File size: 27,083 Bytes
a551f5a |
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 |
---
base_model: Alibaba-NLP/gte-large-en-v1.5
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:700
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What are the expectations for automated systems in relation to
data privacy?
sentences:
- 'https://beta.nsf.gov/funding/opportunities/designing-accountable-software-systems-dass
28. The Leadership Conference Education Fund. The Use Of Pretrial “Risk Assessment”
Instruments: A
Shared Statement Of Civil Rights Concerns. Jul. 30, 2018. http://civilrightsdocs.info/pdf/criminal-justice/
Pretrial-Risk-Assessment-Short.pdf; https://civilrights.org/edfund/pretrial-risk-assessments/'
- "DATA PRIVACY \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations\
\ for automated systems are meant to serve as a blueprint for the development\
\ of additional \ntechnical standards and practices that are tailored for particular\
\ sectors and contexts. \nIn addition to the privacy expectations above\
\ for general non-sensitive data, any system collecting, using, shar-"
- "standing that it may be these users who are most likely to need the human assistance.\
\ Similarly, it should be \ntested to ensure that users with disabilities are\
\ able to find and use human consideration and fallback and also \nrequest reasonable\
\ accommodations or modifications. \nConvenient. Mechanisms for human consideration\
\ and fallback should not be unreasonably burdensome as \ncompared to the automated\
\ system’s equivalent. \n49"
- source_sentence: What is the purpose of the U.S. AI Safety Institute and the AI
Safety Institute Consortium established by NIST?
sentences:
- "AI. NIST established the U.S. AI Safety Institute and the companion AI Safety\
\ Institute Consortium to \ncontinue the efforts set in motion by the E.O. to build\
\ the science necessary for safe, secure, and \ntrustworthy development and use\
\ of AI. \nAcknowledgments: This report was accomplished with the many helpful\
\ comments and contributions"
- "SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\n\
The expectations for automated systems are meant to serve as a blueprint for the\
\ development of additional \ntechnical standards and practices that are tailored\
\ for particular sectors and contexts. \nOngoing monitoring. Automated systems\
\ should have ongoing monitoring procedures, including recalibra"
- "differ from an explanation provided to allow for the possibility of recourse,\
\ an appeal, or one provided in the \ncontext of a dispute or contestation process.\
\ For the purposes of this framework, 'explanation' should be \nconstrued broadly.\
\ An explanation need not be a plain-language statement about causality but could\
\ consist of \nany mechanism that allows the recipient to build the necessary\
\ understanding and intuitions to achieve the"
- source_sentence: What are the consequences faced by individuals when they are unable
to reach a human decision-maker in automated systems?
sentences:
- 'ENDNOTES
85. Mick Dumke and Frank Main. A look inside the watch list Chicago police fought
to keep secret. The
Chicago Sun Times. May 18, 2017.
https://chicago.suntimes.com/2017/5/18/18386116/a-look-inside-the-watch-list-chicago-police-fought
to-keep-secret'
- "presented with no alternative, or are forced to endure a cumbersome process to\
\ reach a human decision-maker once \nthey decide they no longer want to deal\
\ exclusively with the automated system or be impacted by its results. As a result\
\ \nof this lack of human reconsideration, many receive delayed access, or lose\
\ access, to rights, opportunities, benefits, \nand critical services. The American\
\ public deserves the assurance that, when rights, opportunities, or access are"
- "compliance in mind. \nSome state legislatures have placed strong transparency\
\ and validity requirements on \nthe use of pretrial risk assessments. The use\
\ of algorithmic pretrial risk assessments has been a \ncause of concern for civil\
\ rights groups.28 Idaho Code Section 19-1910, enacted in 2019,29 requires that\
\ any \npretrial risk assessment, before use in the state, first be \"shown to\
\ be free of bias against any class of"
- source_sentence: What organizations are mentioned in the appendix alongside individuals
such as Lisa Feldman Barrett and Madeline Owens?
sentences:
- "APPENDIX\nLisa Feldman Barrett \nMadeline Owens \nMarsha Tudor \nMicrosoft Corporation\
\ \nMITRE Corporation \nNational Association for the \nAdvancement of Colored\
\ People \nLegal Defense and Educational \nFund \nNational Association of Criminal\
\ \nDefense Lawyers \nNational Center for Missing & \nExploited Children \nNational\
\ Fair Housing Alliance \nNational Immigration Law Center \nNEC Corporation of\
\ America"
- "or label to ensure the goal of the automated system is appropriately identified\
\ and measured. Additionally, \njustification should be documented for each data\
\ attribute and source to explain why it is appropriate to use \nthat data to\
\ inform the results of the automated system and why such use will not violate\
\ any applicable laws. \nIn cases of high-dimensional and/or derived attributes,\
\ such justifications can be provided as overall \ndescriptions of the attribute\
\ generation process and appropriateness. \n19"
- "ers and other experts across fields and sectors, as well as policymakers throughout\
\ the Federal government—on \nthe issue of algorithmic and data-driven harms and\
\ potential remedies. Through panel discussions, public listen-\ning sessions,\
\ meetings, a formal request for information, and input to a publicly accessible\
\ and widely-publicized \nemail address, people throughout the United States,\
\ public servants across Federal agencies, and members of the"
- source_sentence: What should individuals or organizations provide to ensure that
people impacted by an automated system are informed about significant changes
in use cases or key functionalities?
