laura2243/cross-encoder-expert

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

This is a Cross Encoder model finetuned from cross-encoder/nli-deberta-v3-base using the sentence-transformers 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 <!-- 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
• —Documentation: Cross Encoder Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Cross Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

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': ...}, ...]

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Evaluation

Metrics

Cross Encoder Binary Classification

• —Dataset: paws-val-judge
• —Evaluated with <code>CEBinaryClassificationEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 43,188 training samples
• —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | 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: | sentence0 | sentence1 | 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> with these parameters:

  {
      "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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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

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

@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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