File size: 29,759 Bytes
02da0dc |
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 |
---
base_model: google-bert/bert-base-uncased
datasets:
- sentence-transformers/gooaq
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:3002496
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: extreme old age is called?
sentences:
- The organic process of ageing is called senescence, the medical study of the aging
process is called gerontology, and the study of diseases that afflict the elderly
is called geriatrics. ... Old age is not a definite biological stage, as the chronological
age denoted as "old age" varies culturally and historically.
- The syllabus is described as the summary of the topics covered or units to be
taught in the particular subject. Curriculum refers to the overall content, taught
in an educational system or a course. ... Syllabus is descriptive in nature, but
the curriculum is prescriptive. Syllabus is set for a particular subject.
- Keep records for 3 years from the date you filed your original return or 2 years
from the date you paid the tax, whichever is later, if you file a claim for credit
or refund after you file your return. Keep records for 7 years if you file a claim
for a loss from worthless securities or bad debt deduction.
- source_sentence: has or as when to use?
sentences:
- 'Re: Has or as As is an adverb used in comparisons to refer to the extent or degree
of something; a conjunction 1 used to indicate simultaneous occurrence. 2 used
to indicate by comparison the way that something happens.'
- Go through their posts, likes, comments, and followers to see if the suspect's
username appears. If the user's name appears, click on it. If you click on the
user's profile and are unable to see their content, even though it says they have
a number of posts at the top of their profile, then they have blocked you.
- There's just a 2.6% + $0.30 fee on any portion funded by your credit or debit
card.
- source_sentence: how many inches of snow is good for snowboarding?
sentences:
- All kinds of tomato paste come with a best-by date. Like other condiments, such
as bbq sauce, the unopened paste will easily last months past the date on the
label.
- Data Storage Data in an SD card is stored on a series of electronic components
called NAND chips. These chips allow data to be written and stored on the SD card.
As the chips have no moving parts, data can be transferred from the cards quickly,
far exceeding the speeds available to CD or hard-drive media.
- In these areas, as little as 2-4 inches of snow may be sufficient. Other pistes,
however, may traverse uneven, rocky terrain. In these areas, several inches to
several feet may be necessary to cover the rocky surface. Even more important
than the amount of snowfall is the amount of snow that is retained on the slopes.
- source_sentence: is it normal to have a period after not having one for 8 months?
sentences:
- It is not normal to bleed or spot 12 months or more after your last period. Bleeding
after menopause is usually a sign of a minor health problem but can sometimes
be an early sign of more serious disease.
- '[''What are your recruiting needs for my class? ... '', ''What are the next steps
in the recruiting process with your program? ... '', ''What is your recruiting
timeline? ... '', ''What does a typical day or week look like for a player during
the season? ... '', ''What are the off-season expectations for a player? ... '',
''What are the values of your program?'']'
- Registered retirement savings plans (RRSP) and registered pension plans (RPP)
are both retirement savings plans that are registered with the Canada Revenue
Agency (CRA). RRSPs are individual retirement plans, while RPPs are plans established
by companies to provide pensions to their employees.
- source_sentence: what health services are covered by medicare?
sentences:
- Medicare Part A hospital insurance covers inpatient hospital care, skilled nursing
facility, hospice, lab tests, surgery, home health care.
- Meiocytes are the diploid cells which undergo meiosis to produce gametes. They
are also known as gamete mother cells. The chromosome number in diploid cells
of onion is 16. So meiocytes have 16 chromosomes.
- Elephants have the longest gestation period of all mammals. These gentle giants'
pregnancies last for more than a year and a half. The average gestation period
of an elephant is about 640 to 660 days, or roughly 95 weeks.
