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tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:60398
- loss:BinaryCrossEntropyLoss
pipeline_tag: text-ranking
library_name: sentence-transformers
---
# CrossEncoder
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model trained 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:** [Unknown](https://huggingface.co/unknown) -->
- **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': 'BertForSequenceClassification'})
)
```
## 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 = [
['best way to cook turkey legs', 'The best way to bake chicken legs is to pat them dry, season generously, and bake at 400°F (200°C) for 40-45 minutes. For extra crispiness, start them at a higher temperature (425°F) for the first 20 minutes, then lower to 375°F until the internal temperature reaches 165°F.'],
['best way to burn belly fat', "You cannot spot-reduce stomach fat; the most effective approach combines full-body strength training (like squats, deadlifts, and push-ups) with high-intensity interval training (HIIT) and a calorie-controlled diet. Core exercises like planks and Russian twists build muscle but won't burn the fat on top of them. Prioritize compound movements and consistent cardio to lower overall body fat."],
['crockpot pork tenderloin slow cooker recipes', 'For the best crock pot pork carnitas, season a pork shoulder (or butt) with cumin, oregano, garlic, salt, and pepper, then cook on low for 8-10 hours with orange juice, lime juice, and bay leaves until fall-apart tender. After shredding, spread the meat on a baking sheet and broil for 5-10 minutes to get crispy, golden edges before serving.'],
['bed bath and beyond schaumburg', "Bed Bath & Beyond operated in Canada until its closures in 2023, when all Canadian stores and its e-commerce site were shut down following the company's bankruptcy. The Canadian business was acquired by a private equity firm but ultimately liquidated, so there are no remaining Bed Bath & Beyond locations in Canada."],
['brother printer download for windows 10', 'Try reinstalling the printer driver from Brother\'s official support site, as Windows 10 often needs the specific "Full Driver & Software Package" instead of the basic driver. If it\'s a USB connection, unplug the cable, restart both the printer and PC, then reconnect; for network printers, run the Brother "Printer Setting Tool" or check that the IP address hasn\'t changed. Also, run the Windows built-in "Printer troubleshooter" (Settings > Update & Security > Troubleshoot) to auto-detect and fix common issues.'],
]
scores = model.predict(pairs)
print(scores)
# [-1.6193 1.0399 -0.3764 -1.8634 0.1748]
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'best way to cook turkey legs',
[
'The best way to bake chicken legs is to pat them dry, season generously, and bake at 400°F (200°C) for 40-45 minutes. For extra crispiness, start them at a higher temperature (425°F) for the first 20 minutes, then lower to 375°F until the internal temperature reaches 165°F.',
