File size: 32,372 Bytes
8da4c75 |
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 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:594028
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
- source_sentence: '''আমি'' শব্দটি কোন লিঙ্গ?
A. উভয় লিঙ্গ
B. ক্লীব লিঙ্গ
C. পুংলিঙ্গ
D. স্ত্রী লিঙ্গ'
sentences:
- F.P. Dobroslavin, tibbin müxtəlif sahələri üzrə tanınmış bir alimdir, ancaq daha
çox baş vermiş tədqiqatlara görə seçilir. Onun əməyinin sanitar-gigiyenik sahəyə
təsiri əhəmiyyətlidir.
- 'বাংলা ভাষায় শব্দগুলোর লিঙ্গ সাধারণত তিনটি মূল শ্রেণিতে ভাগ হয়: পুংলিঙ্গ (পুরুষ),
স্ত্রী লিঙ্গ (মহিলা), এবং ক্লীব লিঙ্গ (যার কোনো লিঙ্গ নেই)।'
- Waves are disturbances that transfer energy from one place to another without
transferring matter. Think of a ripple on a pond – the water molecules don't travel
across the pond with the ripple; they mostly move up and down as the energy passes
through them.
- source_sentence: '企业产品组合中所拥有的产品线数目是
A. 产品组合的宽度
B. 产品组合的相关性
C. 产品组合的深度
D. 产品组合的长度'
sentences:
- 产品组合的宽度（Width）是指企业拥有的产品线数目。
- This fluid is produced by the walls of the vagina and the Bartholin's glands.
- "### Assumption of Risk Defined \nAssumption of risk is a legal doctrine used\
\ in tort law that can limit or bar recovery in negligence claims. This doctrine\
\ suggests that if a person voluntarily engages in a risky activity, knowing the\
\ risks involved, they cannot hold another party responsible for resulting injuries.\
\ Common scenarios where this applies include contact sports and recreational\
\ activities, where participants understand the inherent hazards. \n\n### Elements\
\ of Assumption of Risk \nTo successfully argue assumption of risk, certain elements\
\ must be established: \n1. **Knowledge of the Risk**: The individual must have\
\ actual or constructive knowledge of the risk involved. \n2. **Voluntary Exposure**:\
\ The individual must voluntarily choose to expose themselves to that risk. \n\
3. **Informed Consent**: The individual must have consented to take that risk\
\ despite being aware of it. \n\n### Contributory Negligence \nContributory\
\ negligence is a legal concept that exists in some jurisdictions where a plaintiff's\
\ own negligence contributes to their injury. Under this doctrine, if the plaintiff\
\ is found to have played any part in their injury, they may be barred from recovering\
\ damages, or the damage award could be reduced. It emphasizes the responsibility\
\ of the injured party to exercise reasonable care for their own safety. \n\n\
### Interaction of Assumption of Risk and Contributory Negligence \nIn many jurisdictions,\
\ both assumption of risk and contributory negligence can coexist as defenses.\
\ However, some legal systems assert that if a plaintiff is found contributorily\
\ negligent, they cannot also claim assumed risk for the same incident. This overlap\
\ can complicate cases since the determination of the plaintiff's awareness and\
\ behavior prior to the accident can alter the outcome. \n\n### Legal Standard\
\ for Warnings and Liability \nIn negligence cases, the adequacy of warnings\
\ provided is crucial. Courts often assess whether the warnings were sufficient\
\ to inform the individual of the specific hazards present. A simple sign may\
\ not meet the threshold if it fails to clearly communicate the danger involved,\
\ especially if the harm is not immediately obvious or if the context (e.g., a\
\ crowded street) suggests additional risks."
- source_sentence: "While shopping at a grocery store, a customer tripped over a broken\
\ tile, fell, and suffered a concussion. A few months after the accident, the\
\ customer's attorney deposed a store employee. In the deposition, the employee\
\ testified, \"I'd been telling the store manager for years to get that broken\
\ tile fixed, but he wouldn't do it. \" The employee died in an automobile accident\
\ after being deposed. At trial, the deposition should be\nA. admitted, as a dying\
\ declaration. \nB. admitted, as former testimony. \nC. not admitted, because\
\ it is hearsay not within any exception. \nD. not admitted, because the employee\
\ is not available for cross-examination. "
sentences:
- In the context of human evolution, brain size is often compared to body size in
a measurement called the encephalization quotient (EQ). This measure assesses
the expected brain size for an animal of a given body size compared to actual
brain size. An increase in EQ among hominins is often linked to advancements in
cognitive abilities, such as problem-solving and social interaction.
- Another exception to the hearsay rule, though often with specific requirements
related to the declarant's belief of impending death, is the dying declaration.
- In assessing moral actions, it is also essential to consider societal norms. In
the U.S. context in 2020, moral standards often emphasize community well-being
and individual rights. An action like diverting emergency supplies would likely
be condemned in most social circles, while stepping out of rhythm during a line
dancewould not commonly qualify as a serious moral offense. Thus, moral wrongness
is often context-dependent and tied closely to consequences for individuals and
society.
