File size: 36,341 Bytes
ed45995 67a5e14 ed45995 67a5e14 ed45995 4ebdc6e ed45995 e440089 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 |
---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1M<n<10M
- loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
datasets:
- sentence-transformers/gooaq
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
widget:
- source_sentence: 11 is what of 8?
sentences:
- '*RARE* CANDY AXE AND RED NOSED RAIDER IS BACK - FORTNITE ITEM SHOP 8TH DECEMBER
2019.'
- 'Convert fraction (ratio) 8 / 11 Answer: 72.727272727273%'
- Old-age pensions are not included in taxable income under the personal income
tax.
- source_sentence: is 50 shades of grey on prime?
sentences:
- 'Amazon.com: Watch Fifty Shades of Grey. Prime Video.'
- 'How much is 22 out of 100 written as a percentage? Convert fraction (ratio) 22
/ 100 Answer: 22%'
- Petco ferrets are neutered and as social animals, they enjoy each other's company.
- source_sentence: 20 of what is 18?
sentences:
- '20 percent (calculated percentage %) of what number equals 18? Answer: 90.'
- There are 3.35 x 1019 H2O molecules in a 1 mg snowflake.
- There are 104 total Power Moons and 100 Purple Coins in the Mushroom Kingdom.
- source_sentence: 63 up itv when is it on?
sentences:
- Mark Twain Quotes If you tell the truth, you don't have to remember anything.
- 63 Up is on ITV for three consecutive nights, Tuesday 4 – Thursday 6 June, at
9pm.
- In a language, the smallest units of meaning are. Morphemes.
- source_sentence: what is ikit in tagalog?
sentences:
- 'Definition: aunt. the sister of one''s father or mother; the wife of one''s uncle
(n.)'
- 'How much is 12 out of 29 written as a percentage? Convert fraction (ratio) 12
/ 29 Answer: 41.379310344828%'
- Iberia offers Wi-Fi on all long-haul aircraft so that you can stay connected using
your own devices.
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 636.2415070661234
energy_consumed: 1.636836206312608
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 4.514
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on GooAQ Question-Answer tuples
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: gooaq dev
type: gooaq-dev
metrics:
- type: cosine_accuracy@1
value: 0.7198
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.884
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9305
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9709
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7198
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29466666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1861
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09709000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7198
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.884
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9305
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9709
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8490972112228806
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8095713888888812
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8111457785591406
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.7073
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.877
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9244
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9669
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.7073
name: Dot Precision@1
- type: dot_precision@3
value: 0.2923333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.18488000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.09669000000000003
name: Dot Precision@10
- type: dot_recall@1
value: 0.7073
name: Dot Recall@1
- type: dot_recall@3
value: 0.877
name: Dot Recall@3
- type: dot_recall@5
value: 0.9244
name: Dot Recall@5
- type: dot_recall@10
value: 0.9669
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8412144933973646
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8004067857142795
name: Dot Mrr@10
- type: dot_map@100
value: 0.8022667466578848
name: Dot Map@100
---
# MPNet base trained on GooAQ Question-Answer tuples
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) 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.