co2_eq_emissions:
emissions: 408.66249919578786
energy_consumed: 1.0513516760803594
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: 2.832
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: BERT base uncased trained on GooAQ triplets
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: gooaq dev
type: gooaq-dev
metrics:
- type: cosine_accuracy@1
value: 0.576
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7295
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7824
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8462
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.576
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24316666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15648
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08462
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.576
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7295
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7824
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8462
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7089171465159466
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6652589285714262
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6708962490161547
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.5263
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6922
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7494
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8175
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5263
name: Dot Precision@1
- type: dot_precision@3
value: 0.23073333333333335
name: Dot Precision@3
- type: dot_precision@5
value: 0.14987999999999999
name: Dot Precision@5
- type: dot_precision@10
value: 0.08175
name: Dot Precision@10
- type: dot_recall@1
value: 0.5263
name: Dot Recall@1
- type: dot_recall@3
value: 0.6922
name: Dot Recall@3
- type: dot_recall@5
value: 0.7494
name: Dot Recall@5
- type: dot_recall@10
value: 0.8175
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6696727448603579
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.622603690476188
name: Dot Mrr@10
- type: dot_map@100
value: 0.6291100061102131
name: Dot Map@100
---
# BERT base uncased trained on GooAQ triplets
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) 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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **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: PeftModelForFeatureExtraction
(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/bert-base-uncased-gooaq-peft")
# Run inference
sentences = [
'what health services are covered by medicare?',
'Medicare Part A hospital insurance covers inpatient hospital care, skilled nursing facility, hospice, lab tests, surgery, home health care.',
"Elephants have the longest gestation period of all mammals. These gentle giants' pregnancies last for more than a year and a half. The average gestation period of an elephant is about 640 to 660 days, or roughly 95 weeks.",
]
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 | 0.576 |
| cosine_accuracy@3 | 0.7295 |
| cosine_accuracy@5 | 0.7824 |
| cosine_accuracy@10 | 0.8462 |
| cosine_precision@1 | 0.576 |
| cosine_precision@3 | 0.2432 |
| cosine_precision@5 | 0.1565 |
| cosine_precision@10 | 0.0846 |
| cosine_recall@1 | 0.576 |
| cosine_recall@3 | 0.7295 |
| cosine_recall@5 | 0.7824 |
| cosine_recall@10 | 0.8462 |
| cosine_ndcg@10 | 0.7089 |
| cosine_mrr@10 | 0.6653 |
| **cosine_map@100** | **0.6709** |
| dot_accuracy@1 | 0.5263 |
| dot_accuracy@3 | 0.6922 |
| dot_accuracy@5 | 0.7494 |
| dot_accuracy@10 | 0.8175 |
| dot_precision@1 | 0.5263 |
| dot_precision@3 | 0.2307 |
| dot_precision@5 | 0.1499 |
| dot_precision@10 | 0.0818 |
| dot_recall@1 | 0.5263 |
| dot_recall@3 | 0.6922 |
| dot_recall@5 | 0.7494 |
| dot_recall@10 | 0.8175 |
| dot_ndcg@10 | 0.6697 |
| dot_mrr@10 | 0.6226 |
| dot_map@100 | 0.6291 |
<!--
## 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: 11.84 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 60.69 tokens</li><li>max: 149 tokens</li></ul> |
* Samples:
| question | answer |
|:-------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>can dogs get pregnant when on their period?</code> | <code>2. Female dogs can only get pregnant when they're in heat. Some females will show physical signs of readiness – their discharge will lighten in color, and they will “flag,” or lift their tail up and to the side.</code> |
