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timaeus/pubmed_abstracts_max_loss_delta_ablation_l1h2
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timaeus
|
2025-03-18T09:07:54Z
| 14
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[] |
2025-03-18T09:07:50Z
| 0
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---
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rweics5cs7/exo7-realworld-db-combined-deg-enhanced-text-v3
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rweics5cs7
|
2025-09-27T11:57:32Z
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2025-09-27T11:57:18Z
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haorandai/Dec_Banana_Orange_50samples_0constraint
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haorandai
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2024-12-23T07:43:30Z
| 17
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2024-12-23T07:43:28Z
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math-ai/TemplateGSM
|
math-ai
|
2025-03-08T00:44:59Z
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[
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2024-02-01T13:22:35Z
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---
language:
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license: cc-by-4.0
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pretty_name: TemplateGSM
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tags:
- mathematical-reasoning
- reasoning
- finetuning
- pretraining
- llm
---
# Training and Evaluating Language Models with Template-based Data Generation
## TemplateGSM Dataset
The TemplateGSM dataset is a large-scale collection of over 7 million (with potential for unlimited generation) grade school math problems, each paired with both code-based and natural language solutions. Designed to advance mathematical reasoning in language models, this dataset presents a diverse range of challenges to assess and improve model capabilities in solving mathematical problems. The dataset is built from 7473 problem templates, providing a rich and varied benchmark for question-answering tasks within the mathematical domain.
Project Homepage: https://templatemath.github.io
Arxiv link: https://arxiv.org/abs/2411.18104
Paper Link: https://arxiv.org/pdf/2411.18104
GitHub Homepage: https://github.com/iiis-ai/TemplateMath
## Objective
TemplateGSM aims to serve as a benchmark for:
- Assessing language models' proficiency in mathematical reasoning and symbolic computation.
- Training and fine-tuning language models to improve their performance in generating accurate and logically sound mathematical solutions to provided questions.
- Encouraging the development of models capable of understanding and solving complex mathematical problems, thereby bridging the gap between natural language processing and mathematical reasoning.
## Dataset Structure
TemplateGSM is organized into configurations based on the volume of problems generated from each template:
### Configurations
- **templategsm-1000-1k**: Contains 1000 * 1k problems generated from each of the 1000 templates (template 0000-0999), totaling over 1 million individual problems.
- **templategsm-2000-1k**: Contains 2000 * 1k problems generated from each of the 2000 templates (template 0000-1999), culminating in a dataset with 2 million problems.
- **templategsm-4000-1k**: Contains 4000 * 1k problems generated from each of the 4000 templates (template 0000-3999), culminating in a dataset with 4 million problems.
- **templategsm-7473-1k**: Contains 7473 * 1k problems generated from each of the 7473 templates (template 0000-7472), culminating in a dataset with over 7.47 million problems.
### Data Fields
Each problem in the dataset includes the following fields:
- `problem`: The problem statement.
- `solution_code`: A commented solution code that solves the problem in Python.
- `result`: The final answer to the problem.
- `solution_wocode`: The solution in natural language without the use of code.
- `source`: This field indicates the template is constructed from which data source and which seed is used in problem generation, e.g., `gsm8k-train-round2-seed42`.
- `template_id`: This field indicates the template from which the problem was generated, e.g., `0`.
- `problem_id`: An index unique to each problem within its template.
## How to Use
```XML
configs:
- config_name: templategsm-7473-1k
data_files:
- split: train
path:
- data/1k/0000-0999/*.jsonl
- data/1k/1000-1999/*.jsonl
- data/1k/2000-3999/*.jsonl
- data/1k/4000-7472/*.jsonl
default: true
- config_name: templategsm-4000-1k
data_files:
- split: train
path:
- data/1k/0000-0999/*.jsonl
- data/1k/1000-1999/*.jsonl
- data/1k/2000-3999/*.jsonl
- config_name: templategsm-2000-1k
data_files:
- split: train
path:
- data/1k/0000-0999/*.jsonl
- data/1k/1000-1999/*.jsonl
- config_name: templategsm-1000-1k
data_files:
- split: train
path:
- data/1k/0000-0999/*.jsonl
```
To access the TemplateGSM dataset, you can use the Huggingface `datasets` library:
```python
from datasets import load_dataset
# Load a specific configuration
dataset = load_dataset("math-ai/TemplateGSM", "templategsm-7473-1k") # or any valid config_name
```
## License
This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
## Citation
If you utilize Template-based Data Generation (TDG) or the TemplateGSM dataset in your research or application, please consider citing it:
```bibtex
@article{zhang2024training,
title={Training and Evaluating Language Models with Template-based Data Generation},
author={Zhang, Yifan and Luo, Yifan and Yuan, Yang and Yao, Andrew Chi-Chih},
journal={arXiv preprint arXiv:2411.18104},
year={2024},
}
```
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SeppeV/joke_gen_mistral_online_dpo_bert_judge_jo
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SeppeV
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2025-01-06T19:13:34Z
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[] |
2025-01-06T19:13:32Z
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mlfoundations-dev/oh_v1.3_evol_instruct_x.5
|
mlfoundations-dev
|
2024-12-08T18:53:28Z
| 15
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[] |
2024-12-08T18:52:26Z
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---
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DavidCBaines/ebible_corpus
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DavidCBaines
|
2025-05-10T07:51:15Z
| 47
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"language:rgu",
"language:rkb",
"language:rmc",
"language:rmn",
"language:rmy",
"language:ron",
"language:roo",
"language:rop",
"language:row",
"language:rro",
"language:ruf",
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"language:rwo",
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"language:urb",
"language:urd",
"language:uri",
"language:uro",
"language:urt",
"language:urw",
"language:usa",
"language:usp",
"language:uvh",
"language:uvl",
"language:vid",
"language:vie",
"language:viv",
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"language:zpu",
"language:zpv",
"language:zpz",
"language:zsr",
"language:ztq",
"language:zty",
"language:zyp",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"bible",
"translation",
"multilingual",
"religious-text",
"parallel-corpus",
"low-resource-languages"
] |
[] |
2025-05-01T19:57:37Z
| 0
|
---
pretty_name: eBible_corpus
tags:
- bible
- translation
- multilingual
- religious-text
- parallel-corpus
- low-resource-languages
license: unknown
language:
- aai
- aak
- aau
- aaz
- abc
- abt
- abx
- aby
- acf
- acr
- acu
- adz
- aer
- aey
- agd
- agg
- agm
- agn
- agr
- agt
- agu
- ahr
- aia
- aii
- ake
- alp
- alq
- alw
- aly
- ame
- amf
- amh
- amk
- amm
- amn
- amo
- amp
- amr
- amu
- amx
- anh
- anv
- aoi
- aoj
- aom
- aon
- apb
- ape
- apn
- apr
- apu
- apw
- apz
- arb
- are
- arl
- arn
- arp
- asj
- asm
- aso
- ata
- atb
- atd
- atg
- att
- auc
- aui
- auy
- avt
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- awk
- awx
- azb
- azg
- azz
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- bht
- bhu
- big
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- bkq
- bkw
- bkx
- bla
- blw
- blz
- bmh
- bmk
- bmr
- bmu
- bnp
- boa
- bod
- boj
- bon
- box
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- bpx
- bqc
- bqp
- bre
- bsj
- bsn
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- bus
- bvd
- bvr
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- bxh
- byr
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- cao
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- cbk
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- cgc
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- chd
- chf
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- chq
- chz
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- cjv
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- con
- cop
- cot
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- cpb
- cpc
- cpu
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- crx
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- cui
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- cut
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- cwe
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- daa
- dad
- dah
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- deu
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- dgr
- dgz
- dhg
- dif
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- dji
- djk
- djr
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- dop
- dov
- dso
- dwr
- dww
- dwy
- ebk
- ekk
- eko
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- ena
- eng
- enq
- epo
- eri
- ese
- esk
- etr
- ewe
- faa
- fad
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- far
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- fin
- for
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- fue
- fuf
- fuh
- gah
- gai
- gam
- gaq
- gaw
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- gdn
- gdr
- geb
- gfk
- ghs
- glk
- gmv
- gng
- gnn
- gnw
- gof
- grc
- gub
- guh
- gui
- guj
- gul
- gum
- gun
- guo
- gup
- gux
- gvc
- gvf
- gvn
- gvs
- gwi
- gym
- gyr
- hat
- hau
- haw
- hbo
- hch
- heb
- heg
- hin
- hix
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- hmo
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- hus
- huu
- huv
- hvn
- ian
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- ign
- ikk
- ikw
- ilo
- imo
- inb
- ind
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- iou
- ipi
- isl
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- ita
- iws
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- jac
- jae
- jao
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- jid
- jiv
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- jpn
- juy
- jvn
- kan
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- kgp
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- kij
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- kiw
- kiz
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- kjs
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- knv
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- kpg
- kpj
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- kqc
- kqf
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- kqw
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- kyq
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- lit
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- mgw
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- mil
- mio
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- mlp
- mmo
- mmx
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- mni
- mop
- mox
- mph
- mpj
- mpm
- mpp
- mps
- mpt
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- mqb
- mqj
- msb
- msc
- msk
- msm
- msy
- mti
- mto
- mux
- muy
- mva
- mvn
- mwc
- mwe
- mwf
- mwp
- mxb
- mxm
- mxp
- mxq
- mxt
- mya
- myk
- myu
- myw
- myy
- mzz
- nab
- naf
- nag
- nak
- nal
- nas
- nbq
- nca
- nce
- nch
- ncj
- ncl
- ncu
- nde
- ndg
- ndj
- nfa
- ngp
- ngu
- nhe
- nhg
- nhi
- nho
- nhr
- nhu
- nhw
- nhy
- nif
- nii
- nin
- nko
- nld
- nlg
- nmw
- nna
- nnq
- noa
- nob
- nop
- not
- nou
- npi
- npl
- nrz
- nsn
- nss
- ntj
- ntp
- ntu
- nuq
- nuy
- nvm
- nwi
- nya
- nys
- obo
- okv
- omb
- omw
- ong
- ons
- ood
- opm
- ory
- ote
- otm
- otn
- otq
- ots
- pab
- pad
- pah
- pan
- pao
- peg
- pes
- pib
- pio
- pir
- piu
- pjt
- pls
- plu
- pma
- poe
- poh
- poi
- pol
- pon
- por
- poy
- ppo
- prf
- pri
- ptp
- ptu
- pwg
- qub
- quc
- quf
- quh
- qul
- qup
- qvc
- qve
- qvh
- qvm
- qvn
- qvs
- qvw
- qvz
- qwh
- qxh
- qxn
- qxo
- rai
- reg
- rgu
- rkb
- rmc
- rmn
- rmy
- ron
- roo
- rop
- row
- rro
- ruf
- rug
- rus
- rwo
- sab
- san
- sbe
- sbk
- sbs
- sby
- sch
- seh
- sey
- sgb
- sgz
- shj
- shp
- sim
- sja
- sll
- smk
- sna
- snc
- snn
- snp
- snx
- sny
- som
- soq
- soy
- spa
- spl
- spm
- spp
- sps
- spy
- sri
- srm
- srn
- srp
- srq
- ssd
- ssg
- ssx
- stp
- stw
- sua
- sue
- sus
- suz
- swe
- swh
- swp
- sxb
- tac
- taj
- tam
- tav
- taw
- tbc
- tbf
- tbg
- tbl
- tbo
- tbz
- tca
- tcs
- tcz
- tdt
- tdx
- tee
- tel
- ter
- tet
- tew
- tfr
- tgj
- tgk
- tgl
- tgo
- tgp
- tha
- tif
- tim
- tiw
- tiy
- tke
- tkr
- tku
- tlf
- tmd
- tna
- tnc
- tnk
- tnn
- tnp
- toc
- tod
- tof
- toj
- ton
- too
- top
- tos
- tpa
- tpi
- tpt
- tpz
- trc
- tsn
- tsw
- ttc
- tte
- tuc
- tue
- tuf
- tuo
- tur
- tvk
- tvt
- twi
- txq
- txu
- tzj
- tzo
- ubr
- ubu
- udu
- uig
- ukr
- uli
- ulk
- unx
- upv
- ura
- urb
- urd
- uri
- uro
- urt
- urw
- usa
- usp
- uvh
- uvl
- vid
- vie
- viv
- vmy
- waj
- wal
- wap
- wat
- wbi
- wbp
- wed
- wer
- wim
- wiu
- wiv
- wmt
- wmw
- wnc
- wnu
- wol
- wos
- wrk
- wro
- wrs
- wsk
- wuv
- xav
- xbi
- xed
- xla
- xnj
- xnn
- xon
- xsi
- xtd
- xtm
- yaa
- yad
- yal
- yao
- yap
- yaq
- yby
- ycn
- yka
- yle
- yml
- yom
- yon
- yor
- yrb
- yre
- yss
- yuj
- yut
- yuw
- yva
- zaa
- zab
- zac
- zad
- zai
- zaj
- zam
- zao
- zpi
- zar
- zas
- zat
- zav
- zaw
- zca
- zga
- zia
- ziw
- zlm
- zos
- zpc
- zpl
- zpm
- zpo
- zpq
- zpu
- zpv
- zpz
- zsr
- ztq
- zty
- zyp
---
# Dataset Card for eBible Corpus
## Dataset Summary
The eBible Corpus is a large multilingual parallel corpus containing Bible translations sourced from eBible.org. It features text from many low-resource languages and 891 languages in total. This dataset is designed to facilitate research in machine translation, cross-lingual alignment, and linguistic analysis, particularly for low-resource languages.
The dataset consists of two main files:
1. `bible_corpus.parquet`: Contains the verse text aligned by a common verse reference (`book`, `chapter`, `verse`). Each column represents a specific translation identified by its `translationId`.
2. `metadata.csv`: Provides detailed metadata for each translation included in the Parquet file, such as language names, copyright information, license, and source details.
The total compressed size of the dataset files is approximately 1GB.
## Supported Tasks and Leaderboards
This dataset can be used for various NLP tasks, including:
* **Machine Translation:** Especially for low-resource language pairs.
* **Cross-Lingual Word Alignment:** Identifying corresponding words or phrases across translations.
* **Linguistic Analysis:** Studying translation strategies, language evolution, or typological features across a wide range of languages.
## Languages
The dataset covers hundreds of languages. The `metadata.csv` file contains details about each translation's language information (including `languageCode`, `languageName`, `languageNameInEnglish`).
## Dataset Structure
### Data Instances
**`bible_corpus.parquet`**
An example row looks like:
```json
{
"book": "GEN",
"chapter": 1,
"verse": 1,
"eng-engESV": "In the beginning, God created the heavens and the earth.",
"fra-frLSG": "Au commencement, Dieu créa les cieux et la terre.",
"spa-spaRV1909": "EN el principio crió Dios los cielos y la tierra.",
"...": "..."
