File size: 28,190 Bytes
d9b9fbb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 |
---
base_model: intfloat/multilingual-e5-small
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2476
- loss:OnlineContrastiveLoss
widget:
- source_sentence: Why do you want to be to president?
sentences:
- Can you teach me how to cook?
- Recipe for baking cookies
- Would you want to be President?
- source_sentence: What is the speed of sound in air?
sentences:
- Velocity of sound waves in the atmosphere
- What is the most delicious dish you've ever eaten and why?
- The `safe` parameter in the `to_spreadsheet` method determines if a secure conversion
is necessary for certain plant attributes to be stored in a SpreadsheetTable or
Row.
- source_sentence: How many countries are in the European Union?
sentences:
- Number of countries in the European Union
- Artist who painted the Sistine Chapel
- The RecipeManager class is employed to oversee the downloading and unpacking of
recipes.
- source_sentence: What is the currency of the United States?
sentences:
- What's the purpose of life? What is life actually about?
- Iter_zip() is employed to sequentially access and yield files inside ZIP archives.
- Official currency of the USA
- source_sentence: Who wrote the book "To Kill a Mockingbird"?
sentences:
- At what speed does light travel?
- How to set up a yoga studio?
- Who wrote the book "1984"?
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-small
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: pair class dev
type: pair-class-dev
metrics:
- type: cosine_accuracy
value: 0.8768115942028986
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8267427086830139
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8969696969696969
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8267427086830139
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8809523809523809
name: Cosine Precision
- type: cosine_recall
value: 0.9135802469135802
name: Cosine Recall
- type: cosine_ap
value: 0.9300650297384708
name: Cosine Ap
- type: dot_accuracy
value: 0.8768115942028986
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.8267427682876587
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.8969696969696969
name: Dot F1
- type: dot_f1_threshold
value: 0.8267427682876587
name: Dot F1 Threshold
- type: dot_precision
value: 0.8809523809523809
name: Dot Precision
- type: dot_recall
value: 0.9135802469135802
name: Dot Recall
- type: dot_ap
value: 0.9300650297384708
name: Dot Ap
- type: manhattan_accuracy
value: 0.8731884057971014
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 8.953017234802246
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.8929663608562691
name: Manhattan F1
- type: manhattan_f1_threshold
value: 9.028047561645508
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.8848484848484849
name: Manhattan Precision
- type: manhattan_recall
value: 0.9012345679012346
name: Manhattan Recall
- type: manhattan_ap
value: 0.9284992066218356
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.8768115942028986
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.5886479616165161
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.8969696969696969
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.5886479616165161
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.8809523809523809
name: Euclidean Precision
- type: euclidean_recall
value: 0.9135802469135802
name: Euclidean Recall
- type: euclidean_ap
value: 0.9300650297384708
name: Euclidean Ap
- type: max_accuracy
value: 0.8768115942028986
name: Max Accuracy
- type: max_accuracy_threshold
value: 8.953017234802246
name: Max Accuracy Threshold
- type: max_f1
value: 0.8969696969696969
name: Max F1
- type: max_f1_threshold
value: 9.028047561645508
name: Max F1 Threshold
- type: max_precision
value: 0.8848484848484849
name: Max Precision
- type: max_recall
value: 0.9135802469135802
name: Max Recall
- type: max_ap
value: 0.9300650297384708
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: pair class test
