snowflake-arctic-embed-m-klej-dyk

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-m. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Snowflake/snowflake-arctic-embed-m
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Chłopiec z Nariokotome',
    'ile wynosiła objętość mózgu chłopca z Nariokotome?',
    'gdzie znajduje się czwarty polski cmentarz katyński?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1851
cosine_accuracy@3	0.4808
cosine_accuracy@5	0.625
cosine_accuracy@10	0.726
cosine_precision@1	0.1851
cosine_precision@3	0.1603
cosine_precision@5	0.125
cosine_precision@10	0.0726
cosine_recall@1	0.1851
cosine_recall@3	0.4808
cosine_recall@5	0.625
cosine_recall@10	0.726
cosine_ndcg@10	0.4479
cosine_mrr@10	0.359
cosine_map@100	0.3672

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1755
cosine_accuracy@3	0.4712
cosine_accuracy@5	0.613
cosine_accuracy@10	0.7019
cosine_precision@1	0.1755
cosine_precision@3	0.1571
cosine_precision@5	0.1226
cosine_precision@10	0.0702
cosine_recall@1	0.1755
cosine_recall@3	0.4712
cosine_recall@5	0.613
cosine_recall@10	0.7019
cosine_ndcg@10	0.4334
cosine_mrr@10	0.3474
cosine_map@100	0.3564

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1562
cosine_accuracy@3	0.4543
cosine_accuracy@5	0.5649
cosine_accuracy@10	0.6731
cosine_precision@1	0.1562
cosine_precision@3	0.1514
cosine_precision@5	0.113
cosine_precision@10	0.0673
cosine_recall@1	0.1562
cosine_recall@3	0.4543
cosine_recall@5	0.5649
cosine_recall@10	0.6731
cosine_ndcg@10	0.4103
cosine_mrr@10	0.3261
cosine_map@100	0.3351

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1635
cosine_accuracy@3	0.3918
cosine_accuracy@5	0.5072
cosine_accuracy@10	0.6058
cosine_precision@1	0.1635
cosine_precision@3	0.1306
cosine_precision@5	0.1014
cosine_precision@10	0.0606
cosine_recall@1	0.1635
cosine_recall@3	0.3918
cosine_recall@5	0.5072
cosine_recall@10	0.6058
cosine_ndcg@10	0.3758
cosine_mrr@10	0.3027
cosine_map@100	0.3117

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.149
cosine_accuracy@3	0.3389
cosine_accuracy@5	0.4183
cosine_accuracy@10	0.4928
cosine_precision@1	0.149
cosine_precision@3	0.113
cosine_precision@5	0.0837
cosine_precision@10	0.0493
cosine_recall@1	0.149
cosine_recall@3	0.3389
cosine_recall@5	0.4183
cosine_recall@10	0.4928
cosine_ndcg@10	0.3178
cosine_mrr@10	0.2621
cosine_map@100	0.2704

Training Details

Training Dataset

Unnamed Dataset

Size: 3,738 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 6 tokens
mean: 94.61 tokens
max: 512 tokens

min: 10 tokens
mean: 30.71 tokens
max: 76 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 94.61 tokens max: 512 tokens	min: 10 tokens mean: 30.71 tokens max: 76 tokens

Samples:

positive	anchor
`Marsz Ochotników (chin.`	`kto jest kompozytorem chińskiego hymnu narodowego Marsz Ochotników?`
Wybrane przykłady: Święta Rodzina – Maryja z Dzieciątkiem na ręku, niekiedy obok niej stoi św. Józef Rodzina Marii – przedstawienie w którym pojawia się Święta Rodzina oraz postaci spokrewnione z Marią. Maria w połogu (Maria in puerperio) – leżąca na łożu Maria opiekuje się Dzieciątkiem Maria karmiąca (Maria lactans) – Maria karmiąca swą piersią Dzieciątko Orantka – kobieta modląca się z podniesionymi rękami (częsty motyw ikon wschodnich); Sacra Conversazione – Matka Boska tronująca z Dzieciątkiem, otoczona stojącymi postaciami świętych Pietà – opłakująca Jezusa, trzymając na kolanach jego ciało po śmierci na krzyżu; Hodegetria – ujęcie popiersia Maryi, trzymającej na rękach małego Jezusa, częsty motyw w ikonach Eleusa – formalnie podobne do przedstawienia Hodegetrii lecz Maryja policzkiem przytula się do policzka Jezusa Immaculata – Niepokalane Poczęcie Najświętszej Maryi Panny.	`kto zamiast Maryi trzyma nowonarodzonego Jezusa w scenie Bożego Narodzenia przedstawionej na poliptyku z Marią i Dzieciątkiem Jezus?`
`Pomnik Josepha von Eichendorffa w Brzeziu Pomnik Josepha von Eichendorffa – odtworzony w 2006 roku pomnik znanego niemieckiego poety epoki romantyzmu związanego z ziemią raciborską, Josepha von Eichendorffa.`	`po ilu latach odtworzono wysadzony w 1945 roku pomnik Josepha von Eichendorffa w Raciborzu-Brzeziu?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 5
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.0684	1	9.3155	-	-	-	-	-
0.1368	2	9.1788	-	-	-	-	-
0.2051	3	8.8387	-	-	-	-	-
0.2735	4	8.2961	-	-	-	-	-
0.3419	5	8.0242	-	-	-	-	-
0.4103	6	7.2329	-	-	-	-	-
0.4786	7	5.4386	-	-	-	-	-
0.5470	8	6.1186	-	-	-	-	-
0.6154	9	4.9714	-	-	-	-	-
0.6838	10	5.1958	-	-	-	-	-
0.7521	11	5.1135	-	-	-	-	-
0.8205	12	4.6971	-	-	-	-	-
0.8889	13	4.5559	-	-	-	-	-
0.9573	14	3.9357	0.2842	0.3098	0.3191	0.2238	0.3209
1.0256	15	3.7916	-	-	-	-	-
1.0940	16	3.6393	-	-	-	-	-
1.1624	17	3.7733	-	-	-	-	-
1.2308	18	3.6974	-	-	-	-	-
1.2991	19	3.5964	-	-	-	-	-
1.3675	20	3.4118	-	-	-	-	-
1.4359	21	3.2022	-	-	-	-	-
1.5043	22	2.8133	-	-	-	-	-
1.5726	23	3.0871	-	-	-	-	-
1.6410	24	2.9559	-	-	-	-	-
1.7094	25	2.8192	-	-	-	-	-
1.7778	26	3.462	-	-	-	-	-
1.8462	27	3.1435	-	-	-	-	-
1.9145	28	2.8001	-	-	-	-	-
1.9829	29	2.5643	0.3134	0.3359	0.3563	0.2588	0.3671
2.0513	30	2.4295	-	-	-	-	-
2.1197	31	2.3892	-	-	-	-	-
2.1880	32	2.5228	-	-	-	-	-
2.2564	33	2.4906	-	-	-	-	-
2.3248	34	2.5358	-	-	-	-	-
2.3932	35	2.2806	-	-	-	-	-
2.4615	36	2.0083	-	-	-	-	-
2.5299	37	2.5088	-	-	-	-	-
2.5983	38	2.0628	-	-	-	-	-
2.6667	39	2.193	-	-	-	-	-
2.7350	40	2.4783	-	-	-	-	-
2.8034	41	2.382	-	-	-	-	-
2.8718	42	2.2017	-	-	-	-	-
2.9402	43	1.9739	0.3111	0.3392	0.3572	0.2657	0.3659
3.0085	44	2.0332	-	-	-	-	-
3.0769	45	1.9983	-	-	-	-	-
3.1453	46	1.8612	-	-	-	-	-
3.2137	47	1.9897	-	-	-	-	-
3.2821	48	2.2514	-	-	-	-	-
3.3504	49	2.0092	-	-	-	-	-
3.4188	50	1.7399	-	-	-	-	-
3.4872	51	1.5825	-	-	-	-	-
3.5556	52	2.1501	-	-	-	-	-
3.6239	53	1.4505	-	-	-	-	-
3.6923	54	1.8575	-	-	-	-	-
3.7607	55	2.3882	-	-	-	-	-
3.8291	56	2.1119	-	-	-	-	-
3.8974	57	1.8992	-	-	-	-	-
3.9658	58	1.8323	0.3117	0.3365	0.3558	0.2683	0.3670
4.0342	59	1.5938	-	-	-	-	-
4.1026	60	1.552	-	-	-	-	-
4.1709	61	1.907	-	-	-	-	-
4.2393	62	1.8304	-	-	-	-	-
4.3077	63	1.8775	-	-	-	-	-
4.3761	64	1.8654	-	-	-	-	-
4.4444	65	1.7944	-	-	-	-	-
4.5128	66	1.8335	-	-	-	-	-
4.5812	67	1.8823	-	-	-	-	-
4.6496	68	1.6479	-	-	-	-	-
4.7179	69	1.5771	-	-	-	-	-
4.7863	70	2.1911	0.3117	0.3351	0.3564	0.2704	0.3672

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.2
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.3.1
Accelerate: 0.27.2
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

ve88ifz2
/

snowflake-arctic-embed-m-klej-dyk-v0.1

snowflake-arctic-embed-m-klej-dyk

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Model tree for ve88ifz2/snowflake-arctic-embed-m-klej-dyk-v0.1

Evaluation results