"You cannot spot-reduce stomach fat; the most effective approach combines full-body strength training (like squats, deadlifts, and push-ups) with high-intensity interval training (HIIT) and a calorie-controlled diet. Core exercises like planks and Russian twists build muscle but won't burn the fat on top of them. Prioritize compound movements and consistent cardio to lower overall body fat.",
'For the best crock pot pork carnitas, season a pork shoulder (or butt) with cumin, oregano, garlic, salt, and pepper, then cook on low for 8-10 hours with orange juice, lime juice, and bay leaves until fall-apart tender. After shredding, spread the meat on a baking sheet and broil for 5-10 minutes to get crispy, golden edges before serving.',
"Bed Bath & Beyond operated in Canada until its closures in 2023, when all Canadian stores and its e-commerce site were shut down following the company's bankruptcy. The Canadian business was acquired by a private equity firm but ultimately liquidated, so there are no remaining Bed Bath & Beyond locations in Canada.",
'Try reinstalling the printer driver from Brother\'s official support site, as Windows 10 often needs the specific "Full Driver & Software Package" instead of the basic driver. If it\'s a USB connection, unplug the cable, restart both the printer and PC, then reconnect; for network printers, run the Brother "Printer Setting Tool" or check that the IP address hasn\'t changed. Also, run the Windows built-in "Printer troubleshooter" (Settings > Update & Security > Troubleshoot) to auto-detect and fix common issues.',
]
)
# [{'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.*
-->
<!--
## 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: 60,398 training samples
* Columns: <code>query</code>, <code>response</code>, and <code>label</code>
* Approximate statistics based on the first 100 samples:
| | query | response | label |
|:---------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| modality | text | text | |
| details | <ul><li>min: 7 tokens</li><li>mean: 8.26 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 79.28 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.19</li><li>max: 1.0</li></ul> |
* Samples:
| query | response | label |
|:----------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>best way to cook turkey legs</code> | <code>The best way to bake chicken legs is to pat them dry, season generously, and bake at 400°F (200°C) for 40-45 minutes. For extra crispiness, start them at a higher temperature (425°F) for the first 20 minutes, then lower to 375°F until the internal temperature reaches 165°F.</code> | <code>0.0</code> |
| <code>best way to burn belly fat</code> | <code>You cannot spot-reduce stomach fat; the most effective approach combines full-body strength training (like squats, deadlifts, and push-ups) with high-intensity interval training (HIIT) and a calorie-controlled diet. Core exercises like planks and Russian twists build muscle but won't burn the fat on top of them. Prioritize compound movements and consistent cardio to lower overall body fat.</code> | <code>0.0</code> |
