--- 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) - **Maximum Sequence Length:** 512 tokens - **Number of Output Labels:** 1 label ### 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': ...}, ...] ``` ## Evaluation ### Metrics #### Cross Encoder Binary Classification * Dataset: `paws-val-judge` * Evaluated with [CEBinaryClassificationEvaluator](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** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 43,188 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | 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 . | 0.0 | | 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 . | 1.0 | | 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 . | 1.0 | * Loss: [BinaryCrossEntropyLoss](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
Click to expand - `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`: {}
### 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", } ```