_sim_text_texts)
text_image_mean = np.mean(cos_sim_text_images)
text_image_std = np.std(cos_sim_text_images)
query_document_sim_normalized = (cos_sim_query_documents - query_document_mean) / query_document_std
text_image_sim_normalized = (cos_sim_text_images - text_image_mean) / text_image_std
```
|
[
"SCIFACT"
] |
OpenDFM/SciDFM-MoE-A5.6B-v1.0
|
OpenDFM
|
text-generation
|
[
"transformers",
"safetensors",
"SciDFM",
"text-generation",
"AI4S",
"MoE",
"custom_code",
"en",
"zh",
"arxiv:2409.18412",
"license:agpl-3.0",
"autotrain_compatible",
"region:us"
] | 2024-06-26T03:49:32Z |
2024-11-05T02:22:21+00:00
| 27 | 1 |
---
language:
- en
- zh
license: agpl-3.0
tags:
- AI4S
- MoE
---
# SciDFM: Dialogue Foundation Model for Science
SciDFM is the pioneering open-sourced dialogue foundation model tailored for science, which integrates a mixture-of-experts architecture into a transformer-based framework, aiming at enhancing its sophisticated scientific reasoning and understanding capabilities. SciDFM achieves strong performance on general scientific benchmarks such as SciEval and SciQ, and it reachs a SOTA performance on domain-specific benchmark among models of similar size.
## News
* **2024-06-28** The parameter of SciDFM-MoE-A5.6B-v1.0 is open-soursed! Technical report is coming soon.
## Model Details
SciDFM is based on a transformer architecture, and follows modifications of Llama, i.e. RMSNorm, RoPE and SwiGLU. SciDFM use the same hyper-parameters of OpenLLaMa-3B. And in order to better model knowledge of different disciplines, we replace the feed-forward block with Mixture-of-Expert (MoE) layers.
## Training Details
SciDFM is pre-trained on a large corpus containing ~300B science tokens and ~270B general tokens for two epochs, resulting in about 1.1T tokens consuming. And we further fine-tune SciDFM using ~9.3M instruction-following samples for 5 epochs to improve the performances on the downstream benchmarks.
## Usage Details
### Local Inference
To load and run SciDFM locally, here is an example:
```python
import torch
from transformers import LlamaTokenizer, AutoModelForCausalLM
model_name_or_id = "OpenDFM/SciDFM-MoE-A5.6B-v1.0"
tokenizer = LlamaTokenizer.from_pretrained(model_name_or_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
chat_template = "<|user|>:{instruction}<|assistant|>:"
input_text = "What is Mixture-of-Experts (MoE) in computer science?"
input_text = chat_template.format(instruction=input_text)
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
do_sample=True,
top_k=20,
top_p=0.9,
temperature=0.9,
max_new_tokens=1024,
eos_token_id=tokenizer.eos_token_id
)
outputs = model.generate(**inputs, generation_config=generation_config)
generated_text = tokenizer.decode(outputs, skip_special_tokens=True)[0][len(input_text):]
print(generated_text.strip())
```
### SMILES preprocess
When there involves SMILES notation in your input, we recommend to preprocess the SMILES with the `rdkit` package to canonicalize the SMILES. Here is an example:
```python
from rdkit import Chem
def canonicalize_smiles(smiles):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return None
return Chem.MolToSmiles(mol, isomericSmiles=True, kekuleSmiles=False)
```
or directly:
```python
from rdkit import Chem
def canonicalize_smiles(smiles):
return Chem.CanonSmiles(smiles, useChiral=True)
```
### Special Tokens preprocess
If there is SMILES expression in your input, please first process it with the following function:
```python
import sentencepiece as spm
smiles_model = spm.SentencePieceProcessor(model_file="smiles.model")
def convert_smiles(smiles_str):
pieces = smiles_model.encode_as_pieces(smiles_str)[1:]
smiles = "".join([f"[ChemDFM_Start_SMILES_Unit]{piece}[ChemDFM_End_SMILES_Unit]" for piece in pieces])