}
|
mteb/CQADupstackAndroidRetrieval-Fa
|
mteb
|
2025-05-06T09:41:15Z
| 0
| 0
|
[
"region:us"
] |
[] |
2025-05-06T09:41:02Z
| 0
|
---
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 22610167
num_examples: 22998
download_size: 10997289
dataset_size: 22610167
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 43411
num_examples: 1696
download_size: 19480
dataset_size: 43411
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 73650
num_examples: 699
download_size: 39682
dataset_size: 73650
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: qrels
data_files:
- split: test
path: qrels/test-*
- config_name: queries
data_files:
- split: test
path: queries/test-*
---
|
gmm0820/so100_test3
|
gmm0820
|
2024-12-11T07:45:36Z
| 44
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] |
[
"robotics"
] |
2024-12-11T07:44:17Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.0",
"robot_type": "so100",
"total_episodes": 10,
"total_frames": 7481,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_7279ac59-b780-4fc7-8b5c-fc6b66284a5a
|
argilla-internal-testing
|
2024-10-28T14:20:59Z
| 19
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-28T14:20:59Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': positive
'1': negative
splits:
- name: train
num_bytes: 111
num_examples: 3
download_size: 1454
dataset_size: 111
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nhagar/fineweb2-hq_urls
|
nhagar
|
2025-05-15T14:19:03Z
| 63
| 0
|
[
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"doi:10.57967/hf/5484",
"region:us"
] |
[] |
2025-05-08T21:24:40Z
| 0
|
---
license: odc-by
---
# Dataset Card for fineweb2-hq_urls
This dataset provides the URLs and top-level domains associated with training records in [epfml/FineWeb2-HQ](https://huggingface.co/datasets/epfml/FineWeb2-HQ). It is part of a [collection of datasets](https://huggingface.co/collections/nhagar/llm-urls-neurips-681698adac0862be6c65c72b) curated to make exploring LLM training datasets more straightforward and accessible.
## Dataset Details
### Dataset Description
This dataset was created by downloading the source data, extracting URLs and top-level domains, and retaining only those record identifiers. In doing so, it allows researchers and practitioners to explore the contents of these training datasets without having to manage terabytes of raw text. You can explore the pipeline used to construct this dataset on [GitHub](https://github.com/NHagar/cc-genealogy).
- **Curated by:** [Nick Hagar](https://huggingface.co/nhagar) and [Jack Bandy](https://huggingface.co/jackbandy)
- **License:** Same as source dataset
### Dataset Sources
- **Repository:** [epfml/FineWeb2-HQ](https://huggingface.co/datasets/epfml/FineWeb2-HQ)
## Uses
This dataset is intended to allow researchers and practitioners to analyze the contents of large LLM training datasets without having to wade through terabytes of unwieldy text data.
### Direct Use
The main use case for these data is to explore the contents of LLM training datasets at scale. This might involve:
- Identifying the most-used websites
- Categorizing URLs to understand domain- or topic-level dataset composition
- Comparing URLs across datasets
- Digging into inclusion/exclusion patterns for a particular website
### Out-of-Scope Use
This dataset is not intend to replicate or replace the source data, nor is it intended to enable large-scale scraping of the URLs listed. For source text, refer to the original dataset.
## Dataset Structure
This dataset contains every record with a URL from the source dataset. It contains two columns:
- `url`: The raw URL associated with each record
- `domain`: The top-level domain for each URL, extracted with `tldextract`
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
|
ying15/mixed-cod-tokenized-v5
|
ying15
|
2025-05-08T22:03:30Z
| 0
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-08T22:03:28Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12674797.0
num_examples: 1000
download_size: 5771229
dataset_size: 12674797.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AjayP13/mt_bench_101_alpaca_eval
|
AjayP13
|
2025-09-29T18:59:46Z
| 0
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-09-29T18:47:44Z
| 0
|
---
dataset_info:
- config_name: alpaca_eval
features:
- name: dataset
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: generator
dtype: string
splits:
- name: eval
num_bytes: 149562
num_examples: 1388
download_size: 65499
dataset_size: 149562
- config_name: alpaca_eval_gpt4_baseline
features:
- name: dataset
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: generator
dtype: string
splits:
- name: eval
num_bytes: 149562
num_examples: 1388
download_size: 65499
dataset_size: 149562
configs:
- config_name: alpaca_eval
data_files:
- split: eval
path: alpaca_eval/eval-*
- config_name: alpaca_eval_gpt4_baseline
data_files:
- split: eval
path: alpaca_eval_gpt4_baseline/eval-*
---
|
joelkoch/elixir_generated
|
joelkoch
|
2025-02-06T20:22:11Z
| 18
| 0
|
[
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:text-retrieval",
"task_categories:question-answering",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"library:distilabel",
"region:us",
"synthetic",
"distilabel",
"rlaif",
"datacraft"
] |
[
"text-generation",
"text2text-generation",
"text-retrieval",
"question-answering"
] |
2025-02-06T20:22:07Z
| 0
|
---
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
- text-retrieval
- question-answering
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 372568
num_examples: 100
download_size: 152841
dataset_size: 372568
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for elixir_generated
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/joelkoch/elixir_generated/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/joelkoch/elixir_generated/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"context": "Elixir is a dynamic, functional language that runs on the Erlang VM (BEAM), providing built-in support for concurrency, distributed programming, and hot code loading.\n\nElixir\u0027s syntax is similar to Ruby, making it easier for developers familiar with Ruby to learn. \n\nElixir provides a wide range of libraries and frameworks, such as Phoenix, Plug, and Dialyxir.\n\nElixir supports both synchronous and asynchronous programming.\n\nElixir has built-in support for tasks such as compile and run Elixir code on the Erlang VM.\n\nElixir provides the GenServer module for designing and implementing server processes.\n\nThe Elixir documentation is a comprehensive resource for learning Elixir and its many libraries and frameworks.\n\nElixir has a large and active community of developers who contribute to its growth and development.\n\nThe Elixir VM (BEAM) provides crash dumps in case of errors, allowing for easier debugging.\n\nErlang\u0027s pattern matching feature is available in Elixir, allowing for concise and expressive code.\n\nElixir supports pattern matching on structured data types such as tuples, lists, and maps.\n\nElixir\u0027s pipes operator allows for data transformation chaining.\n\nPhoenix is a popular web framework for building applications in Elixir.\n\nPlug is a specification for web applications in Elixir, providing a modular and flexible way of building web applications.\n\nDialyxir is a plugin for the Elixir compiler, providing code analysis and debugging support.\n\nOTP (Open Telecom Platform) is a set of Erlang libraries, including the GenServer module, that provides a set of behaviors for implementing OTP applications.\n\nOTP applications are built using a set of design principles that emphasize fault-tolerant, distributed, and concurrent systems.\n\nOTP applications can be easily distributed across multiple nodes in a cluster.\n\nOTP applications can be started, stopped, and monitored using the name.\n\nOTP provides an interface for distributing processes and managing process supervision.\n\nOTP applications can be compiled and run on the Erlang VM.",
"question": "Is Elixir\u0027s comprehensive documentation a key factor in its adoption among developers?",
"response": "Yes, Elixir\u0027s comprehensive documentation is a key factor in its adoption among developers. The document states, \"The Elixir documentation is a comprehensive resource for learning Elixir and its many libraries and frameworks.\" This suggests that the documentation is a valuable resource for developers to learn and understand Elixir, which would likely contribute to its adoption."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("joelkoch/elixir_generated", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("joelkoch/elixir_generated")
```
</details>
|
rxc5667/3words
|
rxc5667
|
2024-11-30T22:50:25Z
| 17
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-30T22:27:27Z
| 0
|
---
dataset_info:
features:
- name: image_name
dtype: string
- name: image
dtype: image
- name: image_url
dtype: string
- name: image_path
dtype: string
- name: description
dtype: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 54534411.0
num_examples: 451
download_size: 52035970
dataset_size: 54534411.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cambridgeltl/xcopa
|
cambridgeltl
|
2024-01-04T16:55:46Z
| 2,816
| 18
|
[
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"source_datasets:extended|copa",
"language:et",
"language:ht",
"language:id",
"language:it",
"language:qu",
"language:sw",
"language:ta",
"language:th",
"language:tr",
"language:vi",
"language:zh",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[
"question-answering"
] |
2022-03-02T23:29:22Z
| 1
|
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- et
- ht
- id
- it
- qu
- sw
- ta
- th
- tr
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|copa
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: xcopa
pretty_name: XCOPA
dataset_info:
- config_name: et
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
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splits:
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num_examples: 100
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num_examples: 500
download_size: 54200
dataset_size: 68140
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features:
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num_examples: 100
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num_examples: 500
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dataset_size: 70394
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num_examples: 500
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features:
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dtype: string
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features:
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dtype: string
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dtype: string
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dtype: string
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dtype: int32
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dtype: int32
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path: ht/test-*
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path: sw/test-*
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path: translation-et/test-*
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data_files:
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path: translation-ht/test-*
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data_files:
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path: translation-sw/validation-*
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path: translation-sw/test-*
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data_files:
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path: translation-ta/test-*
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path: translation-th/validation-*
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path: translation-th/test-*
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data_files:
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path: translation-tr/validation-*
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path: translation-tr/test-*
- config_name: translation-vi
data_files:
- split: validation
path: translation-vi/validation-*
- split: test
path: translation-vi/test-*
- config_name: translation-zh
data_files:
- split: validation
path: translation-zh/validation-*
- split: test
path: translation-zh/test-*
- config_name: vi
data_files:
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path: vi/validation-*
- split: test
path: vi/test-*
- config_name: zh
data_files:
- split: validation
path: zh/validation-*
- split: test
path: zh/test-*
---
# Dataset Card for "xcopa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/cambridgeltl/xcopa](https://github.com/cambridgeltl/xcopa)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.08 MB
- **Size of the generated dataset:** 1.02 MB
- **Total amount of disk used:** 5.10 MB
### Dataset Summary
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across
languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around
the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the
creation of XCOPA and the implementation of the baselines are available in the paper.
Xcopa language et
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
- et
- ht
- id
- it
- qu
- sw
- ta
- th
- tr
- vi
- zh
## Dataset Structure
### Data Instances
#### et
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.44 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### ht
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.44 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### id
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.45 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### it
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.08 MB
- **Total amount of disk used:** 0.45 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### qu
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.08 MB
- **Total amount of disk used:** 0.45 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
### Data Fields
The data fields are the same among all splits.
#### et
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### ht
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### id
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### it
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### qu
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
### Data Splits
|name|validation|test|
|----|---------:|---:|
|et | 100| 500|
|ht | 100| 500|
|id | 100| 500|
|it | 100| 500|
|qu | 100| 500|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@article{ponti2020xcopa,
title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning},
author={Edoardo M. Ponti, Goran Glava
{s}, Olga Majewska, Qianchu Liu, Ivan Vuli'{c} and Anna Korhonen},
journal={arXiv preprint},
year={2020},
url={https://ducdauge.github.io/files/xcopa.pdf}
}
@inproceedings{roemmele2011choice,
title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
booktitle={2011 AAAI Spring Symposium Series},
year={2011},
url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF},
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
|
twei11/round_12
|
twei11
|
2025-04-10T10:32:46Z
| 16
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-24T09:16:09Z
| 0
|
---
dataset_info:
features:
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dtype: string
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dtype: string
splits:
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num_examples: 1998
download_size: 3477036
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configs:
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data_files:
- split: train
path: data/train-*
---
|
ieuniversity/nlp-ner-train
|
ieuniversity
|
2025-03-06T12:28:18Z
| 71
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-06T12:28:08Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: int64
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list:
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splits:
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num_examples: 677
download_size: 1821648
dataset_size: 3208573
configs:
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data_files:
- split: train
path: data/train-*
---
|
mlfoundations-dev/a1_science_wikipedia_biology
|
mlfoundations-dev
|
2025-04-13T01:19:16Z
| 16
| 0
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] |
[] |
2025-04-12T00:13:00Z
| 0
|
---
dataset_info:
features:
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dtype: string
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dtype: int64
- name: reasoning
dtype: string
- name: deepseek_solution
dtype: string
- name: source
dtype: string
- name: final_reasoning_trace
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 795486738
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download_size: 391172242
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configs:
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---
|
paulnguyen89/coc-captioning-dataset
|
paulnguyen89
|
2025-02-21T09:58:45Z
| 56
| 0
|
[
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"library:polars",
"region:us"
] |
[] |
2025-02-21T08:23:56Z
| 0
|
---
dataset_info:
features:
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dtype:
image:
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configs:
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---
|
000Yash000/yash-cricllama-3.2
|
000Yash000
|
2024-10-25T15:26:51Z
| 23
| 0
|
[
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-25T15:26:49Z
| 0
|
---
dataset_info:
features:
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dtype: string
splits:
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num_bytes: 66623
num_examples: 220
download_size: 26339
dataset_size: 66623
configs:
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data_files:
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path: data/train-*
---
|
mlfoundations-dev/reasoning_shard_38
|
mlfoundations-dev
|
2025-03-05T21:23:56Z
| 15
| 0
|
[
"size_categories:n<1K",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-05T21:23:55Z
| 0
|
---
dataset_info:
features:
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dtype: string
- name: answer
dtype: string
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dtype: string
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dtype: int64
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splits:
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num_bytes: 41983.24130355181
num_examples: 43
download_size: 23740
dataset_size: 41983.24130355181
configs:
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data_files:
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path: data/train-*
---
|
oopere/RetailBanking-Conversations
|
oopere
|
2025-03-30T17:30:03Z
| 72
| 1
|
[
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"doi:10.57967/hf/5533",
"region:us",
"finance",
"banking",
"synthetic",
"llm",
"dialogue",
"retail-banking",
"openai",
"wizardSdata"
] |
[
"text-generation",
"text-classification"
] |
2025-03-30T14:43:35Z
| 0
|
---
license: mit
language:
- en
pretty_name: Retail Banking Conversations
size_categories:
- 1K<n<10K
task_categories:
- text-generation
- text-classification
tags:
- finance
- banking
- synthetic
- llm
- dialogue
- retail-banking
- openai
- wizardSdata
---
# Dataset Description
RetailBanking-Conversations is a synthetic dataset designed to train and evaluate language models in the retail banking domain, it has been created using the open source library [wizardSdata](https://github.com/peremartra/WizardSData) that eable the creation of synthetic datasets in any field.
The dataset contains 320 realistic conversations, across 160 unique financial profiles and 10 key retail banking topics, between financial advisors and clients, covering 10 main categories of banking products and services with various specific subtopics.
Each conversation reflects natural interactions where clients seek information, advice, or solutions for their financial needs, and advisors provide professional guidance tailored to different client profiles
# Dataset Statistics
* Total conversations: 320
* Number of different profiles: 160
* Main topics: 10
* Average conversation length: 4-8 turns
* Language: English
# Structure and Content
The conversations are organized by main topics, including:
* **Cards** - Credit and debit cards
* **Savings & Deposits** - Savings accounts and deposits
* **Mortgages** - Mortgages and real estate financing
* **Personal Loans** - Personal loans
* **Bank Accounts** - Bank accounts
* **Investment Funds** - Investment funds
* **Insurance** - Insurance
* **Digital Banking** - Digital banking and mobile payments
* **Pension Plans** - Pension plans and retirement
* **Customer Rewards** - Loyalty programs and rewards
Each conversation is structured as a multi-turn dialogue between a customer (rol1) and a financial advisor (rol2), following different customer intents, levels of financial knowledge, and risk tolerance.
## Conversation Examples.
```json
{
"id_conversation": "c8f9f6ea-5d30-47ba-8004-459bfc4e536b",
"topic": "Investment Funds",
"sequence": 0,
"rol1": "Hello!",
"rol2": "Hello! It's great to meet you. How can I assist you today with your investment needs? Are you looking to start investing, or do you have specific goals in mind that you'd like to discuss?"
},
{
"id_conversation": "c8f9f6ea-5d30-47ba-8004-459bfc4e536b",
"topic": "Investment Funds",
"sequence": 1,
"rol1": "I'm looking to compare fund performance in the medium-term. Any advice?",
"rol2": "Of course! Comparing fund performance is a crucial step in making informed investment decisions. To get started, could you tell me a bit more about what you mean by \"medium-term\"? Are you thinking of a time frame of 3 to 5 years, or something different?\n\nAdditionally, it would be helpful to know if there are specific types of funds you're interested in, such as equity funds, bond funds, or perhaps a balanced mix. Understanding your risk tolerance and investment goals will also help me provide more tailored advice."
