File size: 39,695 Bytes
6f8f57f |
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 |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:378558
- loss:MultipleNegativesRankingLoss
base_model: intfloat/e5-base-v2
widget:
- source_sentence: Is intraoperative ketorolac an effective substitute for fentanyl
in children undergoing outpatient adenotonsillectomy?
sentences:
- Ketorolac showed no advantage over fentanyl in reducing the incidence of PONV
in children undergoing ADLAT.
- The patients with IgAN and their first relatives showed significant higher Gal
deficient IgA1 level than healthy controls, whereas patients spouses were the
same as healthy controls. It can be suggested that the Gal deficient IgA1 might
be inherited in Chinese patients with IgAN.
- Our results indicated that triptolide enhanced and enriched the stemness in the
PDAC cell lines at a low dose of 12.5 nM, but also resulted in the regression
of tumors derived from these cells.
- source_sentence: Is task specific fall prevention training effective for warfighters
with transtibial amputations?
sentences:
- These results indicate that task specific fall prevention training is an effective
rehabilitation method to reduce falls in persons with lower extremity transtibial
amputations.
- Don t press on the eye. For pain, give acetaminophen Tylenol . Don t give aspirin
or ibuprofen Advil, Motrin , because they can increase bleeding.
- Dermatophytes Trichophyton skin ,hair, ,nail Tri all Three Microsporum skin, hair
My head on head we have skin and hair Epidermophyton skin, nails
- source_sentence: Left horn of sinus venosus forms
sentences:
- Ki 67 expression is predictive of prognosis, and our prognostic model may become
a useful tool for predicting prognosis in patients with stage I II extranodal
NK T cell lymphoma, nasal type.
- Evidence described here suggests that IFN λ is a good candidate inhibitor of viral
replication in dengue infection. Mechanisms for the cellular and organismal interplay
between DENV and IFN λ need to be further studied as they could provide insights
into strategies to treat this disease. Furthermore, we report a novel epithelial
model to study dengue infection in vitro.
- Ans. A Coronary sinusRef Netter s Atlas of Human Embryology 2012 ed. pg. 96Heart
tube embryonic derivativesembryonic structureGives rise to Proximal 1 3rd of bulbus
cordisPrimitive trabeculated left ventricle Middle 1 3rd of bulbus cordisRight
and left ventricular outflow tract Distal 1 3rd of bulbus cordis truncus arteriosus
Ascending aorta and pulmonary trunk Left horn of sinus venosusCoronary sinus Right
horn of sinus venosusSmooth part of right atrium Right common cardinal nerve and
right anterior cardinal nerveSVC superior vena cava
- source_sentence: Is implementation of national diabetes retinal screening programme
associated with a lower proportion of patients referred to ophthalmology?
sentences:
- Introduction of a systematic retinal screening programme can reduce the proportion
of patients referred to the ophthalmology clinic, and use ophthalmology services
more efficiently.
- A Obesity Medications for the treatment of obesity can be classified as catecholaminergic
or serotonergic. Catecholaminergic medications include Amphetamines with high
abuse potential The Non Amphetamine schedule IV appetite suppressants Phentermine,
Diethyl propion Mazindol. The September 1997 withdrawal from the market of Flenfluramine
Defenfluramine has made true serotonergic appetite medications unavailable. The
SSRI antidepressants, E.g.., Fluoxetine Setraline, also have serotonergic activity
but are not approved by the FDA for weight loss.
- A i.e. Protein linked with glycosidic bond
- source_sentence: Does amyloid peptide regulate calcium homoeostasis and arrhythmogenesis
in pulmonary vein cardiomyocytes?
sentences:
- Hydroxy ethyl methaacrylate is a soft, flexible, water absorbing, plastic used
to make soft contact lenses. It is a polymer of 2 hydroxyethyl methacrylate HEMA
, a clear liquid component. Hard contact lenses are made from polymethyl methacrylate
PMMA and Silicon.
- Beta carotene has become popular in part because it s an antioxidant a substance
that may protect cells from damage. A number of studies show that people who eat
lots of fruits and vegetables that are rich in beta carotene and other vitamins
and minerals have a lower risk of some cancers and heart disease. However, so
far studies have not found that beta carotene supplements have the same health
benefits as foods.
