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	---
	tags:
	- sentence-transformers
	- cross-encoder
	- reranker
	- generated_from_trainer
	- dataset_size:43188
	- loss:BinaryCrossEntropyLoss
	base_model: cross-encoder/nli-deberta-v3-base
	pipeline_tag: text-ranking
	library_name: sentence-transformers
	metrics:
	- accuracy
	- accuracy_threshold
	- f1
	- f1_threshold
	- precision
	- recall
	- average_precision
	model-index:
	- name: CrossEncoder based on cross-encoder/nli-deberta-v3-base
	results:
	- task:
	type: cross-encoder-binary-classification
	name: Cross Encoder Binary Classification
	dataset:
	name: paws val judge
	type: paws-val-judge
	metrics:
	- type: accuracy
	value: 0.9645748987854251
	name: Accuracy
	- type: accuracy_threshold
	value: 0.08707074075937271
	name: Accuracy Threshold
	- type: f1
	value: 0.9604876947392187
	name: F1
	- type: f1_threshold
	value: 0.08707074075937271
	name: F1 Threshold
	- type: precision
	value: 0.9470169189670525
	name: Precision
	- type: recall
	value: 0.9743472285845167
	name: Recall
	- type: average_precision
	value: 0.9870268561433264
	name: Average Precision
	---

	# CrossEncoder based on cross-encoder/nli-deberta-v3-base

	This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/nli-deberta-v3-base](https://huggingface.co/cross-encoder/nli-deberta-v3-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: [cross-encoder/nli-deberta-v3-base](https://huggingface.co/cross-encoder/nli-deberta-v3-base) <!-- at revision 6c749ce3425cd33b46d187e45b92bbf96ee12ec7 -->
	- Maximum Sequence Length: 512 tokens
	- Number of Output Labels: 1 label
	<!-- - 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)

	## 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 texts
	pairs = [
	['Route 309 is a Connecticut State Highway in the northwestern Hartford suburbs from Canton to Simsbury .', 'Route 309 runs a Canton State Highway in the northwestern Connecticut suburbs from Hartford to Simsbury .'],
	['During the competition she lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .', 'During the competition , they lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .'],
	['The latter study is one of the few prospective demonstrations that environmental stress with high blood pressure and LVH remains associated .', 'The latter study remains one of the few prospective demonstrations that environmental stress with high blood pressure and LVH is associated .'],
	['The Marignane is located at Marseille Airport in Provence .', 'The Marignane is located in Marseille Provence Airport .'],
	['Birleffi was of Italian descent and Roman - Catholic in a predominantly Protestant state .', 'Birleffi was of Italian ethnicity and Roman Catholic in a predominantly Protestant state .'],
	]
	scores = model.predict(pairs)
	print(scores.shape)
	# (5,)

	# Or rank different texts based on similarity to a single text
	ranks = model.rank(
	'Route 309 is a Connecticut State Highway in the northwestern Hartford suburbs from Canton to Simsbury .',
	[
	'Route 309 runs a Canton State Highway in the northwestern Connecticut suburbs from Hartford to Simsbury .',
	'During the competition , they lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .',
	'The latter study remains one of the few prospective demonstrations that environmental stress with high blood pressure and LVH is associated .',
	'The Marignane is located in Marseille Provence Airport .',
	'Birleffi was of Italian ethnicity and Roman Catholic in a predominantly Protestant state .',
	]
	)
	# [{'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.
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	## Evaluation

	### Metrics

	#### Cross Encoder Binary Classification

	* Dataset: `paws-val-judge`
	* Evaluated with [<code>CEBinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEBinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:----------------------\|:----------\|
	\| accuracy \| 0.9646 \|
	\| accuracy_threshold \| 0.0871 \|
	\| f1 \| 0.9605 \|
	\| f1_threshold \| 0.0871 \|
	\| precision \| 0.947 \|
	\| recall \| 0.9743 \|
	\| average_precision \| 0.987 \|

	<!--
	## 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: 43,188 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 38 characters</li><li>mean: 114.71 characters</li><li>max: 200 characters</li></ul> \| <ul><li>min: 42 characters</li><li>mean: 114.33 characters</li><li>max: 215 characters</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Route 309 is a Connecticut State Highway in the northwestern Hartford suburbs from Canton to Simsbury .</code> \| <code>Route 309 runs a Canton State Highway in the northwestern Connecticut suburbs from Hartford to Simsbury .</code> \| <code>0.0</code> \|
	\| <code>During the competition she lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .</code> \| <code>During the competition , they lost 50-25 to Zimbabwe , 84-16 to Tanzania , 58-24 to South Africa .</code> \| <code>1.0</code> \|
	\| <code>The latter study is one of the few prospective demonstrations that environmental stress with high blood pressure and LVH remains associated .</code> \| <code>The latter study remains one of the few prospective demonstrations that environmental stress with high blood pressure and LVH is associated .</code> \| <code>1.0</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
	- `per_device_eval_batch_size`: 16

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 3
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `bf16`: False
	- `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
	- `project`: huggingface
	- `trackio_space_id`: trackio
	- `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`: no
	- `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`: True
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| paws-val-judge_average_precision \|
	\|:------:\|:----:\|:-------------:\|:--------------------------------:\|
	\| 0.1852 \| 500 \| 0.3758 \| - \|
	\| 0.3704 \| 1000 \| 0.226 \| - \|
	\| 0.5556 \| 1500 \| 0.2176 \| - \|
	\| 0.7407 \| 2000 \| 0.1778 \| - \|
	\| 0.9259 \| 2500 \| 0.1757 \| - \|
	\| 1.0 \| 2700 \| - \| 0.9826 \|
	\| 1.1111 \| 3000 \| 0.1494 \| - \|
	\| 1.2963 \| 3500 \| 0.1271 \| - \|
	\| 1.4815 \| 4000 \| 0.1197 \| - \|
	\| 1.6667 \| 4500 \| 0.1263 \| - \|
	\| 1.8519 \| 5000 \| 0.116 \| - \|
	\| 2.0 \| 5400 \| - \| 0.9852 \|
	\| 2.0370 \| 5500 \| 0.1084 \| - \|
	\| 2.2222 \| 6000 \| 0.0707 \| - \|
	\| 2.4074 \| 6500 \| 0.0741 \| - \|
	\| 2.5926 \| 7000 \| 0.0713 \| - \|
	\| 2.7778 \| 7500 \| 0.0723 \| - \|
	\| 2.9630 \| 8000 \| 0.0727 \| - \|
	\| 3.0 \| 8100 \| - \| 0.9870 \|


	### Framework Versions
	- Python: 3.12.12
	- Sentence Transformers: 5.2.0
	- Transformers: 4.57.3
	- PyTorch: 2.9.0+cu126
	- Accelerate: 1.12.0
	- Datasets: 4.0.0
	- Tokenizers: 0.22.1

	## 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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