},
{
"id_conversation": "c8f9f6ea-5d30-47ba-8004-459bfc4e536b",
"topic": "Investment Funds",
"sequence": 2,
"rol1": "I'm thinking around 3 to 5 years. I'm open to both equity and balanced funds. I have a high risk tolerance, so I'm interested in maximizing potential returns. Any recommendations on how to analyze these funds effectively?",
"rol2": "Great! With a 3 to 5-year time horizon and a high-risk tolerance, you're in a good position to explore equity and balanced funds that can offer higher potential returns. Here are some steps you can take to effectively analyze and compare these funds:\n\n1. **Performance History**: Look at the historical performance of the funds over different periods (1 year, 3 years, 5 years). While past performance doesn't guarantee future results, it can give you an idea of how the fund has performed in various market conditions.\n\n2. **Expense Ratios**: Check the expense ratio of each fund. Lower expense ratios can help enhance your net returns over time. Remember that actively managed funds typically have higher expenses than index funds.\n\n3. **Fund Manager's Track Record**: Investigate the experience and track record of the fund manager(s). Consistent performance by the same manager over time can be a positive indicator.\n\n4. **Portfolio Composition**: Examine the fund's holdings to ensure they align with your risk tolerance and investment goals. For equity funds, look at the sectors and geographic regions they invest in. For balanced funds, see how they allocate between stocks and bonds.\n\n5. **Volatility and Risk Measures**: Review the fund's volatility (standard deviation) and risk measures (such as beta) to understand how much risk the fund has taken to achieve its returns.\n\n6. **Morningstar Ratings**: Consider looking at independent ratings from services like Morningstar, which provide evaluations based on past performance, risk, and cost.\n\n7. **Benchmark Comparison**: Compare each fund's performance against its benchmark"
},
```
# How it was created: Powered by wizardSdata.
This dataset was created using WizardSData, an open-source Python library for generating synthetic datasets using large language models.
The generation process involves:
* Custom persona profiles from the retail banking domain.
* Topic-specific prompt templates for both customer and advisor.
* LLM-powered turn-by-turn generation using gpt-4o.
* Configurable parameters such as temperature, number of questions, and models per role.
## The process used:
* GPT-4o for both client and financial advisor roles
* Diversified profiles: 160 unique profiles specifying demographic characteristics and financial needs
* Jinja2 templates: To create the prompt for user and advisor.
* Differentiated temperature parameters: 0.6 for clients (higher variability) and 0.2 for advisors (greater consistency and accuracy)
Profile Example:
```json
{
"id": 154,
"age": 29,
"gender": "Male",
"marital_status": "Single",
"country": "Italy",
"residence_area": "Urban",
"profession": "Marketing Manager",
"employment_status": "Freelance",
"financial_products": [
"Investment fund",
"Insurance",
"Pension plan"
],
"financial_goal": "Compare current vs. savings accounts",
"topic": "Bank Accounts",
"investment_horizon": "Long-term",
"risk_tolerance": "High",
"conversation_style": "Formal",
"financial_knowledge": "High"
}
```
The wizardSdata library is designed to help researchers and developers create customized conversational datasets for different domains and use cases, significantly reducing the time and effort needed to collect relevant training data.
### Conversation generation.
```python
pip install wizardsdata
import wizardsdata as wsd
# Configure parameters
wsd.set_config(
API_KEY="your_api_key",
template_client_prompt="path/to/client/template.j2",
template_advisor_prompt="path/to/advisor/template.j2",
file_profiles="path/to/profiles.json",
file_output="path/to/output_dataset.json",
model_client="gpt-4o", # or any other compatible model
model_advisor="gpt-4o",
temperature_client=0.7,
temperature_advisor=0.3
)
# Verify configuration
if wsd.is_config_valid():
# Start generation
wsd.start_generation()
```
If you want more information about how to create your own custom dataset refer to [wizardSdata repository](https://github.com/peremartra/WizardSData).
# Citation.
```
@misc{RetailBankingConversations,
author = {Pere Martra},
title = {RetailBankingConversations: A Dataset for Retail Banking Conversations},
year = {2025},
url = {https://huggingface.co/datasets/oopere/RetailBanking-Conversations},
note = {Created using the open-source WizardSData library}
}
```
|
kumarsk9/test_ksk
|
kumarsk9
|
2025-02-20T22:47:07Z
| 15
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-20T00:32:48Z
| 0
|
---
dataset_info:
features:
- name: a
dtype: int64
- name: b
dtype: float64
- name: c
dtype: string
- name: d
dtype: bool
- name: e
dtype: bool
splits:
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num_examples: 7
download_size: 1939
dataset_size: 151
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tuenguyen/logicasker
|
tuenguyen
|
2025-02-04T15:38:14Z
| 15
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-04T15:38:11Z
| 0
|
---
dataset_info:
features:
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dtype: string
- name: Prompt
dtype: string
- name: Solution
dtype: string
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num_examples: 10400
download_size: 425328
dataset_size: 2745955
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jeslev/trec_covid_toyset
|
jeslev
|
2025-01-08T13:48:57Z
| 18
| 0
|
[
"license:cc-by-sa-4.0",
"size_categories:n<1K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"medical"
] |
[] |
2025-01-07T15:50:36Z
| 0
|
---
license: cc-by-sa-4.0
dataset_info:
features:
- name: query-id
dtype: int64
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dtype: string
- name: score
dtype: int64
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num_examples: 25
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dataset_size: 58585
configs:
- config_name: default
data_files:
- split: rel
path: data/rel-*
- split: nonrel
path: data/nonrel-*
tags:
- medical
---
|
twei11/node1_round_45
|
twei11
|
2025-04-16T19:46:32Z
| 15
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-16T19:46:31Z
| 0
|
---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 7002006
num_examples: 1800
download_size: 3405924
dataset_size: 7002006
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Seungjuhan/cont_v2_0_1
|
Seungjuhan
|
2024-11-09T10:44:19Z
| 20
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-09T10:44:18Z
| 0
|
---
dataset_info:
features:
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dtype: string
- name: queries
sequence: string
- name: chain_of_facts
list:
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struct:
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- name: text
dtype: string
- name: topic
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- name: url
dtype: string
- name: text
dtype: string
- name: statement
dtype: string
- name: paraphrased_statement
dtype: string
- name: augmented_statement
dtype: string
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dtype: string
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dtype: string
- name: id
dtype: int64
splits:
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num_examples: 2499
download_size: 7567831
dataset_size: 20177502
configs:
- config_name: default
data_files:
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path: data/train-*
---
|
Nbeau/evaluation_addition_17digits_1000examples_n_plus_less_n.jsonl
|
Nbeau
|
2024-10-09T15:31:36Z
| 21
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-09T15:31:34Z
| 0
|
---
dataset_info:
features:
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dtype: string
- name: scratchpad
dtype: string
- name: result_final
dtype: string
- name: y_digits
dtype: int64
splits:
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num_examples: 1000
download_size: 445245
dataset_size: 1624082
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
DioulaD/Books
|
DioulaD
|
2025-03-05T21:33:14Z
| 24
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-05T21:33:12Z
| 0
|
---
dataset_info:
features:
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dtype: string
- name: content
dtype: string
- name: source
dtype: string
splits:
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num_bytes: 1355156
num_examples: 1151
download_size: 806978
dataset_size: 1355156
configs:
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data_files:
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path: data/train-*
---
|
andlyu/so100_indoor_val_0
|
andlyu
|
2025-03-30T00:58:10Z
| 44
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] |
[
"robotics"
] |
2025-03-30T00:56:02Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 11,
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"fps": 30,
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},
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
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6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.arm": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
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}
},
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480,
640,
3
],
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],
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}
},
"observation.images.base": {
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"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
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"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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1
],
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},
"episode_index": {
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1
],
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},
"index": {
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"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
CohenQu/CoRA_math_reasoning_benchmark_final
|
CohenQu
|
2025-05-15T05:39:39Z
| 0
| 0
|
[
"region:us"
] |
[] |
2025-05-15T03:58:03Z
| 0
|
---
dataset_info:
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---
|
lewtun/test-fast-parser-l1b-v2
|
lewtun
|
2024-11-26T15:02:12Z
| 23
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-26T14:19:45Z
| 0
|
---
dataset_info:
- config_name: None--evals
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dtype: int64
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dtype: float64
- name: acc_weighted
dtype: float64
- name: acc_maj
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download_size: 2556
dataset_size: 288
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configs:
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data_files:
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- config_name: default
data_files:
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path: data/train-*
---
|
GitBag/a_star_final_grpo_math_7_actor_aime-24_eval
|
GitBag
|
2025-05-05T09:58:50Z
| 0
| 0
|
[
"region:us"
] |
[] |
2025-05-05T09:58:43Z
| 0
|
---
dataset_info:
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---
|
h1ee/dataset-01
|
h1ee
|
2024-10-30T03:14:58Z
| 17
| 0
|
[
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-30T02:55:33Z
| 0
|
---
license: apache-2.0
dataset_info:
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num_examples: 10
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dataset_size: 1537
configs:
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data_files:
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path: data/train-*
---
|
alea-institute/kl3m-filter-data-dotgov-www.bjs.gov
|
alea-institute
|
2025-02-03T23:10:38Z
| 15
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-03T23:10:37Z
| 0
|
---
dataset_info:
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download_size: 4443
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configs:
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data_files:
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path: data/train-*
---
|
Geralt-Targaryen/Children-zh
|
Geralt-Targaryen
|
2025-05-03T11:36:21Z
| 0
| 0
|
[
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-02T08:13:47Z
| 0
|
---
license: apache-2.0
---
|
juyoung-trl/databricks-dolly-15k-ja
|
juyoung-trl
|
2024-11-11T02:30:09Z
| 18
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-08T07:49:10Z
| 0
|
---
dataset_info:
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dtype: string
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---
|
rogerscuall/pricer-data
|
rogerscuall
|
2025-05-21T22:35:38Z
| 0
| 0
|
[
"size_categories:100K<n<1M",
"format:parquet",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-21T22:32:15Z
| 0
|
---
dataset_info:
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dtype: string
- name: price
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dtype: string
splits:
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dataset_size: 323100571
configs:
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path: data/test-*
---
|
palli23/spjallromur-30s-chunks80pecentSpit
|
palli23
|
2025-06-23T14:18:12Z
| 0
| 0
|
[
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"library:mlcroissant",
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"region:us"
] |
[] |
2025-06-23T13:41:29Z
| 0
|
---
dataset_info:
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configs:
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---
|
llama-duo/llama3-3b-closedqa-eval-by-gpt4o
|
llama-duo
|
2025-04-02T06:19:25Z
| 17
| 0
|
[
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-02T02:15:13Z
| 0
|
---
dataset_info:
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configs:
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---
|
Den4ikAI/russian_dialogues_2
|
Den4ikAI
|
2024-07-05T12:03:27Z
| 147
| 12
|
[
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:ru",
"license:mit",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[
"conversational",
"text-generation",
"text2text-generation"
] |
2023-07-05T07:16:52Z
| 1
|
---
license: mit
task_categories:
- conversational
- text-generation
- text2text-generation
language:
- ru
size_categories:
- 1M<n<10M
---
### Den4ikAI/russian_dialogues_2
Датасет русских диалогов для обучения диалоговых моделей.
Количество диалогов - 1.6 миллиона
Формат датасета:
```
{
'sample': ['Привет', 'Привет', 'Как дела?']
}
```
### Citation:
```
@MISC{russian_instructions,
author = {Denis Petrov},
title = {Russian context dialogues dataset for conversational agents},
url = {https://huggingface.co/datasets/Den4ikAI/russian_dialogues_2},
year = 2023
}
```
|
davanstrien/outputs-embeddings
|
davanstrien
|
2025-02-13T14:15:08Z
| 21
| 0
|
[
"size_categories:10K<n<100K",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-13T10:17:26Z
| 0
|
---
dataset_info:
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dtype: large_string
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---
|
steveli97/my-question-answer-dataset
|
steveli97
|
2025-04-02T10:00:14Z
| 17
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-02T10:00:11Z
| 0
|
---
dataset_info:
features:
- name: question
dtype: string
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dtype: string
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---
|
datania/demanda_energia_electrica
|
datania
|
2025-03-09T00:23:27Z
| 20
| 0
|
[
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-05T18:58:01Z
| 0
|
---
license: mit
---
# demanda_energia_electrica
Este conjunto de datos ha sido producido y publicado automáticamente por [datania](https://github.com/davidgasquez/datania), una plataforma de datos abiertos moderna.
## Detalles del Conjunto de Datos
- **Número de filas:** 735431
- **Número de columnas:** 3
|
siripragadashashank/mkb
|
siripragadashashank
|
2024-01-18T11:09:02Z
| 121
| 1
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
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"annotations_creators:no-annotation",
"multilinguality:translation",
"source_datasets:original",
"language:bn",
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"language:te",
"language:ur",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"arxiv:2007.07691",
"region:us"
] |
[
"text-generation",
"fill-mask"
] |
2022-03-02T23:29:22Z
| 0
|
---
task_categories:
- text-generation
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annotations_creators:
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size_categories:
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license:
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paperswithcode_id: null
pretty_name: CVIT MKB
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- mr
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- ur
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- ml
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download_size: 52428800
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- name: translation
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- or
- ta
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- name: train
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download_size: 52428800
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features:
- name: translation
dtype:
translation:
languages:
- ta
- te
splits:
- name: train
num_bytes: 1773728
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download_size: 52428800
dataset_size: 1773728
- config_name: gu-or
features:
- name: translation
dtype:
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languages:
- gu
- or
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- name: train
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download_size: 52428800
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features:
- name: translation
dtype:
translation:
languages:
- en
- gu
splits:
- name: train
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download_size: 52428800
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features:
- name: translation
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- hi
- mr
splits:
- name: train
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download_size: 52428800
dataset_size: 1243583
- config_name: mr-ta
features:
- name: translation
dtype:
translation:
languages:
- mr
- ta
splits:
- name: train
num_bytes: 1906073
num_examples: 3175
download_size: 52428800
dataset_size: 1906073
- config_name: en-mr
features:
- name: translation
dtype:
translation:
languages:
- en
- mr
splits:
- name: train
num_bytes: 2140298
num_examples: 5867
download_size: 52428800
dataset_size: 2140298
config_names:
- bn-en
- bn-gu
- bn-hi
- bn-ml
- bn-mr
- bn-or
- bn-ta
- bn-te
- bn-ur
- en-gu
- en-hi
- en-ml
- en-mr
- en-or
- en-ta
- en-te
- en-ur
- gu-hi
- gu-ml
- gu-mr
- gu-or
- gu-ta
- gu-te
- gu-ur
- hi-ml
- hi-mr
- hi-or
- hi-ta
- hi-te
- hi-ur
- ml-mr
- ml-or
- ml-ta
- ml-te
- ml-ur
- mr-or
- mr-ta
- mr-te
- mr-ur
- or-ta
- or-te
- or-ur
- ta-te
- ta-ur
- te-ur
---
# Dataset Card for CVIT MKB
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Link](http://preon.iiit.ac.in/~jerin/bhasha/)
- **Repository:**
- **Paper:** [ARXIV](https://arxiv.org/abs/2007.07691)
- **Leaderboard:**
- **Point of Contact:** [email]([email protected])
### Dataset Summary
Indian Prime Minister's speeches - Mann Ki Baat, on All India Radio, translated into many languages.