- source_sentence: 'Recent research on hominid species dating from the Middle Pliocene
indicates there was (as of 2020):
A. multiple hominid species but with limited diversity.
B. a single species with no diversity.
C. decreased species diversity but increased numbers of hammerstones and flakes,
indicating stone tool manufacture.
D. a single dominant species that outcompeted all others, leading to decreased
diversity.
E. increased species diversity due to a prolonged ice age followed by a severe
drought.
F. decreased species diversity due to a prolonged ice age followed by a severe
drought.
G. a great amount of species diversity, or a single species that exhibited a lot
of diversity.
H. increased species diversity but with decreased population numbers due to harsh
climate conditions.
I. increased species diversity but decreased numbers of hammerstones and flakes,
indicating less stone tool manufacture.
J. very little species diversity during this period and very few hominids.'
sentences:
- Hammerstones and flakes are artifacts associated with early stone tool technology.
Hammerstones are hard rocks used to strike other stones, while flakes are the
sharp pieces produced from such strikes, which could be utilized for tasks like
cutting or scraping, indicating early cognitive and manual skills in tool-making
among certain species.
- The Doppler effect is a phenomenon that occurs when the source of a wave and the
observer are moving relative to each other. It results in a change in the observed
frequency of the wave compared to the source frequency.
- Counseling and therapeutic interventions can play a role in addressing student
behavioral issues, but they should be considered within a broader context of classroom
dynamics and educational strategies. Counseling might help the child develop coping
mechanisms, social skills, and emotional regulation strategies. However, the effectiveness
of counseling is often maximized when the child is supported in the classroom
environment as well, suggesting that changes to the teacher's approach could lead
to improved outcomes.
- source_sentence: 'Hipotalamusi NUK kontrollon sekretimin e hormoneve:
A. FSH dhe LH
B. te rritjes(GH)
C. ACTH
D. te pankreasit'
sentences:
- In the context of estate planning and inheritance law, a will serves as a legal
document outlining how a person's property and assets will be distributed after
their death. The interpretation of a will often hinges on the intent of the testator,
or the person who made the will, which can affect how property interests are determined.
- State laws that regulate matters of legitimate local concern but have an incidental
effect on interstate commerce are subject to a less strict balancing test. Under
this test, a state law will be upheld unless the burden imposed on interstate
commerce is clearly excessive in relation to the putative local benefits.
- Hipotalamusi është një pjesë e trurit që ndodhet nën talamusin. Ai luan një rol
kryesor në lidhjen e sistemit nervor me sistemin endokrin përmes gjëndrës së hipofizës.
datasets:
- DoDucAnh/mcqa-rag-finetune
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [mcqa-rag-finetune](https://huggingface.co/datasets/DoDucAnh/mcqa-rag-finetune) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision 84344a23ee1820ac951bc365f1e91d094a911763 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [mcqa-rag-finetune](https://huggingface.co/datasets/DoDucAnh/mcqa-rag-finetune)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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 = [
'Hipotalamusi NUK kontrollon sekretimin e hormoneve:\nA. FSH dhe LH\nB. te rritjes(GH)\nC. ACTH\nD. te pankreasit',
'Hipotalamusi është një pjesë e trurit që ndodhet nën talamusin. Ai luan një rol kryesor në lidhjen e sistemit nervor me sistemin endokrin përmes gjëndrës së hipofizës.',
'State laws that regulate matters of legitimate local concern but have an incidental effect on interstate commerce are subject to a less strict balancing test. Under this test, a state law will be upheld unless the burden imposed on interstate commerce is clearly excessive in relation to the putative local benefits.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## 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
#### mcqa-rag-finetune
* Dataset: [mcqa-rag-finetune](https://huggingface.co/datasets/DoDucAnh/mcqa-rag-finetune) at [d1f5446](https://huggingface.co/datasets/DoDucAnh/mcqa-rag-finetune/tree/d1f5446a80c070fb8e1abfffef8a9dace426026b)
* Size: 594,028 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 22 tokens</li><li>mean: 105.96 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 70.95 tokens</li><li>max: 478 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Find all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.<br>A. 0<br>B. 1<br>C. 2<br>D. 3</code> | <code>The notation Z_3 refers to the finite field with three elements, often denoted as {0, 1, 2}. This field operates under modular arithmetic, specifically modulo 3. Elements in Z_3 can be added and multiplied according to the rules of modulo 3, where any number can wrap around upon reaching 3.</code> |
