File size: 28,861 Bytes
c1cdb06 | 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 | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:95939
- loss:LambdaLoss
base_model: answerdotai/ModernBERT-base
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: ModernBERT-base trained on GooAQ
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: gooaq dev
type: gooaq-dev
metrics:
- type: map
value: 0.7235
name: Map
- type: mrr@10
value: 0.722
name: Mrr@10
- type: ndcg@10
value: 0.7664
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.44
name: Map
- type: mrr@10
value: 0.4276
name: Mrr@10
- type: ndcg@10
value: 0.5023
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3312
name: Map
- type: mrr@10
value: 0.5754
name: Mrr@10
- type: ndcg@10
value: 0.3743
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.2867
name: Map
- type: mrr@10
value: 0.281
name: Mrr@10
- type: ndcg@10
value: 0.3538
name: Ndcg@10
- task:
type: cross-encoder-nano-beir
name: Cross Encoder Nano BEIR
dataset:
name: NanoBEIR R100 mean
type: NanoBEIR_R100_mean
metrics:
- type: map
value: 0.3526
name: Map
- type: mrr@10
value: 0.428
name: Mrr@10
- type: ndcg@10
value: 0.4101
name: Ndcg@10
---
# ModernBERT-base trained on GooAQ
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 text reranking and semantic search.
## 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
- **Supported Modality:** Text
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apach-2.0
### 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/huggingface/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
### Full Model Architecture
```
CrossEncoder(
(0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'ModernBertForSequenceClassification'})
)
```
## 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 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of inputs
pairs = [
['what are the 5 characteristics of a star?', 'Key Concept: Characteristics used to classify stars include color, temperature, size, composition, and brightness.'],
['what are the 5 characteristics of a star?', 'The biggest difference was compression. The most recent Z-STAR has a compression of 90, while the Z-STAR XV comes in at 102. The original Q-STAR Tour had a compression of 75 (this new version, like Gen 2, is 72), and with its performance characteristics, Srixon could have called it Z-STAR Soft.'],
['what are the 5 characteristics of a star?', "While 4-star hotels provide quality services to its patrons, a 5-star luxury accommodation will have impeccable service delivery. You'll often end up paying extra for the amenities like room service and concierge services in a 4-star hotel. But a 5-star hotel will always offer personalised services."],
['what are the 5 characteristics of a star?', 'However, the brightness of a star depends on its composition and how far it is from the planet. Astronomers define star brightness in terms of apparent magnitude — how bright the star appears from Earth — and absolute magnitude — how bright the star appears at a standard distance of 32.6 light-years, or 10 parsecs.'],
['what are the 5 characteristics of a star?', 'A "falling star" or a "shooting star" has nothing at all to do with a star! These amazing streaks of light you can sometimes see in the night sky are caused by tiny bits of dust and rock called meteoroids falling into the Earth\'s atmosphere and burning up. ... Meteors are commonly called falling stars or shooting stars.'],
]
scores = model.predict(pairs)
print(scores)
# [9.4520e-01 1.7401e-04 4.5739e-03 2.9579e-03 1.3631e-05]
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'what are the 5 characteristics of a star?',
[
'Key Concept: Characteristics used to classify stars include color, temperature, size, composition, and brightness.',
'The biggest difference was compression. The most recent Z-STAR has a compression of 90, while the Z-STAR XV comes in at 102. The original Q-STAR Tour had a compression of 75 (this new version, like Gen 2, is 72), and with its performance characteristics, Srixon could have called it Z-STAR Soft.',
"While 4-star hotels provide quality services to its patrons, a 5-star luxury accommodation will have impeccable service delivery. You'll often end up paying extra for the amenities like room service and concierge services in a 4-star hotel. But a 5-star hotel will always offer personalised services.",
'However, the brightness of a star depends on its composition and how far it is from the planet. Astronomers define star brightness in terms of apparent magnitude — how bright the star appears from Earth — and absolute magnitude — how bright the star appears at a standard distance of 32.6 light-years, or 10 parsecs.',
'A "falling star" or a "shooting star" has nothing at all to do with a star! These amazing streaks of light you can sometimes see in the night sky are caused by tiny bits of dust and rock called meteoroids falling into the Earth\'s atmosphere and burning up. ... Meteors are commonly called falling stars or shooting stars.',