### Supported Tasks and Leaderboards
[MORE INFORMATION NEEDED]
### Languages
Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English
## Dataset Structure
### Data Instances
[MORE INFORMATION NEEDED]
### Data Fields
- `src_tag`: `string` text in source language
- `tgt_tag`: `string` translation of source language in target language
### Data Splits
[MORE INFORMATION NEEDED]
## Dataset Creation
### Curation Rationale
[MORE INFORMATION NEEDED]
### Source Data
[MORE INFORMATION NEEDED]
#### Initial Data Collection and Normalization
[MORE INFORMATION NEEDED]
#### Who are the source language producers?
[MORE INFORMATION NEEDED]
### Annotations
#### Annotation process
[MORE INFORMATION NEEDED]
#### Who are the annotators?
[MORE INFORMATION NEEDED]
### Personal and Sensitive Information
[MORE INFORMATION NEEDED]
## Considerations for Using the Data
### Social Impact of Dataset
[MORE INFORMATION NEEDED]
### Discussion of Biases
[MORE INFORMATION NEEDED]
### Other Known Limitations
[MORE INFORMATION NEEDED]
## Additional Information
### Dataset Curators
[MORE INFORMATION NEEDED]
### Licensing Information
The datasets and pretrained models provided here are licensed under Creative Commons Attribution-ShareAlike 4.0 International License.
### Citation Information
```
@misc{siripragada2020multilingual,
title={A Multilingual Parallel Corpora Collection Effort for Indian Languages},
author={Shashank Siripragada and Jerin Philip and Vinay P. Namboodiri and C V Jawahar},
year={2020},
eprint={2007.07691},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.
|
bobertonthebuilder/zxyxxxl_batch_21
|
bobertonthebuilder
|
2025-03-20T05:51:02Z
| 16
| 0
|
[
"format:parquet",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-20T05:51:01Z
| 0
|
---
dataset_info:
features: []
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 324
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dhruv0808/indic_sentiment_analyzer
|
dhruv0808
|
2025-01-16T08:51:55Z
| 23
| 0
|
[
"language:en",
"language:hi",
"language:te",
"language:ta",
"language:kn",
"language:or",
"language:bn",
"language:gu",
"language:pa",
"language:ml",
"language:mr",
"language:as",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"synthetic",
"sentiment",
"multilingual",
"NLP"
] |
[] |
2025-01-16T05:39:47Z
| 0
|
---
license: cc-by-4.0
language:
- en
- hi
- te
- ta
- kn
- or
- bn
- gu
- pa
- ml
- mr
- as
tags:
- synthetic
- sentiment
- multilingual
- NLP
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
# Multilingual Sentiment Analysis Dataset for Indian Languages
## Overview
This repository contains a comprehensive sentiment analysis dataset covering 11 Indian languages and English. The dataset is designed to support sentiment analysis tasks across multiple domains and languages, making it valuable for developing multilingual sentiment analysis models and applications.
## Languages Covered
- English (en) - Original
- Hindi (hi)
- Telugu (te)
- Tamil (ta)
- Kannada (kn)
- Odia/Oriya (or)
- Bengali (bn)
- Gujarati (gu)
- Punjabi (pa)
- Malayalam (ml)
- Marathi (mr)
- Assamese (as)
## Dataset Composition
### Sources
1. **Generated Data**
- Created using state-of-the-art language models via Groq API:
- Gemma 2 9B IT
- LLaMA 3.1 70B Versatile
- LLaMA 3.1 8B Instant
- LLaMA 3.2 1B Preview
- LLaMA 3.2 3B Preview
- Mixtral 8x7B 32768
2. **Existing Data Integration**
- Incorporated approximately 1,000 data points per language from ai4bharat/IndicSentiment dataset
### Domain Coverage
The dataset encompasses a broad spectrum of domains, providing comprehensive coverage across various sectors and industries.
It includes sentiment-labeled text from customer feedback and market research, extending to political analysis and financial
markets. The dataset covers customer service interactions, brand monitoring efforts, and content recommendation systems. It also
incorporates data from healthcare settings, social media monitoring, and employee satisfaction surveys. The scope extends to banking
services, product reviews, and general survey analysis across different sectors. The educational sector, crisis management scenarios,
and entertainment industry are represented, along with e-commerce transactions and legal sector communications. The dataset further includes
sentiments from human resources contexts, sports analytics, and the tourism and hospitality industry, ensuring a well-rounded representation of
various professional and commercial contexts.
## Data Generation Process
1. Initial data generation in English using multiple LLMs through Groq API
2. Translation into target Indian languages
3. Integration with existing IndicSentiment dataset
## Dataset Statistics
[Include relevant statistics about your dataset:
- Total number of entries per language- 10.5K
- Sentiment classes- "Positive", "Negative" and "Neutral"]
## Usage
#### Loading the Dataset
```python
from datasets import load_dataset
ds = load_dataset("dhruv0808/indic_sentiment_analyzer", data_files="Multilingual_sentiment_analysis.csv")
```
## Citation
If you use this dataset in your research, please cite:
```
If you use this dataset in your research, please cite:
```bibtex
@misc{bhatnagar2024multilingual,
title={Multilingual Sentiment Analysis Dataset for Indian Languages},
author={Bhatnagar, Dhruv},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/datasets/dhruv0808/indic_sentiment_analyzer}},
note={A comprehensive multilingual sentiment dataset covering 12 languages and multiple domains}
}
```
For plain text citation:
```
Bhatnagar, D. (2024). Multilingual Sentiment Analysis Dataset for Indian Languages.
Available at: https://huggingface.co/datasets/dhruv0808/indic_sentiment_analyzer
```
```
## License
This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
This license means:
- ✅ You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- ❌ Restrictions:
- NonCommercial — You may not use the material for commercial purposes
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made
For more details, see the [full license text](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
```
## Acknowledgments
- Groq API for providing access to state-of-the-art language models
- AI4Bharat for the IndicSentiment dataset
## Contact
Dhruv Bhatnagar
Email: [email protected]
|
Duy1412/Grasp5
|
Duy1412
|
2025-05-16T19:30:57Z
| 0
| 0
|
[
"task_categories:robotics",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] |
[
"robotics"
] |
2025-05-16T19:29:35Z
| 0
|
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# Grasp5
**This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).**
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
|
Jerry2003/SMM_data2
|
Jerry2003
|
2025-05-30T20:48:12Z
| 70
| 0
|
[
"license:apache-2.0",
"modality:image",
"region:us"
] |
[] |
2025-05-30T13:48:21Z
| 0
|
---
license: apache-2.0
---
|
dtocre/cgr_instruct_dataset_test
|
dtocre
|
2025-04-06T04:48:06Z
| 16
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-06T04:48:00Z
| 0
|
---
dataset_info:
features:
- name: input
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 5240087
num_examples: 2208
- name: eval
num_bytes: 251818
num_examples: 112
download_size: 1658223
dataset_size: 5491905
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
---
|
T145/connections
|
T145
|
2025-01-05T19:47:17Z
| 11
| 0
|
[
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-01-05T19:38:17Z
| 0
|
---
dataset_info:
features:
- name: level
dtype: int64
- name: level_id
dtype: string
- name: category
dtype: string
- name: words
sequence: string
splits:
- name: train
num_bytes: 55055
num_examples: 711
download_size: 32798
dataset_size: 55055
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
language:
- en
pretty_name: NYT Connections Answers
size_categories:
- n<1K
---
Made with the following script using a local copy of [this website](https://word.tips/todays-nyt-connections-answers/):
```python
import re
from bs4 import BeautifulSoup
from datasets import Dataset
with open("Today’s NYT Connections Answers Jan 5, #574 - Daily Updates & Hints - Word Tips.htm", encoding="utf-8") as f:
html = f.read()
soup = BeautifulSoup(html, "html.parser")
texts = re.findall(r'"([^"]*)"', "".join(soup.find_all("script")[9]))
texts = [" ".join(text.split()).replace(" ,", ", ") for text in texts if ":" in text and (text.startswith("🟡") or text.startswith("🟢") or text.startswith("🔵") or text.startswith("🟣"))]
levels = {
"🟡": 1,
"🟢": 2,
"🔵": 3,
"🟣": 4
}
def gen():
for group in texts:
level_id = group[:1]
group = group[2:]
category, group = group.split(":")
entry = {
"level": levels[level_id],
"level_id": level_id,
"category": category,
"words": [word.strip() for word in group.split(",")]
}
#pprint(entry)
yield entry
dataset = Dataset.from_generator(gen)
dataset.push_to_hub("T145/connections")
```
|
Hoang123/structured-generation-information-extraction-vlms-openbmb-RLAIF-V-Dataset
|
Hoang123
|
2025-05-13T13:53:18Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-13T13:53:16Z
| 0
|
---
dataset_info:
features:
- name: ds_name
dtype: string
- name: image
dtype: image
- name: question
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: origin_dataset
dtype: string
- name: origin_split
dtype: string
- name: idx
dtype: string
- name: image_path
dtype: string
- name: synthetic_question
dtype: string
- name: synthetic_description
dtype: string
- name: synthetic_quality
dtype: string
splits:
- name: train
num_bytes: 1606811.0
num_examples: 10
download_size: 1611701
dataset_size: 1606811.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_eb16c643-99b5-4c8e-abc0-022e9eda4d55
|
argilla-internal-testing
|
2024-12-11T09:14:37Z
| 15
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-11T09:14:37Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': positive
'1': negative
splits:
- name: train
num_bytes: 111
num_examples: 3
download_size: 1256
dataset_size: 111
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LeRobot-worldwide-hackathon/267-CheckLock-parasoll-sort10
|
LeRobot-worldwide-hackathon
|
2025-06-14T17:40:03Z
| 48
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] |
[
"robotics"
] |
2025-06-14T17:39:57Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101_follower",
"total_episodes": 10,
"total_frames": 4415,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
"observation.images.front": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.top": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
nhagar/fineweb-2_urls
|
nhagar
|
2025-05-15T05:04:19Z
| 132
| 0
|
[
"task_categories:text-generation",
"license:odc-by",
"size_categories:1B<n<10B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"doi:10.57967/hf/5443",
"region:us"
] |
[
"text-generation"
] |
2025-04-23T19:09:53Z
| 0
|
---
license: odc-by
task_categories:
- text-generation
size_categories:
- 10B<n<100B
configs:
- config_name: default
data_files:
- split: train
path: batch*.parquet
---
# Dataset Card for fineweb-2_urls
This dataset provides the URLs and top-level domains associated with training records in [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2). It is part of a [collection of datasets](https://huggingface.co/collections/nhagar/llm-urls-neurips-681698adac0862be6c65c72b) curated to make exploring LLM training datasets more straightforward and accessible.
## Dataset Details
### Dataset Description
This dataset was created by downloading the source data, extracting URLs and top-level domains, and retaining only those record identifiers. In doing so, it allows researchers and practitioners to explore the contents of these training datasets without having to manage terabytes of raw text. You can explore the pipeline used to construct this dataset on [GitHub](https://github.com/NHagar/cc-genealogy).
- **Curated by:** [Nick Hagar](https://huggingface.co/nhagar) and [Jack Bandy](https://huggingface.co/jackbandy)
- **License:** Same as source dataset
### Dataset Sources
- **Repository:** [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2)
## Uses
This dataset is intended to allow researchers and practitioners to analyze the contents of large LLM training datasets without having to wade through terabytes of unwieldy text data.
### Direct Use
The main use case for these data is to explore the contents of LLM training datasets at scale. This might involve:
- Identifying the most-used websites
- Categorizing URLs to understand domain- or topic-level dataset composition
- Comparing URLs across datasets
- Digging into inclusion/exclusion patterns for a particular website
### Out-of-Scope Use
This dataset is not intend to replicate or replace the source data, nor is it intended to enable large-scale scraping of the URLs listed. For source text, refer to the original dataset.
## Dataset Structure
This dataset contains every record with a URL from the source dataset. It contains two columns:
- `url`: The raw URL associated with each record
- `domain`: The top-level domain for each URL, extracted with `tldextract`
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
|
douzi999/so100_test_25
|
douzi999
|
2025-04-03T06:45:51Z
| 27
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] |
[
"robotics"
] |
2025-04-03T06:44:29Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 40,
"total_frames": 19701,
"total_tasks": 1,
"total_videos": 120,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:40"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
12
],
"names": [
"left_shoulder_pan",
"left_shoulder_lift",
"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
12
],
"names": [
"left_shoulder_pan",
"left_shoulder_lift",
"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.top": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
Imran1/jenny-tts-tags-40s-v2
|
Imran1
|
2024-10-02T07:02:01Z
| 29
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-02T06:59:51Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
- name: language
dtype: string
- name: utterance_pitch_mean
dtype: float32
- name: utterance_pitch_std
dtype: float32
- name: snr
dtype: float64
- name: c50
dtype: float64
- name: speaking_rate
dtype: string
- name: phonemes
dtype: string
- name: stoi
dtype: float64
- name: si-sdr
dtype: float64
- name: pesq
dtype: float64
- name: noise
dtype: string
- name: reverberation
dtype: string
- name: speech_monotony
dtype: string
- name: sdr_noise
dtype: string
- name: pesq_speech_quality
dtype: string
splits:
- name: train
num_bytes: 13948
num_examples: 40
download_size: 15957
dataset_size: 13948
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Kyleyee/arranged_eval_data_imdb
|
Kyleyee
|
2025-03-22T16:21:47Z
| 6
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-22T16:08:19Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
- name: Kyleyee_Qwen2-0.5B-DPO-imdb-tm-tp_scores
dtype: float64
- name: Kyleyee_Qwen2-0.5B-DPO-imdb-bm_scores
dtype: float64
- name: Kyleyee_Qwen2-0.5B-DRDPO-imdb-tm-tp_scores
dtype: float64
- name: Kyleyee_Qwen2-0.5B-DRDPO-imdb-tm-ep_scores
dtype: float64
- name: Kyleyee_Qwen2-0.5B-DRDPO-imdb-tm-wp_scores
dtype: float64
- name: Kyleyee_Qwen2-0.5B-DRDPO-imdb-tm-rp_scores
dtype: float64
- name: Kyleyee_Qwen2-0.5B-DRDPO-imdb-bm-tp_scores
dtype: float64
- name: Kyleyee_Qwen2-0.5B-DRDPO-imdb-bm-wp_scores
dtype: float64
splits:
- name: temperature_0
num_bytes: 144960
num_examples: 100
- name: temperature_0.25
num_bytes: 144960
num_examples: 100
- name: temperature_0.5
num_bytes: 144960
num_examples: 100
- name: temperature_0.75
num_bytes: 144960
num_examples: 100
- name: temperature_1
num_bytes: 144960
num_examples: 100
download_size: 529784
dataset_size: 724800
configs:
- config_name: default
data_files:
- split: temperature_0
path: data/temperature_0-*
- split: temperature_0.25
path: data/temperature_0.25-*
- split: temperature_0.5
path: data/temperature_0.5-*
- split: temperature_0.75
path: data/temperature_0.75-*
- split: temperature_1
path: data/temperature_1-*
---
|
junkim100/multilingual_instruction_tuning_lima_bactrian
|
junkim100
|
2025-06-01T17:26:29Z
| 30
| 0
|
[
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-01T17:25:49Z
| 0
|
---
dataset_info:
- config_name: bactrian
features:
- name: en_input
dtype: string
- name: en_output
dtype: string
- name: de_input
dtype: string
- name: de_output
dtype: string
- name: es_input
dtype: string
- name: es_output
dtype: string
- name: fr_input
dtype: string
- name: fr_output
dtype: string
- name: it_input
dtype: string
- name: it_output
dtype: string
- name: id_original_bactrian
dtype: string
splits:
- name: train
num_bytes: 210461464.65934017
num_examples: 53613
- name: val
num_bytes: 26309155.170329917
num_examples: 6702
- name: test
num_bytes: 26309155.170329917
num_examples: 6702
download_size: 158883391
dataset_size: 263079775.00000003
- config_name: combined
features:
- name: de_input
dtype: string
- name: de_output
dtype: string
- name: en_input
dtype: string
- name: en_output
dtype: string
- name: es_input
dtype: string
- name: es_output
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- name: fr_input
dtype: string
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dtype: string
- name: it_input
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- name: it_output
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splits:
- name: train
num_bytes: 222538006
num_examples: 54437
- name: val
num_bytes: 28031723
num_examples: 6805
- name: test
num_bytes: 27434172
num_examples: 6805
download_size: 167513898
dataset_size: 278003901
- config_name: lima
features:
- name: id_numeric
dtype: int64
- name: en_input
dtype: string
- name: en_output
dtype: string
- name: de_input
dtype: string
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dtype: string
- name: es_input
dtype: string
- name: es_output
dtype: string
- name: fr_input
dtype: string
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dtype: string
- name: it_input
dtype: string
- name: it_output
dtype: string
splits:
- name: train
num_bytes: 12773928.8
num_examples: 824
- name: val
num_bytes: 1596741.1
num_examples: 103
- name: test
num_bytes: 1596741.1
num_examples: 103
download_size: 9302187
dataset_size: 15967411.0
configs:
- config_name: bactrian
data_files:
- split: train
path: bactrian/train-*
- split: val
path: bactrian/val-*
- split: test
path: bactrian/test-*
- config_name: combined
data_files:
- split: train
path: combined/train-*
- split: val
path: combined/val-*
- split: test
path: combined/test-*
- config_name: lima
data_files:
- split: train
path: lima/train-*
- split: val
path: lima/val-*
- split: test
path: lima/test-*
---
|
Hieuman/nyt_comments_hard
|
Hieuman
|
2025-03-05T03:19:27Z
| 16
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-05T03:19:22Z
| 0
|
---
dataset_info:
features:
- name: authorIDs
dtype: string
- name: fullText
sequence:
sequence: string
- name: cluster
dtype: int64
- name: retrieval_idx
sequence: int64
splits:
- name: train
num_bytes: 131578274
num_examples: 1306
download_size: 77771547
dataset_size: 131578274
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
french-datasets/AmazonScience-mintaka
|
french-datasets
|
2025-03-31T09:12:18Z
| 68
| 0
|
[
"multilinguality:multilingual",
"language:fra",
"language:eng",
"language:arb",
"language:deu",
"language:hi",
"language:ita",
"language:jpn",
"language:por",
"language:spa",
"region:us"
] |
[] |
2025-03-30T17:10:40Z
| 0
|
---
language:
- fra
- eng
- arb
- deu
- hi
- ita
- jpn
- por
- spa
multilinguality:
- multilingual
viewer: false
---
Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données https://huggingface.co/datasets/AmazonScience/mintaka.