| <code>crockpot pork tenderloin slow cooker recipes</code> | <code>For the best crock pot pork carnitas, season a pork shoulder (or butt) with cumin, oregano, garlic, salt, and pepper, then cook on low for 8-10 hours with orange juice, lime juice, and bay leaves until fall-apart tender. After shredding, spread the meat on a baking sheet and broil for 5-10 minutes to get crispy, golden edges before serving.</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`: 16
- `num_train_epochs`: 1
- `disable_tqdm`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 16
- `num_train_epochs`: 1
- `max_steps`: -1
- `learning_rate`: 5e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0
- `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`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: False
- `fp16`: False
- `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`: True
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 8
- `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`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `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`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `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
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0003 | 1 | 1.4164 |
| 0.0026 | 10 | 1.6867 |
| 0.0053 | 20 | 1.0120 |
| 0.0079 | 30 | 0.8804 |
| 0.0106 | 40 | 0.7323 |
| 0.0132 | 50 | 0.7835 |
| 0.0159 | 60 | 0.7049 |
| 0.0185 | 70 | 0.6971 |
| 0.0212 | 80 | 0.6359 |
| 0.0238 | 90 | 0.7263 |
| 0.0265 | 100 | 0.7078 |
| 0.0291 | 110 | 0.6384 |
| 0.0318 | 120 | 0.6927 |
| 0.0344 | 130 | 0.6432 |
| 0.0371 | 140 | 0.6371 |
| 0.0397 | 150 | 0.6497 |
| 0.0424 | 160 | 0.6716 |
| 0.0450 | 170 | 0.6637 |
| 0.0477 | 180 | 0.6525 |
| 0.0503 | 190 | 0.6316 |
| 0.0530 | 200 | 0.6499 |
| 0.0556 | 210 | 0.6469 |
| 0.0583 | 220 | 0.6191 |
| 0.0609 | 230 | 0.5827 |
| 0.0636 | 240 | 0.7219 |
| 0.0662 | 250 | 0.7073 |
| 0.0689 | 260 | 0.7059 |
| 0.0715 | 270 | 0.6503 |
| 0.0742 | 280 | 0.6666 |
| 0.0768 | 290 | 0.6783 |
| 0.0795 | 300 | 0.6520 |
| 0.0821 | 310 | 0.7278 |
| 0.0848 | 320 | 0.6807 |
| 0.0874 | 330 | 0.6322 |
| 0.0901 | 340 | 0.6281 |
| 0.0927 | 350 | 0.6566 |
| 0.0954 | 360 | 0.6422 |
| 0.0980 | 370 | 0.6129 |
| 0.1007 | 380 | 0.6519 |
| 0.1033 | 390 | 0.6105 |
| 0.1060 | 400 | 0.7017 |
| 0.1086 | 410 | 0.6183 |
| 0.1113 | 420 | 0.6435 |
| 0.1139 | 430 | 0.6163 |
| 0.1166 | 440 | 0.6073 |
| 0.1192 | 450 | 0.6686 |
| 0.1219 | 460 | 0.5915 |
| 0.1245 | 470 | 0.6867 |
| 0.1272 | 480 | 0.6514 |
| 0.1298 | 490 | 0.6234 |
| 0.1325 | 500 | 0.7231 |
| 0.1351 | 510 | 0.6752 |
| 0.1377 | 520 | 0.6850 |
| 0.1404 | 530 | 0.6246 |
| 0.1430 | 540 | 0.6448 |
| 0.1457 | 550 | 0.6384 |
| 0.1483 | 560 | 0.6507 |
