File size: 18,863 Bytes
b8be1b1 |
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 |
---
base_model: sentence-transformers/all-MiniLM-L12-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100000
- loss:CosineSimilarityLoss
widget:
- source_sentence: NIPA personal income includes pension contributions by employers
in the year income is earned , and benefits paid at retirement are not a component
of NIPA income .
sentences:
- While not the only makeup of income , NIPA is one of the more well known income
distinctions .
- Les temples de karnak et de Louxor ont été démolis pour faire place à des projets
de construction en Cisjordanie .
- Les restaurants sont tenus à des règles strictes pour contenir leur odeur .
- source_sentence: right right you know the one that 's one reason we bought a house
here in Plano we were hoping you know well the school district 's gonna be good
you know for resale value and so on and so forth but
sentences:
- We moved to Plano because we thought the school district was good .
- These and those .
- L' obsession a suscité une suggestion que tous étaient des boucs émissaires de
la guerre .
- source_sentence: Dans l' homme invisible , le talentueux dixième narrateur doit
surmonter non seulement les différentes idéologies qui lui sont présentées comme
masques ou subversions d' identité , mais aussi les différents rôles et prescriptions
pour le leadership que sa propre race lui souhaite de réaliser .
sentences:
- '" We ''re too uptight now ! " Said Tommy'
- Le talentueux dixième narrateur doit surmonter les idéologies .
- Saddam is not taking advantage of the current Arab love towards the United States
- source_sentence: Les lacunes trouvées au cours de la surveillance en cours ou au
moyen d' évaluations distinctes devraient être communiquées à l' individu responsable
de la fonction et à au moins un niveau de gestion au-dessus de cet individu .
sentences:
- L' économie diminuera également si les conditions du marché changent .
- The Watergate comparison wasn 't just for Democratic bashing .
- Il n' y a pas lieu de signaler les lacunes .
- source_sentence: it looks fertile and it it um i mean it rains enough they have
the climate and the rain and if not it 's like i 've been to Saint Thomas and
it just starts from the ocean up
sentences:
- Il n' a jamais triché .
- They don 't know how to do it .
- They have the rain and the climate so I imagine the lands would be fertile .
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: snli dev
type: snli-dev
metrics:
- type: pearson_cosine
value: 0.3725313255221131
name: Pearson Cosine
- type: spearman_cosine
value: 0.3729470854776107
name: Spearman Cosine
- type: pearson_manhattan
value: 0.3650227128515394
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.37250760289182383
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.36567325497563746
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.37294699995093694
name: Spearman Euclidean
- type: pearson_dot
value: 0.3725313190046259
name: Pearson Dot
- type: spearman_dot
value: 0.3729474276296007
name: Spearman Dot
- type: pearson_max
value: 0.3725313255221131
name: Pearson Max
- type: spearman_max
value: 0.3729474276296007
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision a05860a77cef7b37e0048a7864658139bc18a854 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("cherifkhalifah/finetuned-snli-MiniLM-L12-v2-100k-en-fr")
# Run inference
sentences = [
"it looks fertile and it it um i mean it rains enough they have the climate and the rain and if not it 's like i 've been to Saint Thomas and it just starts from the ocean up",
