File size: 25,582 Bytes
edde102 |
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 |
---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: Net cash used in financing activities in 2023 was $2,430 million.
sentences:
- What criteria does Airbnb, Inc. use to assess if an available-for-sale security
should be recorded as impaired on their financial statements?
- What was the total amount of net cash used in financing activities in 2023?
- How much did Visa authorize for its share repurchase program in October 2023?
- source_sentence: Microsoft® and Windows® are either registered trademarks or trademarks
of Microsoft Corporation in the United States and/or other countries.
sentences:
- Where does Eli Lilly and Company manufacture and distribute its products?
- What is the significance of Microsoft® and Windows® in relation to Microsoft Corporation?
- What percentage of total net revenue did the Americas region contribute in 2023?
- source_sentence: We make available free of charge on the Investor Relations section
of our corporate website all of the reports we file with or furnish to the SEC
as soon as reasonably practicable, after the reports are filed or furnished.
sentences:
- Is there a cost to access reports filed by Intuit Inc. with the SEC?
- What amount of cash, cash equivalents, and restricted cash did the company have
at the end of the period?
- Where in IBM’s 2023 Form 10-K can the Financial Statement Schedule be found?
- source_sentence: The U.S. Automobile Information and Disclosure Act also requires
manufacturers of motor vehicles to disclose certain information regarding the
manufacturer’s suggested retail price, optional equipment and pricing.
sentences:
- What does the Adjusted Effective Tax Rate measure exclude?
- What was the fair value of the total consideration transferred for the acquisition
discussed, and how was it composed?
- Which act requires U.S. automobile manufacturers to disclose certain pricing and
equipment information?
- source_sentence: Under the Insurance Act, Chubb's Bermuda domiciled subsidiaries
are prohibited from declaring or paying any dividends of more than 25 percent
of total statutory capital and surplus, as shown in its previous financial year
statutory balance sheet, unless at least seven days before payment of the dividends,
it files with the BMA an affidavit signed by at least two directors of the relevant
Bermuda domiciled subsidiary (one of whom must be a director resident in Bermuda)
and by the relevant Bermuda domiciled subsidiary’s principal representative, that
it will continue to meet its required solvency margins. Furthermore, Bermuda domiciled
subsidiaries may only declare and pay a dividend from retained earnings and a
dividend or distribution from contributed surplus if it has no reasonable grounds
for believing that it is, or would after the payment be, unable to pay its liabilities
as they become due, or if the realizable value of its assets would be less than
the aggregate of its liabilities. In addition, Chubb's Bermuda domiciled subsidiaries
must obtain the BMA's prior approval before reducing total statutory capital,
as shown in its previous financial year's financial statements, by 15 percent
or more.
sentences:
- What are the restrictions and requirements for Bermuda domiciled subsidiaries
regarding the distribution of dividends under the Insurance Act?
- What section deals with financial statements and supplementary data?
- What measures has the company implemented to ensure workplace safety?
pipeline_tag: sentence-similarity
model-index:
- name: BGE small Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7042857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8457142857142858
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.88
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9242857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7042857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28190476190476194
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.176
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09242857142857142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7042857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8457142857142858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.88
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9242857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8153543862763872
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7803667800453513
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7829122109320609
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7057142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8471428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8685714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9242857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7057142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28238095238095234
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17371428571428568
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09242857142857142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7057142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8471428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8685714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9242857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.815124112835889
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7802040816326532
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7828080021041772
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.7071428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8385714285714285
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8757142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9228571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7071428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27952380952380956
