File size: 29,258 Bytes
79c2367 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 |
---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4517388
- loss:ContrastiveLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: 640 prt ashley floor 10 chula vista california 91913
sentences:
- 10523 howard parks apartment 8 cockseysville md 21030
- 640 prt ashley floor 10 East Gregory PW 91913
- trailwoods radial loveland oh 4514
- source_sentence: 9036 taylorsville road louisville ky 40299-1750
sentences:
- '16331 northwest gearin junctn floor num 6 apt # 4 f tigard or 97223-2808'
- 19 Brian Key walk voorhees township n. j. 08026
- 9036 taylorsville boulevard louisville 40299-175
- source_sentence: 11 simek ln middletown township n j 07758
sentences:
- 248 strawberry meadows place apt 1 springdale 72764-3759
- 11 Daniel Drive knl middletown township MT 41761
- 1135 s westgate ave Mileshaven ca 90049
- source_sentence: so west prospect street aloha or 97078
sentences:
- '1300 Brittney Club plains lot # b new york cty NY 10459'
- 527 Nicole Springs bypas rupert CA 05776
- so wdest prospect street aloha 97078
- source_sentence: 8234 harvest bend lane laurel md 20707
sentences:
- 8234 harvest bend lane laurel md
- 8702 wahl crse basement santee ca 92071
- 310 ella street Jamesborough ne 68310
datasets:
- jarredparrett/deepparse_address_mutations_comb_3
pipeline_tag: sentence-similarity
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
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: jarredparrett/deepparse address mutations comb 3
type: jarredparrett/deepparse_address_mutations_comb_3
metrics:
- type: cosine_accuracy
value: 0.9770643339132159
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7712496519088745
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9784053285401372
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7712496519088745
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.960100255219399
name: Cosine Precision
- type: cosine_recall
value: 0.9974219699718995
name: Cosine Recall
- type: cosine_ap
value: 0.9864940067102314
name: Cosine Ap
- type: dot_accuracy
value: 0.9770643339132159
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.7712496519088745
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9784053285401372
name: Dot F1
- type: dot_f1_threshold
value: 0.7712496519088745
name: Dot F1 Threshold
- type: dot_precision
value: 0.960100255219399
name: Dot Precision
- type: dot_recall
value: 0.9974219699718995
name: Dot Recall
- type: dot_ap
value: 0.986499063941509
name: Dot Ap
- type: manhattan_accuracy
value: 0.9770395408321384
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 10.601512908935547
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.978383036334317
name: Manhattan F1
- type: manhattan_f1_threshold
value: 10.611783027648926
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.9600334406666756
name: Manhattan Precision
- type: manhattan_recall
value: 0.9974477502721805
name: Manhattan Recall
- type: manhattan_ap
value: 0.9865423177462433
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9770643339132159
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.6763879060745239
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9784053285401372
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.6763879060745239
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.960100255219399
name: Euclidean Precision
- type: euclidean_recall
value: 0.9974219699718995
name: Euclidean Recall
- type: euclidean_ap
value: 0.9865515796011742
name: Euclidean Ap
- type: max_accuracy
value: 0.9770643339132159
name: Max Accuracy
- type: max_accuracy_threshold
value: 10.601512908935547
name: Max Accuracy Threshold
- type: max_f1
value: 0.9784053285401372
name: Max F1
- type: max_f1_threshold
value: 10.611783027648926
name: Max F1 Threshold
- type: max_precision
value: 0.960100255219399
name: Max Precision
- type: max_recall
value: 0.9974477502721805
name: Max Recall
- type: max_ap
value: 0.9865515796011742
name: Max Ap
- type: cosine_accuracy
value: 0.9770612347780813
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7710819244384766
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9783854448042815
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7710819244384766
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9600473761629129
name: Cosine Precision
- type: cosine_recall
value: 0.9974377142267394
name: Cosine Recall
- type: cosine_ap
value: 0.9865423807819248
name: Cosine Ap
- type: dot_accuracy
value: 0.9770612347780813
