| --- |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:942069 |
| - loss:BinaryCrossEntropyLoss |
| base_model: microsoft/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 microsoft/deberta-v3-base |
| results: |
| - task: |
| type: cross-encoder-classification |
| name: Cross Encoder Classification |
| dataset: |
| name: AllNLI norm dev |
| type: AllNLI-norm-dev |
| metrics: |
| - type: accuracy |
| value: 0.6807244238693595 |
| name: Accuracy |
| - type: accuracy_threshold |
| value: 0.4375791847705841 |
| name: Accuracy Threshold |
| - type: f1 |
| value: 0.5465734265734266 |
| name: F1 |
| - type: f1_threshold |
| value: 0.00438243243843317 |
| name: F1 Threshold |
| - type: precision |
| value: 0.40035514274006284 |
| name: Precision |
| - type: recall |
| value: 0.8610458284371327 |
| name: Recall |
| - type: average_precision |
| value: 0.49929639749742777 |
| name: Average Precision |
| - task: |
| type: cross-encoder-classification |
| name: Cross Encoder Classification |
| dataset: |
| name: AllNLI test |
| type: AllNLI-test |
| metrics: |
| - type: accuracy |
| value: 0.6814204314204314 |
| name: Accuracy |
| - type: accuracy_threshold |
| value: 0.5599576234817505 |
| name: Accuracy Threshold |
| - type: f1 |
| value: 0.5269568771714694 |
| name: F1 |
| - type: f1_threshold |
| value: 0.0010413693962618709 |
| name: F1 Threshold |
| - type: precision |
| value: 0.3655438357718045 |
| name: Precision |
| - type: recall |
| value: 0.9436392914653784 |
| name: Recall |
| - type: average_precision |
| value: 0.48186391164637776 |
| name: Average Precision |
| --- |
| |
| # CrossEncoder based on microsoft/deberta-v3-base |
|
|
| This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [microsoft/deberta-v3-base](https://huggingface.co/microsoft/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:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) <!-- at revision 8ccc9b6f36199bec6961081d44eb72fb3f7353f3 --> |
| - **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/UKPLab/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("tani-at-nola/reranker-deberta-v3-base-nli") |
| # Get scores for pairs of texts |
| pairs = [ |
| ['The sisters are hugging goodbye while holding to go packages after just eating lunch.', 'Two women are embracing while holding to go packages.'], |
| ['Two woman are holding packages.', 'Two women are embracing while holding to go packages.'], |
| ['The men are fighting outside a deli.', 'Two women are embracing while holding to go packages.'], |
| ['Two kids in numbered jerseys wash their hands.', 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.'], |
| ['Two kids at a ballgame wash their hands.', 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.'], |
| ] |
| scores = model.predict(pairs) |
| print(scores.shape) |
| # (5,) |
| |
| # Or rank different texts based on similarity to a single text |
| ranks = model.rank( |
| 'The sisters are hugging goodbye while holding to go packages after just eating lunch.', |
| [ |
| 'Two women are embracing while holding to go packages.', |
| 'Two women are embracing while holding to go packages.', |
| 'Two women are embracing while holding to go packages.', |
| 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', |
| 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', |
| ] |
| ) |
| # [{'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.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Cross Encoder Classification |
|
|
| * Datasets: `AllNLI-norm-dev` and `AllNLI-test` |
| * Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator) |
|
|
| | Metric | AllNLI-norm-dev | AllNLI-test | |
| |:----------------------|:----------------|:------------| |
| | accuracy | 0.6807 | 0.6814 | |
| | accuracy_threshold | 0.4376 | 0.56 | |
| | f1 | 0.5466 | 0.527 | |
| | f1_threshold | 0.0044 | 0.001 | |
| | precision | 0.4004 | 0.3655 | |
| | recall | 0.861 | 0.9436 | |
| | **average_precision** | **0.4993** | **0.4819** | |
| |
| <!-- |
| ## 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: 942,069 training samples |
| * Columns: <code>hypothesis</code>, <code>premise</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | hypothesis | premise | label | |
| |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 11 characters</li><li>mean: 38.26 characters</li><li>max: 131 characters</li></ul> | <ul><li>min: 23 characters</li><li>mean: 69.54 characters</li><li>max: 227 characters</li></ul> | <ul><li>0: ~66.60%</li><li>1: ~33.40%</li></ul> | |
| * Samples: |
| | hypothesis | premise | label | |
| |:---------------------------------------------------------------|:--------------------------------------------------------------------|:---------------| |
| | <code>A person is training his horse for a competition.</code> | <code>A person on a horse jumps over a broken down airplane.</code> | <code>0</code> | |
| | <code>A person is at a diner, ordering an omelette.</code> | <code>A person on a horse jumps over a broken down airplane.</code> | <code>0</code> | |
| | <code>A person is outdoors, on a horse.</code> | <code>A person on a horse jumps over a broken down airplane.</code> | <code>1</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 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 19,657 evaluation samples |
| * Columns: <code>hypothesis</code>, <code>premise</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | hypothesis | premise | label | |
| |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 11 characters</li><li>mean: 37.66 characters</li><li>max: 116 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 75.01 characters</li><li>max: 229 characters</li></ul> | <ul><li>0: ~66.90%</li><li>1: ~33.10%</li></ul> | |
| * Samples: |
| | hypothesis | premise | label | |
| |:---------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:---------------| |
| | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>Two women are embracing while holding to go packages.</code> | <code>0</code> | |
| | <code>Two woman are holding packages.</code> | <code>Two women are embracing while holding to go packages.</code> | <code>1</code> | |
| | <code>The men are fighting outside a deli.</code> | <code>Two women are embracing while holding to go packages.</code> | <code>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 |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `num_train_epochs`: 5 |
| - `warmup_ratio`: 0.1 |
| - `bf16`: True |
| - `load_best_model_at_end`: True |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `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`: 1.0 |
| - `num_train_epochs`: 5 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.1 |
| - `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 |
| - `use_ipex`: False |
| - `bf16`: True |
| - `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`: True |
| - `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`: True |
| - `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} |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `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`: False |
| - `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`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | AllNLI-norm-dev_average_precision | AllNLI-test_average_precision | |
| |:----------:|:--------:|:-------------:|:---------------:|:---------------------------------:|:-----------------------------:| |
| | -1 | -1 | - | - | 0.3614 | - | |
| | 0.0068 | 100 | 0.7205 | - | - | - | |
| | 0.0136 | 200 | 0.6972 | - | - | - | |
| | 0.0204 | 300 | 0.6086 | - | - | - | |
| | 0.0272 | 400 | 0.4855 | - | - | - | |
| | 0.0340 | 500 | 0.3991 | - | - | - | |
| | 0.0408 | 600 | 0.3409 | - | - | - | |
| | 0.0476 | 700 | 0.2987 | - | - | - | |
| | 0.0544 | 800 | 0.2841 | - | - | - | |
| | 0.0611 | 900 | 0.2729 | - | - | - | |
| | 0.0679 | 1000 | 0.2627 | - | - | - | |
| | 0.0747 | 1100 | 0.2517 | - | - | - | |
| | 0.0815 | 1200 | 0.2286 | - | - | - | |
| | 0.0883 | 1300 | 0.2385 | - | - | - | |
| | 0.0951 | 1400 | 0.2329 | - | - | - | |
| | 0.1019 | 1500 | 0.2213 | 0.1959 | 0.4997 | - | |
| | 0.1087 | 1600 | 0.22 | - | - | - | |
| | 0.1155 | 1700 | 0.2295 | - | - | - | |
| | 0.1223 | 1800 | 0.2236 | - | - | - | |
| | 0.1291 | 1900 | 0.2273 | - | - | - | |
| | 0.1359 | 2000 | 0.2071 | - | - | - | |
| | 0.1427 | 2100 | 0.2254 | - | - | - | |
| | 0.1495 | 2200 | 0.2217 | - | - | - | |
| | 0.1563 | 2300 | 0.2093 | - | - | - | |
| | 0.1631 | 2400 | 0.2112 | - | - | - | |
| | 0.1698 | 2500 | 0.2176 | - | - | - | |
| | 0.1766 | 2600 | 0.2195 | - | - | - | |
| | 0.1834 | 2700 | 0.2107 | - | - | - | |
| | 0.1902 | 2800 | 0.2164 | - | - | - | |
| | 0.1970 | 2900 | 0.213 | - | - | - | |
| | **0.2038** | **3000** | **0.2055** | **0.1726** | **0.4789** | **-** | |
| | 0.2106 | 3100 | 0.2039 | - | - | - | |
| | 0.2174 | 3200 | 0.2157 | - | - | - | |
| | 0.2242 | 3300 | 0.2155 | - | - | - | |
| | 0.2310 | 3400 | 0.2017 | - | - | - | |
| | 0.2378 | 3500 | 0.2068 | - | - | - | |
| | 0.2446 | 3600 | 0.2111 | - | - | - | |
| | 0.2514 | 3700 | 0.2062 | - | - | - | |
| | 0.2582 | 3800 | 0.2062 | - | - | - | |
| | 0.2650 | 3900 | 0.2217 | - | - | - | |
| | 0.2718 | 4000 | 0.2012 | - | - | - | |
| | 0.2786 | 4100 | 0.2127 | - | - | - | |
| | 0.2853 | 4200 | 0.212 | - | - | - | |
| | 0.2921 | 4300 | 0.2075 | - | - | - | |
| | 0.2989 | 4400 | 0.2099 | - | - | - | |
| | 0.3057 | 4500 | 0.2134 | 0.1644 | 0.4993 | - | |
| | -1 | -1 | - | - | - | 0.4819 | |
| |
| * The bold row denotes the saved checkpoint. |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 5.0.0 |
| - Transformers: 4.53.2 |
| - PyTorch: 2.7.1+cu126 |
| - Accelerate: 1.9.0 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.21.2 |
| |
| ## 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", |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
| <!-- |
| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
| |
| <!-- |
| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