sentences:
- "with an intent or reasonably foreseeable possibility of endangering \nyour safety\
\ or the safety of your community. They should be designed \nto proactively protect\
\ you from harms stemming from unintended, \nyet foreseeable, uses or impacts\
\ of automated systems. You should be \nprotected from inappropriate or irrelevant\
\ data use in the design, de\nvelopment, and deployment of automated systems,\
\ and from the \ncompounded harm of its reuse. Independent evaluation and report"
- "use, the individual or organization responsible for the system, and ex\nplanations\
\ of outcomes that are clear, timely, and accessible. Such \nnotice should be\
\ kept up-to-date and people impacted by the system \nshould be notified of significant\
\ use case or key functionality chang\nes. You should know how and why an outcome\
\ impacting you was de\ntermined by an automated system, including when the automated"
- 'software-algorithms-and-artificial-intelligence; U.S. Department of Justice.
Algorithms, Artificial
Intelligence, and Disability Discrimination in Hiring. May 12, 2022. https://beta.ada.gov/resources/ai
guidance/
54. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan.
Dissecting racial bias in'
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.8666666666666667
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9866666666666667
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8666666666666667
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3288888888888888
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19999999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8666666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9866666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9481205912028868
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.93
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.93
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.8666666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 1.0
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1.0
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.8666666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.33333333333333326
name: Dot Precision@3
- type: dot_precision@5
value: 0.19999999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.8666666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 1.0
name: Dot Recall@3
- type: dot_recall@5
value: 1.0
name: Dot Recall@5
- type: dot_recall@10
value: 1.0
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9490449037619082
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9311111111111112
name: Dot Mrr@10
- type: dot_map@100
value: 0.931111111111111
name: Dot Map@100
---
# SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) <!-- at revision 104333d6af6f97649377c2afbde10a7704870c7b -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'What should individuals or organizations provide to ensure that people impacted by an automated system are informed about significant changes in use cases or key functionalities?',
'use, the individual or organization responsible for the system, and ex\xad\nplanations of outcomes that are clear, timely, and accessible. Such \nnotice should be kept up-to-date and people impacted by the system \nshould be notified of significant use case or key functionality chang\xad\nes. You should know how and why an outcome impacting you was de\xad\ntermined by an automated system, including when the automated',
'software-algorithms-and-artificial-intelligence; U.S. Department of Justice. Algorithms, Artificial\nIntelligence, and Disability Discrimination in Hiring. May 12, 2022. https://beta.ada.gov/resources/ai\xad\nguidance/\n54. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. Dissecting racial bias in',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:---------|
| cosine_accuracy@1 | 0.8667 |
| cosine_accuracy@3 | 0.9867 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8667 |
| cosine_precision@3 | 0.3289 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8667 |
| cosine_recall@3 | 0.9867 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9481 |
| cosine_mrr@10 | 0.93 |
| **cosine_map@100** | **0.93** |
| dot_accuracy@1 | 0.8667 |
| dot_accuracy@3 | 1.0 |
| dot_accuracy@5 | 1.0 |
| dot_accuracy@10 | 1.0 |
| dot_precision@1 | 0.8667 |
| dot_precision@3 | 0.3333 |
| dot_precision@5 | 0.2 |
| dot_precision@10 | 0.1 |
| dot_recall@1 | 0.8667 |
| dot_recall@3 | 1.0 |
| dot_recall@5 | 1.0 |
| dot_recall@10 | 1.0 |
| dot_ndcg@10 | 0.949 |
| dot_mrr@10 | 0.9311 |
| dot_map@100 | 0.9311 |
<!--
## 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
#### json
* Dataset: json
* Size: 700 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 700 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 22.12 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 80.96 tokens</li><li>max: 571 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the primary purpose of the AI Bill of Rights outlined in the October 2022 blueprint?</code> | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> |
| <code>What is the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy?</code> | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered</code> |
| <code>What initiative did the OSTP announce a year prior to the release of the framework for a bill of rights for an AI-powered world?</code> | <code>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 7
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `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`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 7
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | cosine_map@100 |
|:----------:|:-----:|:--------------:|
| 0.7273 | 1 | 0.8548 |
| 1.4545 | 2 | 0.8811 |
| 2.9091 | 4 | 0.9233 |
| **3.6364** | **5** | **0.9311** |
| 4.3636 | 6 | 0.93 |
| 5.0909 | 7 | 0.93 |
* The bold row denotes the saved checkpoint.
### 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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.*
--> |