]
)
# [{'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
* Dataset: `gooaq-dev`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": false
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.7235 (+0.1924) |
| mrr@10 | 0.7220 (+0.1981) |
| **ndcg@10** | **0.7664 (+0.1751)** |
#### Cross Encoder Reranking
* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
|:------------|:---------------------|:---------------------|:---------------------|
| map | 0.4400 (-0.0496) | 0.3312 (+0.0702) | 0.2867 (-0.1329) |
| mrr@10 | 0.4276 (-0.0499) | 0.5754 (+0.0756) | 0.2810 (-0.1457) |
| **ndcg@10** | **0.5023 (-0.0381)** | **0.3743 (+0.0492)** | **0.3538 (-0.1468)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_R100_mean`
* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"dataset_id": "sentence-transformers/NanoBEIR-en",
"rerank_k": 100,
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.3526 (-0.0374) |
| mrr@10 | 0.4280 (-0.0400) |
| **ndcg@10** | **0.4101 (-0.0452)** |
<!--
## 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: 95,939 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>labels</code>
* Approximate statistics based on the first 100 samples:
| | question | answer | labels |
|:---------|:----------------------------------------------------------------------------------|:-----------------------------------|:-----------------------------------|
| type | string | list | list |
| modality | text | | |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.69 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>size: 6 elements</li></ul> | <ul><li>size: 6 elements</li></ul> |
* Samples:
| question | answer | labels |
|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>what are the 5 characteristics of a star?</code> | <code>['Key Concept: Characteristics used to classify stars include color, temperature, size, composition, and brightness.', 'The biggest difference was compression. The most recent Z-STAR has a compression of 90, while the Z-STAR XV comes in at 102. The original Q-STAR Tour had a compression of 75 (this new version, like Gen 2, is 72), and with its performance characteristics, Srixon could have called it Z-STAR Soft.', "While 4-star hotels provide quality services to its patrons, a 5-star luxury accommodation will have impeccable service delivery. You'll often end up paying extra for the amenities like room service and concierge services in a 4-star hotel. But a 5-star hotel will always offer personalised services.", 'However, the brightness of a star depends on its composition and how far it is from the planet. Astronomers define star brightness in terms of apparent magnitude — how bright the star appears from Earth — and absolute magnitude — how bright the star appears at a standard distance of 32.6 light-years, or 10 parsecs.', 'A "falling star" or a "shooting star" has nothing at all to do with a star! These amazing streaks of light you can sometimes see in the night sky are caused by tiny bits of dust and rock called meteoroids falling into the Earth\'s atmosphere and burning up. ... Meteors are commonly called falling stars or shooting stars.', ...]</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>are copic markers alcohol ink?</code> | <code>['Copic Ink is alcohol-based and flammable. Keep away from direct sunlight and extreme temperatures.', 'Copic Sketch markers are alcohol-based, refillable dye markers with replaceable nibs for full versatility. ... Great for comic drawing, scrapbooking, fine art, illustration, product design, architecture, and more, this marker is an essential for artists, designers and crafters. Details: Available in multiple colors.', 'Alcohol based markers differ from water based markers in that the color (dye or pigment) is suspended in an alcohol or another fast evaporating solvent, rather than water or glycerin. This means that alcohol based markers are not water soluble, but may be alcohol soluble.', 'Gel ink pens are primarily made to be used on paper substrates and not on plastics, glass, or fabrics. While some gel ink versions can adhere to other surfaces, this greatly affects the overall "performance" of the ink. (i.e. the gel inks might be easily washed out or scratched off the surface.)', 'Permanent markers are made from pigment and not dyes. Dyes are water soluble but pigment are not soluble in water. If you clean the markers with a no polar solvent like alcohol or acetone the markers are not truly permanent on certain materials. ... The purpose is to determine if permanent marker are truly permanent.', ...]</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>what is the difference between appellate term and appellate division?