|
dgambettavuw/D_gen3_run0_llama2-7b_xlsum_doc1000_real32_synt96_vuw
|
dgambettavuw
|
2024-12-19T08:23:45Z
| 17
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-19T08:23:37Z
| 0
|
---
dataset_info:
features:
- name: id
dtype: int64
- name: doc
dtype: string
splits:
- name: train
num_bytes: 538542
num_examples: 1000
download_size: 275283
dataset_size: 538542
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
electricsheepafrica/Healthy-Life-Expectancy-At-Birth-Years-for-African-Countries
|
electricsheepafrica
|
2025-06-21T13:54:34Z
| 0
| 0
|
[
"license:apache-2.0",
"size_categories:n<1K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-21T13:39:10Z
| 0
|
---
license: apache-2.0
---
# Healthy life expectancy at birth (years) for African Countries
## Dataset Description
This dataset contains 'Healthy life expectancy at birth (years)' data for all 54 African countries, sourced from the World Health Organization (WHO). The data is structured with years as rows and countries as columns, facilitating time-series analysis.
The data is measured in: years.
Missing values have been handled using linear interpolation followed by forward and backward filling to ensure a complete dataset.
## How to Use
You can load the data using pandas:
```python
import pandas as pd
df = pd.read_csv('hf://datasets/electricsheepafrica/Healthy-Life-Expectancy-At-Birth-Years-for-African-Countries/healthy_life_expectancy_at_birth__years_.csv')
print(df.head())
```
|
mlfoundations-dev/load_in_science_biology_standardized
|
mlfoundations-dev
|
2025-04-17T00:51:39Z
| 16
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-15T23:22:50Z
| 0
|
---
dataset_info:
features:
- name: instruction_seed
dtype: string
- name: conversation_id
dtype: int64
- name: _source
dtype: string
splits:
- name: train
num_bytes: 59954985
num_examples: 19825
download_size: 27599020
dataset_size: 59954985
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Coobiw/agiqa3k_qual
|
Coobiw
|
2025-04-27T17:26:54Z
| 26
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-27T17:24:15Z
| 0
|
---
dataset_info:
features:
- name: images
sequence: image
- name: problem
dtype: string
- name: answer
dtype: float64
splits:
- name: train
num_bytes: 667070998.875
num_examples: 2385
- name: test
num_bytes: 164424543.0
num_examples: 597
download_size: 829462977
dataset_size: 831495541.875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
hzy/math-prm-rollouts-labels-0.5-start_2-end_3
|
hzy
|
2025-03-15T07:47:00Z
| 16
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-15T07:46:59Z
| 0
|
---
dataset_info:
features:
- name: problem
dtype: string
- name: level
dtype: string
- name: solution
dtype: string
- name: type
dtype: string
- name: completion
dtype: string
- name: all_one_or_zero
dtype: float64
- name: rollout_points
sequence: int64
- name: step_labels
sequence: float64
splits:
- name: train
num_bytes: 12086908
num_examples: 3261
- name: test
num_bytes: 7282356
num_examples: 2243
download_size: 3984532
dataset_size: 19369264
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
danigambit/D_ep2_run0_llama2-7b_wiki_doc100_tok25
|
danigambit
|
2024-10-23T23:02:58Z
| 18
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-23T23:02:55Z
| 0
|
---
dataset_info:
features:
- name: id
dtype: int64
- name: doc
dtype: string
splits:
- name: train
num_bytes: 192161
num_examples: 100
download_size: 51027
dataset_size: 192161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Yuyeong/rw_roman-empire_node2vec3_6_public_masked
|
Yuyeong
|
2025-05-24T05:04:57Z
| 0
| 0
|
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-24T05:03:04Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
'10': '10'
'11': '11'
'12': '12'
'13': '13'
'14': '14'
'15': '15'
'16': '16'
'17': '17'
- name: group_idx
dtype: int64
- name: node_idx
dtype: int64
- name: train_0
dtype: bool
- name: validation_0
dtype: bool
- name: test_0
dtype: bool
- name: train_1
dtype: bool
- name: validation_1
dtype: bool
- name: test_1
dtype: bool
- name: train_2
dtype: bool
- name: validation_2
dtype: bool
- name: test_2
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dtype: bool
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dtype: bool
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dtype: bool
- name: validation_7
dtype: bool
- name: test_7
dtype: bool
- name: train_8
dtype: bool
- name: validation_8
dtype: bool
- name: test_8
dtype: bool
- name: train_9
dtype: bool
- name: validation_9
dtype: bool
- name: test_9
dtype: bool
splits:
- name: train
num_bytes: 4000719255
num_examples: 2266200
download_size: 908736557
dataset_size: 4000719255
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
omlab/VLM-R1
|
omlab
|
2025-04-23T11:05:37Z
| 1,252
| 15
|
[
"license:unknown",
"modality:image",
"arxiv:2504.07615",
"region:us"
] |
[
"visual-grounding"
] |
2025-02-14T16:44:40Z
| 0
|
---
task_categories:
- visual-grounding
license: unknown
---
This repository contains the dataset used in the paper [VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model](https://huggingface.co/papers/2504.07615).
Code: https://github.com/om-ai-lab/VLM-R1
|
HungVu2003/opt-350m_beta_0.2_alpha_0.2_dataset_0_for_gen_5
|
HungVu2003
|
2025-04-07T06:00:19Z
| 15
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-07T06:00:18Z
| 0
|
---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 1260227
num_examples: 11250
download_size: 772467
dataset_size: 1260227
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
baxromovcomston/raft_dataset
|
baxromovcomston
|
2024-12-21T13:50:19Z
| 42
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-21T11:50:43Z
| 0
|
---
dataset_info:
features:
- name: id
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: context
struct:
- name: sentences
sequence:
sequence: string
- name: title
sequence:
sequence: string
- name: oracle_context
dtype: string
- name: cot_answer
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 64664.1
num_examples: 9
- name: test
num_bytes: 7521
num_examples: 1
download_size: 74829
dataset_size: 72185.1
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Ersins/trpyschology.csv
|
Ersins
|
2025-05-21T22:00:57Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-21T22:00:22Z
| 0
|
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 740894.1573426573
num_examples: 514
- name: test
num_bytes: 83602.84265734266
num_examples: 58
download_size: 414392
dataset_size: 824497.0
---
# Dataset Card for "trpyschology.csv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TristonNV/Llama-3.2-1B-Instruct-best_of_n-prm-completions
|
TristonNV
|
2025-01-16T23:08:42Z
| 18
| 0
|
[
"license:apache-2.0",
"size_categories:10K<n<100K",
"region:us"
] |
[] |
2025-01-16T22:53:32Z
| 0
|
---
license: apache-2.0
pretty_name: Search-n-learn test
size_categories:
- 10K<n<100K
---
|
robintomar/record-test-2
|
robintomar
|
2025-06-14T07:37:49Z
| 0
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] |
[
"robotics"
] |
2025-06-14T07:05:36Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100_follower",
"total_episodes": 14,
"total_frames": 4802,
"total_tasks": 1,
"total_videos": 14,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:14"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
"observation.state": {
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6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
"observation.images.top": {
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"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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1
],
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},
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1
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1
],
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},
"task_index": {
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"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
Uns0/flappy_dataset
|
Uns0
|
2025-03-28T18:59:25Z
| 8
| 0
|
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-28T18:44:07Z
| 0
|
---
dataset_info:
features:
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dtype: int32
- name: frames
dtype: binary
- name: actions
dtype: int8
- name: health
dtype: float32
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dtype: int64
splits:
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num_bytes: 184280440
num_examples: 26836
- name: batch_1
num_bytes: 204439422
num_examples: 29422
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num_bytes: 229412263
num_examples: 32609
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num_bytes: 276249807
num_examples: 38477
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num_bytes: 344239459
num_examples: 46914
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num_bytes: 457815508
num_examples: 60366
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num_bytes: 629928824
num_examples: 80251
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num_bytes: 2174362311
num_examples: 256747
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num_bytes: 2155692652
num_examples: 249749
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num_bytes: 2163235870
num_examples: 247986
- name: batch_10
num_bytes: 2238754558
num_examples: 256989
download_size: 5756437427
dataset_size: 11058411114
configs:
- config_name: default
data_files:
- split: batch_0
path: data/batch_0-*
- split: batch_1
path: data/batch_1-*
- split: batch_2
path: data/batch_2-*
- split: batch_3
path: data/batch_3-*
- split: batch_4
path: data/batch_4-*
- split: batch_5
path: data/batch_5-*
- split: batch_6
path: data/batch_6-*
- split: batch_7
path: data/batch_7-*
- split: batch_8
path: data/batch_8-*
- split: batch_9
path: data/batch_9-*
- split: batch_10
path: data/batch_10-*
---
|
dgambettaphd/D_llm3_gen10_WXS_doc1000_synt64_lr1e-04_acm_MPP
|
dgambettaphd
|
2025-05-07T19:32:14Z
| 0
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-07T19:32:10Z
| 0
|
---
dataset_info:
features:
- name: id_doc
dtype: int64
- name: text
dtype: string
- name: dataset
dtype: string
- name: gen
dtype: int64
- name: synt
dtype: int64
- name: MPP
dtype: float64
splits:
- name: train
num_bytes: 16855384
num_examples: 26000
download_size: 10228375
dataset_size: 16855384
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
taxitain/so100_obj_to_bin_top0_180
|
taxitain
|
2025-05-06T07:35:53Z
| 37
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"obj_to_bin"
] |
[
"robotics"
] |
2025-05-05T20:20:51Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- obj_to_bin
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 1,
"total_frames": 872,
"total_tasks": 1,
"total_videos": 2,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:1"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.top0": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.side1": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
abdullah-alamodi/aqedah-data
|
abdullah-alamodi
|
2024-12-12T10:58:05Z
| 22
| 0
|
[
"language:ar",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-12T10:56:14Z
| 0
|
---
license: mit
language:
- ar
pretty_name: aqedah-data
---
|
zeahub/camus-sample
|
zeahub
|
2025-06-08T14:01:06Z
| 0
| 0
|
[
"source_datasets:https://www.creatis.insa-lyon.fr/Challenge/camus/index.html",
"region:us",
"medical-imaging",
"ultrasound",
"cardiac",
"benchmark",
"zea"
] |
[] |
2025-06-08T13:52:24Z
| 0
|
---
pretty_name: Cardiac Acquisitions for Multi-structure Ultrasound Segmentation
source_datasets:
- https://www.creatis.insa-lyon.fr/Challenge/camus/index.html
tags:
- medical-imaging
- ultrasound
- cardiac
- benchmark
- zea
converted_by: "@tristan-deep"
---
## Dataset Description
This contains one example for each split in the [CAMUS](https://www.creatis.insa-lyon.fr/Challenge/camus/index.html) (Cardiac Acquisitions for Multi-structure Ultrasound Segmentation) dataset. Converted to the [zea format](https://usbmd-toolbox.web.app/data-acquisition.html) for use with the [zea toolbox](https://github.com/tue-bmd/zea).