- Aβ 25 35 has direct electrophysiological effects on PV cardiomyocytes.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: MPNet base trained on AllNLI triplets
results:
- task:
type: triplet
name: Triplet
dataset:
name: eval dataset
type: eval-dataset
metrics:
- type: cosine_accuracy
value: 0.9937447168216399
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: test dataset
type: test-dataset
metrics:
- type: cosine_accuracy
value: 0.9964285714285714
name: Cosine Accuracy
---
# MPNet base trained on AllNLI triplets
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). 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:** [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) <!-- at revision 1c644c92ad3ba1efdad3f1451a637716616a20e8 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **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': False}) with Transformer model: BertModel
(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})
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'Does amyloid peptide regulate calcium homoeostasis and arrhythmogenesis in pulmonary vein cardiomyocytes?',
'Aβ 25 35 has direct electrophysiological effects on PV cardiomyocytes.',
'Beta carotene has become popular in part because it s an antioxidant a substance that may protect cells from damage. A number of studies show that people who eat lots of fruits and vegetables that are rich in beta carotene and other vitamins and minerals have a lower risk of some cancers and heart disease. However, so far studies have not found that beta carotene supplements have the same health benefits as foods.',
]
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
#### Triplet
* Datasets: `eval-dataset` and `test-dataset`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | eval-dataset | test-dataset |
|:--------------------|:-------------|:-------------|
| **cosine_accuracy** | **0.9937** | **0.9964** |
<!--
## 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: 378,558 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 24.72 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 88.11 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Does tolbutamide alter glucose transport and metabolism in the embryonic mouse heart?</code> | <code>Tolbutamide stimulates glucose uptake and metabolism in the embryonic heart, as occurs in adult extra pancreatic tissues. Glut 1 and HKI, but not GRP78, are likely involved in tolbutamide induced cardiac dysmorphogenesis.</code> | <code>1.0</code> |
| <code>Do flk1 cells derived from mouse embryonic stem cells reconstitute hematopoiesis in vivo in SCID mice?</code> | <code>The Flk1 hematopoietic cells derived from ES cells reconstitute hematopoiesis in vivo and may become an alternative donor source for bone marrow transplantation.</code> | <code>1.0</code> |
| <code>Does systematic aging of degradable nanosuspension ameliorate vibrating mesh nebulizer performance?</code> | <code>Nebulization of purified nanosuspensions resulted in droplet diameters of 7.0 µm. However, electrolyte supplementation and storage, which led to an increase in sample conductivity 10 20 µS cm , were capable of providing smaller droplet diameters during vibrating mesh nebulization 5.0 µm . No relevant change of NP properties i.e. size, morphology, remaining mass and molecular weight of the employed polymer was observed when incubated at 22 C for two weeks.</code> | <code>1.0</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