| <code>are there different forms of als?</code> | <code>['Sporadic ALS is the most common form. It affects up to 95% of people with the disease. Sporadic means it happens sometimes without a clear cause.', 'Familial ALS (FALS) runs in families. About 5% to 10% of people with ALS have this type. FALS is caused by changes to a gene.']</code> |
| <code>what is the difference between stayman and jacoby transfer?</code> | <code>1. The Stayman Convention is used only with a 4-Card Major suit looking for a 4-Card Major suit fit. Jacoby Transfer bids are used with a 5-Card suit looking for a 3-Card fit.</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: 12.01 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 61.37 tokens</li><li>max: 138 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is there a season 5 animal kingdom?</code> | <code>the good news for the fans is that the season five was confirmed by TNT in July, 2019. The season five of Animal Kingdom was expected to release in May, 2020.</code> |
| <code>what are cmos voltage levels?</code> | <code>CMOS gate circuits have input and output signal specifications that are quite different from TTL. For a CMOS gate operating at a power supply voltage of 5 volts, the acceptable input signal voltages range from 0 volts to 1.5 volts for a “low” logic state, and 3.5 volts to 5 volts for a “high” logic state.</code> |
| <code>dangers of drinking coke when pregnant?</code> | <code>Drinking it during pregnancy was linked to poorer fine motor, visual, spatial and visual motor abilities in early childhood (around age 3). By mid-childhood (age 7), kids whose moms drank diet sodas while pregnant had poorer verbal abilities, the study findings reported.</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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 128
- `per_device_eval_batch_size`: 128
- `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
| Epoch | Step | Training Loss | loss | gooaq-dev_cosine_map@100 |
|:------:|:-----:|:-------------:|:------:|:------------------------:|
| 0 | 0 | - | - | 0.2017 |
| 0.0000 | 1 | 2.584 | - | - |
| 0.0213 | 500 | 2.4164 | - | - |
| 0.0426 | 1000 | 1.1421 | - | - |
| 0.0639 | 1500 | 0.5215 | - | - |
| 0.0853 | 2000 | 0.3645 | 0.2763 | 0.6087 |
| 0.1066 | 2500 | 0.3046 | - | - |
| 0.1279 | 3000 | 0.2782 | - | - |
| 0.1492 | 3500 | 0.2601 | - | - |
| 0.1705 | 4000 | 0.2457 | 0.2013 | 0.6396 |
| 0.1918 | 4500 | 0.2363 | - | - |
| 0.2132 | 5000 | 0.2291 | - | - |
| 0.2345 | 5500 | 0.2217 | - | - |
| 0.2558 | 6000 | 0.2137 | 0.1770 | 0.6521 |
| 0.2771 | 6500 | 0.215 | - | - |
| 0.2984 | 7000 | 0.2057 | - | - |
| 0.3197 | 7500 | 0.198 | - | - |
| 0.3410 | 8000 | 0.196 | 0.1626 | 0.6594 |
| 0.3624 | 8500 | 0.1938 | - | - |
| 0.3837 | 9000 | 0.195 | - | - |
| 0.4050 | 9500 | 0.1895 | - | - |
| 0.4263 | 10000 | 0.186 | 0.1542 | 0.6628 |
| 0.4476 | 10500 | 0.1886 | - | - |
| 0.4689 | 11000 | 0.1835 | - | - |
| 0.4903 | 11500 | 0.1825 | - | - |
| 0.5116 | 12000 | 0.1804 | 0.1484 | 0.6638 |
| 0.5329 | 12500 | 0.176 | - | - |
| 0.5542 | 13000 | 0.1825 | - | - |
| 0.5755 | 13500 | 0.1785 | - | - |
| 0.5968 | 14000 | 0.1766 | 0.1436 | 0.6672 |
| 0.6182 | 14500 | 0.1718 | - | - |
| 0.6395 | 15000 | 0.1717 | - | - |
| 0.6608 | 15500 | 0.1674 | - | - |
| 0.6821 | 16000 | 0.1691 | 0.1406 | 0.6704 |
| 0.7034 | 16500 | 0.1705 | - | - |
| 0.7247 | 17000 | 0.1693 | - | - |
| 0.7460 | 17500 | 0.166 | - | - |
| 0.7674 | 18000 | 0.1676 | 0.1385 | 0.6721 |
| 0.7887 | 18500 | 0.1666 | - | - |
| 0.8100 | 19000 | 0.1658 | - | - |
| 0.8313 | 19500 | 0.1682 | - | - |
| 0.8526 | 20000 | 0.1639 | 0.1370 | 0.6705 |
| 0.8739 | 20500 | 0.1711 | - | - |
| 0.8953 | 21000 | 0.1667 | - | - |
| 0.9166 | 21500 | 0.165 | - | - |
| 0.9379 | 22000 | 0.1658 | 0.1356 | 0.6711 |
| 0.9592 | 22500 | 0.1665 | - | - |
| 0.9805 | 23000 | 0.1636 | - | - |
| 1.0 | 23457 | - | - | 0.6709 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 1.051 kWh
- **Carbon Emitted**: 0.409 kg of CO2
- **Hours Used**: 2.832 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: 0.31.0
- Datasets: 2.20.0
- 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.*
--> |