|
jkot/dataset_merged_preprocesssed_v2
|
jkot
|
2023-04-28T20:06:15Z
| 10,470
| 0
|
[
"size_categories:100K<n<1M",
"format:parquet",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2023-04-28T16:23:57Z
| 0
|
---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 229523006640
num_examples: 238899
- name: test
num_bytes: 12170045648
num_examples: 12669
download_size: 72324319243
dataset_size: 241693052288
---
# Dataset Card for "dataset_merged_preprocesssed_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TSXu/Physion_Dominoes_Ex3
|
TSXu
|
2025-04-02T06:00:18Z
| 48
| 0
|
[
"license:apache-2.0",
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] |
[] |
2025-04-02T05:02:00Z
| 0
|
---
license: apache-2.0
---
|
QuanHoangNgoc/lock_dataset_prc
|
QuanHoangNgoc
|
2025-06-08T04:51:06Z
| 96
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-05T02:50:57Z
| 0
|
---
dataset_info:
features:
- name: input_values
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 18795072252.0
num_examples: 15023
- name: dev
num_bytes: 118626196.0
num_examples: 95
download_size: 18886526489
dataset_size: 18913698448.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
|
tsilva/GymnasiumRecording__ALE_KungFuMaster_v5
|
tsilva
|
2025-06-04T16:15:04Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:image",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-04T16:14:02Z
| 0
|
---
dataset_info:
features:
- name: episode_id
dtype: int64
- name: image
dtype: image
- name: step
dtype: int64
- name: action
sequence: int64
splits:
- name: train
num_bytes: 376263.0
num_examples: 248
download_size: 254711
dataset_size: 376263.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
saintlyk1d/dont-say-it-prompts-player1-test-set-variant-C
|
saintlyk1d
|
2025-05-24T05:31:37Z
| 53
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-16T05:33:07Z
| 0
|
---
dataset_info:
features:
- name: player_word
dtype: string
- name: opponent_word
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 61457
num_examples: 100
download_size: 8466
dataset_size: 61457
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MAINLAND/xview_part4
|
MAINLAND
|
2024-11-20T12:43:01Z
| 112
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-20T12:32:34Z
| 0
|
---
dataset_info:
features:
- name: image
dtype: image
- name: messages
list:
- name: content
list:
- name: index
dtype: int64
- name: text
dtype: string
- name: type
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 42913675946.962
num_examples: 34913
download_size: 4758139345
dataset_size: 42913675946.962
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
datacomp/imagenet-1k-random90.0
|
datacomp
|
2024-11-07T22:42:36Z
| 27
| 1
|
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-05T07:42:19Z
| 0
|
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': tench, Tinca tinca
'1': goldfish, Carassius auratus
'2': great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
'3': tiger shark, Galeocerdo cuvieri
'4': hammerhead, hammerhead shark
'5': electric ray, crampfish, numbfish, torpedo
'6': stingray
'7': cock
'8': hen
'9': ostrich, Struthio camelus
'10': brambling, Fringilla montifringilla
'11': goldfinch, Carduelis carduelis
'12': house finch, linnet, Carpodacus mexicanus
'13': junco, snowbird
'14': indigo bunting, indigo finch, indigo bird, Passerina cyanea
'15': robin, American robin, Turdus migratorius
'16': bulbul
'17': jay
'18': magpie
'19': chickadee
'20': water ouzel, dipper
'21': kite
'22': bald eagle, American eagle, Haliaeetus leucocephalus
'23': vulture
'24': great grey owl, great gray owl, Strix nebulosa
'25': European fire salamander, Salamandra salamandra
'26': common newt, Triturus vulgaris
'27': eft
'28': spotted salamander, Ambystoma maculatum
'29': axolotl, mud puppy, Ambystoma mexicanum
'30': bullfrog, Rana catesbeiana
'31': tree frog, tree-frog
'32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
'33': loggerhead, loggerhead turtle, Caretta caretta
'34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
'35': mud turtle
'36': terrapin
'37': box turtle, box tortoise
'38': banded gecko
'39': common iguana, iguana, Iguana iguana
'40': American chameleon, anole, Anolis carolinensis
'41': whiptail, whiptail lizard
'42': agama
'43': frilled lizard, Chlamydosaurus kingi
'44': alligator lizard
'45': Gila monster, Heloderma suspectum
'46': green lizard, Lacerta viridis
'47': African chameleon, Chamaeleo chamaeleon
'48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus
komodoensis
'49': African crocodile, Nile crocodile, Crocodylus niloticus
'50': American alligator, Alligator mississipiensis
'51': triceratops
'52': thunder snake, worm snake, Carphophis amoenus
'53': ringneck snake, ring-necked snake, ring snake
'54': hognose snake, puff adder, sand viper
'55': green snake, grass snake
'56': king snake, kingsnake
'57': garter snake, grass snake
'58': water snake
'59': vine snake
'60': night snake, Hypsiglena torquata
'61': boa constrictor, Constrictor constrictor
'62': rock python, rock snake, Python sebae
'63': Indian cobra, Naja naja
'64': green mamba
'65': sea snake
'66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
'67': diamondback, diamondback rattlesnake, Crotalus adamanteus
'68': sidewinder, horned rattlesnake, Crotalus cerastes
'69': trilobite
'70': harvestman, daddy longlegs, Phalangium opilio
'71': scorpion
'72': black and gold garden spider, Argiope aurantia
'73': barn spider, Araneus cavaticus
'74': garden spider, Aranea diademata
'75': black widow, Latrodectus mactans
'76': tarantula
'77': wolf spider, hunting spider
'78': tick
'79': centipede
'80': black grouse
'81': ptarmigan
'82': ruffed grouse, partridge, Bonasa umbellus
'83': prairie chicken, prairie grouse, prairie fowl
'84': peacock
'85': quail
'86': partridge
'87': African grey, African gray, Psittacus erithacus
'88': macaw
'89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
'90': lorikeet
'91': coucal
'92': bee eater
'93': hornbill
'94': hummingbird
'95': jacamar
'96': toucan
'97': drake
'98': red-breasted merganser, Mergus serrator
'99': goose
'100': black swan, Cygnus atratus
'101': tusker
'102': echidna, spiny anteater, anteater
'103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
'104': wallaby, brush kangaroo
'105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
'106': wombat
'107': jellyfish
'108': sea anemone, anemone
'109': brain coral
'110': flatworm, platyhelminth
'111': nematode, nematode worm, roundworm
'112': conch
'113': snail
'114': slug
'115': sea slug, nudibranch
'116': chiton, coat-of-mail shell, sea cradle, polyplacophore
'117': chambered nautilus, pearly nautilus, nautilus
'118': Dungeness crab, Cancer magister
'119': rock crab, Cancer irroratus
'120': fiddler crab
'121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
'122': American lobster, Northern lobster, Maine lobster, Homarus americanus
'123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
'124': crayfish, crawfish, crawdad, crawdaddy
'125': hermit crab
'126': isopod
'127': white stork, Ciconia ciconia
'128': black stork, Ciconia nigra
'129': spoonbill
'130': flamingo
'131': little blue heron, Egretta caerulea
'132': American egret, great white heron, Egretta albus
'133': bittern
'134': crane
'135': limpkin, Aramus pictus
'136': European gallinule, Porphyrio porphyrio
'137': American coot, marsh hen, mud hen, water hen, Fulica americana
'138': bustard
'139': ruddy turnstone, Arenaria interpres
'140': red-backed sandpiper, dunlin, Erolia alpina
'141': redshank, Tringa totanus
'142': dowitcher
'143': oystercatcher, oyster catcher
'144': pelican
'145': king penguin, Aptenodytes patagonica
'146': albatross, mollymawk
'147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
'148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca
'149': dugong, Dugong dugon
'150': sea lion
'151': Chihuahua
'152': Japanese spaniel
'153': Maltese dog, Maltese terrier, Maltese
'154': Pekinese, Pekingese, Peke
'155': Shih-Tzu
'156': Blenheim spaniel
'157': papillon
'158': toy terrier
'159': Rhodesian ridgeback
'160': Afghan hound, Afghan
'161': basset, basset hound
'162': beagle
'163': bloodhound, sleuthhound
'164': bluetick
'165': black-and-tan coonhound
'166': Walker hound, Walker foxhound
'167': English foxhound
'168': redbone
'169': borzoi, Russian wolfhound
'170': Irish wolfhound
'171': Italian greyhound
'172': whippet
'173': Ibizan hound, Ibizan Podenco
'174': Norwegian elkhound, elkhound
'175': otterhound, otter hound
'176': Saluki, gazelle hound
'177': Scottish deerhound, deerhound
'178': Weimaraner
'179': Staffordshire bullterrier, Staffordshire bull terrier
'180': American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
'181': Bedlington terrier
'182': Border terrier
'183': Kerry blue terrier
'184': Irish terrier
'185': Norfolk terrier
'186': Norwich terrier
'187': Yorkshire terrier
'188': wire-haired fox terrier
'189': Lakeland terrier
'190': Sealyham terrier, Sealyham
'191': Airedale, Airedale terrier
'192': cairn, cairn terrier
'193': Australian terrier
'194': Dandie Dinmont, Dandie Dinmont terrier
'195': Boston bull, Boston terrier
'196': miniature schnauzer
'197': giant schnauzer
'198': standard schnauzer
'199': Scotch terrier, Scottish terrier, Scottie
'200': Tibetan terrier, chrysanthemum dog
'201': silky terrier, Sydney silky
'202': soft-coated wheaten terrier
'203': West Highland white terrier
'204': Lhasa, Lhasa apso
'205': flat-coated retriever
'206': curly-coated retriever
'207': golden retriever
'208': Labrador retriever
'209': Chesapeake Bay retriever
'210': German short-haired pointer
'211': vizsla, Hungarian pointer
'212': English setter
'213': Irish setter, red setter
'214': Gordon setter
'215': Brittany spaniel
'216': clumber, clumber spaniel
'217': English springer, English springer spaniel
'218': Welsh springer spaniel
'219': cocker spaniel, English cocker spaniel, cocker
'220': Sussex spaniel
'221': Irish water spaniel
'222': kuvasz
'223': schipperke
'224': groenendael
'225': malinois
'226': briard
'227': kelpie
'228': komondor
'229': Old English sheepdog, bobtail
'230': Shetland sheepdog, Shetland sheep dog, Shetland
'231': collie
'232': Border collie
'233': Bouvier des Flandres, Bouviers des Flandres
'234': Rottweiler
'235': German shepherd, German shepherd dog, German police dog, alsatian
'236': Doberman, Doberman pinscher
'237': miniature pinscher
'238': Greater Swiss Mountain dog
'239': Bernese mountain dog
'240': Appenzeller
'241': EntleBucher
'242': boxer
'243': bull mastiff
'244': Tibetan mastiff
'245': French bulldog
'246': Great Dane
'247': Saint Bernard, St Bernard
'248': Eskimo dog, husky
'249': malamute, malemute, Alaskan malamute
'250': Siberian husky
'251': dalmatian, coach dog, carriage dog
'252': affenpinscher, monkey pinscher, monkey dog
'253': basenji
'254': pug, pug-dog
'255': Leonberg
'256': Newfoundland, Newfoundland dog
'257': Great Pyrenees
'258': Samoyed, Samoyede
'259': Pomeranian
'260': chow, chow chow
'261': keeshond
'262': Brabancon griffon
'263': Pembroke, Pembroke Welsh corgi
'264': Cardigan, Cardigan Welsh corgi
'265': toy poodle
'266': miniature poodle
'267': standard poodle
'268': Mexican hairless
'269': timber wolf, grey wolf, gray wolf, Canis lupus
'270': white wolf, Arctic wolf, Canis lupus tundrarum
'271': red wolf, maned wolf, Canis rufus, Canis niger
'272': coyote, prairie wolf, brush wolf, Canis latrans
'273': dingo, warrigal, warragal, Canis dingo
'274': dhole, Cuon alpinus
'275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
'276': hyena, hyaena
'277': red fox, Vulpes vulpes
'278': kit fox, Vulpes macrotis
'279': Arctic fox, white fox, Alopex lagopus
'280': grey fox, gray fox, Urocyon cinereoargenteus
'281': tabby, tabby cat
'282': tiger cat
'283': Persian cat
'284': Siamese cat, Siamese
'285': Egyptian cat
'286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
'287': lynx, catamount
'288': leopard, Panthera pardus
'289': snow leopard, ounce, Panthera uncia
'290': jaguar, panther, Panthera onca, Felis onca
'291': lion, king of beasts, Panthera leo
'292': tiger, Panthera tigris
'293': cheetah, chetah, Acinonyx jubatus
'294': brown bear, bruin, Ursus arctos
'295': American black bear, black bear, Ursus americanus, Euarctos americanus
'296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
'297': sloth bear, Melursus ursinus, Ursus ursinus
'298': mongoose
'299': meerkat, mierkat
'300': tiger beetle
'301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
'302': ground beetle, carabid beetle
'303': long-horned beetle, longicorn, longicorn beetle
'304': leaf beetle, chrysomelid
'305': dung beetle
'306': rhinoceros beetle
'307': weevil
'308': fly
'309': bee
'310': ant, emmet, pismire
'311': grasshopper, hopper
'312': cricket
'313': walking stick, walkingstick, stick insect
'314': cockroach, roach
'315': mantis, mantid
'316': cicada, cicala
'317': leafhopper
'318': lacewing, lacewing fly
'319': dragonfly, darning needle, devil's darning needle, sewing needle,
snake feeder, snake doctor, mosquito hawk, skeeter hawk
'320': damselfly
'321': admiral
'322': ringlet, ringlet butterfly
'323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
'324': cabbage butterfly
'325': sulphur butterfly, sulfur butterfly
'326': lycaenid, lycaenid butterfly
'327': starfish, sea star
'328': sea urchin
'329': sea cucumber, holothurian
'330': wood rabbit, cottontail, cottontail rabbit
'331': hare
'332': Angora, Angora rabbit
'333': hamster
'334': porcupine, hedgehog
'335': fox squirrel, eastern fox squirrel, Sciurus niger
'336': marmot
'337': beaver
'338': guinea pig, Cavia cobaya
'339': sorrel
'340': zebra
'341': hog, pig, grunter, squealer, Sus scrofa
'342': wild boar, boar, Sus scrofa
'343': warthog
'344': hippopotamus, hippo, river horse, Hippopotamus amphibius
'345': ox
'346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
'347': bison
'348': ram, tup
'349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
'350': ibex, Capra ibex
'351': hartebeest
'352': impala, Aepyceros melampus
'353': gazelle
'354': Arabian camel, dromedary, Camelus dromedarius
'355': llama
'356': weasel
'357': mink
'358': polecat, fitch, foulmart, foumart, Mustela putorius
'359': black-footed ferret, ferret, Mustela nigripes
'360': otter
'361': skunk, polecat, wood pussy
'362': badger
'363': armadillo
'364': three-toed sloth, ai, Bradypus tridactylus
'365': orangutan, orang, orangutang, Pongo pygmaeus
'366': gorilla, Gorilla gorilla
'367': chimpanzee, chimp, Pan troglodytes
'368': gibbon, Hylobates lar
'369': siamang, Hylobates syndactylus, Symphalangus syndactylus
'370': guenon, guenon monkey
'371': patas, hussar monkey, Erythrocebus patas
'372': baboon
'373': macaque
'374': langur
'375': colobus, colobus monkey
'376': proboscis monkey, Nasalis larvatus
'377': marmoset
'378': capuchin, ringtail, Cebus capucinus
'379': howler monkey, howler
'380': titi, titi monkey
'381': spider monkey, Ateles geoffroyi
'382': squirrel monkey, Saimiri sciureus
'383': Madagascar cat, ring-tailed lemur, Lemur catta
'384': indri, indris, Indri indri, Indri brevicaudatus
'385': Indian elephant, Elephas maximus
'386': African elephant, Loxodonta africana
'387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
'388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
'389': barracouta, snoek
'390': eel
'391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus
kisutch
'392': rock beauty, Holocanthus tricolor
'393': anemone fish
'394': sturgeon
'395': gar, garfish, garpike, billfish, Lepisosteus osseus
'396': lionfish
'397': puffer, pufferfish, blowfish, globefish
'398': abacus
'399': abaya
'400': academic gown, academic robe, judge's robe
'401': accordion, piano accordion, squeeze box
'402': acoustic guitar
'403': aircraft carrier, carrier, flattop, attack aircraft carrier
'404': airliner
'405': airship, dirigible
'406': altar
'407': ambulance
'408': amphibian, amphibious vehicle
'409': analog clock
'410': apiary, bee house
'411': apron
'412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
'413': assault rifle, assault gun
'414': backpack, back pack, knapsack, packsack, rucksack, haversack
'415': bakery, bakeshop, bakehouse
'416': balance beam, beam