This model was trained using the [train_script.py](train_script.py) code.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **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': False}) with Transformer model: MPNetModel
(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("tomaarsen/mpnet-base-gooaq")
# Run inference
sentences = [
'11 is what of 8?',
'Convert fraction (ratio) 8 / 11 Answer: 72.727272727273%',
'Old-age pensions are not included in taxable income under the personal income tax.',
]
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: `gooaq-dev`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7198 |
| cosine_accuracy@3 | 0.884 |
| cosine_accuracy@5 | 0.9305 |
| cosine_accuracy@10 | 0.9709 |
| cosine_precision@1 | 0.7198 |
| cosine_precision@3 | 0.2947 |
| cosine_precision@5 | 0.1861 |
| cosine_precision@10 | 0.0971 |
| cosine_recall@1 | 0.7198 |
| cosine_recall@3 | 0.884 |
| cosine_recall@5 | 0.9305 |
| cosine_recall@10 | 0.9709 |
| cosine_ndcg@10 | 0.8491 |
| cosine_mrr@10 | 0.8096 |
| **cosine_map@100** | **0.8111** |
| dot_accuracy@1 | 0.7073 |
| dot_accuracy@3 | 0.877 |
| dot_accuracy@5 | 0.9244 |
| dot_accuracy@10 | 0.9669 |
| dot_precision@1 | 0.7073 |
| dot_precision@3 | 0.2923 |
| dot_precision@5 | 0.1849 |
| dot_precision@10 | 0.0967 |
| dot_recall@1 | 0.7073 |
| dot_recall@3 | 0.877 |
| dot_recall@5 | 0.9244 |
| dot_recall@10 | 0.9669 |
| dot_ndcg@10 | 0.8412 |
| dot_mrr@10 | 0.8004 |
| dot_map@100 | 0.8023 |
<!--
## 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
#### sentence-transformers/gooaq
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,002,496 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.89 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 60.37 tokens</li><li>max: 147 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>biotechnology is best defined as?</code> | <code>Biotechnology is best defined as_______________? The science that involves using living organisms to produce needed materials. Which of the following tools of biotechnology, to do investigation, is used when trying crime?</code> |
| <code>how to open xye file?</code> | <code>Firstly, use File then Open and make sure that you can see All Files (*. *) and not just Excel files (the default option!) in the folder containing the *. xye file: Select the file you wish to open and Excel will bring up a wizard menu for importing plain text data into Excel (as shown below).</code> |
| <code>how much does california spend?</code> | <code>Estimated 2016 expenditures The total estimated government spending in California in fiscal year 2016 was $265.9 billion. Per-capita figures are calculated by taking the state's total spending and dividing by the number of state residents according to United States Census Bureau estimates.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### sentence-transformers/gooaq
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 10,000 evaluation samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.82 tokens</li><li>max: 166 tokens</li></ul> |