return smiles
convert_smiles("C(C(=O)O)N")
```
And if there is protein sequece in your input, please first process it with the following function:
```python
def convert_protein(p_str):
res = [f"<<protein>>{s}" for s in p_str]
return "".join(res)
convert_protein("MIRLGAPQTL")
```
## Evaluation
We briefly compare SciDFM-MoE-A5.6B-v1.0 with similar-sized instruction-tuned LLMs on scientific evaluation benchmarks. The results are shown below:
| Model | SciEval | SciQ | ARC\_c | ARC\_e | GSM8K | MATH | MedQA | MMCQA | PMQA | Avg |
|--------------------|---------|-------|--------|--------|-------|-------|-------|-------|-------|-------|
| LLaMa2-7B | 27.06 | 57.00 | 36.43 | 46.59 | 3.94 | 3.96 | 26.32 | 29.84 | 66.80 | 32.95 |
| Galactica-6.7B | 46.28 | 74.20 | 44.28 | 61.83 | 2.80 | 6.32 | 30.48 | 36.46 | 48.80 | 38.91 |
| LLaMa2-13B | 33.88 | 78.10 | 56.66 | 72.35 | 22.82 | 3.90 | 32.68 | 34.28 | 77.80 | 45.45 |
| ChatGLM2-6B | 54.25 | 75.80 | 57.08 | 73.57 | 25.09 | 7.18 | 27.42 | 34.21 | 60.40 | 45.94 |
| Galactica-30B | 54.24 | 83.10 | 57.85 | 75.04 | 13.65 | 8.66 | 37.71 | 48.43 | 58.80 | 48.35 |
| LLaMa3-8B | 59.70 | 90.00 | 71.16 | 84.05 | 5.91 | 7.00 | 48.78 | 52.74 | 26.60 | 49.59 |
| ChatGLM3-6B | 51.13 | 77.60 | 60.84 | 75.97 | 60.27 | 23.52 | 24.59 | 31.39 | 51.80 | 50.53 |
| SciGLM-6B | 61.22 | 88.70 | 77.47 | 86.57 | 42.23 | 16.40 | 42.81 | 44.94 | 73.60 | 59.12 |
| SciDFM | 62.48 | 88.00 | 64.76 | 81.48 | 59.14 | 27.28 | 44.54 | 53.10 | 78.00 | 61.56 |
| ChatGLM3-6B-base | 60.34 | 89.00 | 78.58 | 87.37 | 59.82 | 22.64 | 42.73 | 45.14 | 74.40 | 61.96 |
| Llama3-8B-Instruct | 64.91 | 91.60 | 76.45 | 87.33 | 76.57 | 26.26 | 56.48 | 59.31 | 72.00 | 67.44 |
## Citation
```
@article{sun2024scidfm,
title={SciDFM: A Large Language Model with Mixture-of-Experts for Science},
author={Sun, Liangtai and Luo, Danyu and Ma, Da and Zhao, Zihan and Chen, Baocai and Shen, Zhennan and Zhu, Su and Chen, Lu and Chen, Xin and Yu, Kai},
journal={arXiv preprint arXiv:2409.18412},
year={2024}
}
```
|
[
"MEDQA",
"SCIQ"
] |
RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf
|
RichardErkhov
| null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-07-17T08:16:58Z |
2024-07-17T17:05:10+00:00
| 27 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Collaiborator-MEDLLM-Llama-3-8B-v2 - GGUF
- Model creator: https://huggingface.co/collaiborateorg/
- Original model: https://huggingface.co/collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q2_K.gguf) | Q2_K | 2.96GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K.gguf) | Q3_K | 3.74GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K.gguf) | Q4_K | 4.58GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K.gguf) | Q5_K | 5.34GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q6_K.gguf) | Q6_K | 6.14GB |
| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
license: llama3
library_name: transformers
tags:
- generated_from_trainer
- Healthcare & Lifesciences
- BioMed
- Medical
- CollAIborate
base_model: meta-llama/Meta-Llama-3-8B-Instruct
thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
model-index:
- name: Collaiborator-MEDLLM-Llama-3-8B-v2
results: []
datasets:
- collaiborateorg/BioMedData
---
# Collaiborator-MEDLLM-Llama-3-8B-v2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on our custom "BioMedData" dataset.
## Model details
Model Name: Collaiborator-MEDLLM-Llama-3-8b-v2
Base Model: Llama-3-8B-Instruct
Parameter Count: 8 billion
Training Data: Custom high-quality biomedical dataset
Number of Entries in Dataset: 500,000+
Dataset Composition: The dataset comprises both synthetic and manually curated samples, ensuring a diverse and comprehensive coverage of biomedical knowledge.