type: pair-class-test
metrics:
- type: cosine_accuracy
value: 0.8768115942028986
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8267427086830139
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8969696969696969
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8267427086830139
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8809523809523809
name: Cosine Precision
- type: cosine_recall
value: 0.9135802469135802
name: Cosine Recall
- type: cosine_ap
value: 0.9300650297384708
name: Cosine Ap
- type: dot_accuracy
value: 0.8768115942028986
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.8267427682876587
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.8969696969696969
name: Dot F1
- type: dot_f1_threshold
value: 0.8267427682876587
name: Dot F1 Threshold
- type: dot_precision
value: 0.8809523809523809
name: Dot Precision
- type: dot_recall
value: 0.9135802469135802
name: Dot Recall
- type: dot_ap
value: 0.9300650297384708
name: Dot Ap
- type: manhattan_accuracy
value: 0.8731884057971014
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 8.953017234802246
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.8929663608562691
name: Manhattan F1
- type: manhattan_f1_threshold
value: 9.028047561645508
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.8848484848484849
name: Manhattan Precision
- type: manhattan_recall
value: 0.9012345679012346
name: Manhattan Recall
- type: manhattan_ap
value: 0.9284992066218356
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.8768115942028986
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.5886479616165161
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.8969696969696969
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.5886479616165161
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.8809523809523809
name: Euclidean Precision
- type: euclidean_recall
value: 0.9135802469135802
name: Euclidean Recall
- type: euclidean_ap
value: 0.9300650297384708
name: Euclidean Ap
- type: max_accuracy
value: 0.8768115942028986
name: Max Accuracy
- type: max_accuracy_threshold
value: 8.953017234802246
name: Max Accuracy Threshold
- type: max_f1
value: 0.8969696969696969
name: Max F1
- type: max_f1_threshold
value: 9.028047561645508
name: Max F1 Threshold
- type: max_precision
value: 0.8848484848484849
name: Max Precision
- type: max_recall
value: 0.9135802469135802
name: Max Recall
- type: max_ap
value: 0.9300650297384708
name: Max Ap
---
# SentenceTransformer based on intfloat/multilingual-e5-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
- **Maximum Sequence Length:** 512 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': 512, '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("srikarvar/fine_tuned_model_15")
# Run inference
sentences = [
'Who wrote the book "To Kill a Mockingbird"?',
'Who wrote the book "1984"?',
'At what speed does light travel?',
]
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
#### Binary Classification
* Dataset: `pair-class-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.8768 |
| cosine_accuracy_threshold | 0.8267 |
| cosine_f1 | 0.897 |
| cosine_f1_threshold | 0.8267 |
| cosine_precision | 0.881 |
| cosine_recall | 0.9136 |
| cosine_ap | 0.9301 |
| dot_accuracy | 0.8768 |
| dot_accuracy_threshold | 0.8267 |
| dot_f1 | 0.897 |
| dot_f1_threshold | 0.8267 |
| dot_precision | 0.881 |
| dot_recall | 0.9136 |
| dot_ap | 0.9301 |
| manhattan_accuracy | 0.8732 |
| manhattan_accuracy_threshold | 8.953 |
| manhattan_f1 | 0.893 |
| manhattan_f1_threshold | 9.028 |
| manhattan_precision | 0.8848 |
| manhattan_recall | 0.9012 |
| manhattan_ap | 0.9285 |
| euclidean_accuracy | 0.8768 |
| euclidean_accuracy_threshold | 0.5886 |
| euclidean_f1 | 0.897 |
| euclidean_f1_threshold | 0.5886 |
| euclidean_precision | 0.881 |
| euclidean_recall | 0.9136 |
| euclidean_ap | 0.9301 |
| max_accuracy | 0.8768 |
| max_accuracy_threshold | 8.953 |
| max_f1 | 0.897 |
| max_f1_threshold | 9.028 |
| max_precision | 0.8848 |
| max_recall | 0.9136 |
| **max_ap** | **0.9301** |