| <code>Find all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.<br>A. 0<br>B. 1<br>C. 2<br>D. 3</code> | <code>A field is a set equipped with two operations, addition and multiplication, satisfying certain properties: associativity, commutativity, distributivity, the existence of additive and multiplicative identities, and the existence of additive inverses and multiplicative inverses (for all elements except the zero element). In order for Z_3[x]/(f(x)) to be a field, the polynomial f(x) must be irreducible over Z_3.</code> |
| <code>Find all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.<br>A. 0<br>B. 1<br>C. 2<br>D. 3</code> | <code>The expression Z_3[x] indicates the set of all polynomials with coefficients in Z_3. A polynomial is said to be irreducible over Z_3 if it cannot be factored into the product of two non-constant polynomials with coefficients in Z_3. In the case of quadratic polynomials like x^2 + c, irreducibility depends on whether it has any roots in the field Z_3.</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
#### mcqa-rag-finetune
* Dataset: [mcqa-rag-finetune](https://huggingface.co/datasets/DoDucAnh/mcqa-rag-finetune) at [d1f5446](https://huggingface.co/datasets/DoDucAnh/mcqa-rag-finetune/tree/d1f5446a80c070fb8e1abfffef8a9dace426026b)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 22 tokens</li><li>mean: 98.74 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 59.88 tokens</li><li>max: 501 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>ക്രൂരകോഷ്ഠം ഉള്ള ഒരാളിൽ കോപിച്ചിരിക്കുന്ന ദോഷം താഴെപ്പറയുന്നവയിൽ ഏതാണ്?<br>A. കഫം<br>B. പിത്തം<br>C. വാതം<br>D. രക്തം</code> | <code>ഓരോ ദോഷത്തിനും അതിന്റേതായ സ്വഭാവങ്ങളും ശരീരത്തിൽ അത് ഉണ്ടാക്കുന്ന ഫലങ്ങളും ഉണ്ട്.</code> |
| <code>Melyik tényező nem befolyásolja a fagylalt keresleti függvényét?<br>A. A fagylalt árának változása.<br>B. Mindegyik tényező befolyásolja.<br>C. A jégkrém árának változása.<br>D. A fagylalttölcsér árának változása.</code> | <code>A keresleti függvény negatív meredekségű, ami azt jelenti, hogy az ár növekedésével a keresett mennyiség csökken (csökkenő kereslet törvénye).</code> |
| <code>In contrast to _______, _______ aim to reward favourable behaviour by companies. The success of such campaigns have been heightened through the use of ___________, which allow campaigns to facilitate the company in achieving _________ .<br>A. Boycotts, Buyalls, Blockchain technology, Increased Sales<br>B. Buycotts, Boycotts, Digital technology, Decreased Sales<br>C. Boycotts, Buycotts, Digital technology, Decreased Sales<br>D. Buycotts, Boycotts, Blockchain technology, Charitable donations<br>E. Boycotts, Buyalls, Blockchain technology, Charitable donations<br>F. Boycotts, Buycotts, Digital technology, Increased Sales<br>G. Buycotts, Boycotts, Digital technology, Increased Sales<br>H. Boycotts, Buycotts, Physical technology, Increased Sales<br>I. Buycotts, Buyalls, Blockchain technology, Charitable donations<br>J. Boycotts, Buycotts, Blockchain technology, Decreased Sales</code> | <code>**Consumer Activism**: This term refers to the actions taken by consumers to promote social, political, or environmental causes. These actions can include boycotting certain companies or buycotting others, influencing market dynamics based on ethical considerations. The effectiveness of consumer activism can vary but has gained prominence in recent years with increased visibility through social media.</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`: 12
- `per_device_eval_batch_size`: 12
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `warmup_steps`: 5000
- `fp16`: True
- `load_best_model_at_end`: True
#### 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`: 12
- `per_device_eval_batch_size`: 12
- `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`: 3e-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`: 0.0
- `warmup_steps`: 5000
- `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`: None
- `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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:--------:|:--------:|:-------------:|:---------------:|
| **0.05** | **2476** | **0.1209** | **0.0347** |
| 0.1000 | 4952 | 0.0737 | 0.0459 |
| 0.1501 | 7428 | 0.087 | 0.0732 |
| 0.2001 | 9904 | 0.0825 | 0.1209 |
| 0.2501 | 12380 | 0.0783 | 0.0934 |
| 0.3001 | 14856 | 0.071 | 0.0793 |
| 0.3501 | 17332 | 0.0661 | 0.0855 |
| 0.4001 | 19808 | 0.0652 | 0.0964 |
| 0.4502 | 22284 | 0.063 | 0.0892 |
| 0.5002 | 24760 | 0.056 | 0.0923 |
| 0.5502 | 27236 | 0.0509 | 0.1016 |
| 0.6002 | 29712 | 0.045 | 0.0918 |
| 0.6502 | 32188 | 0.0472 | 0.0896 |
| 0.7002 | 34664 | 0.0396 | 0.0959 |
| 0.7503 | 37140 | 0.0371 | 0.0819 |
| 0.8003 | 39616 | 0.0341 | 0.0845 |
| 0.8503 | 42092 | 0.0344 | 0.0790 |
| 0.9003 | 44568 | 0.0288 | 0.0863 |
| 0.9503 | 47044 | 0.03 | 0.0767 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 4.1.0
- Transformers: 4.52.3
- PyTorch: 2.7.0+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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.*
--> |