</code> | <code>['Appellate terms An appellate term is an intermediate appellate court that hears appeals from the inferior courts within their designated counties or judicial districts, and are intended to ease the workload on the Appellate Division and provide a less expensive forum closer to the people.', 'As verbs the difference between request and appeal is that request is to express the need or desire for while appeal is (obsolete) to accuse (someone of something).', 'As nouns the difference between fraction and division is that fraction is a part of a whole, especially a comparatively small part while division is (uncountable) the act or process of dividing anything.', 'As nouns the difference between divide and division is that divide is a thing that divides while division is (uncountable) the act or process of dividing anything.', 'definition and meaning , “A feature is a distinctive characteristic of a good or service that sets it apart from similar items. Customers, however, want a benefit..” The same source also defines objective: “A specific result that a person or system aims to achieve within a time frame and with available resources.', ...]</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
* Loss: [<code>LambdaLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#lambdaloss) with these parameters:
```json
{
"weighting_scheme": "sentence_transformers.cross_encoder.losses.lambda_loss.NDCGLoss2PPScheme",
"k": null,
"sigma": 1.0,
"eps": 1e-10,
"reduction_log": "binary",
"activation_fn": "torch.nn.modules.linear.Identity",
"mini_batch_size": 8
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `num_train_epochs`: 1
- `learning_rate`: 2e-05
- `warmup_steps`: 0.1
- `gradient_accumulation_steps`: 4
- `fp16`: True
- `per_device_eval_batch_size`: 16
- `load_best_model_at_end`: True
- `seed`: 12
- `dataloader_num_workers`: 2
- `dataloader_persistent_workers`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 16
- `num_train_epochs`: 1
- `max_steps`: -1
- `learning_rate`: 2e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 4
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 16
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 12
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 2
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: True
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | gooaq-dev_ndcg@10 | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|:-------:|:--------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
| -1 | -1 | - | 0.1341 (-0.4572) | 0.0124 (-0.5280) | 0.2983 (-0.0268) | 0.0067 (-0.4940) | 0.1058 (-0.3496) |
| 0.0007 | 1 | 2.1925 | - | - | - | - | - |
| 0.0667 | 100 | 2.0361 | - | - | - | - | - |
| 0.1334 | 200 | 1.0558 | - | - | - | - | - |
| 0.1668 | 250 | - | 0.7213 (+0.1300) | 0.4535 (-0.0869) | 0.3667 (+0.0417) | 0.3618 (-0.1389) | 0.3940 (-0.0614) |
| 0.2001 | 300 | 0.6286 | - | - | - | - | - |
| 0.2668 | 400 | 0.5332 | - | - | - | - | - |
| 0.3335 | 500 | 0.5208 | 0.7468 (+0.1556) | 0.4826 (-0.0578) | 0.3783 (+0.0533) | 0.4192 (-0.0815) | 0.4267 (-0.0287) |
| 0.4002 | 600 | 0.4892 | - | - | - | - | - |
| 0.4669 | 700 | 0.4780 | - | - | - | - | - |
| 0.5003 | 750 | - | 0.7538 (+0.1626) | 0.5041 (-0.0363) | 0.4069 (+0.0819) | 0.3785 (-0.1221) | 0.4299 (-0.0255) |
| 0.5336 | 800 | 0.4448 | - | - | - | - | - |
| 0.6003 | 900 | 0.4455 | - | - | - | - | - |
| 0.6670 | 1000 | 0.4427 | 0.7611 (+0.1698) | 0.4584 (-0.0820) | 0.3731 (+0.0481) | 0.3098 (-0.1908) | 0.3804 (-0.0749) |
| 0.7337 | 1100 | 0.4307 | - | - | - | - | - |
| 0.8004 | 1200 | 0.4269 | - | - | - | - | - |
| 0.8338 | 1250 | - | 0.7640 (+0.1727) | 0.4978 (-0.0426) | 0.3887 (+0.0636) | 0.3634 (-0.1373) | 0.4166 (-0.0388) |
| 0.8671 | 1300 | 0.4119 | - | - | - | - | - |
| 0.9338 | 1400 | 0.4104 | - | - | - | - | - |
| **1.0** | **1500** | **0.4018** | **0.7664 (+0.1751)** | **0.5023 (-0.0381)** | **0.3743 (+0.0492)** | **0.3538 (-0.1468)** | **0.4101 (-0.0452)** |
| -1 | -1 | - | 0.7664 (+0.1751) | 0.5023 (-0.0381) | 0.3743 (+0.0492) | 0.3538 (-0.1468) | 0.4101 (-0.0452) |
* The bold row denotes the saved checkpoint.
### Training Time
- **Training**: 2.2 hours
### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.6.0
- Transformers: 5.13.1
- PyTorch: 2.11.0+cu128
- Accelerate: 1.14.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2
## Additional Resources
- [Training and Finetuning Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-reranker): the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
- [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video reranker models through the same API.
- [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): training multimodal Cross Encoders.
## 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",
}
```
#### LambdaLoss
```bibtex
@inproceedings{wang2018lambdaloss,
title={The LambdaLoss Framework for Ranking Metric Optimization},
author={Wang, Xuanhui and Li, Cheng and Golbandi, Nadav and Bendersky, Michael and Najork, Marc},
booktitle={Proceedings of the 27th ACM international conference on information and knowledge management},
pages={1313--1322},
year={2018}
}
```
<!--
## 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.*
--> |