| 0.1510 | 570 | 0.6698 |
| 0.1536 | 580 | 0.6437 |
| 0.1563 | 590 | 0.6700 |
| 0.1589 | 600 | 0.6922 |
| 0.1616 | 610 | 0.6558 |
| 0.1642 | 620 | 0.6543 |
| 0.1669 | 630 | 0.6623 |
| 0.1695 | 640 | 0.6519 |
| 0.1722 | 650 | 0.7129 |
| 0.1748 | 660 | 0.6629 |
| 0.1775 | 670 | 0.7004 |
| 0.1801 | 680 | 0.6801 |
| 0.1828 | 690 | 0.6362 |
| 0.1854 | 700 | 0.6492 |
| 0.1881 | 710 | 0.6198 |
| 0.1907 | 720 | 0.6550 |
| 0.1934 | 730 | 0.6379 |
| 0.1960 | 740 | 0.6613 |
| 0.1987 | 750 | 0.5927 |
| 0.2013 | 760 | 0.6787 |
| 0.2040 | 770 | 0.6305 |
| 0.2066 | 780 | 0.6491 |
| 0.2093 | 790 | 0.6448 |
| 0.2119 | 800 | 0.6268 |
| 0.2146 | 810 | 0.6884 |
| 0.2172 | 820 | 0.6439 |
| 0.2199 | 830 | 0.5978 |
| 0.2225 | 840 | 0.6046 |
| 0.2252 | 850 | 0.6475 |
| 0.2278 | 860 | 0.7012 |
| 0.2305 | 870 | 0.7130 |
| 0.2331 | 880 | 0.6389 |
| 0.2358 | 890 | 0.6361 |
| 0.2384 | 900 | 0.6038 |
| 0.2411 | 910 | 0.6292 |
| 0.2437 | 920 | 0.6372 |
| 0.2464 | 930 | 0.6516 |
| 0.2490 | 940 | 0.6012 |
| 0.2517 | 950 | 0.6210 |
| 0.2543 | 960 | 0.6331 |
| 0.2570 | 970 | 0.6447 |
| 0.2596 | 980 | 0.6170 |
| 0.2623 | 990 | 0.6522 |
| 0.2649 | 1000 | 0.6478 |
| 0.2675 | 1010 | 0.6565 |
| 0.2702 | 1020 | 0.6779 |
| 0.2728 | 1030 | 0.5857 |
| 0.2755 | 1040 | 0.6579 |
| 0.2781 | 1050 | 0.6550 |
| 0.2808 | 1060 | 0.6872 |
| 0.2834 | 1070 | 0.6193 |
| 0.2861 | 1080 | 0.6520 |
| 0.2887 | 1090 | 0.6288 |
| 0.2914 | 1100 | 0.6431 |
| 0.2940 | 1110 | 0.6261 |
| 0.2967 | 1120 | 0.6611 |
| 0.2993 | 1130 | 0.6272 |
| 0.3020 | 1140 | 0.6233 |
| 0.3046 | 1150 | 0.6476 |
| 0.3073 | 1160 | 0.6371 |
| 0.3099 | 1170 | 0.6419 |
| 0.3126 | 1180 | 0.6269 |
| 0.3152 | 1190 | 0.5902 |
| 0.3179 | 1200 | 0.6759 |
| 0.3205 | 1210 | 0.6018 |
| 0.3232 | 1220 | 0.5933 |
| 0.3258 | 1230 | 0.6813 |
| 0.3285 | 1240 | 0.6602 |
| 0.3311 | 1250 | 0.6151 |
| 0.3338 | 1260 | 0.6993 |
| 0.3364 | 1270 | 0.6090 |
| 0.3391 | 1280 | 0.6280 |
| 0.3417 | 1290 | 0.6401 |
| 0.3444 | 1300 | 0.6348 |
| 0.3470 | 1310 | 0.6247 |
| 0.3497 | 1320 | 0.6340 |
| 0.3523 | 1330 | 0.6235 |
| 0.3550 | 1340 | 0.6398 |
| 0.3576 | 1350 | 0.6428 |
| 0.3603 | 1360 | 0.5905 |
| 0.3629 | 1370 | 0.6111 |
| 0.3656 | 1380 | 0.6149 |
| 0.3682 | 1390 | 0.6170 |
| 0.3709 | 1400 | 0.6312 |
| 0.3735 | 1410 | 0.6468 |
| 0.3762 | 1420 | 0.6192 |
| 0.3788 | 1430 | 0.6379 |
| 0.3815 | 1440 | 0.6349 |
| 0.3841 | 1450 | 0.6483 |
| 0.3868 | 1460 | 0.5781 |
| 0.3894 | 1470 | 0.6124 |
| 0.3921 | 1480 | 0.6726 |
| 0.3947 | 1490 | 0.6514 |
| 0.3974 | 1500 | 0.6686 |
| 0.4 | 1510 | 0.6760 |
| 0.4026 | 1520 | 0.6189 |
| 0.4053 | 1530 | 0.6204 |
| 0.4079 | 1540 | 0.5860 |
| 0.4106 | 1550 | 0.6148 |
| 0.4132 | 1560 | 0.6168 |
| 0.4159 | 1570 | 0.6297 |
| 0.4185 | 1580 | 0.6276 |
| 0.4212 | 1590 | 0.6515 |
| 0.4238 | 1600 | 0.5978 |
| 0.4265 | 1610 | 0.6310 |
| 0.4291 | 1620 | 0.6304 |
| 0.4318 | 1630 | 0.6473 |
| 0.4344 | 1640 | 0.6485 |
| 0.4371 | 1650 | 0.6604 |
| 0.4397 | 1660 | 0.6157 |
| 0.4424 | 1670 | 0.6129 |
| 0.4450 | 1680 | 0.7063 |
