File size: 39,550 Bytes
dc36518 |
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 |
---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- text-classification
- generated_from_trainer
- dataset_size:583872
- loss:BinaryCrossEntropyLoss
base_model: answerdotai/ModernBERT-base
datasets:
- sentence-transformers/natural-questions
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: CrossEncoder based on answerdotai/ModernBERT-base
results: []
---
# CrossEncoder based on answerdotai/ModernBERT-base
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
- **Maximum Sequence Length:** 8192 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## 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 CrossEncoder
# Download from the 馃 Hub
model = CrossEncoder("tomaarsen/reranker-ModernBERT-base-nq-bce")
# Get scores for pairs of texts
pairs = [
['difference between russian blue and british blue cat', 'Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.'],
['who played the little girl on mrs doubtfire', 'Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.'],
['what year did the movie the sound of music come out', 'The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time鈥攕urpassing Gone with the Wind鈥攁nd held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.'],
['where was the movie dawn of the dead filmed', 'Dawn of the Dead (2004 film) The mall scenes and rooftop scenes were shot in the former Thornhill Square Shopping Centre in Thornhill, Ontario, and the other scenes were shot in the Aileen-Willowbrook neighborhood of Thornhill. The set for Ana and Luis\'s bedroom was constructed in a back room of the mall.[7] The mall was defunct, which is the reason the production used it; the movie crew completely renovated the structure, and stocked it with fictitious stores after Starbucks and numerous other corporations refused to let their names be used[7] (two exceptions to this are Roots and Panasonic). Most of the mall was demolished shortly after the film was shot. The fictitious stores include a coffee shop called Hallowed Grounds (a lyric from Johnny Cash\'s song "The Man Comes Around", which was used over the opening credits), and an upscale department store called Gaylen Ross (an in-joke reference to one of the stars of the original 1978 film).'],
['where is the 2018 nba draft being held', "2018 NBA draft The 2018 NBA draft was held on June 21, 2018, at Barclays Center in Brooklyn, New York. National Basketball Association (NBA) teams took turns selecting amateur United States college basketball players and other eligible players, including international players. It was televised nationally by ESPN. This draft was the last to use the original weighted lottery system that gives teams near the bottom of the NBA draft better odds at the top three picks of the draft while teams higher up had worse odds in the process; the rule was agreed upon by the NBA on September 28, 2017, but would not be implemented until the 2019 draft.[2] With the last year of what was, at the time, the most recent lottery system (with the NBA draft lottery being held in Chicago instead of in New York), the Phoenix Suns won the first overall pick on May 15, 2018, with the Sacramento Kings at the second overall pick and the Atlanta Hawks at third overall pick.[3] The Suns' selection is their first No. 1 overall selection in franchise history. They would use that selection on the Bahamian center DeAndre Ayton from the nearby University of Arizona."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'difference between russian blue and british blue cat',
[
'Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.',
'Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.',
'The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time鈥攕urpassing Gone with the Wind鈥攁nd held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.',
'Dawn of the Dead (2004 film) The mall scenes and rooftop scenes were shot in the former Thornhill Square Shopping Centre in Thornhill, Ontario, and the other scenes were shot in the Aileen-Willowbrook neighborhood of Thornhill. The set for Ana and Luis\'s bedroom was constructed in a back room of the mall.[7] The mall was defunct, which is the reason the production used it; the movie crew completely renovated the structure, and stocked it with fictitious stores after Starbucks and numerous other corporations refused to let their names be used[7] (two exceptions to this are Roots and Panasonic). Most of the mall was demolished shortly after the film was shot. The fictitious stores include a coffee shop called Hallowed Grounds (a lyric from Johnny Cash\'s song "The Man Comes Around", which was used over the opening credits), and an upscale department store called Gaylen Ross (an in-joke reference to one of the stars of the original 1978 film).',
"2018 NBA draft The 2018 NBA draft was held on June 21, 2018, at Barclays Center in Brooklyn, New York. National Basketball Association (NBA) teams took turns selecting amateur United States college basketball players and other eligible players, including international players. It was televised nationally by ESPN. This draft was the last to use the original weighted lottery system that gives teams near the bottom of the NBA draft better odds at the top three picks of the draft while teams higher up had worse odds in the process; the rule was agreed upon by the NBA on September 28, 2017, but would not be implemented until the 2019 draft.[2] With the last year of what was, at the time, the most recent lottery system (with the NBA draft lottery being held in Chicago instead of in New York), the Phoenix Suns won the first overall pick on May 15, 2018, with the Sacramento Kings at the second overall pick and the Atlanta Hawks at third overall pick.[3] The Suns' selection is their first No. 1 overall selection in franchise history. They would use that selection on the Bahamian center DeAndre Ayton from the nearby University of Arizona.",
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### 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
#### Cross Encoder Reranking
* Datasets: `nq-dev`, `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
* Evaluated with [<code>CERerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CERerankingEvaluator)
| Metric | nq-dev | NanoMSMARCO | NanoNFCorpus | NanoNQ |
|:------------|:---------------------|:---------------------|:---------------------|:---------------------|
| map | 0.7329 (+0.0308) | 0.5611 (+0.0716) | 0.3497 (+0.0793) | 0.6827 (+0.2620) |
| mrr@10 | 0.7315 (+0.0325) | 0.5546 (+0.0771) | 0.5310 (+0.0312) | 0.6992 (+0.2725) |
| **ndcg@10** | **0.7913 (+0.0313)** | **0.6363 (+0.0959)** | **0.3956 (+0.0706)** | **0.7285 (+0.2279)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>CENanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CENanoBEIREvaluator)
| Metric | Value |
|:------------|:---------------------|
| map | 0.5312 (+0.1376) |
| mrr@10 | 0.5949 (+0.1269) |
| **ndcg@10** | **0.5868 (+0.1315)** |
<!--
## 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: 583,872 training samples
* Columns: <code>query</code>, <code>response</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | response | label |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 29 characters</li><li>mean: 47.27 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 26 characters</li><li>mean: 615.6 characters</li><li>max: 3146 characters</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
| query | response | label |
|:---------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>who plays harry in the amazing spiderman 2</code> | <code>Dane DeHaan Dane William DeHaan (/d蓹藞h蓱藧n/ d蓹-HAHN; born February 6, 1986[2][3][4]) is an American actor. His roles include Andrew Detmer in Chronicle (2012), Harry Osborn in The Amazing Spider-Man 2 (2014), Lockhart in Gore Verbinski's A Cure for Wellness (2016), and the title character in Luc Besson's Valerian and the City of a Thousand Planets (2017). He has also appeared in several advertisements for Prada.</code> | <code>1</code> |
| <code>when was the united federation of planets founded</code> | <code>United Federation of Planets In the series Star Trek: Enterprise, Earth Minister Nathan Samuels advocated the Coalition of Planets and invited other alien species, initially the Vulcans, Andorians and Tellarites, to become a part of this. The formation of the Coalition seems to have been the event that provoked the xenophobic Terra Prime incident in the episodes "Demons" and "Terra Prime". After Terra Prime leader John Frederick Paxton exploited the xenophobia on Earth, many of the aliens were unnerved and nearly abandoned the idea of a coalition. However, they were convinced by a speech from Captain Jonathan Archer to give the idea of a united organization of worlds a chance. Six years later in 2161,[8] the United Federation of Planets was organized.</code> | <code>1</code> |
| <code>who plays flores in orange is the new black</code> | <code>Laura G贸mez (actress) Laura G贸mez (born 1979) is a Dominican actress, speaker, writer, and director. She belongs to SAG-AFTRA and lives in New York City. G贸mez is best known for her portrayal of the character Blanca Flores, an astute and disheveled prison inmate in the award-winning Netflix series Orange Is The New Black. In the fall of 2012 she won the NYU Technisphere Award for her short film To Kill a Roach.</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fct": "torch.nn.modules.linear.Identity",
"pos_weight": 5
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 100,231 evaluation samples
* Columns: <code>query</code>, <code>response</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | response | label |
|:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 27 characters</li><li>mean: 47.03 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 26 characters</li><li>mean: 608.17 characters</li><li>max: 2639 characters</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