#### Binary Classification
* Dataset: `pair-class-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.8768 |
| cosine_accuracy_threshold | 0.8267 |
| cosine_f1 | 0.897 |
| cosine_f1_threshold | 0.8267 |
| cosine_precision | 0.881 |
| cosine_recall | 0.9136 |
| cosine_ap | 0.9301 |
| dot_accuracy | 0.8768 |
| dot_accuracy_threshold | 0.8267 |
| dot_f1 | 0.897 |
| dot_f1_threshold | 0.8267 |
| dot_precision | 0.881 |
| dot_recall | 0.9136 |
| dot_ap | 0.9301 |
| manhattan_accuracy | 0.8732 |
| manhattan_accuracy_threshold | 8.953 |
| manhattan_f1 | 0.893 |
| manhattan_f1_threshold | 9.028 |
| manhattan_precision | 0.8848 |
| manhattan_recall | 0.9012 |
| manhattan_ap | 0.9285 |
| euclidean_accuracy | 0.8768 |
| euclidean_accuracy_threshold | 0.5886 |
| euclidean_f1 | 0.897 |
| euclidean_f1_threshold | 0.5886 |
| euclidean_precision | 0.881 |
| euclidean_recall | 0.9136 |
| euclidean_ap | 0.9301 |
| max_accuracy | 0.8768 |
| max_accuracy_threshold | 8.953 |
| max_f1 | 0.897 |
| max_f1_threshold | 9.028 |
| max_precision | 0.8848 |
| max_recall | 0.9136 |
| **max_ap** | **0.9301** |
<!--
## 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: 2,476 training samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~40.20%</li><li>1: ~59.80%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.35 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.06 tokens</li><li>max: 98 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:---------------|:----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
| <code>1</code> | <code>The ImageNet dataset is used for training models to classify images into various categories.</code> | <code>A model is trained using the ImageNet dataset to classify images into distinct categories.</code> |
| <code>1</code> | <code>No, it doesn't exist in version 5.3.1.</code> | <code>Version 5.3.1 does not contain it.</code> |
| <code>0</code> | <code>Can you help me with my homework?</code> | <code>Can you do my homework for me?</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 276 evaluation samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 276 samples:
| | label | sentence1 | sentence2 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~41.30%</li><li>1: ~58.70%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.56 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.34 tokens</li><li>max: 86 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
| <code>0</code> | <code>What are the challenges of AI in cybersecurity?</code> | <code>How is AI used to enhance cybersecurity?</code> |
| <code>1</code> | <code>You can find the SYSTEM log documentation on the main version. Click on the provided link to redirect to the main version of the documentation.</code> | <code>The SYSTEM log documentation can be accessed by clicking on the link which will take you to the main version.</code> |
| <code>1</code> | <code>What is the capital of Italy?</code> | <code>Name the capital city of Italy</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap |
|:-------:|:-------:|:-------------:|:----------:|:---------------------:|:----------------------:|
| 0 | 0 | - | - | 0.7876 | - |
| 0.2564 | 10 | 1.5794 | - | - | - |
| 0.5128 | 20 | 0.8392 | - | - | - |
| 0.7692 | 30 | 0.7812 | - | - | - |
| 1.0 | 39 | - | 0.8081 | 0.9138 | - |
| 1.0256 | 40 | 0.6505 | - | - | - |
| 1.2821 | 50 | 0.57 | - | - | - |
| 1.5385 | 60 | 0.3015 | - | - | - |
| 1.7949 | 70 | 0.3091 | - | - | - |
| 2.0 | 78 | - | 0.7483 | 0.9267 | - |
| 2.0513 | 80 | 0.3988 | - | - | - |
| 2.3077 | 90 | 0.1801 | - | - | - |
| 2.5641 | 100 | 0.1166 | - | - | - |
| 2.8205 | 110 | 0.1255 | - | - | - |
| 3.0 | 117 | - | 0.7106 | 0.9284 | - |
| 3.0769 | 120 | 0.2034 | - | - | - |
| 3.3333 | 130 | 0.0329 | - | - | - |
| 3.5897 | 140 | 0.0805 | - | - | - |
| 3.8462 | 150 | 0.0816 | - | - | - |
| **4.0** | **156** | **-** | **0.6969** | **0.9301** | **0.9301** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- 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.*
--> |