| 0.4477 | 1690 | 0.6170 |
| 0.4503 | 1700 | 0.6413 |
| 0.4530 | 1710 | 0.6405 |
| 0.4556 | 1720 | 0.6849 |
| 0.4583 | 1730 | 0.6015 |
| 0.4609 | 1740 | 0.6486 |
| 0.4636 | 1750 | 0.6748 |
| 0.4662 | 1760 | 0.6085 |
| 0.4689 | 1770 | 0.6067 |
| 0.4715 | 1780 | 0.6963 |
| 0.4742 | 1790 | 0.6343 |
| 0.4768 | 1800 | 0.6466 |
| 0.4795 | 1810 | 0.6349 |
| 0.4821 | 1820 | 0.6210 |
| 0.4848 | 1830 | 0.6484 |
| 0.4874 | 1840 | 0.6274 |
| 0.4901 | 1850 | 0.6578 |
| 0.4927 | 1860 | 0.6256 |
| 0.4954 | 1870 | 0.7020 |
| 0.4980 | 1880 | 0.6198 |
| 0.5007 | 1890 | 0.6457 |
| 0.5033 | 1900 | 0.6248 |
| 0.5060 | 1910 | 0.5870 |
| 0.5086 | 1920 | 0.6228 |
| 0.5113 | 1930 | 0.6996 |
| 0.5139 | 1940 | 0.6112 |
| 0.5166 | 1950 | 0.6389 |
| 0.5192 | 1960 | 0.6378 |
| 0.5219 | 1970 | 0.5988 |
| 0.5245 | 1980 | 0.6403 |
| 0.5272 | 1990 | 0.6261 |
| 0.5298 | 2000 | 0.5839 |
| 0.5325 | 2010 | 0.6167 |
| 0.5351 | 2020 | 0.6513 |
| 0.5377 | 2030 | 0.6450 |
| 0.5404 | 2040 | 0.6429 |
| 0.5430 | 2050 | 0.6528 |
| 0.5457 | 2060 | 0.5987 |
| 0.5483 | 2070 | 0.6270 |
| 0.5510 | 2080 | 0.6091 |
| 0.5536 | 2090 | 0.6311 |
| 0.5563 | 2100 | 0.6079 |
| 0.5589 | 2110 | 0.6404 |
| 0.5616 | 2120 | 0.5812 |
| 0.5642 | 2130 | 0.5952 |
| 0.5669 | 2140 | 0.5462 |
| 0.5695 | 2150 | 0.5837 |
| 0.5722 | 2160 | 0.6012 |
| 0.5748 | 2170 | 0.6098 |
| 0.5775 | 2180 | 0.6354 |
| 0.5801 | 2190 | 0.6451 |
| 0.5828 | 2200 | 0.6939 |
| 0.5854 | 2210 | 0.5740 |
| 0.5881 | 2220 | 0.6262 |
| 0.5907 | 2230 | 0.6177 |
| 0.5934 | 2240 | 0.6054 |
| 0.5960 | 2250 | 0.6323 |
| 0.5987 | 2260 | 0.6014 |
| 0.6013 | 2270 | 0.6029 |
| 0.6040 | 2280 | 0.6510 |
| 0.6066 | 2290 | 0.6106 |
| 0.6093 | 2300 | 0.6658 |
| 0.6119 | 2310 | 0.6148 |
| 0.6146 | 2320 | 0.5705 |
| 0.6172 | 2330 | 0.6343 |
| 0.6199 | 2340 | 0.6189 |
| 0.6225 | 2350 | 0.6195 |
| 0.6252 | 2360 | 0.6301 |
| 0.6278 | 2370 | 0.6341 |
| 0.6305 | 2380 | 0.6970 |
| 0.6331 | 2390 | 0.6382 |
| 0.6358 | 2400 | 0.6147 |
| 0.6384 | 2410 | 0.6227 |
| 0.6411 | 2420 | 0.6444 |
| 0.6437 | 2430 | 0.6539 |
| 0.6464 | 2440 | 0.6036 |
| 0.6490 | 2450 | 0.5803 |
| 0.6517 | 2460 | 0.5509 |
| 0.6543 | 2470 | 0.6734 |
| 0.6570 | 2480 | 0.6047 |
| 0.6596 | 2490 | 0.6228 |
| 0.6623 | 2500 | 0.6495 |
| 0.6649 | 2510 | 0.5809 |
| 0.6675 | 2520 | 0.5985 |
| 0.6702 | 2530 | 0.6411 |
| 0.6728 | 2540 | 0.6028 |
| 0.6755 | 2550 | 0.6446 |
| 0.6781 | 2560 | 0.6517 |
| 0.6808 | 2570 | 0.6912 |
| 0.6834 | 2580 | 0.6082 |
| 0.6861 | 2590 | 0.6456 |
| 0.6887 | 2600 | 0.5687 |
| 0.6914 | 2610 | 0.6497 |
| 0.6940 | 2620 | 0.6519 |
| 0.6967 | 2630 | 0.6755 |
| 0.6993 | 2640 | 0.6039 |
| 0.7020 | 2650 | 0.6252 |
| 0.7046 | 2660 | 0.5952 |
| 0.7073 | 2670 | 0.6513 |
| 0.7099 | 2680 | 0.5913 |
| 0.7126 | 2690 | 0.6494 |
| 0.7152 | 2700 | 0.6464 |
| 0.7179 | 2710 | 0.6148 |
| 0.7205 | 2720 | 0.6189 |
| 0.7232 | 2730 | 0.6096 |
| 0.7258 | 2740 | 0.6257 |
| 0.7285 | 2750 | 0.6643 |
| 0.7311 | 2760 | 0.6037 |
| 0.7338 | 2770 | 0.5765 |
| 0.7364 | 2780 | 0.6253 |
| 0.7391 | 2790 | 0.6314 |