'417': balloon
'418': ballpoint, ballpoint pen, ballpen, Biro
'419': Band Aid
'420': banjo
'421': bannister, banister, balustrade, balusters, handrail
'422': barbell
'423': barber chair
'424': barbershop
'425': barn
'426': barometer
'427': barrel, cask
'428': barrow, garden cart, lawn cart, wheelbarrow
'429': baseball
'430': basketball
'431': bassinet
'432': bassoon
'433': bathing cap, swimming cap
'434': bath towel
'435': bathtub, bathing tub, bath, tub
'436': beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
'437': beacon, lighthouse, beacon light, pharos
'438': beaker
'439': bearskin, busby, shako
'440': beer bottle
'441': beer glass
'442': bell cote, bell cot
'443': bib
'444': bicycle-built-for-two, tandem bicycle, tandem
'445': bikini, two-piece
'446': binder, ring-binder
'447': binoculars, field glasses, opera glasses
'448': birdhouse
'449': boathouse
'450': bobsled, bobsleigh, bob
'451': bolo tie, bolo, bola tie, bola
'452': bonnet, poke bonnet
'453': bookcase
'454': bookshop, bookstore, bookstall
'455': bottlecap
'456': bow
'457': bow tie, bow-tie, bowtie
'458': brass, memorial tablet, plaque
'459': brassiere, bra, bandeau
'460': breakwater, groin, groyne, mole, bulwark, seawall, jetty
'461': breastplate, aegis, egis
'462': broom
'463': bucket, pail
'464': buckle
'465': bulletproof vest
'466': bullet train, bullet
'467': butcher shop, meat market
'468': cab, hack, taxi, taxicab
'469': caldron, cauldron
'470': candle, taper, wax light
'471': cannon
'472': canoe
'473': can opener, tin opener
'474': cardigan
'475': car mirror
'476': carousel, carrousel, merry-go-round, roundabout, whirligig
'477': carpenter's kit, tool kit
'478': carton
'479': car wheel
'480': cash machine, cash dispenser, automated teller machine, automatic
teller machine, automated teller, automatic teller, ATM
'481': cassette
'482': cassette player
'483': castle
'484': catamaran
'485': CD player
'486': cello, violoncello
'487': cellular telephone, cellular phone, cellphone, cell, mobile phone
'488': chain
'489': chainlink fence
'490': chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
'491': chain saw, chainsaw
'492': chest
'493': chiffonier, commode
'494': chime, bell, gong
'495': china cabinet, china closet
'496': Christmas stocking
'497': church, church building
'498': cinema, movie theater, movie theatre, movie house, picture palace
'499': cleaver, meat cleaver, chopper
'500': cliff dwelling
'501': cloak
'502': clog, geta, patten, sabot
'503': cocktail shaker
'504': coffee mug
'505': coffeepot
'506': coil, spiral, volute, whorl, helix
'507': combination lock
'508': computer keyboard, keypad
'509': confectionery, confectionary, candy store
'510': container ship, containership, container vessel
'511': convertible
'512': corkscrew, bottle screw
'513': cornet, horn, trumpet, trump
'514': cowboy boot
'515': cowboy hat, ten-gallon hat
'516': cradle
'517': crane2
'518': crash helmet
'519': crate
'520': crib, cot
'521': Crock Pot
'522': croquet ball
'523': crutch
'524': cuirass
'525': dam, dike, dyke
'526': desk
'527': desktop computer
'528': dial telephone, dial phone
'529': diaper, nappy, napkin
'530': digital clock
'531': digital watch
'532': dining table, board
'533': dishrag, dishcloth
'534': dishwasher, dish washer, dishwashing machine
'535': disk brake, disc brake
'536': dock, dockage, docking facility
'537': dogsled, dog sled, dog sleigh
'538': dome
'539': doormat, welcome mat
'540': drilling platform, offshore rig
'541': drum, membranophone, tympan
'542': drumstick
'543': dumbbell
'544': Dutch oven
'545': electric fan, blower
'546': electric guitar
'547': electric locomotive
'548': entertainment center
'549': envelope
'550': espresso maker
'551': face powder
'552': feather boa, boa
'553': file, file cabinet, filing cabinet
'554': fireboat
'555': fire engine, fire truck
'556': fire screen, fireguard
'557': flagpole, flagstaff
'558': flute, transverse flute
'559': folding chair
'560': football helmet
'561': forklift
'562': fountain
'563': fountain pen
'564': four-poster
'565': freight car
'566': French horn, horn
'567': frying pan, frypan, skillet
'568': fur coat
'569': garbage truck, dustcart
'570': gasmask, respirator, gas helmet
'571': gas pump, gasoline pump, petrol pump, island dispenser
'572': goblet
'573': go-kart
'574': golf ball
'575': golfcart, golf cart
'576': gondola
'577': gong, tam-tam
'578': gown
'579': grand piano, grand
'580': greenhouse, nursery, glasshouse
'581': grille, radiator grille
'582': grocery store, grocery, food market, market
'583': guillotine
'584': hair slide
'585': hair spray
'586': half track
'587': hammer
'588': hamper
'589': hand blower, blow dryer, blow drier, hair dryer, hair drier
'590': hand-held computer, hand-held microcomputer
'591': handkerchief, hankie, hanky, hankey
'592': hard disc, hard disk, fixed disk
'593': harmonica, mouth organ, harp, mouth harp
'594': harp
'595': harvester, reaper
'596': hatchet
'597': holster
'598': home theater, home theatre
'599': honeycomb
'600': hook, claw
'601': hoopskirt, crinoline
'602': horizontal bar, high bar
'603': horse cart, horse-cart
'604': hourglass
'605': iPod
'606': iron, smoothing iron
'607': jack-o'-lantern
'608': jean, blue jean, denim
'609': jeep, landrover
'610': jersey, T-shirt, tee shirt
'611': jigsaw puzzle
'612': jinrikisha, ricksha, rickshaw
'613': joystick
'614': kimono
'615': knee pad
'616': knot
'617': lab coat, laboratory coat
'618': ladle
'619': lampshade, lamp shade
'620': laptop, laptop computer
'621': lawn mower, mower
'622': lens cap, lens cover
'623': letter opener, paper knife, paperknife
'624': library
'625': lifeboat
'626': lighter, light, igniter, ignitor
'627': limousine, limo
'628': liner, ocean liner
'629': lipstick, lip rouge
'630': Loafer
'631': lotion
'632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
'633': loupe, jeweler's loupe
'634': lumbermill, sawmill
'635': magnetic compass
'636': mailbag, postbag
'637': mailbox, letter box
'638': maillot
'639': maillot, tank suit
'640': manhole cover
'641': maraca
'642': marimba, xylophone
'643': mask
'644': matchstick
'645': maypole
'646': maze, labyrinth
'647': measuring cup
'648': medicine chest, medicine cabinet
'649': megalith, megalithic structure
'650': microphone, mike
'651': microwave, microwave oven
'652': military uniform
'653': milk can
'654': minibus
'655': miniskirt, mini
'656': minivan
'657': missile
'658': mitten
'659': mixing bowl
'660': mobile home, manufactured home
'661': Model T
'662': modem
'663': monastery
'664': monitor
'665': moped
'666': mortar
'667': mortarboard
'668': mosque
'669': mosquito net
'670': motor scooter, scooter
'671': mountain bike, all-terrain bike, off-roader
'672': mountain tent
'673': mouse, computer mouse
'674': mousetrap
'675': moving van
'676': muzzle
'677': nail
'678': neck brace
'679': necklace
'680': nipple
'681': notebook, notebook computer
'682': obelisk
'683': oboe, hautboy, hautbois
'684': ocarina, sweet potato
'685': odometer, hodometer, mileometer, milometer
'686': oil filter
'687': organ, pipe organ
'688': oscilloscope, scope, cathode-ray oscilloscope, CRO
'689': overskirt
'690': oxcart
'691': oxygen mask
'692': packet
'693': paddle, boat paddle
'694': paddlewheel, paddle wheel
'695': padlock
'696': paintbrush
'697': pajama, pyjama, pj's, jammies
'698': palace
'699': panpipe, pandean pipe, syrinx
'700': paper towel
'701': parachute, chute
'702': parallel bars, bars
'703': park bench
'704': parking meter
'705': passenger car, coach, carriage
'706': patio, terrace
'707': pay-phone, pay-station
'708': pedestal, plinth, footstall
'709': pencil box, pencil case
'710': pencil sharpener
'711': perfume, essence
'712': Petri dish
'713': photocopier
'714': pick, plectrum, plectron
'715': pickelhaube
'716': picket fence, paling
'717': pickup, pickup truck
'718': pier
'719': piggy bank, penny bank
'720': pill bottle
'721': pillow
'722': ping-pong ball
'723': pinwheel
'724': pirate, pirate ship
'725': pitcher, ewer
'726': plane, carpenter's plane, woodworking plane
'727': planetarium
'728': plastic bag
'729': plate rack
'730': plow, plough
'731': plunger, plumber's helper
'732': Polaroid camera, Polaroid Land camera
'733': pole
'734': police van, police wagon, paddy wagon, patrol wagon, wagon, black
Maria
'735': poncho
'736': pool table, billiard table, snooker table
'737': pop bottle, soda bottle
'738': pot, flowerpot
'739': potter's wheel
'740': power drill
'741': prayer rug, prayer mat
'742': printer
'743': prison, prison house
'744': projectile, missile
'745': projector
'746': puck, hockey puck
'747': punching bag, punch bag, punching ball, punchball
'748': purse
'749': quill, quill pen
'750': quilt, comforter, comfort, puff
'751': racer, race car, racing car
'752': racket, racquet
'753': radiator
'754': radio, wireless
'755': radio telescope, radio reflector
'756': rain barrel
'757': recreational vehicle, RV, R.V.
'758': reel
'759': reflex camera
'760': refrigerator, icebox
'761': remote control, remote
'762': restaurant, eating house, eating place, eatery
'763': revolver, six-gun, six-shooter
'764': rifle
'765': rocking chair, rocker
'766': rotisserie
'767': rubber eraser, rubber, pencil eraser
'768': rugby ball
'769': rule, ruler
'770': running shoe
'771': safe
'772': safety pin
'773': saltshaker, salt shaker
'774': sandal
'775': sarong
'776': sax, saxophone
'777': scabbard
'778': scale, weighing machine
'779': school bus
'780': schooner
'781': scoreboard
'782': screen, CRT screen
'783': screw
'784': screwdriver
'785': seat belt, seatbelt
'786': sewing machine
'787': shield, buckler
'788': shoe shop, shoe-shop, shoe store
'789': shoji
'790': shopping basket
'791': shopping cart
'792': shovel
'793': shower cap
'794': shower curtain
'795': ski
'796': ski mask
'797': sleeping bag
'798': slide rule, slipstick
'799': sliding door
'800': slot, one-armed bandit
'801': snorkel
'802': snowmobile
'803': snowplow, snowplough
'804': soap dispenser
'805': soccer ball
'806': sock
'807': solar dish, solar collector, solar furnace
'808': sombrero
'809': soup bowl
'810': space bar
'811': space heater
'812': space shuttle
'813': spatula
'814': speedboat
'815': spider web, spider's web
'816': spindle
'817': sports car, sport car
'818': spotlight, spot
'819': stage
'820': steam locomotive
'821': steel arch bridge
'822': steel drum
'823': stethoscope
'824': stole
'825': stone wall
'826': stopwatch, stop watch
'827': stove
'828': strainer
'829': streetcar, tram, tramcar, trolley, trolley car
'830': stretcher
'831': studio couch, day bed
'832': stupa, tope
'833': submarine, pigboat, sub, U-boat
'834': suit, suit of clothes
'835': sundial
'836': sunglass
'837': sunglasses, dark glasses, shades
'838': sunscreen, sunblock, sun blocker
'839': suspension bridge
'840': swab, swob, mop
'841': sweatshirt
'842': swimming trunks, bathing trunks
'843': swing
'844': switch, electric switch, electrical switch
'845': syringe
'846': table lamp
'847': tank, army tank, armored combat vehicle, armoured combat vehicle
'848': tape player
'849': teapot
'850': teddy, teddy bear
'851': television, television system
'852': tennis ball
'853': thatch, thatched roof
'854': theater curtain, theatre curtain
'855': thimble
'856': thresher, thrasher, threshing machine
'857': throne
'858': tile roof
'859': toaster
'860': tobacco shop, tobacconist shop, tobacconist
'861': toilet seat
'862': torch
'863': totem pole
'864': tow truck, tow car, wrecker
'865': toyshop
'866': tractor
'867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
'868': tray
'869': trench coat
'870': tricycle, trike, velocipede
'871': trimaran
'872': tripod
'873': triumphal arch
'874': trolleybus, trolley coach, trackless trolley
'875': trombone
'876': tub, vat
'877': turnstile
'878': typewriter keyboard
'879': umbrella
'880': unicycle, monocycle
'881': upright, upright piano
'882': vacuum, vacuum cleaner
'883': vase
'884': vault
'885': velvet
'886': vending machine
'887': vestment
'888': viaduct
'889': violin, fiddle
'890': volleyball
'891': waffle iron
'892': wall clock
'893': wallet, billfold, notecase, pocketbook
'894': wardrobe, closet, press
'895': warplane, military plane
'896': washbasin, handbasin, washbowl, lavabo, wash-hand basin
'897': washer, automatic washer, washing machine
'898': water bottle
'899': water jug
'900': water tower
'901': whiskey jug
'902': whistle
'903': wig
'904': window screen
'905': window shade
'906': Windsor tie
'907': wine bottle
'908': wing
'909': wok
'910': wooden spoon
'911': wool, woolen, woollen
'912': worm fence, snake fence, snake-rail fence, Virginia fence
'913': wreck
'914': yawl
'915': yurt
'916': web site, website, internet site, site
'917': comic book
'918': crossword puzzle, crossword
'919': street sign
'920': traffic light, traffic signal, stoplight
'921': book jacket, dust cover, dust jacket, dust wrapper
'922': menu
'923': plate
'924': guacamole
'925': consomme
'926': hot pot, hotpot
'927': trifle
'928': ice cream, icecream
'929': ice lolly, lolly, lollipop, popsicle
'930': French loaf
'931': bagel, beigel
'932': pretzel
'933': cheeseburger
'934': hotdog, hot dog, red hot
'935': mashed potato
'936': head cabbage
'937': broccoli
'938': cauliflower
'939': zucchini, courgette
'940': spaghetti squash
'941': acorn squash
'942': butternut squash
'943': cucumber, cuke
'944': artichoke, globe artichoke
'945': bell pepper
'946': cardoon
'947': mushroom
'948': Granny Smith
'949': strawberry
'950': orange
'951': lemon
'952': fig
'953': pineapple, ananas
'954': banana
'955': jackfruit, jak, jack
'956': custard apple
'957': pomegranate
'958': hay
'959': carbonara
'960': chocolate sauce, chocolate syrup
'961': dough
'962': meat loaf, meatloaf
'963': pizza, pizza pie
'964': potpie
'965': burrito
'966': red wine
'967': espresso
'968': cup
'969': eggnog
'970': alp
'971': bubble
'972': cliff, drop, drop-off
'973': coral reef
'974': geyser
'975': lakeside, lakeshore
'976': promontory, headland, head, foreland
'977': sandbar, sand bar
'978': seashore, coast, seacoast, sea-coast
'979': valley, vale
'980': volcano
'981': ballplayer, baseball player
'982': groom, bridegroom
'983': scuba diver
'984': rapeseed
'985': daisy
'986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
'987': corn
'988': acorn
'989': hip, rose hip, rosehip
'990': buckeye, horse chestnut, conker
'991': coral fungus
'992': agaric
'993': gyromitra
'994': stinkhorn, carrion fungus
'995': earthstar
'996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola
frondosa
'997': bolete
'998': ear, spike, capitulum
'999': toilet tissue, toilet paper, bathroom tissue
splits:
- name: train
num_bytes: 103121063842.125
num_examples: 1281167
- name: validation
num_bytes: 6706896736.0
num_examples: 50000
- name: test
num_bytes: 13610348261.0
num_examples: 100000
download_size: 123394795208
dataset_size: 123438308839.125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
facebook/voxpopuli
|
facebook
|
2022-10-14T13:43:12Z
| 7,898
| 110
|
[
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"language:en",
"language:de",
"language:fr",
"language:es",
"language:pl",
"language:it",
"language:ro",
"language:hu",
"language:cs",
"language:nl",
"language:fi",
"language:hr",
"language:sk",
"language:sl",
"language:et",
"language:lt",
"license:cc0-1.0",
"license:other",
"size_categories:100K<n<1M",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2101.00390",
"region:us"
] |
[
"automatic-speech-recognition"
] |
2022-05-10T14:42:49Z
| 0
|
---
annotations_creators: []
language:
- en
- de
- fr
- es
- pl
- it
- ro
- hu
- cs
- nl
- fi
- hr
- sk
- sl
- et
- lt
language_creators: []
license:
- cc0-1.0
- other
multilinguality:
- multilingual
pretty_name: VoxPopuli
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for Voxpopuli
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/facebookresearch/voxpopuli
- **Repository:** https://github.com/facebookresearch/voxpopuli
- **Paper:** https://arxiv.org/abs/2101.00390
- **Point of Contact:** [[email protected]](mailto:[email protected]), [[email protected]](mailto:[email protected]), [[email protected]](mailto:[email protected])
### Dataset Summary
VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials.
