File size: 22,295 Bytes
f09530d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 |
---
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:CoSENTLoss
base_model: ymelka/camembert-cosmetic-finetuned
datasets:
- PhilipMay/stsb_multi_mt
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Nous nous déplaçons "... par rapport au cadre de repos cosmique
en mouvement ... à environ 371 km/s vers la constellation du Lion".
sentences:
- La dame a fait frire la viande panée dans de l'huile chaude.
- Il n'y a pas d'alambic qui ne soit pas relatif à un autre objet.
- Le joueur de basket-ball est sur le point de marquer des points pour son équipe.
- source_sentence: Le professeur Burkhauser a effectué des recherches approfondies
sur les personnes qui sont pénalisées par l'augmentation du salaire minimum.
sentences:
- Un adolescent parle à une fille par le biais d'une webcam.
- Une femme est en train de couper des oignons verts.
- Les lois sur le salaire minimum nuisent le plus aux personnes les moins qualifiées
et les moins productives.
- source_sentence: Bien que le terme "reine" puisse faire référence à la fois à la
reine régente (souveraine) ou à la reine consort, le roi a toujours été le souverain.
sentences:
- Des moutons paissent dans le champ devant une rangée d'arbres.
- Il y a une très bonne raison de ne pas appeler le conjoint de la Reine "Roi" -
parce qu'il n'est pas le Roi.
- Un groupe de personnes âgées pose autour d'une table à manger.
- source_sentence: Deux pygargues à tête blanche perchés sur une branche.
sentences:
- Un groupe de militaires joue dans un quintette de cuivres.
- Deux aigles sont perchés sur une branche.
- Un homme qui joue de la guitare sous la pluie.
- source_sentence: Un homme joue de la guitare.
sentences:
- Il est possible qu'un système solaire comme le nôtre existe en dehors d'une galaxie.
- Un homme joue de la flûte.
- Un homme est en train de manger une banane.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on ymelka/camembert-cosmetic-finetuned
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb fr dev
type: stsb-fr-dev
metrics:
- type: pearson_cosine
value: 0.6401461834329478
name: Pearson Cosine
- type: spearman_cosine
value: 0.6661576168424006
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7077411059971963
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7104395816607704
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6183470655093759
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6339424060254548
name: Spearman Euclidean
- type: pearson_dot
value: 0.18614455072383299
name: Pearson Dot
- type: spearman_dot
value: 0.21677402345623561
name: Spearman Dot
- type: pearson_max
value: 0.7077411059971963
name: Pearson Max
- type: spearman_max
value: 0.7104395816607704
name: Spearman Max
- type: pearson_cosine
value: 0.834390325106948
name: Pearson Cosine
- type: spearman_cosine
value: 0.8564941342147334
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8518548236293758
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.854193303324745
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8541012365072966
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8555434573522197
name: Spearman Euclidean
- type: pearson_dot
value: 0.4989804086580052
name: Pearson Dot
- type: spearman_dot
value: 0.5094008186566353
name: Spearman Dot
- type: pearson_max
value: 0.8541012365072966
name: Pearson Max
- type: spearman_max
value: 0.8564941342147334
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb fr test
type: stsb-fr-test
metrics:
- type: pearson_cosine
value: 0.7979696368103
name: Pearson Cosine
- type: spearman_cosine
value: 0.8219240068315988
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8237827107867745
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8221440625680553
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8230384709547542
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8218369251066925
name: Spearman Euclidean
- type: pearson_dot
value: 0.4089365107737232
name: Pearson Dot
- type: spearman_dot
value: 0.4588995887587045
name: Spearman Dot
- type: pearson_max
value: 0.8237827107867745
name: Pearson Max
- type: spearman_max
value: 0.8221440625680553