| 0.7417 | 2800 | 0.6280 |
| 0.7444 | 2810 | 0.6428 |
| 0.7470 | 2820 | 0.6571 |
| 0.7497 | 2830 | 0.6249 |
| 0.7523 | 2840 | 0.6677 |
| 0.7550 | 2850 | 0.6373 |
| 0.7576 | 2860 | 0.5973 |
| 0.7603 | 2870 | 0.6336 |
| 0.7629 | 2880 | 0.5572 |
| 0.7656 | 2890 | 0.6219 |
| 0.7682 | 2900 | 0.6223 |
| 0.7709 | 2910 | 0.6338 |
| 0.7735 | 2920 | 0.6454 |
| 0.7762 | 2930 | 0.6187 |
| 0.7788 | 2940 | 0.6364 |
| 0.7815 | 2950 | 0.6503 |
| 0.7841 | 2960 | 0.6184 |
| 0.7868 | 2970 | 0.6502 |
| 0.7894 | 2980 | 0.5683 |
| 0.7921 | 2990 | 0.5877 |
| 0.7947 | 3000 | 0.6358 |
| 0.7974 | 3010 | 0.6351 |
| 0.8 | 3020 | 0.6345 |
| 0.8026 | 3030 | 0.6210 |
| 0.8053 | 3040 | 0.5930 |
| 0.8079 | 3050 | 0.5895 |
| 0.8106 | 3060 | 0.6353 |
| 0.8132 | 3070 | 0.5700 |
| 0.8159 | 3080 | 0.6433 |
| 0.8185 | 3090 | 0.6550 |
| 0.8212 | 3100 | 0.6339 |
| 0.8238 | 3110 | 0.6142 |
| 0.8265 | 3120 | 0.6546 |
| 0.8291 | 3130 | 0.6292 |
| 0.8318 | 3140 | 0.5963 |
| 0.8344 | 3150 | 0.6108 |
| 0.8371 | 3160 | 0.6355 |
| 0.8397 | 3170 | 0.5676 |
| 0.8424 | 3180 | 0.6031 |
| 0.8450 | 3190 | 0.5923 |
| 0.8477 | 3200 | 0.6513 |
| 0.8503 | 3210 | 0.6188 |
| 0.8530 | 3220 | 0.6284 |
| 0.8556 | 3230 | 0.6340 |
| 0.8583 | 3240 | 0.6147 |
| 0.8609 | 3250 | 0.5917 |
| 0.8636 | 3260 | 0.6779 |
| 0.8662 | 3270 | 0.6350 |
| 0.8689 | 3280 | 0.6473 |
| 0.8715 | 3290 | 0.6474 |
| 0.8742 | 3300 | 0.5871 |
| 0.8768 | 3310 | 0.6273 |
| 0.8795 | 3320 | 0.6149 |
| 0.8821 | 3330 | 0.6161 |
| 0.8848 | 3340 | 0.6093 |
| 0.8874 | 3350 | 0.6370 |
| 0.8901 | 3360 | 0.5999 |
| 0.8927 | 3370 | 0.5798 |
| 0.8954 | 3380 | 0.6397 |
| 0.8980 | 3390 | 0.6465 |
| 0.9007 | 3400 | 0.5869 |
| 0.9033 | 3410 | 0.6462 |
| 0.9060 | 3420 | 0.6473 |
| 0.9086 | 3430 | 0.6154 |
| 0.9113 | 3440 | 0.6151 |
| 0.9139 | 3450 | 0.6234 |
| 0.9166 | 3460 | 0.5975 |
| 0.9192 | 3470 | 0.6232 |
| 0.9219 | 3480 | 0.6272 |
| 0.9245 | 3490 | 0.6518 |
| 0.9272 | 3500 | 0.6128 |
| 0.9298 | 3510 | 0.5916 |
| 0.9325 | 3520 | 0.5841 |
| 0.9351 | 3530 | 0.5625 |
| 0.9377 | 3540 | 0.6427 |
| 0.9404 | 3550 | 0.5752 |
| 0.9430 | 3560 | 0.6391 |
| 0.9457 | 3570 | 0.6066 |
| 0.9483 | 3580 | 0.6980 |
| 0.9510 | 3590 | 0.6205 |
| 0.9536 | 3600 | 0.6102 |
| 0.9563 | 3610 | 0.6038 |
| 0.9589 | 3620 | 0.5955 |
| 0.9616 | 3630 | 0.6513 |
| 0.9642 | 3640 | 0.6105 |
| 0.9669 | 3650 | 0.6158 |
| 0.9695 | 3660 | 0.6189 |
| 0.9722 | 3670 | 0.6710 |
| 0.9748 | 3680 | 0.6418 |
| 0.9775 | 3690 | 0.6060 |
| 0.9801 | 3700 | 0.6531 |
| 0.9828 | 3710 | 0.5995 |
| 0.9854 | 3720 | 0.5584 |
| 0.9881 | 3730 | 0.6247 |
| 0.9907 | 3740 | 0.6302 |
| 0.9934 | 3750 | 0.5997 |
| 0.9960 | 3760 | 0.6169 |
| 0.9987 | 3770 | 0.6731 |
</details>
### Training Time
- **Training**: 3.9 minutes
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 5.6.1
- Transformers: 5.14.1
- PyTorch: 2.13.0+cu130
- Accelerate: 1.14.0
- Datasets: 5.0.0
- 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",
}
```
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