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17514285714285713
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09228571428571428
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7071428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8385714285714285
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8757142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9228571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.815223056195625
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7808248299319727
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7833488292208493
name: Cosine Map@100
---
# BGE small Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
(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("haophancs/bge-base-financial-matryoshka")
# Run inference
sentences = [
"Under the Insurance Act, Chubb's Bermuda domiciled subsidiaries are prohibited from declaring or paying any dividends of more than 25 percent of total statutory capital and surplus, as shown in its previous financial year statutory balance sheet, unless at least seven days before payment of the dividends, it files with the BMA an affidavit signed by at least two directors of the relevant Bermuda domiciled subsidiary (one of whom must be a director resident in Bermuda) and by the relevant Bermuda domiciled subsidiary’s principal representative, that it will continue to meet its required solvency margins. Furthermore, Bermuda domiciled subsidiaries may only declare and pay a dividend from retained earnings and a dividend or distribution from contributed surplus if it has no reasonable grounds for believing that it is, or would after the payment be, unable to pay its liabilities as they become due, or if the realizable value of its assets would be less than the aggregate of its liabilities. In addition, Chubb's Bermuda domiciled subsidiaries must obtain the BMA's prior approval before reducing total statutory capital, as shown in its previous financial year's financial statements, by 15 percent or more.",
'What are the restrictions and requirements for Bermuda domiciled subsidiaries regarding the distribution of dividends under the Insurance Act?',
'What section deals with financial statements and supplementary data?',
]
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
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7043 |
| cosine_accuracy@3 | 0.8457 |
| cosine_accuracy@5 | 0.88 |
| cosine_accuracy@10 | 0.9243 |
| cosine_precision@1 | 0.7043 |
| cosine_precision@3 | 0.2819 |
| cosine_precision@5 | 0.176 |
| cosine_precision@10 | 0.0924 |
| cosine_recall@1 | 0.7043 |
| cosine_recall@3 | 0.8457 |
| cosine_recall@5 | 0.88 |
| cosine_recall@10 | 0.9243 |
| cosine_ndcg@10 | 0.8154 |
| cosine_mrr@10 | 0.7804 |
| **cosine_map@100** | **0.7829** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7057 |
| cosine_accuracy@3 | 0.8471 |
| cosine_accuracy@5 | 0.8686 |
| cosine_accuracy@10 | 0.9243 |
| cosine_precision@1 | 0.7057 |
| cosine_precision@3 | 0.2824 |
| cosine_precision@5 | 0.1737 |
| cosine_precision@10 | 0.0924 |
| cosine_recall@1 | 0.7057 |
| cosine_recall@3 | 0.8471 |
| cosine_recall@5 | 0.8686 |
| cosine_recall@10 | 0.9243 |
| cosine_ndcg@10 | 0.8151 |
| cosine_mrr@10 | 0.7802 |
| **cosine_map@100** | **0.7828** |
#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7071 |
| cosine_accuracy@3 | 0.8386 |
| cosine_accuracy@5 | 0.8757 |
| cosine_accuracy@10 | 0.9229 |
| cosine_precision@1 | 0.7071 |
| cosine_precision@3 | 0.2795 |
| cosine_precision@5 | 0.1751 |
| cosine_precision@10 | 0.0923 |
| cosine_recall@1 | 0.7071 |
| cosine_recall@3 | 0.8386 |
| cosine_recall@5 | 0.8757 |
| cosine_recall@10 | 0.9229 |
| cosine_ndcg@10 | 0.8152 |
| cosine_mrr@10 | 0.7808 |
| **cosine_map@100** | **0.7833** |
<!--
## 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: 6,300 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 45.4 tokens</li><li>max: 252 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.43 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|
| <code>In 2023, $2.2 billion or 5% was primarily related to patient co-pay assistance, cash discounts for prompt payment, distributor fees, and sales return provisions.</code> | <code>What was the amount of sales return provisions in 2023 as part of gross-to-net deductions?</code> |
| <code>Cash and cash equivalents were $21.9 billion at the end of 2023 as compared to $14.1 billion at the end of 2022, showing a $7.8 billion increase.</code> | <code>How much did cash and cash equivalents increase by the end of 2023 compared to the end of 2022?</code> |
| <code>The net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022 was $72 million.</code> | <code>What was the net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
384
],
"matryoshka_weights": [
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`: 4
- `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
- `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`: 4
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_384_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|
| 0.8122 | 10 | 0.8256 | - | - | - |
| 0.9746 | 12 | - | 0.7719 | 0.7679 | 0.7652 |
| 1.6244 | 20 | 0.2984 | - | - | - |
| 1.9492 | 24 | - | 0.7784 | 0.7810 | 0.7791 |
| 2.4365 | 30 | 0.201 | - | - | - |
| 2.9239 | 36 | - | 0.7835 | 0.7832 | 0.7828 |
| 3.2487 | 40 | 0.1705 | - | - | - |
| **3.8985** | **48** | **-** | **0.7833** | **0.7828** | **0.7829** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+cu121
- Accelerate: 0.31.0
- 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",
}
```
#### 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.*
--> |