* Samples:
| question | answer |
|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how to open nx file?</code> | <code>['Click File > Open. The File Open dialog box opens.', 'Select NX File (*. prt) in the Type box. ... ', 'Select an NX . ... ', 'Select Import in the File Open dialog box. ... ', 'If you do not want to retain the import profile in use, select an import profile from the Profile list. ... ', 'Click OK in the Import New Model dialog box.']</code> |
| <code>how to recover deleted photos from blackberry priv?</code> | <code>['Run Android Data Recovery. ... ', 'Enable USB Debugging Mode. ... ', 'Scan Your BlackBerry PRIV to Find Deleted Photos. ... ', 'Recover Deleted Photos from BlackBerry PRIV.']</code> |
| <code>which subatomic particles are found within the nucleus of an atom?</code> | <code>In the middle of every atom is the nucleus. The nucleus contains two types of subatomic particles, protons and neutrons. The protons have a positive electrical charge and the neutrons have no electrical charge. A third type of subatomic particle, electrons, move around the nucleus.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: 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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 1
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | gooaq-dev_cosine_map@100 |
|:------:|:-----:|:-------------:|:------:|:------------------------:|
| 0 | 0 | - | - | 0.1379 |
| 0.0000 | 1 | 3.6452 | - | - |
| 0.0053 | 250 | 2.4418 | - | - |
| 0.0107 | 500 | 0.373 | - | - |
| 0.0160 | 750 | 0.183 | - | - |
| 0.0213 | 1000 | 0.1286 | 0.0805 | 0.6796 |
| 0.0266 | 1250 | 0.1099 | - | - |
| 0.0320 | 1500 | 0.091 | - | - |
| 0.0373 | 1750 | 0.0768 | - | - |
| 0.0426 | 2000 | 0.0665 | 0.0526 | 0.7162 |
| 0.0480 | 2250 | 0.0659 | - | - |
| 0.0533 | 2500 | 0.0602 | - | - |
| 0.0586 | 2750 | 0.0548 | - | - |
| 0.0639 | 3000 | 0.0543 | 0.0426 | 0.7328 |
| 0.0693 | 3250 | 0.0523 | - | - |
| 0.0746 | 3500 | 0.0494 | - | - |
| 0.0799 | 3750 | 0.0468 | - | - |
| 0.0853 | 4000 | 0.0494 | 0.0362 | 0.7450 |
| 0.0906 | 4250 | 0.048 | - | - |
| 0.0959 | 4500 | 0.0442 | - | - |
| 0.1012 | 4750 | 0.0442 | - | - |
| 0.1066 | 5000 | 0.0408 | 0.0332 | 0.7519 |
| 0.1119 | 5250 | 0.0396 | - | - |
| 0.1172 | 5500 | 0.0379 | - | - |
| 0.1226 | 5750 | 0.0392 | - | - |
| 0.1279 | 6000 | 0.0395 | 0.0300 | 0.7505 |
| 0.1332 | 6250 | 0.0349 | - | - |
| 0.1386 | 6500 | 0.0383 | - | - |
| 0.1439 | 6750 | 0.0335 | - | - |
| 0.1492 | 7000 | 0.0323 | 0.0253 | 0.7624 |
| 0.1545 | 7250 | 0.0342 | - | - |
| 0.1599 | 7500 | 0.0292 | - | - |
| 0.1652 | 7750 | 0.0309 | - | - |
| 0.1705 | 8000 | 0.0335 | 0.0249 | 0.7631 |
| 0.1759 | 8250 | 0.0304 | - | - |
| 0.1812 | 8500 | 0.0318 | - | - |
| 0.1865 | 8750 | 0.0271 | - | - |
| 0.1918 | 9000 | 0.029 | 0.0230 | 0.7615 |
| 0.1972 | 9250 | 0.0309 | - | - |
| 0.2025 | 9500 | 0.0305 | - | - |
| 0.2078 | 9750 | 0.0237 | - | - |