| query | response | label |
|:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>difference between russian blue and british blue cat</code> | <code>Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.</code> | <code>1</code> |
| <code>who played the little girl on mrs doubtfire</code> | <code>Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.</code> | <code>1</code> |
| <code>what year did the movie the sound of music come out</code> | <code>The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time鈥攕urpassing Gone with the Wind鈥攁nd held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fct": "torch.nn.modules.linear.Identity",
"pos_weight": 5
}
```
### 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
- `seed`: 12
- `bf16`: True
- `dataloader_num_workers`: 4
- `load_best_model_at_end`: 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
- `torch_empty_cache_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`: 12
- `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`: 4
- `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
- `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
| Epoch | Step | Training Loss | Validation Loss | nq-dev_ndcg@10 | NanoMSMARCO_ndcg@10 | NanoNFCorpus_ndcg@10 | NanoNQ_ndcg@10 | NanoBEIR_mean_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:---------------------:|
| -1 | -1 | - | - | 0.1432 (-0.6168) | 0.0574 (-0.4830) | 0.2881 (-0.0369) | 0.0121 (-0.4886) | 0.1192 (-0.3362) |
| 0.0001 | 1 | 1.1359 | - | - | - | - | - | - |
| 0.0219 | 200 | 1.164 | - | - | - | - | - | - |
| 0.0438 | 400 | 0.9314 | - | - | - | - | - | - |
| 0.0658 | 600 | 0.403 | - | - | - | - | - | - |
| 0.0877 | 800 | 0.2647 | - | - | - | - | - | - |
| 0.1096 | 1000 | 0.2651 | 0.9483 | 0.7421 (-0.0179) | 0.5703 (+0.0298) | 0.3594 (+0.0344) | 0.6641 (+0.1635) | 0.5313 (+0.0759) |
| 0.1315 | 1200 | 0.2159 | - | - | - | - | - | - |
| 0.1535 | 1400 | 0.185 | - | - | - | - | - | - |
| 0.1754 | 1600 | 0.1925 | - | - | - | - | - | - |
| 0.1973 | 1800 | 0.1814 | - | - | - | - | - | - |
| 0.2192 | 2000 | 0.164 | 0.9816 | 0.7700 (+0.0100) | 0.5722 (+0.0318) | 0.3713 (+0.0462) | 0.7361 (+0.2354) | 0.5599 (+0.1045) |
| 0.2411 | 2200 | 0.159 | - | - | - | - | - | - |
| 0.2631 | 2400 | 0.1429 | - | - | - | - | - | - |
| 0.2850 | 2600 | 0.1611 | - | - | - | - | - | - |
| 0.3069 | 2800 | 0.1464 | - | - | - | - | - | - |
| 0.3288 | 3000 | 0.1489 | 0.8411 | 0.7819 (+0.0219) | 0.6213 (+0.0809) | 0.3578 (+0.0328) | 0.7051 (+0.2045) | 0.5614 (+0.1061) |
| 0.3508 | 3200 | 0.1352 | - | - | - | - | - | - |
| 0.3727 | 3400 | 0.1179 | - | - | - | - | - | - |
| 0.3946 | 3600 | 0.1328 | - | - | - | - | - | - |
| 0.4165 | 3800 | 0.1237 | - | - | - | - | - | - |
| 0.4385 | 4000 | 0.1216 | 1.2462 | 0.7888 (+0.0288) | 0.6164 (+0.0760) | 0.3792 (+0.0542) | 0.7152 (+0.2146) | 0.5703 (+0.1149) |
| 0.4604 | 4200 | 0.1174 | - | - | - | - | - | - |
| 0.4823 | 4400 | 0.1129 | - | - | - | - | - | - |
| 0.5042 | 4600 | 0.1252 | - | - | - | - | - | - |
| 0.5261 | 4800 | 0.1131 | - | - | - | - | - | - |
| 0.5481 | 5000 | 0.121 | 0.8000 | 0.7849 (+0.0249) | 0.6144 (+0.0740) | 0.3792 (+0.0541) | 0.7235 (+0.2228) | 0.5723 (+0.1170) |
| 0.5700 | 5200 | 0.1201 | - | - | - | - | - | - |
| 0.5919 | 5400 | 0.1245 | - | - | - | - | - | - |
| 0.6138 | 5600 | 0.1007 | - | - | - | - | - | - |
| 0.6358 | 5800 | 0.1065 | - | - | - | - | - | - |
| 0.6577 | 6000 | 0.117 | 1.0459 | 0.7868 (+0.0268) | 0.5901 (+0.0497) | 0.3675 (+0.0425) | 0.7425 (+0.2419) | 0.5667 (+0.1114) |
| 0.6796 | 6200 | 0.1128 | - | - | - | - | - | - |
| 0.7015 | 6400 | 0.1002 | - | - | - | - | - | - |
| 0.7234 | 6600 | 0.1049 | - | - | - | - | - | - |
| 0.7454 | 6800 | 0.1071 | - | - | - | - | - | - |
| 0.7673 | 7000 | 0.0955 | 0.9440 | 0.7937 (+0.0337) | 0.6168 (+0.0764) | 0.3771 (+0.0521) | 0.7289 (+0.2283) | 0.5743 (+0.1189) |
| 0.7892 | 7200 | 0.0839 | - | - | - | - | - | - |
| 0.8111 | 7400 | 0.1071 | - | - | - | - | - | - |
| 0.8331 | 7600 | 0.0977 | - | - | - | - | - | - |
| 0.8550 | 7800 | 0.1013 | - | - | - | - | - | - |
| 0.8769 | 8000 | 0.1047 | 0.8250 | 0.7893 (+0.0293) | 0.6071 (+0.0667) | 0.3946 (+0.0695) | 0.7216 (+0.2210) | 0.5744 (+0.1191) |
| 0.8988 | 8200 | 0.0956 | - | - | - | - | - | - |
| 0.9207 | 8400 | 0.0879 | - | - | - | - | - | - |
| 0.9427 | 8600 | 0.091 | - | - | - | - | - | - |
| 0.9646 | 8800 | 0.0952 | - | - | - | - | - | - |
| **0.9865** | **9000** | **0.078** | **1.0437** | **0.7913 (+0.0313)** | **0.6363 (+0.0959)** | **0.3956 (+0.0706)** | **0.7285 (+0.2279)** | **0.5868 (+0.1315)** |
| -1 | -1 | - | - | 0.7913 (+0.0313) | 0.6363 (+0.0959) | 0.3956 (+0.0706) | 0.7285 (+0.2279) | 0.5868 (+0.1315) |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0.dev0
- PyTorch: 2.6.0.dev20241112+cu121
- Accelerate: 1.2.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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",
}
```
<!--
## 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.*
--> |