## Model description
Collaiborator-MEDLLM-Llama-3-8b-v2 is a specialized large language model designed for biomedical applications. It is finetuned from the Llama-3-8B-Instruct model using a custom dataset containing over 500,000 diverse entries. These entries include a mix of synthetic and manually curated data, ensuring high quality and broad coverage of biomedical topics.
The model is trained to understand and generate text related to various biomedical fields, making it a valuable tool for researchers, clinicians, and other professionals in the biomedical domain.
## Evaluation Metrics
Collaiborator-MEDLLM-Llama-3-8b-v2 outperforms many of the leading LLMs and find below its metrics evaluated using the Eleuther AI Language Model Evaluation Harness framework against the tasks medmcqa, medqa_4options, mmlu_anatomy, mmlu_clinical_knowledge, mmlu_college_biology, mmlu_college_medicine, mmlu_medical_genetics, mmlu_professional_medicine and pubmedqa
## Benchmark Results
## Quick Demo
<video controls autoplay src="https://hf.fast360.xyz/production/uploads/653f5b93cd52f288490edc83/piGRPwvcBTLmcgExL89zp.mp4"></video>
## Intended uses & limitations
Collaiborator-MEDLLM-Llama-3-8b-v2 is intended for a wide range of applications within the biomedical field, including:
1. Research Support: Assisting researchers in literature review and data extraction from biomedical texts.
2. Clinical Decision Support: Providing information to support clinical decision-making processes.
3. Educational Tool: Serving as a resource for medical students and professionals seeking to expand their knowledge base.
## Limitations and Ethical Considerations
While Collaiborator-MEDLLM-Llama-3-8b-v2 performs well in various biomedical NLP tasks, users should be aware of the following limitations:
Biases: The model may inherit biases present in the training data. Efforts have been made to curate a balanced dataset, but some biases may persist.
Accuracy: The model's responses are based on patterns in the data it has seen and may not always be accurate or up-to-date. Users should verify critical information from reliable sources.
Ethical Use: The model should be used responsibly, particularly in clinical settings where the stakes are high. It should complement, not replace, professional judgment and expertise.
## Training and evaluation
Collaiborator-MEDLLM-Llama-3-8b-v2 was trained using NVIDIA A40 GPU's, which provides the computational power necessary for handling large-scale data and model parameters efficiently. Rigorous evaluation protocols have been implemented to benchmark its performance against similar models, ensuring its robustness and reliability in real-world applications.
## How to use
import transformers
import torch
model_id = "collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v2"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"},
{"role": "user", "content": "I'm a 35-year-old male and for the past few months, I've been experiencing fatigue, increased sensitivity to cold, and dry, itchy skin. What is the diagnosis here?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
### Contact Information
For further information, inquiries, or issues related to Biomed-LLM, please contact:
Email: [email protected]
Website: https://www.collaiborate.com
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.11.0
- Transformers 4.40.2
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
### Citation
If you use Collaiborator-MEDLLM-Llama-3-8b in your research or applications, please cite it as follows:
@misc{Collaiborator_MEDLLM,
author = Collaiborator,
title = {Collaiborator-MEDLLM-Llama-3-8b: A High-Performance Biomedical Language Model},
year = {2024},
howpublished = {https://huggingface.co/collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v2},
}
|
[
"MEDQA",
"PUBMEDQA"
] |
RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf
|
RichardErkhov
| null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-08-05T08:24:37Z |
2024-08-05T17:55:54+00:00
| 27 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Viet-Sailor-4B-Instruct - GGUF
- Model creator: https://huggingface.co/5CD-AI/
- Original model: https://huggingface.co/5CD-AI/Viet-Sailor-4B-Instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Viet-Sailor-4B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q2_K.gguf) | Q2_K | 1.51GB |
| [Viet-Sailor-4B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.IQ3_XS.gguf) | IQ3_XS | 1.66GB |
| [Viet-Sailor-4B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.IQ3_S.gguf) | IQ3_S | 1.73GB |
| [Viet-Sailor-4B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q3_K_S.gguf) | Q3_K_S | 1.73GB |
| [Viet-Sailor-4B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.IQ3_M.gguf) | IQ3_M | 1.81GB |
| [Viet-Sailor-4B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q3_K.gguf) | Q3_K | 1.89GB |
| [Viet-Sailor-4B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q3_K_M.gguf) | Q3_K_M | 1.89GB |
| [Viet-Sailor-4B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q3_K_L.gguf) | Q3_K_L | 2.03GB |
| [Viet-Sailor-4B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.IQ4_XS.gguf) | IQ4_XS | 2.08GB |
| [Viet-Sailor-4B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q4_0.gguf) | Q4_0 | 2.17GB |
| [Viet-Sailor-4B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.IQ4_NL.gguf) | IQ4_NL | 2.18GB |
| [Viet-Sailor-4B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q4_K_S.gguf) | Q4_K_S | 2.18GB |
| [Viet-Sailor-4B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q4_K.gguf) | Q4_K | 2.29GB |
| [Viet-Sailor-4B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q4_K_M.gguf) | Q4_K_M | 2.29GB |
| [Viet-Sailor-4B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q4_1.gguf) | Q4_1 | 2.38GB |
| [Viet-Sailor-4B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q5_0.gguf) | Q5_0 | 2.58GB |
| [Viet-Sailor-4B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q5_K_S.gguf) | Q5_K_S | 2.58GB |
| [Viet-Sailor-4B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q5_K.gguf) | Q5_K | 2.64GB |
| [Viet-Sailor-4B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q5_K_M.gguf) | Q5_K_M | 2.64GB |
| [Viet-Sailor-4B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q5_1.gguf) | Q5_1 | 2.79GB |
| [Viet-Sailor-4B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q6_K.gguf) | Q6_K | 3.03GB |
| [Viet-Sailor-4B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/5CD-AI_-_Viet-Sailor-4B-Instruct-gguf/blob/main/Viet-Sailor-4B-Instruct.Q8_0.gguf) | Q8_0 | 3.92GB |
Original model description:
---
library_name: transformers
datasets:
- vilm/OpenOrca-Viet
- bkai-foundation-models/vi-alpaca
- 5CD-AI/Vietnamese-395k-meta-math-MetaMathQA-gg-translated
- 5CD-AI/Vietnamese-Locutusque-function-calling-chatml-gg-translated
- 5CD-AI/Vietnamese-1m5-kaist-CoT-gg-translated-unrefined
- 5CD-AI/Vietnamese-mabryCodes-tiny-cot-alpaca-gg-translated
- 5CD-AI/Vietnamese-nampdn-ai-tiny-webtext-gg-translated
- 5CD-AI/Vietnamese-Openorca-Multiplechoice-gg-translated
- 5CD-AI/Vietnamese-Multi-turn-Chat-Alpaca
- 5CD-AI/Visocial-Instructions
---
<div align="center">
<img src="viet-sailor-4b-logo.png" width="700"/>
</div>
## Viet-Sailor-4B-Instruct Version 2
[Sailor-4B](https://huggingface.co/sail/Sailor-4B) 🌊 is a model that has undergone additional pre-training on datasets from Southeast Asian countries 🌏, resulting in impressive performance 🚀. Building on this foundation, we have fine-tuned the model with a specific focus on Vietnamese language capabilities 🇻🇳.
This is a good choice for models under 7B parameters on the VMLU leaderboard 📊.
## Training details 📚
The **1,000,000 samples** fine-tuning training dataset was meticulously sampled in part from the following datasets:
- [OpenOrca-Viet 🐋](https://huggingface.co/datasets/vilm/OpenOrca-Viet)
- [vi-alpaca 🦙](https://huggingface.co/datasets/bkai-foundation-models/vi-alpaca)
- [Vietnamese-395k-meta-math-MetaMathQA-gg-translated 📐](https://huggingface.co/datasets/5CD-AI/Vietnamese-395k-meta-math-MetaMathQA-gg-translated)
- [Vietnamese-Locutusque-function-calling-chatml-gg-translated 🧠](https://huggingface.co/datasets/5CD-AI/Vietnamese-Locutusque-function-calling-chatml-gg-translated)
- [Vietnamese-1m5-kaist-CoT-gg-translated-unrefined 🧵](https://huggingface.co/datasets/5CD-AI/Vietnamese-1m5-kaist-CoT-gg-translated-unrefined)
- [Vietnamese-mabryCodes-tiny-cot-alpaca-gg-translated 🧠](https://huggingface.co/datasets/5CD-AI/Vietnamese-mabryCodes-tiny-cot-alpaca-gg-translated)
- [Vietnamese-nampdn-ai-tiny-webtext-gg-translated 🧠](https://huggingface.co/datasets/5CD-AI/Vietnamese-nampdn-ai-tiny-webtext-gg-translated)
- [Vietnamese-Openorca-Multiplechoice-gg-translated 🐋](https://huggingface.co/datasets/5CD-AI/Vietnamese-Openorca-Multiplechoice-gg-translated)
- [Vietnamese-Multi-turn-Chat-Alpaca 💬](https://huggingface.co/datasets/5CD-AI/Vietnamese-Multi-turn-Chat-Alpaca)
- [Visocial-Instructions 💬](https://huggingface.co/datasets/5CD-AI/Visocial-Instructions)
## Benchmarks 📈
We evaluated our model using the VMLU leaderboard:
<div align="center">
<img src="vmlu.png" width="1000"/>
</div>
| # | MODEL | CREATOR | BASE MODEL | STEM | SOCIAL SCIENCE | HUMANITIES | OTHERS | AVG |
|----|----------------------|------------------|---------------------|-------|----------------|------------|--------|-------|
| 1 | VNPTAI.IO-14B | VNPT AI | Qwen1.5-14B-Chat | 51.64 | 61.75 | 58.09 | 54.51 | 55.83 |
| 2 | SeaLLM-7B-v2.5 | DAMO Academy | llama-2-7b | 49.35 | 60.66 | 55.95 | 49.05 | 53.30 |
| 3 | MI4uLLM-7B-Chat | ML4U | Mistral-7B-v0.1 | 44.72 | 58.69 | 56.86 | 52.36 | 52.08 |
| 4 | Vistral-7B-Chat | UONLP x Ontocord | Mistral-7B-v0.1 | 43.32 | 57.02 | 55.12 | 48.01 | 50.07 |
| 5 | SDSRV-7B-chat | SDSRV teams | Mistral-7B-v0.1 | 36.29 | 60.55 | 55.95 | 49.05 | 48.55 |
| 6 | Arcanic Cono 1.5 | Arcanic AI | Mistral-7B-v0.1 | 45.11 | 52.44 | 51.97 | 45.36 | 47.45 |
| 7 | SeaLLM-7b-v2 | DAMO Academy | llama-2-7b | 39.95 | 52.02 | 49.38 | 45.27 | 45.79 |
| <b>8 | <b>Viet-Sailor-4B-Instruct | <b>5CD-AI | <b>Sailor-4B | <b>36.83 | <b>49.13 | <b>48.18 | <b>41.76 | <b>43.24</b> |
| 9 | bloomz-7b1 | BigScience | Bloom-7b1 | 32.63 | 45.73 | 41.85 | 39.89 | 38.87 |
| 10 | T-llama-7b | FPTU HCM | llama-2-7b | 32.2 | 43.15 | 40.31 | 36.57 | 37.28 |
| 11 | vbd-llama2-7b-50b... | Vin BigData | llama-2-7b | 31.45 | 40.34 | 40.24 | 39.62 | 36.98 |
| .. | ...... | ..... | .... | ... | ... | ... | ... | ... |
## Examples
Function-calling
```
<|im_start|>system
Bạn là một trợ lý hữu ích với quyền truy cập vào các chức năng sau. Sử dụng chúng nếu cần thiết -{
"name": "search_movies",
"description": "Search for movies based on title or genre",
"parameters": {
"type": "object",
"properties": {
"title": {
"type": "string",
"description": "The title of the movie"
},
"genre": {
"type": "string",
"description": "The genre of the movie"
}
}
}
}<|im_end|>
<|im_start|>question
Bạn có thể tìm cho tôi một bộ phim có tựa đề "Inception" được không?<|im_end|>
<|im_start|>answer
<function-call> {"name": "search_movies", "arguments": {"title": "Inception"}}<|im_end|>
<|im_start|>question
<function-response> {"movies": [{"title": "Inception", "genre": "Science Fiction", "director": "Christopher Nolan", "release_year": 2010}]}<|im_end|>
<|im_start|>answer
Tôi đã tìm thấy một bộ phim có tựa đề "Inception". Đó là một bộ phim Khoa học viễn tưởng của đạo diễn Christopher Nolan và được phát hành vào năm 2010.<|im_end|>
```
Sentiment analysis
```
<|im_start|>system
Bạn là trợ lý AI hữu ích<|im_end|>
<|im_start|>question
Bạn là trợ lý AI. Hãy phân loại cảm xúc của bình luận sau theo từng khía cạnh của chủ thể.