This implementation contains transcribed speech data for 18 languages.
It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents)
### Example usage
VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name:
```python
from datasets import load_dataset
voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr")
```
To load all the languages in a single dataset use "multilang" config name:
```python
voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang")
```
To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter:
```python
voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"])
```
To load accented English data, use "en_accented" config name:
```python
voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented")
```
**Note that L2 English subset contains only `test` split.**
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
Accented English subset can also be used for research in ASR for accented speech (15 L2 accents)
### Languages
VoxPopuli contains labelled (transcribed) data for 18 languages:
| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
| English | En | 543 | 1313 | 4.8M |
| German | De | 282 | 531 | 2.3M |
| French | Fr | 211 | 534 | 2.1M |
| Spanish | Es | 166 | 305 | 1.6M |
| Polish | Pl | 111 | 282 | 802K |
| Italian | It | 91 | 306 | 757K |
| Romanian | Ro | 89 | 164 | 739K |
| Hungarian | Hu | 63 | 143 | 431K |
| Czech | Cs | 62 | 138 | 461K |
| Dutch | Nl | 53 | 221 | 488K |
| Finnish | Fi | 27 | 84 | 160K |
| Croatian | Hr | 43 | 83 | 337K |
| Slovak | Sk | 35 | 96 | 270K |
| Slovene | Sl | 10 | 45 | 76K |
| Estonian | Et | 3 | 29 | 18K |
| Lithuanian | Lt | 2 | 21 | 10K |
| Total | | 1791 | 4295 | 15M |
Accented speech transcribed data has 15 various L2 accents:
| Accent | Code | Transcribed Hours | Transcribed Speakers |
|:---:|:---:|:---:|:---:|
| Dutch | en_nl | 3.52 | 45 |
| German | en_de | 3.52 | 84 |
| Czech | en_cs | 3.30 | 26 |
| Polish | en_pl | 3.23 | 33 |
| French | en_fr | 2.56 | 27 |
| Hungarian | en_hu | 2.33 | 23 |
| Finnish | en_fi | 2.18 | 20 |
| Romanian | en_ro | 1.85 | 27 |
| Slovak | en_sk | 1.46 | 17 |
| Spanish | en_es | 1.42 | 18 |
| Italian | en_it | 1.11 | 15 |
| Estonian | en_et | 1.08 | 6 |
| Lithuanian | en_lt | 0.65 | 7 |
| Croatian | en_hr | 0.42 | 9 |
| Slovene | en_sl | 0.25 | 7 |
## Dataset Structure
### Data Instances
```python
{
'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5',
'language': 11, # "hr"
'audio': {
'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav',
'array': array([-0.01434326, -0.01055908, 0.00106812, ..., 0.00646973], dtype=float32),
'sampling_rate': 16000
},
'raw_text': '',
'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.',
'gender': 'female',
'speaker_id': '119431',
'is_gold_transcript': True,
'accent': 'None'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `language` (datasets.ClassLabel) - numerical id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `raw_text` (string) - original (orthographic) audio segment text
* `normalized_text` (string) - normalized audio segment transcription
* `gender` (string) - gender of speaker
* `speaker_id` (string) - id of speaker
* `is_gold_transcript` (bool) - ?
* `accent` (string) - type of accent, for example "en_lt", if applicable, else "None".
### Data Splits
All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home)
#### Initial Data Collection and Normalization
The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation.
Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.
The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.
The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment.
We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).
#### Who are the source language producers?
Speakers are participants of the European Parliament events, many of them are EU officials.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data.
VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers.
The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials.
### Other Known Limitations
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data.
### Citation Information
Please cite this paper:
```bibtex
@inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya and
Haziza, Daniel and
Williamson, Mary and
Pino, Juan and
Dupoux, Emmanuel",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.80",
pages = "993--1003",
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
|
jyang-ca/so101_test_grab_place
|
jyang-ca
|
2025-06-14T15:48:48Z
| 0
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101",
"tutorial"
] |
[
"robotics"
] |
2025-06-14T15:42:54Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 51,
"total_frames": 23783,
"total_tasks": 1,
"total_videos": 102,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:51"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.Orbbec": {
"dtype": "video",
"shape": [
880,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 880,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
Doae16/Arabic_summaries_batch21
|
Doae16
|
2025-03-06T12:59:07Z
| 16
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-05T20:29:25Z
| 0
|
---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
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dtype: string
splits:
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num_examples: 2100
download_size: 5266634
dataset_size: 11204825
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Shivak666/testing_3
|
Shivak666
|
2025-02-20T08:41:45Z
| 7
| 0
|
[
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-20T08:37:01Z
| 0
|
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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struct:
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struct:
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struct:
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struct:
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dtype: string
splits:
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num_bytes: 1870
num_examples: 1
download_size: 37531
dataset_size: 1870
---
|
Mineru/ETH
|
Mineru
|
2024-11-30T00:44:50Z
| 27
| 0
|
[
"license:cc-by-sa-4.0",
"size_categories:1M<n<10M",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-30T00:44:24Z
| 0
|
---
license: cc-by-sa-4.0
---
|
nadavo11/contrastive-seams
|
nadavo11
|
2025-09-29T07:05:28Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-09-29T07:05:00Z
| 0
|
---
dataset_info:
features:
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dtype: image
- name: seam
dtype: image
- name: is_fake
dtype: bool
- name: method
dtype: string
- name: image_path
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splits:
- name: test
num_bytes: 6054332
num_examples: 50
- name: train
num_bytes: 23758371
num_examples: 200
download_size: 29825732
dataset_size: 29812703
configs:
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data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
---
|
liu-nlp/estonian-blimp-nom-sg-to-nom-pl-experimental
|
liu-nlp
|
2025-09-19T16:12:32Z
| 84
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-09-19T16:12:29Z
| 0
|
---
dataset_info:
features:
- name: original
dtype: string
- name: corrupted
dtype: string
splits:
- name: train
num_bytes: 357969
num_examples: 1316
download_size: 242847
dataset_size: 357969
configs:
- config_name: default
data_files:
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path: data/train-*
---
|
adaadig/candorNEW_P7tags
|
adaadig
|
2025-02-16T05:45:13Z
| 8
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-16T05:44:40Z
| 0
|
---
dataset_info:
features:
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dtype: string
- name: avg_log_prob
dtype: float64
- name: whisperbase_transcript_list
dtype: string
- name: confidence_score
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- name: utterance_pitch_mean
dtype: float32
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dtype: float64
- name: c50
dtype: float64
- name: speaking_rate
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- name: phonemes
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- name: reverberation
dtype: string
- name: speech_monotony
dtype: string
splits:
- name: train
num_bytes: 8517515
num_examples: 9932
download_size: 3020421
dataset_size: 8517515
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Taywon/HH_sycophancy_biased_15k
|
Taywon
|
2024-12-05T07:26:18Z
| 15
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-05T07:23:38Z
| 0
|
---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: flipped
dtype: bool
splits:
- name: train
num_bytes: 51774507
num_examples: 15000
- name: test
num_bytes: 3580088
num_examples: 1071
download_size: 31290366
dataset_size: 55354595
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
OpenPipe/best-hn-comment-pairs-v2
|
OpenPipe
|
2024-10-17T18:37:30Z
| 31
| 1
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-17T18:37:22Z
| 0
|
---
dataset_info:
features:
- name: chosen
dtype: int64
- name: rejected
dtype: int64
- name: chosen_rank
dtype: int64
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dtype: int64
- name: top_level_parent
dtype: int64
- name: split
dtype: large_string
- name: chosen_prompt
dtype: large_string
- name: rejected_prompt
dtype: large_string
splits:
- name: train
num_bytes: 115409826
num_examples: 30000
- name: validation
num_bytes: 1964047
num_examples: 500
- name: test
num_bytes: 3860648
num_examples: 1000
download_size: 68080869
dataset_size: 121234521
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
mastermani305/ps-story-content
|
mastermani305
|
2024-10-12T09:30:14Z
| 17
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-12T09:30:09Z
| 0
|
---
dataset_info:
features:
- name: url
dtype: string
- name: part_no
dtype: int64
- name: content_as_array_of_para
sequence: string
- name: content
dtype: string
- name: chapter_no
dtype: string
splits:
- name: train
num_bytes: 21025439
num_examples: 294
download_size: 7276519
dataset_size: 21025439
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ohgnues/domain_fineweb-edu
|
ohgnues
|
2025-03-08T11:58:47Z
| 26
| 0
|
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-08T11:43:17Z
| 0
|
---
dataset_info:
- config_name: Business_and_Industrial
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4752343210
num_examples: 1000000
download_size: 2717936517
dataset_size: 4752343210
- config_name: Computers_and_Electronics
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4208795253
num_examples: 1000000
download_size: 2393073581
dataset_size: 4208795253
- config_name: Health
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4670790951
num_examples: 1000000
download_size: 2705536051
dataset_size: 4670790951
- config_name: Law_and_Government
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6342709607
num_examples: 1000000
download_size: 3600415941
dataset_size: 6342709607
- config_name: Science
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4394530292
num_examples: 1000000
download_size: 2589361241
dataset_size: 4394530292
configs:
- config_name: Business_and_Industrial
data_files:
- split: train
path: Business_and_Industrial/train-*
- config_name: Computers_and_Electronics
data_files:
- split: train
path: Computers_and_Electronics/train-*
- config_name: Health
data_files:
- split: train
path: Health/train-*
- config_name: Law_and_Government
data_files:
- split: train
path: Law_and_Government/train-*
- config_name: Science
data_files:
- split: train
path: Science/train-*
---
|
parler-tts/libritts_r_filtered
|
parler-tts
|
2024-08-06T16:45:54Z
| 1,736
| 20
|
[
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2305.18802",
"arxiv:2406.07969",
"region:us"
] |
[
"text-to-speech",
"automatic-speech-recognition"
] |
2024-06-13T15:48:39Z
| 1
|
---
license: cc-by-4.0
task_categories:
- text-to-speech
- automatic-speech-recognition
language:
- en
size_categories:
- 10K<n<100K
dataset_info:
- config_name: clean
features:
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dtype:
audio:
sampling_rate: 24000
- name: text_normalized
dtype: string
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splits:
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num_bytes: 1506311977.8882804
num_examples: 5589
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num_bytes: 1432099582.6705585
num_examples: 4689
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num_bytes: 8985618654.720787
num_examples: 32215
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num_bytes: 31794257100.91056
num_examples: 112326
download_size: 44461321972
dataset_size: 43718287316.190186
- config_name: other
features:
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dtype:
audio:
sampling_rate: 24000
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
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num_bytes: 1042714063.4789225
num_examples: 4342
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num_bytes: 1061489621.2561874
num_examples: 4716
- name: train.other.500
num_bytes: 50718457351.73659
num_examples: 194626
download_size: 54153699917
dataset_size: 52822661036.471695
configs:
- config_name: clean
data_files:
- split: dev.clean
path: clean/dev.clean-*
- split: test.clean
path: clean/test.clean-*
- split: train.clean.100
path: clean/train.clean.100-*
- split: train.clean.360
path: clean/train.clean.360-*
- config_name: other
data_files:
- split: dev.other
path: other/dev.other-*
- split: test.other
path: other/test.other-*
- split: train.other.500
path: other/train.other.500-*
pretty_name: Filtered LibriTTS-R
---
# Dataset Card for Filtered LibriTTS-R
This is a filtered version of [LibriTTS-R](https://huggingface.co/datasets/mythicinfinity/libritts_r). It has been filtered based on two sources:
1. LibriTTS-R paper [1], which lists samples for which speech restoration have failed
2. LibriTTS-P [2] list of [excluded speakers](https://github.com/line/LibriTTS-P/blob/main/data/excluded_spk_list.txt) for which multiple speakers have been detected.
LibriTTS-R [1] is a sound quality improved version of the [LibriTTS corpus](http://www.openslr.org/60/) which is a multi-speaker English corpus of approximately
585 hours of read English speech at 24kHz sampling rate, published in 2019.
## Usage
### Example
Loading the `clean` config with only the `train.clean.360` split.
```py
from datasets import load_dataset
load_dataset("blabble-io/libritts_r", "clean", split="train.clean.100")
```
Streaming is also supported.
```py
from datasets import load_dataset
load_dataset("blabble-io/libritts_r", streaming=True)
```
### Splits
There are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):
- dev.clean
- dev.other
- test.clean
- test.other
- train.clean.100
- train.clean.360
- train.other.500
### Configurations
There are 3 configurations, each which limits the splits the `load_dataset()` function will download.
The default configuration is "all".
- "dev": only the "dev.clean" split (good for testing the dataset quickly)
- "clean": contains only "clean" splits
- "other": contains only "other" splits
- "all": contains only "all" splits
### Columns
```
{
"audio": datasets.Audio(sampling_rate=24_000),
"text_normalized": datasets.Value("string"),
"text_original": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"path": datasets.Value("string"),
"chapter_id": datasets.Value("string"),
"id": datasets.Value("string"),
}
```
### Example Row
```
{
'audio': {
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS_R/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'array': ...,
'sampling_rate': 24000
},
'text_normalized': 'How quickly he disappeared!"',
'text_original': 'How quickly he disappeared!"',
'speaker_id': '3081',
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS_R/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'chapter_id': '166546',
'id': '3081_166546_000028_000002'
}
```
## Dataset Details
### Dataset Description
- **License:** CC BY 4.0
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Homepage:** https://www.openslr.org/141/
- **Paper:** https://arxiv.org/abs/2305.18802
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```
@ARTICLE{Koizumi2023-hs,
title = "{LibriTTS-R}: A restored multi-speaker text-to-speech corpus",
author = "Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding,
Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani,
Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur",
abstract = "This paper introduces a new speech dataset called
``LibriTTS-R'' designed for text-to-speech (TTS) use. It is
derived by applying speech restoration to the LibriTTS
corpus, which consists of 585 hours of speech data at 24 kHz
sampling rate from 2,456 speakers and the corresponding
texts. The constituent samples of LibriTTS-R are identical
to those of LibriTTS, with only the sound quality improved.
Experimental results show that the LibriTTS-R ground-truth
samples showed significantly improved sound quality compared
to those in LibriTTS. In addition, neural end-to-end TTS
trained with LibriTTS-R achieved speech naturalness on par
with that of the ground-truth samples. The corpus is freely
available for download from
\textbackslashurl\{http://www.openslr.org/141/\}.",
month = may,
year = 2023,
copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
archivePrefix = "arXiv",
primaryClass = "eess.AS",
eprint = "2305.18802"
}
```
```
@misc{kawamura2024librittspcorpusspeakingstyle,
title={LibriTTS-P: A Corpus with Speaking Style and Speaker Identity Prompts for Text-to-Speech and Style Captioning},
author={Masaya Kawamura and Ryuichi Yamamoto and Yuma Shirahata and Takuya Hasumi and Kentaro Tachibana},
year={2024},
eprint={2406.07969},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2406.07969},
}
```
|
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