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.7710819244384766
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9783854448042815
name: Dot F1
- type: dot_f1_threshold
value: 0.7710819244384766
name: Dot F1 Threshold
- type: dot_precision
value: 0.9600473761629129
name: Dot Precision
- type: dot_recall
value: 0.9974377142267394
name: Dot Recall
- type: dot_ap
value: 0.9865613743522202
name: Dot Ap
- type: manhattan_accuracy
value: 0.9770395408321384
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 10.510114669799805
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.9783637843035726
name: Manhattan F1
- type: manhattan_f1_threshold
value: 10.637184143066406
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.9599119169895931
name: Manhattan Precision
- type: manhattan_recall
value: 0.9975389354307954
name: Manhattan Recall
- type: manhattan_ap
value: 0.9865931109650937
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9770612347780813
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.6766358613967896
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9783854448042815
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.6766358613967896
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.9600473761629129
name: Euclidean Precision
- type: euclidean_recall
value: 0.9974377142267394
name: Euclidean Recall
- type: euclidean_ap
value: 0.9866061739963429
name: Euclidean Ap
- type: max_accuracy
value: 0.9770612347780813
name: Max Accuracy
- type: max_accuracy_threshold
value: 10.510114669799805
name: Max Accuracy Threshold
- type: max_f1
value: 0.9783854448042815
name: Max F1
- type: max_f1_threshold
value: 10.637184143066406
name: Max F1 Threshold
- type: max_precision
value: 0.9600473761629129
name: Max Precision
- type: max_recall
value: 0.9975389354307954
name: Max Recall
- type: max_ap
value: 0.9866061739963429
name: Max Ap
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) dataset. 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-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3)
- **Language:** en
<!-- - **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': 256, '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("jarredparrett/all-MiniLM-L6-v2_tuned_on_deepparse_address_mutations_comb_3")
# Run inference
sentences = [
'8234 harvest bend lane laurel md 20707',
'8234 harvest bend lane laurel md',
'8702 wahl crse basement santee ca 92071',
]
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: `jarredparrett/deepparse_address_mutations_comb_3`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9771 |
| cosine_accuracy_threshold | 0.7712 |
| cosine_f1 | 0.9784 |
| cosine_f1_threshold | 0.7712 |
| cosine_precision | 0.9601 |
| cosine_recall | 0.9974 |
| cosine_ap | 0.9865 |
| dot_accuracy | 0.9771 |
| dot_accuracy_threshold | 0.7712 |
| dot_f1 | 0.9784 |
| dot_f1_threshold | 0.7712 |
| dot_precision | 0.9601 |
| dot_recall | 0.9974 |
| dot_ap | 0.9865 |
| manhattan_accuracy | 0.977 |
| manhattan_accuracy_threshold | 10.6015 |
| manhattan_f1 | 0.9784 |
| manhattan_f1_threshold | 10.6118 |
| manhattan_precision | 0.96 |
| manhattan_recall | 0.9974 |
| manhattan_ap | 0.9865 |
| euclidean_accuracy | 0.9771 |
| euclidean_accuracy_threshold | 0.6764 |
| euclidean_f1 | 0.9784 |
| euclidean_f1_threshold | 0.6764 |
| euclidean_precision | 0.9601 |
| euclidean_recall | 0.9974 |
| euclidean_ap | 0.9866 |
| max_accuracy | 0.9771 |
| max_accuracy_threshold | 10.6015 |
| max_f1 | 0.9784 |
| max_f1_threshold | 10.6118 |
| max_precision | 0.9601 |
| max_recall | 0.9974 |
| **max_ap** | **0.9866** |
#### Binary Classification
* Dataset: `jarredparrett/deepparse_address_mutations_comb_3`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9771 |
| cosine_accuracy_threshold | 0.7711 |
| cosine_f1 | 0.9784 |
| cosine_f1_threshold | 0.7711 |
| cosine_precision | 0.96 |
| cosine_recall | 0.9974 |
| cosine_ap | 0.9865 |
| dot_accuracy | 0.9771 |
| dot_accuracy_threshold | 0.7711 |
| dot_f1 | 0.9784 |
| dot_f1_threshold | 0.7711 |
| dot_precision | 0.96 |
| dot_recall | 0.9974 |
| dot_ap | 0.9866 |
| manhattan_accuracy | 0.977 |
| manhattan_accuracy_threshold | 10.5101 |
| manhattan_f1 | 0.9784 |
| manhattan_f1_threshold | 10.6372 |
| manhattan_precision | 0.9599 |
| manhattan_recall | 0.9975 |
| manhattan_ap | 0.9866 |
| euclidean_accuracy | 0.9771 |
| euclidean_accuracy_threshold | 0.6766 |
| euclidean_f1 | 0.9784 |
| euclidean_f1_threshold | 0.6766 |
| euclidean_precision | 0.96 |
| euclidean_recall | 0.9974 |
| euclidean_ap | 0.9866 |
| max_accuracy | 0.9771 |
| max_accuracy_threshold | 10.5101 |
| max_f1 | 0.9784 |
| max_f1_threshold | 10.6372 |
| max_precision | 0.96 |
| max_recall | 0.9975 |
| **max_ap** | **0.9866** |
<!--
## 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
#### deepparse_address_mutations_comb_3
* Dataset: [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) at [7162fdc](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3/tree/7162fdce4cfcb8114dc8f64d0631dc7a48c5ab7a)
* Size: 4,517,388 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 | torch.Tensor | string | string |
| details | <ul><li></li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.21 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.54 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:----------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|
| <code>tensor(1, device='cuda:0')</code> | <code>12737 chesdin landng dr chesterfield va 23838</code> | <code>12737 chesdin landng dr chesterfield va</code> |
| <code>tensor(1, device='cuda:0')</code> | <code>6080 norh oak trafficway gladstone mo 64118</code> | <code>6080 norh oak trafficway gladstone 64118-4896</code> |
| <code>tensor(0, device='cuda:0')</code> | <code>242 pierce view cir wentzville mo 63385</code> | <code>242 pierce view cir wentzville LA 63385</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Evaluation Dataset
#### deepparse_address_mutations_comb_3
* Dataset: [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) at [7162fdc](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3/tree/7162fdce4cfcb8114dc8f64d0631dc7a48c5ab7a)
* Size: 968,012 evaluation samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:-------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | torch.Tensor | string | string |
| details | <ul><li></li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.24 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.45 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:----------------------------------------|:------------------------------------------------------|:--------------------------------------------------------|
| <code>tensor(1, device='cuda:0')</code> | <code>1 vincent avenue essex maryland 21221</code> | <code>1 vincent avenue essedx MD 21221</code> |
| <code>tensor(1, device='cuda:0')</code> | <code>139 berg avenue hamilton tshp n.j. 08610</code> | <code>139 bcrg avenue hamilton tshp n.j. 08610</code> |
| <code>tensor(1, device='cuda:0')</code> | <code>714 havard rd houston texas 77336</code> | <code>714 havaplns plns houston texas 77336-3120</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `fp16`: 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`: 1024
- `per_device_eval_batch_size`: 1024
- `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`: 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`: 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`: 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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | jarredparrett/deepparse_address_mutations_comb_3_max_ap |
|:------:|:-----:|:-------------:|:------:|:-------------------------------------------------------:|
| 0.1133 | 500 | 0.0191 | 0.0131 | 0.8459 |
| 0.2267 | 1000 | 0.0112 | 0.0091 | 0.8887 |
| 0.3400 | 1500 | 0.0086 | 0.0067 | 0.9346 |
| 0.4533 | 2000 | 0.0064 | 0.0044 | 0.9604 |
| 0.5666 | 2500 | 0.0049 | 0.0037 | 0.9722 |
| 0.6800 | 3000 | 0.0042 | 0.0033 | 0.9761 |
| 0.7933 | 3500 | 0.0039 | 0.0032 | 0.9808 |
| 0.9066 | 4000 | 0.0037 | 0.0029 | 0.9825 |
| 1.0197 | 4500 | 0.0035 | 0.0028 | 0.9826 |
| 1.1330 | 5000 | 0.0033 | 0.0028 | 0.9836 |
| 1.2464 | 5500 | 0.0032 | 0.0027 | 0.9845 |
| 1.3597 | 6000 | 0.0031 | 0.0026 | 0.9853 |
| 1.4730 | 6500 | 0.003 | 0.0025 | 0.9857 |
| 1.5864 | 7000 | 0.003 | 0.0025 | 0.9859 |
| 1.6997 | 7500 | 0.0029 | 0.0025 | 0.9862 |
| 1.8130 | 8000 | 0.0028 | 0.0024 | 0.9864 |
| 1.9263 | 8500 | 0.0028 | 0.0024 | 0.9861 |
| 2.0394 | 9000 | 0.0028 | 0.0024 | 0.9864 |
| 2.1528 | 9500 | 0.0027 | 0.0024 | 0.9864 |
| 2.2661 | 10000 | 0.0027 | 0.0024 | 0.9865 |
| 2.3794 | 10500 | 0.0027 | 0.0023 | 0.9866 |
| 2.4927 | 11000 | 0.0026 | 0.0023 | 0.9866 |
| 2.6061 | 11500 | 0.0026 | 0.0023 | 0.9865 |
| 2.7194 | 12000 | 0.0026 | 0.0023 | 0.9865 |
| 2.8327 | 12500 | 0.0026 | 0.0023 | 0.9865 |
| 2.9461 | 13000 | 0.0026 | 0.0023 | 0.9866 |
| 2.9995 | 13236 | - | - | 0.9866 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.2.0
- Tokenizers: 0.20.3
## 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",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
<!--
## 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.*
--> |