name: Spearman Max
---
# SentenceTransformer based on ymelka/camembert-cosmetic-finetuned
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ymelka/camembert-cosmetic-finetuned](https://huggingface.co/ymelka/camembert-cosmetic-finetuned) on the [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [ymelka/camembert-cosmetic-finetuned](https://huggingface.co/ymelka/camembert-cosmetic-finetuned) <!-- at revision cd4cb90f9388340c5f02740130efd30336c08905 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- **Languages:** de, en, es, fr, it, nl, pl, pt, ru, zh
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: CamembertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("ymelka/camembert-cosmetic-similarity")
# Run inference
sentences = [
'Un homme joue de la guitare.',
'Un homme est en train de manger une banane.',
'Un homme joue de la flûte.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `stsb-fr-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6401 |
| **spearman_cosine** | **0.6662** |
| pearson_manhattan | 0.7077 |
| spearman_manhattan | 0.7104 |
| pearson_euclidean | 0.6183 |
| spearman_euclidean | 0.6339 |
| pearson_dot | 0.1861 |
| spearman_dot | 0.2168 |
| pearson_max | 0.7077 |
| spearman_max | 0.7104 |
#### Semantic Similarity
* Dataset: `stsb-fr-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8344 |
| **spearman_cosine** | **0.8565** |
| pearson_manhattan | 0.8519 |
| spearman_manhattan | 0.8542 |
| pearson_euclidean | 0.8541 |
| spearman_euclidean | 0.8555 |
| pearson_dot | 0.499 |
| spearman_dot | 0.5094 |
| pearson_max | 0.8541 |
| spearman_max | 0.8565 |
#### Semantic Similarity
* Dataset: `stsb-fr-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.798 |
| **spearman_cosine** | **0.8219** |
| pearson_manhattan | 0.8238 |
| spearman_manhattan | 0.8221 |
| pearson_euclidean | 0.823 |
| spearman_euclidean | 0.8218 |
| pearson_dot | 0.4089 |
| spearman_dot | 0.4589 |
| pearson_max | 0.8238 |
| spearman_max | 0.8221 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### PhilipMay/stsb_multi_mt
* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 11.1 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.7</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------|
| <code>Un avion est en train de décoller.</code> | <code>Un avion est en train de décoller.</code> | <code>5.0</code> |
| <code>Un homme joue d'une grande flûte.</code> | <code>Un homme joue de la flûte.</code> | <code>3.799999952316284</code> |
| <code>Un homme étale du fromage râpé sur une pizza.</code> | <code>Un homme étale du fromage râpé sur une pizza non cuite.</code> | <code>3.799999952316284</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### PhilipMay/stsb_multi_mt
* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.45 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.35 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.36</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:------------------|
| <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>5.0</code> |
| <code>Un jeune enfant monte à cheval.</code> | <code>Un enfant monte à cheval.</code> | <code>4.75</code> |
| <code>Un homme donne une souris à un serpent.</code> | <code>L'homme donne une souris au serpent.</code> | <code>5.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: 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
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | stsb-fr-dev_spearman_cosine | stsb-fr-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------------------:|:----------------------------:|
| 0 | 0 | - | - | 0.6661 | - |
| 0.2778 | 100 | 4.9452 | 4.4417 | 0.7733 | - |
| 0.5556 | 200 | 4.667 | 4.4273 | 0.7986 | - |
| 0.8333 | 300 | 4.4904 | 4.3058 | 0.8338 | - |
| 1.1111 | 400 | 4.1679 | 4.2723 | 0.8491 | - |
| 1.3889 | 500 | 4.138 | 4.3575 | 0.8464 | - |
| 1.6667 | 600 | 4.5737 | 4.3427 | 0.8479 | - |
| 1.9444 | 700 | 4.3086 | 4.4455 | 0.8510 | - |
| 2.2222 | 800 | 3.8711 | 4.4135 | 0.8590 | - |
| 2.5 | 900 | 4.064 | 4.4775 | 0.8567 | - |
| 2.7778 | 1000 | 4.2255 | 4.4733 | 0.8565 | - |
| 3.0 | 1080 | - | - | - | 0.8219 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.2
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |