File size: 33,172 Bytes
0b58985 | 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 | ---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:769572
- loss:BinaryCrossEntropyLoss
base_model: BAAI/bge-reranker-v2-m3
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: CrossEncoder based on BAAI/bge-reranker-v2-m3
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: query note test
type: query_note_test
metrics:
- type: map
value: 0.45654587062161356
name: Map
- type: mrr@10
value: 0.5283685579312956
name: Mrr@10
- type: ndcg@10
value: 0.527108528024005
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: query note val
type: query_note_val
metrics:
- type: map
value: 0.45724051914340746
name: Map
- type: mrr@10
value: 0.5246903296465979
name: Mrr@10
- type: ndcg@10
value: 0.5328170850876233
name: Ndcg@10
---
# CrossEncoder based on BAAI/bge-reranker-v2-m3
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) 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:** [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) <!-- at revision 953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
- **Supported Modality:** Text
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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/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': 'XLMRobertaForSequenceClassification'})
)
```
## 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("steerrec/bge-reranker-v2-m3-query-note")
# Get scores for pairs of inputs
pairs = [
['infp男和infj女', '谁懂啊啊啊‼️💗INFP×INFJ相处好戳我\n绿老头和小蝴蝶都是高敏易碎人群，消息秒回啊，朋友圈点赞啊，这种细微且偏爱的行为，都能让对方感到安心和减少内耗。\n小蝴蝶的话，有事情就不要藏着掖着，对绿老头也不要有过多的试探行为，有问题就直接问，直接说，只要把事情拖到情绪都上来了，那这问题就更难解决了。因为绿老头只是看起来淡然，但其实他们的内心是很火热躁动的，他们只是不好意思主动表达。绿老头得学会调整，明知道小蝴蝶容易想多内耗，那就多主动表现情绪，不要让小蝴蝶觉得你对他淡淡的，要是能偶尔打个只球，明确的表达一下内心，那就更好了。#心理[话题]# #mbti[话题]# #infp[话题]# #infp日常[话题]# #infp和infj[话题]# #INFJ[话题]# #infj的世界[话题]# #我的日常[话题]# #温暖治愈[话题]# '],
['电动车远光灯刺眼反击', '电动车在这些情况下是全责哦！注意了哦！\n骑电动车一定要小心这些情况哦！[暗中观察R]#交通事故理赔[话题]# #交通事故[话题]# #法律求助[话题]# #电动车[话题]# #法律咨询[话题]# #交通安全[话题]# '],
['电动车远光灯刺眼反击', '支付宝上这个骑行险有用吗\n#车险[话题]# #电动车骑行险[话题]# #支付宝[话题]# '],
['牛奶品牌排名', '注意❗注意❗杯子到货，买就送📢\n杯子已经到货啦📣\n依然还是35.9到手2瓶4斤装的鲜奶，在额外赠送2个mini玻璃杯哦~\n顺丰冷链发货，包邮到家📦\n\xa0#乍甸牛奶[话题]#\xa0\xa0#云南游[话题]#\xa0\xa0#鲜奶[话题]#\xa0\xa0#可爱杯子[话题]#\xa0\xa0#杯子分享[话题]#\xa0\xa0#杯子控必入系列[话题]#\xa0\xa0#我就是个杯子控[话题]#\xa0\xa0#杯子[话题]#\xa0\xa0#鲜奶酸奶怎么挑[话题]#\xa0\xa0#鲜奶推荐[话题]#\xa0\xa0#鲜牛乳[话题]#\xa0\xa0#乍甸牛奶福利种草官[话题]#\xa0\xa0#乍甸牛奶也很好喝[话题]#\xa0\xa0#乍甸鲜奶[话题]#\xa0\t\n'],
['mbti人格', 'INFP小蝴蝶🦋请谨慎破防😅\n感觉本infp的确心灵上有些许脆弱，在面对朋友或者家人，别人的一句批评或者不认同，会影响我一天的心情～感觉这个习惯跟刻在骨子里一样，一边安慰自己，却一边焦虑😮\u200d💨很难想象有时候自己却很开朗乐观，其实内心很脆弱，一点就破⊙﹏⊙\n\t\n内容纯属娱乐🌚如有雷同纯属巧合🌚\n请大家对号入座哈哈哈哈😂#MBTI16型人格[话题]# #mbti梗图[话题]# #infp精神世界[话题]# #infp日常[话题]# '],
]
scores = model.predict(pairs)
print(scores)
# [0.3757 0.2134 0.1656 0.179 0.272 ]
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'infp男和infj女',
[
'谁懂啊啊啊‼️💗INFP×INFJ相处好戳我\n绿老头和小蝴蝶都是高敏易碎人群，消息秒回啊，朋友圈点赞啊，这种细微且偏爱的行为，都能让对方感到安心和减少内耗。\n小蝴蝶的话，有事情就不要藏着掖着，对绿老头也不要有过多的试探行为，有问题就直接问，直接说，只要把事情拖到情绪都上来了，那这问题就更难解决了。因为绿老头只是看起来淡然，但其实他们的内心是很火热躁动的，他们只是不好意思主动表达。绿老头得学会调整，明知道小蝴蝶容易想多内耗，那就多主动表现情绪，不要让小蝴蝶觉得你对他淡淡的，要是能偶尔打个只球，明确的表达一下内心，那就更好了。#心理[话题]# #mbti[话题]# #infp[话题]# #infp日常[话题]# #infp和infj[话题]# #INFJ[话题]# #infj的世界[话题]# #我的日常[话题]# #温暖治愈[话题]# ',
'电动车在这些情况下是全责哦！注意了哦！\n骑电动车一定要小心这些情况哦！[暗中观察R]#交通事故理赔[话题]# #交通事故[话题]# #法律求助[话题]# #电动车[话题]# #法律咨询[话题]# #交通安全[话题]# ',
'支付宝上这个骑行险有用吗\n#车险[话题]# #电动车骑行险[话题]# #支付宝[话题]# ',
'注意❗注意❗杯子到货，买就送📢\n杯子已经到货啦📣\n依然还是35.9到手2瓶4斤装的鲜奶，在额外赠送2个mini玻璃杯哦~\n顺丰冷链发货，包邮到家📦\n\xa0#乍甸牛奶[话题]#\xa0\xa0#云南游[话题]#\xa0\xa0#鲜奶[话题]#\xa0\xa0#可爱杯子[话题]#\xa0\xa0#杯子分享[话题]#\xa0\xa0#杯子控必入系列[话题]#\xa0\xa0#我就是个杯子控[话题]#\xa0\xa0#杯子[话题]#\xa0\xa0#鲜奶酸奶怎么挑[话题]#\xa0\xa0#鲜奶推荐[话题]#\xa0\xa0#鲜牛乳[话题]#\xa0\xa0#乍甸牛奶福利种草官[话题]#\xa0\xa0#乍甸牛奶也很好喝[话题]#\xa0\xa0#乍甸鲜奶[话题]#\xa0\t\n',
'INFP小蝴蝶🦋请谨慎破防😅\n感觉本infp的确心灵上有些许脆弱，在面对朋友或者家人，别人的一句批评或者不认同，会影响我一天的心情～感觉这个习惯跟刻在骨子里一样，一边安慰自己，却一边焦虑😮\u200d💨很难想象有时候自己却很开朗乐观，其实内心很脆弱，一点就破⊙﹏⊙\n\t\n内容纯属娱乐🌚如有雷同纯属巧合🌚\n请大家对号入座哈哈哈哈😂#MBTI16型人格[话题]# #mbti梗图[话题]# #infp精神世界[话题]# #infp日常[话题]# ',
]
)
# [{'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: `query_note_test` and `query_note_val`
* 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
}
```
| Metric | query_note_test | query_note_val |
|:------------|:----------------|:---------------|
| map | 0.4565 | 0.4572 |
| mrr@10 | 0.5284 | 0.5247 |
| **ndcg@10** | **0.5271** | **0.5328** |
<!--
## 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: 769,572 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 100 samples:
| | sentence1 | sentence2 | label |
|:---------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| modality | text | text | |
| details | <ul><li>min: 5 tokens</li><li>mean: 7.92 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 208.42 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.0</li><li>max: 0.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>infp男和infj女</code> | <code>谁懂啊啊啊‼️💗INFP×INFJ相处好戳我<br>绿老头和小蝴蝶都是高敏易碎人群，消息秒回啊，朋友圈点赞啊，这种细微且偏爱的行为，都能让对方感到安心和减少内耗。<br>小蝴蝶的话，有事情就不要藏着掖着，对绿老头也不要有过多的试探行为，有问题就直接问，直接说，只要把事情拖到情绪都上来了，那这问题就更难解决了。因为绿老头只是看起来淡然，但其实他们的内心是很火热躁动的，他们只是不好意思主动表达。绿老头得学会调整，明知道小蝴蝶容易想多内耗，那就多主动表现情绪，不要让小蝴蝶觉得你对他淡淡的，要是能偶尔打个只球，明确的表达一下内心，那就更好了。#心理[话题]# #mbti[话题]# #infp[话题]# #infp日常[话题]# #infp和infj[话题]# #INFJ[话题]# #infj的世界[话题]# #我的日常[话题]# #温暖治愈[话题]# </code> | <code>0.0</code> |
| <code>电动车远光灯刺眼反击</code> | <code>电动车在这些情况下是全责哦！注意了哦！<br>骑电动车一定要小心这些情况哦！[暗中观察R]#交通事故理赔[话题]# #交通事故[话题]# #法律求助[话题]# #电动车[话题]# #法律咨询[话题]# #交通安全[话题]# </code> | <code>0.0</code> |
| <code>电动车远光灯刺眼反击</code> | <code>支付宝上这个骑行险有用吗<br>#车险[话题]# #电动车骑行险[话题]# #支付宝[话题]# </code> | <code>0.0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `learning_rate`: 0.0001
- `weight_decay`: 0.01
- `num_train_epochs`: 1
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.05
- `seed`: 3407
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 8
- `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`: 0.0001
- `weight_decay`: 0.01
- `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`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `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`: 3407
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | query_note_test_ndcg@10 | query_note_val_ndcg@10 |
|:------:|:-----:|:-------------:|:-----------------------:|:----------------------:|
| -1 | -1 | - | 0.5206 | - |
| 0.7048 | 16950 | 0.6932 | - | - |
| 0.7069 | 17000 | 0.5323 | - | - |
| 0.7089 | 17050 | 0.5209 | - | - |
| 0.7110 | 17100 | 0.5281 | - | - |
| 0.7131 | 17150 | 0.5267 | - | - |
| 0.7152 | 17200 | 0.5124 | - | - |
| 0.7173 | 17250 | 0.5199 | - | - |
| 0.7193 | 17300 | 0.5189 | - | - |
| 0.7214 | 17350 | 0.5059 | - | - |
| 0.7235 | 17400 | 0.5326 | - | - |
| 0.7256 | 17450 | 0.5159 | - | - |
| 0.7277 | 17500 | 0.5246 | - | - |
| 0.7297 | 17550 | 0.5128 | - | - |
| 0.7318 | 17600 | 0.5078 | - | - |
| 0.7339 | 17650 | 0.4966 | - | - |
| 0.7360 | 17700 | 0.506 | - | - |
| 0.7380 | 17750 | 0.499 | - | - |
| 0.7401 | 17800 | 0.5069 | - | - |
| 0.7422 | 17850 | 0.5387 | - | - |
| 0.7443 | 17900 | 0.5124 | - | - |
| 0.7464 | 17950 | 0.522 | - | - |
| 0.7484 | 18000 | 0.5103 | - | - |
| 0.7505 | 18050 | 0.5217 | - | - |
| 0.7526 | 18100 | 0.4939 | - | - |
| 0.7547 | 18150 | 0.5151 | - | - |
| 0.7568 | 18200 | 0.4804 | - | - |
| 0.7588 | 18250 | 0.4969 | - | - |
| 0.7609 | 18300 | 0.5277 | - | - |
| 0.7630 | 18350 | 0.5143 | - | - |
| 0.7651 | 18400 | 0.5063 | - | - |
| 0.7672 | 18450 | 0.4899 | - | - |
| 0.7692 | 18500 | 0.5144 | - | - |
| 0.7713 | 18550 | 0.528 | - | - |
| 0.7734 | 18600 | 0.5032 | - | - |
| 0.7755 | 18650 | 0.4956 | - | - |
| 0.7775 | 18700 | 0.5144 | - | - |
| 0.7796 | 18750 | 0.5145 | - | - |
| 0.7817 | 18800 | 0.4971 | - | - |
| 0.7838 | 18850 | 0.5188 | - | - |
| 0.7859 | 18900 | 0.501 | - | - |
| 0.7879 | 18950 | 0.4892 | - | - |
| 0.7900 | 19000 | 0.4752 | - | - |
| 0.7921 | 19050 | 0.4984 | - | - |
| 0.7942 | 19100 | 0.5001 | - | - |
| 0.7963 | 19150 | 0.4809 | - | - |
| 0.7983 | 19200 | 0.5085 | - | - |
| 0.8004 | 19250 | 0.5122 | - | - |
| 0.8025 | 19300 | 0.5122 | - | - |
| 0.8046 | 19350 | 0.4909 | - | - |
| 0.8067 | 19400 | 0.5341 | - | - |
| 0.8087 | 19450 | 0.5147 | - | - |
| 0.8108 | 19500 | 0.5095 | - | - |
| 0.8129 | 19550 | 0.4945 | - | - |
| 0.8150 | 19600 | 0.4971 | - | - |
| 0.8170 | 19650 | 0.4967 | - | - |
| 0.8191 | 19700 | 0.5108 | - | - |
| 0.8212 | 19750 | 0.4983 | - | - |
| 0.8233 | 19800 | 0.5154 | - | - |
| 0.8254 | 19850 | 0.5214 | - | - |
| 0.8274 | 19900 | 0.4953 | - | - |
| 0.8295 | 19950 | 0.5079 | - | - |
| 0.8316 | 20000 | 0.5252 | - | - |
| 0.8337 | 20050 | 0.4966 | - | - |
| 0.8358 | 20100 | 0.492 | - | - |
| 0.8378 | 20150 | 0.5065 | - | - |
| 0.8399 | 20200 | 0.4825 | - | - |
| 0.8420 | 20250 | 0.4879 | - | - |
| 0.8441 | 20300 | 0.5351 | - | - |
| 0.8462 | 20350 | 0.4904 | - | - |
| 0.8482 | 20400 | 0.5141 | - | - |
| 0.8503 | 20450 | 0.5146 | - | - |
| 0.8524 | 20500 | 0.508 | - | - |
| 0.8545 | 20550 | 0.5271 | - | - |
| 0.8565 | 20600 | 0.5057 | - | - |
| 0.8586 | 20650 | 0.4757 | - | - |
| 0.8607 | 20700 | 0.5151 | - | - |
| 0.8628 | 20750 | 0.486 | - | - |
| 0.8649 | 20800 | 0.4908 | - | - |
| 0.8669 | 20850 | 0.5287 | - | - |
| 0.8690 | 20900 | 0.5223 | - | - |
| 0.8711 | 20950 | 0.5086 | - | - |
| 0.8732 | 21000 | 0.5066 | - | - |
| 0.8753 | 21050 | 0.5042 | - | - |
| 0.8773 | 21100 | 0.5032 | - | - |
| 0.8794 | 21150 | 0.5123 | - | - |
| 0.8815 | 21200 | 0.4825 | - | - |
| 0.8836 | 21250 | 0.5222 | - | - |
| 0.8857 | 21300 | 0.5044 | - | - |
| 0.8877 | 21350 | 0.5034 | - | - |
| 0.8898 | 21400 | 0.5193 | - | - |
| 0.8919 | 21450 | 0.4975 | - | - |
| 0.8940 | 21500 | 0.4754 | - | - |
| 0.8960 | 21550 | 0.5209 | - | - |
| 0.8981 | 21600 | 0.5024 | - | - |
| 0.9002 | 21650 | 0.5206 | - | - |
| 0.9023 | 21700 | 0.5032 | - | - |
| 0.9044 | 21750 | 0.5264 | - | - |
| 0.9064 | 21800 | 0.499 | - | - |
| 0.9085 | 21850 | 0.4967 | - | - |
| 0.9106 | 21900 | 0.491 | - | - |
| 0.9127 | 21950 | 0.5056 | - | - |
| 0.9148 | 22000 | 0.4996 | - | - |
| 0.9168 | 22050 | 0.4994 | - | - |
| 0.9189 | 22100 | 0.5254 | - | - |
| 0.9210 | 22150 | 0.5034 | - | - |
| 0.9231 | 22200 | 0.5123 | - | - |
| 0.9252 | 22250 | 0.4956 | - | - |
| 0.9272 | 22300 | 0.5194 | - | - |
| 0.9293 | 22350 | 0.474 | - | - |
| 0.9314 | 22400 | 0.4842 | - | - |
| 0.9335 | 22450 | 0.4914 | - | - |
| 0.9356 | 22500 | 0.4925 | - | - |
| 0.9376 | 22550 | 0.4938 | - | - |
| 0.9397 | 22600 | 0.5086 | - | - |
| 0.9418 | 22650 | 0.457 | - | - |
| 0.9439 | 22700 | 0.5185 | - | - |
| 0.9459 | 22750 | 0.5268 | - | - |
| 0.9480 | 22800 | 0.4872 | - | - |
| 0.9501 | 22850 | 0.5048 | - | - |
| 0.9522 | 22900 | 0.5103 | - | - |
| 0.9543 | 22950 | 0.5236 | - | - |
| 0.9563 | 23000 | 0.5049 | - | - |
| 0.9584 | 23050 | 0.5041 | - | - |
| 0.9605 | 23100 | 0.5066 | - | - |
| 0.9626 | 23150 | 0.5206 | - | - |
| 0.9647 | 23200 | 0.4732 | - | - |
| 0.9667 | 23250 | 0.4881 | - | - |
| 0.9688 | 23300 | 0.5099 | - | - |
| 0.9709 | 23350 | 0.5226 | - | - |
| 0.9730 | 23400 | 0.5322 | - | - |
| 0.9751 | 23450 | 0.4993 | - | - |
| 0.9771 | 23500 | 0.4856 | - | - |
| 0.9792 | 23550 | 0.4727 | - | - |
| 0.9813 | 23600 | 0.5093 | - | - |
| 0.9834 | 23650 | 0.5073 | - | - |
| 0.9854 | 23700 | 0.5153 | - | - |
| 0.9875 | 23750 | 0.4979 | - | - |
| 0.9896 | 23800 | 0.4961 | - | - |
| 0.9917 | 23850 | 0.5093 | - | - |
| 0.9938 | 23900 | 0.4811 | - | - |
| 0.9958 | 23950 | 0.5008 | - | - |
| 0.9979 | 24000 | 0.5151 | - | - |
| 1.0 | 24050 | 0.5318 | - | 0.5328 |
| -1 | -1 | - | 0.5271 | - |
</details>
### Training Time
- **Training**: 57.2 minutes
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.5.1
- Transformers: 4.56.2
- PyTorch: 2.11.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.3.0
- 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",
}
```
<!--
## 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.*
--> |