| 0.2132 | 10000 | 0.0274 | 0.0220 | 0.7667 |
| 0.2185 | 10250 | 0.0248 | - | - |
| 0.2238 | 10500 | 0.0249 | - | - |
| 0.2291 | 10750 | 0.0272 | - | - |
| 0.2345 | 11000 | 0.0289 | 0.0230 | 0.7664 |
| 0.2398 | 11250 | 0.027 | - | - |
| 0.2451 | 11500 | 0.0259 | - | - |
| 0.2505 | 11750 | 0.0237 | - | - |
| 0.2558 | 12000 | 0.0245 | 0.0220 | 0.7694 |
| 0.2611 | 12250 | 0.0251 | - | - |
| 0.2664 | 12500 | 0.0243 | - | - |
| 0.2718 | 12750 | 0.0229 | - | - |
| 0.2771 | 13000 | 0.0273 | 0.0201 | 0.7725 |
| 0.2824 | 13250 | 0.0244 | - | - |
| 0.2878 | 13500 | 0.0248 | - | - |
| 0.2931 | 13750 | 0.0255 | - | - |
| 0.2984 | 14000 | 0.0244 | 0.0192 | 0.7729 |
| 0.3037 | 14250 | 0.0242 | - | - |
| 0.3091 | 14500 | 0.0235 | - | - |
| 0.3144 | 14750 | 0.0231 | - | - |
| 0.3197 | 15000 | 0.0228 | 0.0190 | 0.7823 |
| 0.3251 | 15250 | 0.0229 | - | - |
| 0.3304 | 15500 | 0.0224 | - | - |
| 0.3357 | 15750 | 0.0216 | - | - |
| 0.3410 | 16000 | 0.0218 | 0.0186 | 0.7787 |
| 0.3464 | 16250 | 0.022 | - | - |
| 0.3517 | 16500 | 0.0233 | - | - |
| 0.3570 | 16750 | 0.0216 | - | - |
| 0.3624 | 17000 | 0.0226 | 0.0169 | 0.7862 |
| 0.3677 | 17250 | 0.0215 | - | - |
| 0.3730 | 17500 | 0.0212 | - | - |
| 0.3784 | 17750 | 0.0178 | - | - |
| 0.3837 | 18000 | 0.0217 | 0.0161 | 0.7813 |
| 0.3890 | 18250 | 0.0217 | - | - |
| 0.3943 | 18500 | 0.0191 | - | - |
| 0.3997 | 18750 | 0.0216 | - | - |
| 0.4050 | 19000 | 0.022 | 0.0157 | 0.7868 |
| 0.4103 | 19250 | 0.0223 | - | - |
| 0.4157 | 19500 | 0.021 | - | - |
| 0.4210 | 19750 | 0.0176 | - | - |
| 0.4263 | 20000 | 0.021 | 0.0162 | 0.7873 |
| 0.4316 | 20250 | 0.0206 | - | - |
| 0.4370 | 20500 | 0.0196 | - | - |
| 0.4423 | 20750 | 0.0186 | - | - |
| 0.4476 | 21000 | 0.0197 | 0.0158 | 0.7907 |
| 0.4530 | 21250 | 0.0156 | - | - |
| 0.4583 | 21500 | 0.0178 | - | - |
| 0.4636 | 21750 | 0.0175 | - | - |
| 0.4689 | 22000 | 0.0187 | 0.0151 | 0.7937 |
| 0.4743 | 22250 | 0.0182 | - | - |
| 0.4796 | 22500 | 0.0185 | - | - |
| 0.4849 | 22750 | 0.0217 | - | - |
| 0.4903 | 23000 | 0.0179 | 0.0156 | 0.7937 |
| 0.4956 | 23250 | 0.0193 | - | - |
| 0.5009 | 23500 | 0.015 | - | - |
| 0.5062 | 23750 | 0.0181 | - | - |
| 0.5116 | 24000 | 0.0173 | 0.0150 | 0.7924 |
| 0.5169 | 24250 | 0.0177 | - | - |
| 0.5222 | 24500 | 0.0183 | - | - |
| 0.5276 | 24750 | 0.0171 | - | - |
| 0.5329 | 25000 | 0.0185 | 0.0140 | 0.7955 |
| 0.5382 | 25250 | 0.0178 | - | - |
| 0.5435 | 25500 | 0.015 | - | - |
| 0.5489 | 25750 | 0.017 | - | - |
| 0.5542 | 26000 | 0.0171 | 0.0139 | 0.7931 |
| 0.5595 | 26250 | 0.0164 | - | - |
| 0.5649 | 26500 | 0.0175 | - | - |
| 0.5702 | 26750 | 0.0175 | - | - |
| 0.5755 | 27000 | 0.0163 | 0.0133 | 0.7954 |
| 0.5809 | 27250 | 0.0179 | - | - |
| 0.5862 | 27500 | 0.016 | - | - |
| 0.5915 | 27750 | 0.0155 | - | - |
| 0.5968 | 28000 | 0.0162 | 0.0138 | 0.7979 |
| 0.6022 | 28250 | 0.0164 | - | - |
| 0.6075 | 28500 | 0.0148 | - | - |
| 0.6128 | 28750 | 0.0152 | - | - |
| 0.6182 | 29000 | 0.0166 | 0.0134 | 0.7987 |
| 0.6235 | 29250 | 0.0159 | - | - |
| 0.6288 | 29500 | 0.0168 | - | - |
