File size: 25,135 Bytes
04dc468 |
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 |
---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:314315
- loss:AdaptiveLayerLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-small
datasets:
- stanfordnlp/snli
- sentence-transformers/stsb
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- 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
widget:
- source_sentence: Two teenage girls conversing next to lockers.
sentences:
- Girls talking about their problems next to lockers.
- A bully tries to pop a balloon without being caught in the act.
- Two dogs standing together in the yard.
- source_sentence: A young man in a heavy brown winter coat stands in front of a blue
railing with his arms spread.
sentences:
- a boy holding onto the wall of an old brick house's raised foundation as construction
occurs
- The railing is in front of a frozen lake.
- A skateboarder is doing tricks for a competition.
- source_sentence: A shirtless man with a white hat and no shoes sitting crisscross
with his back against the wall holding up a white plastic cup.
sentences:
- A long-haired boy riding his skateboard at a fast pace over a stone wall with
graffiti.
- A man is sitting crisscross
- a child in a black ninja suit does a kick
- source_sentence: A light colored dog leaps over a hurdle.
sentences:
- Men sit on the bus going to work,
- A dog jumps over a obstacel.
- a man standing on his motorbike.
- source_sentence: people are standing near water with a boat heading their direction
sentences:
- People are standing near water with a large blue boat heading their direction.
- Two people climbing on a wooden scaffold.
- The dogs are near the toy.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.7660217567682521
name: Pearson Cosine
- type: spearman_cosine
value: 0.7681125489633884
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7917532885619117
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.794675885405013
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7860948725725584
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7895594746178918
name: Spearman Euclidean
- type: pearson_dot
value: 0.644843928972524
name: Pearson Dot
- type: spearman_dot
value: 0.6427588138459626
name: Spearman Dot
- type: pearson_max
value: 0.7917532885619117
name: Pearson Max
- type: spearman_max
value: 0.794675885405013
name: Spearman Max
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.6730608840700584
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.5814725160598755
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7170495061078964
name: Cosine F1
- type: cosine_f1_threshold
value: 0.4670722782611847
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5977392321184954
name: Cosine Precision
- type: cosine_recall
value: 0.895866802979407
name: Cosine Recall
- type: cosine_ap
value: 0.7193483203625508
name: Cosine Ap
- type: dot_accuracy
value: 0.6444764576541057
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 71.95508575439453
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7094262988661364
name: Dot F1
- type: dot_f1_threshold
value: 53.77289581298828
name: Dot F1 Threshold
- type: dot_precision
value: 0.5779411764705882
name: Dot Precision
- type: dot_recall
value: 0.9183584051409376
name: Dot Recall
- type: dot_ap
value: 0.6828334101602328
name: Dot Ap
- type: manhattan_accuracy
value: 0.6664644779740693
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 213.6251678466797
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7047102517243412
name: Manhattan F1
- type: manhattan_f1_threshold
value: 245.20578002929688
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.5908461842625544
name: Manhattan Precision
- type: manhattan_recall
value: 0.8729370527238206
name: Manhattan Recall
- type: manhattan_ap
value: 0.7132026586783923
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.6621426946698006
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 10.358880996704102
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7024081560907013
name: Euclidean F1
- type: euclidean_f1_threshold
value: 12.010871887207031
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.5864970645792563
name: Euclidean Precision
- type: euclidean_recall
value: 0.8754198919234701
name: Euclidean Recall
- type: euclidean_ap
value: 0.7101786172295015
name: Euclidean Ap
- type: max_accuracy
value: 0.6730608840700584
name: Max Accuracy
- type: max_accuracy_threshold
value: 213.6251678466797
name: Max Accuracy Threshold
- type: max_f1
value: 0.7170495061078964
name: Max F1
- type: max_f1_threshold
value: 245.20578002929688
name: Max F1 Threshold
- type: max_precision
value: 0.5977392321184954
name: Max Precision
- type: max_recall
value: 0.9183584051409376
name: Max Recall
- type: max_ap
value: 0.7193483203625508
name: Max Ap
---