#### Unnamed Dataset
* Size: 47,320 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 24.45 tokens</li><li>max: 253 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 87.68 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Does thrombospondin 2 gene silencing in human aortic smooth muscle cells improve cell attachment?</code> | <code>siRNA mediated TSP 2 silencing of human aortic HAoSMCs improved cell attachment but had no effect on cell migration or proliferation. The effect on cell attachment was unrelated to changes in MMP activity.</code> | <code>1.0</code> |
| <code>What can you do to manage polycythemia vera?</code> | <code>Most people with polycythemia vera take low dose aspirin. There are a lot of ways you can keep yourself comfortable and as healthy as possible Don t smoke or chew tobacco. Tobacco makes blood vessels narrow, which can make blood clots more likely. Get some light exercise, such as walking, to help your circulation and keep your heart healthy. Do leg and ankle exercises to stop clots from forming in the veins of your legs. Your doctor or a physical therapist can show you how. Bathe or shower in cool water if warm water makes you itch. Keep your skin moist with lotion, and try not to scratch.</code> | <code>1.0</code> |
| <code>Is weekly nab paclitaxel safe and effective in 65 years old patients with metastatic breast cancer a post hoc analysis?</code> | <code>Weekly nab paclitaxel was safe and more efficacious compared with the q3w schedule and with solvent based taxanes in older patients with MBC.</code> | <code>1.0</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
- `do_predict`: True
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: True
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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
- `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
- `include_for_metrics`: []
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | eval-dataset_cosine_accuracy | test-dataset_cosine_accuracy |
|:----------:|:--------:|:-------------:|:---------------:|:----------------------------:|:----------------------------:|
| 0 | 0 | - | - | 0.9813 | - |
| 0.0085 | 50 | 1.8471 | - | - | - |
| 0.0169 | 100 | 0.5244 | - | - | - |
| 0.0254 | 150 | 0.2175 | - | - | - |
| 0.0338 | 200 | 0.1392 | - | - | - |
| 0.0423 | 250 | 0.1437 | - | - | - |
| 0.0507 | 300 | 0.142 | - | - | - |
| 0.0592 | 350 | 0.1295 | - | - | - |
| 0.0676 | 400 | 0.1238 | - | - | - |
| 0.0761 | 450 | 0.14 | - | - | - |
| 0.0845 | 500 | 0.1173 | 0.1006 | 0.9931 | - |
| 0.0930 | 550 | 0.1236 | - | - | - |
| 0.1014 | 600 | 0.1127 | - | - | - |
| 0.1099 | 650 | 0.1338 | - | - | - |
| 0.1183 | 700 | 0.1071 | - | - | - |
| 0.1268 | 750 | 0.1149 | - | - | - |
| 0.1352 | 800 | 0.1072 | - | - | - |
| 0.1437 | 850 | 0.1117 | - | - | - |
| 0.1522 | 900 | 0.1087 | - | - | - |
| 0.1606 | 950 | 0.1242 | - | - | - |
| **0.1691** | **1000** | **0.1039** | **0.091** | **0.9965** | **-** |
| 0.1775 | 1050 | 0.1043 | - | - | - |
| 0.1860 | 1100 | 0.1193 | - | - | - |
| 0.1944 | 1150 | 0.1028 | - | - | - |
| 0.2029 | 1200 | 0.1027 | - | - | - |
| 0.2113 | 1250 | 0.1075 | - | - | - |
| 0.2198 | 1300 | 0.1177 | - | - | - |
| 0.2282 | 1350 | 0.0937 | - | - | - |
| 0.2367 | 1400 | 0.1095 | - | - | - |
| 0.2451 | 1450 | 0.1054 | - | - | - |
| 0.2536 | 1500 | 0.1003 | 0.0798 | 0.9958 | - |
| 0.2620 | 1550 | 0.0952 | - | - | - |
| 0.2705 | 1600 | 0.1028 | - | - | - |
| 0.2790 | 1650 | 0.0988 | - | - | - |
| 0.2874 | 1700 | 0.0887 | - | - | - |
| 0.2959 | 1750 | 0.1027 | - | - | - |