'They have the rain and the climate so I imagine the lands would be fertile .',
"They don 't know how to do it .",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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: `snli-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.3725 |
| spearman_cosine | 0.3729 |
| pearson_manhattan | 0.365 |
| spearman_manhattan | 0.3725 |
| pearson_euclidean | 0.3657 |
| spearman_euclidean | 0.3729 |
| pearson_dot | 0.3725 |
| spearman_dot | 0.3729 |
| pearson_max | 0.3725 |
| **spearman_max** | **0.3729** |
<!--
## 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
#### Unnamed Dataset
* Size: 100,000 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 35.27 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.46 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----------------|
| <code>Natalia M' a regardé .</code> | <code>Natalia a regardé et attend que je lui donne l' épée .</code> | <code>0.5</code> |
| <code>And he sounded sincere .</code> | <code>He sounded sincere.He was sounding sincere in his words .</code> | <code>0.0</code> |
| <code>There 's a small zoo area where you can see snakes , lizards , birds of prey , wolves , hyenas , foxes , and various desert cats , including cheetahs and leopards .</code> | <code>The zoo is home to some endangered desert animals .</code> | <code>0.5</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### 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
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | snli-dev_spearman_max |
|:------:|:-----:|:-------------:|:---------------------:|
| 0.08 | 500 | 0.2008 | 0.0433 |
| 0.16 | 1000 | 0.1757 | 0.1024 |
| 0.24 | 1500 | 0.1732 | 0.1503 |
| 0.32 | 2000 | 0.1685 | 0.2168 |
| 0.4 | 2500 | 0.1702 | 0.2206 |
| 0.48 | 3000 | 0.1676 | 0.2117 |
| 0.56 | 3500 | 0.1637 | 0.2624 |
| 0.64 | 4000 | 0.1636 | 0.2169 |
| 0.72 | 4500 | 0.1608 | 0.0051 |
| 0.8 | 5000 | 0.1601 | 0.2236 |
| 0.88 | 5500 | 0.1597 | 0.2471 |
| 0.96 | 6000 | 0.1596 | 0.2934 |
| 1.0 | 6250 | - | 0.2905 |
| 1.04 | 6500 | 0.1602 | 0.3001 |
| 1.12 | 7000 | 0.1571 | 0.3116 |
| 1.2 | 7500 | 0.1588 | 0.3145 |
| 1.28 | 8000 | 0.1562 | 0.3304 |
| 1.3600 | 8500 | 0.1548 | 0.3376 |
| 1.44 | 9000 | 0.156 | 0.3359 |
| 1.52 | 9500 | 0.1552 | 0.3194 |
| 1.6 | 10000 | 0.153 | 0.3474 |
| 1.6800 | 10500 | 0.1529 | 0.3220 |
| 1.76 | 11000 | 0.1518 | 0.3255 |
| 1.8400 | 11500 | 0.1499 | 0.3332 |
| 1.92 | 12000 | 0.1524 | 0.3521 |
| 2.0 | 12500 | 0.1512 | 0.3425 |
| 2.08 | 13000 | 0.1514 | 0.3462 |
| 2.16 | 13500 | 0.1516 | 0.3414 |
| 2.24 | 14000 | 0.1532 | 0.3453 |
| 2.32 | 14500 | 0.1459 | 0.3699 |
| 2.4 | 15000 | 0.1524 | 0.3576 |
| 2.48 | 15500 | 0.1506 | 0.3418 |
| 2.56 | 16000 | 0.1488 | 0.3559 |
| 2.64 | 16500 | 0.1486 | 0.3597 |
| 2.7200 | 17000 | 0.1469 | 0.3552 |
| 2.8 | 17500 | 0.1448 | 0.3459 |
| 2.88 | 18000 | 0.1458 | 0.3503 |
| 2.96 | 18500 | 0.1468 | 0.3647 |
| 3.0 | 18750 | - | 0.3611 |
| 3.04 | 19000 | 0.1472 | 0.3741 |
| 3.12 | 19500 | 0.1457 | 0.3603 |
| 3.2 | 20000 | 0.147 | 0.3576 |
| 3.2800 | 20500 | 0.1451 | 0.3663 |
| 3.36 | 21000 | 0.1438 | 0.3734 |
| 3.44 | 21500 | 0.1471 | 0.3698 |
| 3.52 | 22000 | 0.1462 | 0.3646 |
| 3.6 | 22500 | 0.1436 | 0.3740 |
| 3.68 | 23000 | 0.1441 | 0.3696 |
| 3.76 | 23500 | 0.1423 | 0.3636 |
| 3.84 | 24000 | 0.1411 | 0.3713 |
| 3.92 | 24500 | 0.1438 | 0.3706 |
| 4.0 | 25000 | 0.1421 | 0.3729 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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",
}
```
<!--
## 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.*
--> |