| 0.6341 | 29750 | 0.0187 | - | - |
| 0.6395 | 30000 | 0.017 | 0.0137 | 0.7980 |
| 0.6448 | 30250 | 0.0168 | - | - |
| 0.6501 | 30500 | 0.0149 | - | - |
| 0.6555 | 30750 | 0.0159 | - | - |
| 0.6608 | 31000 | 0.0149 | 0.0131 | 0.8017 |
| 0.6661 | 31250 | 0.0149 | - | - |
| 0.6714 | 31500 | 0.0147 | - | - |
| 0.6768 | 31750 | 0.0157 | - | - |
| 0.6821 | 32000 | 0.0151 | 0.0125 | 0.8011 |
| 0.6874 | 32250 | 0.015 | - | - |
| 0.6928 | 32500 | 0.0157 | - | - |
| 0.6981 | 32750 | 0.0153 | - | - |
| 0.7034 | 33000 | 0.0141 | 0.0123 | 0.8012 |
| 0.7087 | 33250 | 0.0143 | - | - |
| 0.7141 | 33500 | 0.0121 | - | - |
| 0.7194 | 33750 | 0.0164 | - | - |
| 0.7247 | 34000 | 0.014 | 0.0121 | 0.8014 |
| 0.7301 | 34250 | 0.0147 | - | - |
| 0.7354 | 34500 | 0.0149 | - | - |
| 0.7407 | 34750 | 0.014 | - | - |
| 0.7460 | 35000 | 0.0156 | 0.0117 | 0.8022 |
| 0.7514 | 35250 | 0.0153 | - | - |
| 0.7567 | 35500 | 0.0146 | - | - |
| 0.7620 | 35750 | 0.0144 | - | - |
| 0.7674 | 36000 | 0.0139 | 0.0111 | 0.8035 |
| 0.7727 | 36250 | 0.0134 | - | - |
| 0.7780 | 36500 | 0.013 | - | - |
| 0.7833 | 36750 | 0.0156 | - | - |
| 0.7887 | 37000 | 0.0144 | 0.0108 | 0.8048 |
| 0.7940 | 37250 | 0.0133 | - | - |
| 0.7993 | 37500 | 0.0154 | - | - |
| 0.8047 | 37750 | 0.0132 | - | - |
| 0.8100 | 38000 | 0.013 | 0.0108 | 0.8063 |
| 0.8153 | 38250 | 0.0126 | - | - |
| 0.8207 | 38500 | 0.0135 | - | - |
| 0.8260 | 38750 | 0.014 | - | - |
| 0.8313 | 39000 | 0.013 | 0.0109 | 0.8086 |
| 0.8366 | 39250 | 0.0136 | - | - |
| 0.8420 | 39500 | 0.0141 | - | - |
| 0.8473 | 39750 | 0.0155 | - | - |
| 0.8526 | 40000 | 0.0153 | 0.0106 | 0.8075 |
| 0.8580 | 40250 | 0.0131 | - | - |
| 0.8633 | 40500 | 0.0128 | - | - |
| 0.8686 | 40750 | 0.013 | - | - |
| 0.8739 | 41000 | 0.0133 | 0.0109 | 0.8060 |
| 0.8793 | 41250 | 0.0119 | - | - |
| 0.8846 | 41500 | 0.0144 | - | - |
| 0.8899 | 41750 | 0.0142 | - | - |
| 0.8953 | 42000 | 0.0138 | 0.0105 | 0.8083 |
| 0.9006 | 42250 | 0.014 | - | - |
| 0.9059 | 42500 | 0.0134 | - | - |
| 0.9112 | 42750 | 0.0134 | - | - |
| 0.9166 | 43000 | 0.0124 | 0.0106 | 0.8113 |
| 0.9219 | 43250 | 0.0122 | - | - |
| 0.9272 | 43500 | 0.0126 | - | - |
| 0.9326 | 43750 | 0.0121 | - | - |
| 0.9379 | 44000 | 0.0137 | 0.0103 | 0.8105 |
| 0.9432 | 44250 | 0.0132 | - | - |
| 0.9485 | 44500 | 0.012 | - | - |
| 0.9539 | 44750 | 0.0136 | - | - |
| 0.9592 | 45000 | 0.0133 | 0.0104 | 0.8112 |
| 0.9645 | 45250 | 0.0118 | - | - |
| 0.9699 | 45500 | 0.0132 | - | - |
| 0.9752 | 45750 | 0.0118 | - | - |
| 0.9805 | 46000 | 0.012 | 0.0102 | 0.8104 |
| 0.9858 | 46250 | 0.0127 | - | - |
| 0.9912 | 46500 | 0.0134 | - | - |
| 0.9965 | 46750 | 0.0121 | - | - |
| 1.0 | 46914 | - | - | 0.8111 |
</details>
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 1.637 kWh
- **Carbon Emitted**: 0.636 kg of CO2
- **Hours Used**: 4.514 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- 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",
}
```
#### 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.*
--> |