# SentenceTransformer based on microsoft/deberta-v3-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) 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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
- **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': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(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("bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerBaseline")
# Run inference
sentences = [
'people are standing near water with a boat heading their direction',
'People are standing near water with a large blue boat heading their direction.',
'The dogs are near the toy.',
]
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
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.766 |
| **spearman_cosine** | **0.7681** |
| pearson_manhattan | 0.7918 |
| spearman_manhattan | 0.7947 |
| pearson_euclidean | 0.7861 |
| spearman_euclidean | 0.7896 |
| pearson_dot | 0.6448 |
| spearman_dot | 0.6428 |
| pearson_max | 0.7918 |
| spearman_max | 0.7947 |
#### Binary Classification
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.6731 |
| cosine_accuracy_threshold | 0.5815 |
| cosine_f1 | 0.717 |
| cosine_f1_threshold | 0.4671 |
| cosine_precision | 0.5977 |
| cosine_recall | 0.8959 |
| cosine_ap | 0.7193 |
| dot_accuracy | 0.6445 |
| dot_accuracy_threshold | 71.9551 |
| dot_f1 | 0.7094 |
| dot_f1_threshold | 53.7729 |
| dot_precision | 0.5779 |
| dot_recall | 0.9184 |
| dot_ap | 0.6828 |
| manhattan_accuracy | 0.6665 |
| manhattan_accuracy_threshold | 213.6252 |
| manhattan_f1 | 0.7047 |
| manhattan_f1_threshold | 245.2058 |
| manhattan_precision | 0.5908 |
| manhattan_recall | 0.8729 |
| manhattan_ap | 0.7132 |
| euclidean_accuracy | 0.6621 |
| euclidean_accuracy_threshold | 10.3589 |
| euclidean_f1 | 0.7024 |
| euclidean_f1_threshold | 12.0109 |
| euclidean_precision | 0.5865 |
| euclidean_recall | 0.8754 |
| euclidean_ap | 0.7102 |
| max_accuracy | 0.6731 |
| max_accuracy_threshold | 213.6252 |
| max_f1 | 0.717 |
| max_f1_threshold | 245.2058 |
| max_precision | 0.5977 |
| max_recall | 0.9184 |
| **max_ap** | **0.7193** |
<!--
## 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
#### stanfordnlp/snli
* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 314,315 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>0</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"n_layers_per_step": 1,
"last_layer_weight": 1,
"prior_layers_weight": 1,
"kl_div_weight": 1.2,
"kl_temperature": 1.2
}
```
### Evaluation Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* 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: 5 tokens</li><li>mean: 14.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"n_layers_per_step": 1,
"last_layer_weight": 1,
"prior_layers_weight": 1,
"kl_div_weight": 1.2,
"kl_temperature": 1.2
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 5e-06
- `weight_decay`: 1e-07
- `num_train_epochs`: 2
- `warmup_ratio`: 0.5
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerBaselinen
- `hub_strategy`: checkpoint
- `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`: 32
- `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`: 5e-06
- `weight_decay`: 1e-07
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.5
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerBaselinen
- `hub_strategy`: checkpoint
- `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 | max_ap | spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:------:|:---------------:|
| None | 0 | - | 4.1425 | - | 0.4276 |
| 0.1001 | 983 | 4.7699 | 3.8387 | 0.6364 | - |
| 0.2001 | 1966 | 3.5997 | 2.7649 | 0.6722 | - |
| 0.3002 | 2949 | 2.811 | 2.3520 | 0.6838 | - |
| 0.4003 | 3932 | 2.414 | 2.0700 | 0.6883 | - |
| 0.5004 | 4915 | 2.186 | 1.8993 | 0.6913 | - |
| 0.6004 | 5898 | 1.8523 | 1.5632 | 0.7045 | - |
| 0.7005 | 6881 | 0.6415 | 1.4902 | 0.7082 | - |
| 0.8006 | 7864 | 0.5016 | 1.4636 | 0.7108 | - |
| 0.9006 | 8847 | 0.4194 | 1.3875 | 0.7121 | - |
| 1.0007 | 9830 | 0.3737 | 1.3077 | 0.7117 | - |
| 1.1008 | 10813 | 1.8087 | 1.0903 | 0.7172 | - |
| 1.2009 | 11796 | 1.6631 | 1.0388 | 0.7180 | - |
| 1.3009 | 12779 | 1.6161 | 1.0177 | 0.7169 | - |
| 1.4010 | 13762 | 1.5378 | 1.0136 | 0.7148 | - |
| 1.5011 | 14745 | 1.5215 | 1.0053 | 0.7159 | - |
| 1.6011 | 15728 | 1.2887 | 0.9600 | 0.7166 | - |
| 1.7012 | 16711 | 0.3058 | 0.9949 | 0.7180 | - |
| 1.8013 | 17694 | 0.2897 | 0.9792 | 0.7186 | - |
| 1.9014 | 18677 | 0.275 | 0.9598 | 0.7192 | - |
| 2.0 | 19646 | - | 0.9796 | 0.7193 | - |
| None | 0 | - | 2.4594 | 0.7193 | 0.7681 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- 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",
}
```
#### AdaptiveLayerLoss
```bibtex
@misc{li20242d,
title={2D Matryoshka Sentence Embeddings},
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
year={2024},
eprint={2402.14776},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@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}
}
```
<!--
## 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.*
--> |