Bình luận: về cách ăn mặc của cô thì quá 😡 😡 . nhưng về cách nói chuyện thì rất 😍.<|im_end|>
<|im_start|>answer
Cảm xúc của bình luận này có thể được phân loại như sau:
- Cảm xúc tiêu cực đối với cách ăn mặc của cô ấy: 😡
- Cảm xúc tích cực đối với cách nói chuyện của cô ấy: 😍<|im_end|>
```
Simple math
```
<|im_start|>system
Bạn là một trợ lý AI hữu ích. Một nhà toán học.<|im_end|>
<|im_start|>question
Trên sân có 2 con vịt và 9 con bò hỏi tổng cộng có mấy cái chân ?<|im_end|>
<|im_start|>answer
Tổng số chân trên sân là 11. Mỗi con vịt có 2 chân và mỗi con bò có 4 chân, vì vậy 2 con vịt có tổng cộng 2*2 = 4 chân và 9 con bò có tổng cộng 9*4 = 36 chân. Do đó, tổng số chân trên sân là 4+36 = 40.<|im_end|>
```
Writing
```
<|im_start|>system
Bạn là một trợ lý AI hữu ích, trung thực, trung lập. Hãy trả lời một cách đầy đủ <|im_end|>
<|im_start|>question
Người dùng sẽ cung cấp cho bạn một nhiệm vụ. Hãy thực hiện lần lượt 2 nhiệm vụ sau:
Bưới 1: Hãy mô tả ý nghĩa hình ảnh của chuỗi emoji 😡 👫 👨👩👦.
Bước 2: Hãy tạo 1 câu chuyện ngắn kết hợp đầy đủ ý nghĩa giữa chúng với nhau.<|im_end|>
<|im_start|>answer
Bước 1: Mô tả ý nghĩa hình ảnh của chuỗi emoji 😡 👫 👨👩👦.
Ý nghĩa hình ảnh của chuỗi emoji có thể được giải thích như sau:
- 😡: Biểu tượng cảm xúc này thường được sử dụng để thể hiện sự buồn bã, đau khổ hoặc thất vọng. Trong trường hợp này, nó có thể biểu thị rằng ai đó đang trải qua một số cảm xúc tiêu cực.
- 👫: Biểu tượng cảm xúc này thường được sử dụng để thể hiện tình yêu và sự quan tâm. Trong trường hợp này, nó có thể biểu thị rằng ai đó đang thể hiện tình yêu và sự quan tâm của họ đối với ai đó.
- 👨👩👦: Biểu tượng cảm xúc này thường được sử dụng để thể hiện tình yêu gia đình. Trong trường hợp này, nó có thể biểu thị rằng ai đó đang thể hiện tình yêu và sự quan tâm của họ đối với một thành viên trong gia đình.
Bước 2: Tạo một câu chuyện ngắn kết hợp các biểu tượng cảm xúc với nhau.
Câu chuyện: Trong một thị trấn nhỏ, nơi mọi người đều biết nhau, có một gia đình hạnh phúc sống trong một ngôi nhà nhỏ xinh xắn. Cha mẹ yêu thương và chăm sóc con cái của mình rất nhiều. Một ngày nọ, họ nhận được tin dữ rằng đứa con út của họ đã qua đời vì một căn bệnh hiếm gặp. Cả gia đình đều đau buồn, đặc biệt là cha mẹ của đứa trẻ. Họ cố gắng vượt qua nỗi đau bằng cách dành thời gian bên nhau, ôm nhau và chia sẻ những ký ức đẹp đẽ về đứa con yêu dấu của họ.<|im_end|>
```
## Requirements
The code of Sailor has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`.
## Quickstart
Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = "cuda"
model = AutoModelForCausalLM.from_pretrained(
'5CD-AI/Viet-Sailor-4B-Instruct',
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained('5CD-AI/Viet-Sailor-4B-Instruct')
system_prompt = 'Bạn là một trợ lý AI hữu ích. Bạn rất cẩn thận và trung lập. Hãy trả lời trung thực và đầy đủ. Chỉ trả lời khi bạn biết thông tin chính xác.'
prompt = """Hãy phân loại cảm xúc của bình luận sau theo từng khía cạnh của chủ thể.
Bình luận: về cách ăn mặc của cô thì quá 😡 😡 . nhưng về cách nói chuyện thì rất 😍."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "question", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
input_ids = model_inputs.input_ids.to(device)
generated_ids = model.generate(
input_ids,
max_new_tokens=256,
num_beams=3,
top_k=20,
top_p= 0.5,
temperature=0.9,
repetition_penalty = 1.5,
length_penalty = 1.0,
do_sample=True
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
|
[
"CHIA"
] |
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