| 0.3043 | 1800 | 0.0937 | - | - | - |
| 0.3128 | 1850 | 0.1031 | - | - | - |
| 0.3212 | 1900 | 0.0857 | - | - | - |
| 0.3297 | 1950 | 0.094 | - | - | - |
| 0.3381 | 2000 | 0.1044 | 0.0721 | 0.9954 | - |
| 0.3466 | 2050 | 0.0829 | - | - | - |
| 0.3550 | 2100 | 0.0934 | - | - | - |
| 0.3635 | 2150 | 0.0785 | - | - | - |
| 0.3719 | 2200 | 0.0938 | - | - | - |
| 0.3804 | 2250 | 0.0885 | - | - | - |
| 0.3888 | 2300 | 0.0907 | - | - | - |
| 0.3973 | 2350 | 0.0911 | - | - | - |
| 0.4057 | 2400 | 0.0891 | - | - | - |
| 0.4142 | 2450 | 0.0798 | - | - | - |
| 0.4227 | 2500 | 0.0856 | 0.0655 | 0.9935 | - |
| 0.4311 | 2550 | 0.0925 | - | - | - |
| 0.4396 | 2600 | 0.0778 | - | - | - |
| 0.4480 | 2650 | 0.0871 | - | - | - |
| 0.4565 | 2700 | 0.0769 | - | - | - |
| 0.4649 | 2750 | 0.0815 | - | - | - |
| 0.4734 | 2800 | 0.0697 | - | - | - |
| 0.4818 | 2850 | 0.0714 | - | - | - |
| 0.4903 | 2900 | 0.0788 | - | - | - |
| 0.4987 | 2950 | 0.0772 | - | - | - |
| 0.5072 | 3000 | 0.0825 | 0.0618 | 0.9917 | - |
| 0.5156 | 3050 | 0.0742 | - | - | - |
| 0.5241 | 3100 | 0.0784 | - | - | - |
| 0.5325 | 3150 | 0.0697 | - | - | - |
| 0.5410 | 3200 | 0.0791 | - | - | - |
| 0.5495 | 3250 | 0.0657 | - | - | - |
| 0.5579 | 3300 | 0.0779 | - | - | - |
| 0.5664 | 3350 | 0.0719 | - | - | - |
| 0.5748 | 3400 | 0.0656 | - | - | - |
| 0.5833 | 3450 | 0.0698 | - | - | - |
| 0.5917 | 3500 | 0.0678 | 0.0578 | 0.9903 | - |
| 0.6002 | 3550 | 0.0771 | - | - | - |
| 0.6086 | 3600 | 0.0645 | - | - | - |
| 0.6171 | 3650 | 0.078 | - | - | - |
| 0.6255 | 3700 | 0.064 | - | - | - |
| 0.6340 | 3750 | 0.0691 | - | - | - |
| 0.6424 | 3800 | 0.0634 | - | - | - |
| 0.6509 | 3850 | 0.0732 | - | - | - |
| 0.6593 | 3900 | 0.059 | - | - | - |
| 0.6678 | 3950 | 0.0671 | - | - | - |
| 0.6762 | 4000 | 0.0633 | 0.0552 | 0.9936 | - |
| 0.6847 | 4050 | 0.0732 | - | - | - |
| 0.6932 | 4100 | 0.0593 | - | - | - |
| 0.7016 | 4150 | 0.0639 | - | - | - |
| 0.7101 | 4200 | 0.0672 | - | - | - |
| 0.7185 | 4250 | 0.0604 | - | - | - |
| 0.7270 | 4300 | 0.0666 | - | - | - |
| 0.7354 | 4350 | 0.0594 | - | - | - |
| 0.7439 | 4400 | 0.0783 | - | - | - |
| 0.7523 | 4450 | 0.0654 | - | - | - |
| 0.7608 | 4500 | 0.0596 | 0.0520 | 0.9937 | - |
| 0.7692 | 4550 | 0.0654 | - | - | - |
| 0.7777 | 4600 | 0.0511 | - | - | - |
| 0.7861 | 4650 | 0.0641 | - | - | - |
| 0.7946 | 4700 | 0.0609 | - | - | - |
| 0.8030 | 4750 | 0.0591 | - | - | - |
| 0.8115 | 4800 | 0.0496 | - | - | - |
| 0.8199 | 4850 | 0.0624 | - | - | - |
| 0.8284 | 4900 | 0.0639 | - | - | - |
| 0.8369 | 4950 | 0.056 | - | - | - |
| 0.8453 | 5000 | 0.0641 | 0.0487 | 0.9947 | - |
| 0.8538 | 5050 | 0.0608 | - | - | - |
| 0.8622 | 5100 | 0.0725 | - | - | - |
| 0.8707 | 5150 | 0.055 | - | - | - |
| 0.8791 | 5200 | 0.0556 | - | - | - |
| 0.8876 | 5250 | 0.0489 | - | - | - |
| 0.8960 | 5300 | 0.0513 | - | - | - |
| 0.9045 | 5350 | 0.0493 | - | - | - |
| 0.9129 | 5400 | 0.0574 | - | - | - |
| 0.9214 | 5450 | 0.0665 | - | - | - |
| 0.9298 | 5500 | 0.0588 | 0.0475 | 0.9937 | - |
| 0.9383 | 5550 | 0.0557 | - | - | - |
| 0.9467 | 5600 | 0.0497 | - | - | - |
| 0.9552 | 5650 | 0.0592 | - | - | - |
| 0.9637 | 5700 | 0.0526 | - | - | - |
| 0.9721 | 5750 | 0.0683 | - | - | - |
| 0.9806 | 5800 | 0.0588 | - | - | - |
| 0.9890 | 5850 | 0.0541 | - | - | - |
| 0.9975 | 5900 | 0.0636 | - | - | - |
| 1.0 | 5915 | - | - | - | 0.9964 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.3.0
- Transformers: 4.46.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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.*
--> |