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	---
	tags:
	- sentence-transformers
	- cross-encoder
	- reranker
	- generated_from_trainer
	- dataset_size:11928
	- 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 = [
	['How do you control your horniness?', 'How do I control my horny emotions?'],
	['What do i do after my MBBS ?', 'What can one do after MBBS?'],
	['What is the county of Edgware and how does the lifestyle compare to the London Borough of Enfield?', 'What is the district of Edgware and how does the lifestyle compare to the London Borough of Islington?'],
	['What is a qualified SAP ERP key user?', 'What is the responsibility of SAP ERP key user?'],
	['Which is the best book for tensor calculus?', 'Which is the best book to study TENSOR for general relativity from basic?'],
	]
	scores = model.predict(pairs)
	print(scores)
	# [ 0.0335 0.6294 -2.3788 -0.096 -0.4309]

	# Or rank different texts based on similarity to a single text
	ranks = model.rank(
	'How do you control your horniness?',
	[
	'How do I control my horny emotions?',
	'What can one do after MBBS?',
	'What is the district of Edgware and how does the lifestyle compare to the London Borough of Islington?',
	'What is the responsibility of SAP ERP key user?',
	'Which is the best book to study TENSOR for general relativity from basic?',
	]
	)
	# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
	```

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	### Downstream Usage (Sentence Transformers)

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 11,928 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: 14.07 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 14.39 tokens</li><li>max: 31 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.62</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| response \| label \|
	\|:----------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>How do you control your horniness?</code> \| <code>How do I control my horny emotions?</code> \| <code>1.0</code> \|
	\| <code>What do i do after my MBBS ?</code> \| <code>What can one do after MBBS?</code> \| <code>1.0</code> \|
	\| <code>What is the county of Edgware and how does the lifestyle compare to the London Borough of Enfield?</code> \| <code>What is the district of Edgware and how does the lifestyle compare to the London Borough of Islington?</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
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 0.0013 \| 1 \| 2.9614 \|
	\| 0.0134 \| 10 \| 0.8912 \|
	\| 0.0268 \| 20 \| 0.8821 \|
	\| 0.0402 \| 30 \| 0.6854 \|
	\| 0.0536 \| 40 \| 0.7558 \|
	\| 0.0670 \| 50 \| 0.6960 \|
	\| 0.0804 \| 60 \| 0.6753 \|
	\| 0.0938 \| 70 \| 0.6979 \|
	\| 0.1072 \| 80 \| 0.6919 \|
	\| 0.1206 \| 90 \| 0.6373 \|
	\| 0.1340 \| 100 \| 0.6750 \|
	\| 0.1475 \| 110 \| 0.7235 \|
	\| 0.1609 \| 120 \| 0.6508 \|
	\| 0.1743 \| 130 \| 0.6698 \|
	\| 0.1877 \| 140 \| 0.6603 \|
	\| 0.2011 \| 150 \| 0.6601 \|
	\| 0.2145 \| 160 \| 0.6269 \|
	\| 0.2279 \| 170 \| 0.6568 \|
	\| 0.2413 \| 180 \| 0.5662 \|
	\| 0.2547 \| 190 \| 0.6341 \|
	\| 0.2681 \| 200 \| 0.6649 \|
	\| 0.2815 \| 210 \| 0.6582 \|
	\| 0.2949 \| 220 \| 0.6966 \|
	\| 0.3083 \| 230 \| 0.5850 \|
	\| 0.3217 \| 240 \| 0.5919 \|
	\| 0.3351 \| 250 \| 0.6952 \|
	\| 0.3485 \| 260 \| 0.6682 \|
	\| 0.3619 \| 270 \| 0.6402 \|
	\| 0.3753 \| 280 \| 0.6923 \|
	\| 0.3887 \| 290 \| 0.5896 \|
	\| 0.4021 \| 300 \| 0.6448 \|
	\| 0.4155 \| 310 \| 0.6208 \|
	\| 0.4290 \| 320 \| 0.6557 \|
	\| 0.4424 \| 330 \| 0.6780 \|
	\| 0.4558 \| 340 \| 0.6057 \|
	\| 0.4692 \| 350 \| 0.6660 \|
	\| 0.4826 \| 360 \| 0.6834 \|
	\| 0.4960 \| 370 \| 0.6351 \|
	\| 0.5094 \| 380 \| 0.6442 \|
	\| 0.5228 \| 390 \| 0.6002 \|
	\| 0.5362 \| 400 \| 0.6454 \|
	\| 0.5496 \| 410 \| 0.6431 \|
	\| 0.5630 \| 420 \| 0.6146 \|
	\| 0.5764 \| 430 \| 0.5826 \|
	\| 0.5898 \| 440 \| 0.6906 \|
	\| 0.6032 \| 450 \| 0.6260 \|
	\| 0.6166 \| 460 \| 0.6390 \|
	\| 0.6300 \| 470 \| 0.6107 \|
	\| 0.6434 \| 480 \| 0.6381 \|
	\| 0.6568 \| 490 \| 0.6296 \|
	\| 0.6702 \| 500 \| 0.6163 \|
	\| 0.6836 \| 510 \| 0.5750 \|
	\| 0.6971 \| 520 \| 0.6387 \|
	\| 0.7105 \| 530 \| 0.6353 \|
	\| 0.7239 \| 540 \| 0.5639 \|
	\| 0.7373 \| 550 \| 0.5501 \|
	\| 0.7507 \| 560 \| 0.6608 \|
	\| 0.7641 \| 570 \| 0.6868 \|
	\| 0.7775 \| 580 \| 0.5937 \|
	\| 0.7909 \| 590 \| 0.6198 \|
	\| 0.8043 \| 600 \| 0.6683 \|
	\| 0.8177 \| 610 \| 0.6228 \|
	\| 0.8311 \| 620 \| 0.5776 \|
	\| 0.8445 \| 630 \| 0.6115 \|
	\| 0.8579 \| 640 \| 0.6536 \|
	\| 0.8713 \| 650 \| 0.6366 \|
	\| 0.8847 \| 660 \| 0.6278 \|
	\| 0.8981 \| 670 \| 0.6331 \|
	\| 0.9115 \| 680 \| 0.5928 \|
	\| 0.9249 \| 690 \| 0.6246 \|
	\| 0.9383 \| 700 \| 0.6273 \|
	\| 0.9517 \| 710 \| 0.6254 \|
	\| 0.9651 \| 720 \| 0.5991 \|
	\| 0.9786 \| 730 \| 0.6309 \|
	\| 0.9920 \| 740 \| 0.5972 \|


	### Training Time
	- Training: 19.5 seconds

	### 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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