| --- |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:2879 |
| - loss:BinaryCrossEntropyLoss |
| base_model: BAAI/bge-reranker-v2-m3 |
| 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 BAAI/bge-reranker-v2-m3 |
| results: |
| - task: |
| type: cross-encoder-classification |
| name: Cross Encoder Classification |
| dataset: |
| name: entity matching |
| type: entity-matching |
| metrics: |
| - type: accuracy |
| value: 0.8525121555915721 |
| name: Accuracy |
| - type: accuracy_threshold |
| value: 0.44037526845932007 |
| name: Accuracy Threshold |
| - type: f1 |
| value: 0.8783068783068781 |
| name: F1 |
| - type: f1_threshold |
| value: 0.3608097732067108 |
| name: F1 Threshold |
| - type: precision |
| value: 0.827930174563591 |
| name: Precision |
| - type: recall |
| value: 0.9352112676056338 |
| name: Recall |
| - type: average_precision |
| value: 0.9356992398880613 |
| name: Average Precision |
| --- |
| |
| # CrossEncoder based on BAAI/bge-reranker-v2-m3 |
|
|
| This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) 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:** [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) <!-- at revision 953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e --> |
| - **Maximum Sequence Length:** 64 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("pujithapsx/test_fine_flow") |
| # Get scores for pairs of texts |
| pairs = [ |
| ['Yamini Durga Fernandes', 'Roy Yamini Durga'], |
| ['C/O Ramesh Yadav Village Bairiya Post Bairiya Ballia', 'Village Bairiya C/O Ramesh Yadav Post Bairiya Ballia'], |
| ['Flat 5 Lotus Tower Brigade Road Bengaluru', 'Flat 6 Lotus Tower Brigade Road Bangalore'], |
| ['House 7 Tinsukia Village Post Tinsukia Assam Assam', 'Tinsukia Village Assam'], |
| ['Rudra', 'Rudhraa'], |
| ] |
| scores = model.predict(pairs) |
| print(scores.shape) |
| # (5,) |
| |
| # Or rank different texts based on similarity to a single text |
| ranks = model.rank( |
| 'Yamini Durga Fernandes', |
| [ |
| 'Roy Yamini Durga', |
| 'Village Bairiya C/O Ramesh Yadav Post Bairiya Ballia', |
| 'Flat 6 Lotus Tower Brigade Road Bangalore', |
| 'Tinsukia Village Assam', |
| 'Rudhraa', |
| ] |
| ) |
| # [{'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 |
|
|
| * Dataset: `entity-matching` |
| * Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator) |
|
|
| | Metric | Value | |
| |:----------------------|:-----------| |
| | accuracy | 0.8525 | |
| | accuracy_threshold | 0.4404 | |
| | f1 | 0.8783 | |
| | f1_threshold | 0.3608 | |
| | precision | 0.8279 | |
| | recall | 0.9352 | |
| | **average_precision** | **0.9357** | |
| |
| <!-- |
| ## 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: 2,879 training samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 4 characters</li><li>mean: 30.29 characters</li><li>max: 90 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 31.45 characters</li><li>max: 106 characters</li></ul> | <ul><li>0: ~42.10%</li><li>1: ~57.90%</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:----------------------------------|:----------------------------------------|:---------------| |
| | <code>Village Buxar Bihar</code> | <code>Village Buxar Rohtas Bihar</code> | <code>0</code> | |
| | <code>Dhruv</code> | <code>Dhruvi</code> | <code>0</code> | |
| | <code>Venkat Prakash Verma</code> | <code>Venkat P Verma</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: 617 evaluation samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 617 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 4 characters</li><li>mean: 30.88 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 4 characters</li><li>mean: 31.67 characters</li><li>max: 100 characters</li></ul> | <ul><li>0: ~42.46%</li><li>1: ~57.54%</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:------------------------------------------------------------------|:------------------------------------------------------------------|:---------------| |
| | <code>Yamini Durga Fernandes</code> | <code>Roy Yamini Durga</code> | <code>0</code> | |
| | <code>C/O Ramesh Yadav Village Bairiya Post Bairiya Ballia</code> | <code>Village Bairiya C/O Ramesh Yadav Post Bairiya Ballia</code> | <code>1</code> | |
| | <code>Flat 5 Lotus Tower Brigade Road Bengaluru</code> | <code>Flat 6 Lotus Tower Brigade Road Bangalore</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`: 256 |
| - `per_device_eval_batch_size`: 32 |
| - `learning_rate`: 2e-05 |
| - `weight_decay`: 0.01 |
| - `num_train_epochs`: 1 |
| - `warmup_ratio`: 0.1 |
| - `use_cpu`: True |
| - `bf16`: True |
| - `half_precision_backend`: cpu_amp |
| - `load_best_model_at_end`: True |
| - `dataloader_pin_memory`: False |
| |
| #### 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`: 256 |
| - `per_device_eval_batch_size`: 32 |
| - `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`: 2e-05 |
| - `weight_decay`: 0.01 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `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`: True |
| - `use_mps_device`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `jit_mode_eval`: False |
| - `bf16`: True |
| - `fp16`: False |
| - `fp16_opt_level`: O1 |
| - `half_precision_backend`: cpu_amp |
| - `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`: 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} |
| - `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`: False |
| - `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 | Validation Loss | entity-matching_average_precision | |
| |:----------:|:------:|:---------------:|:---------------------------------:| |
| | 0.1667 | 2 | 0.4423 | 0.9298 | |
| | 0.3333 | 4 | 0.4188 | 0.9319 | |
| | 0.5 | 6 | 0.4032 | 0.9335 | |
| | 0.6667 | 8 | 0.3935 | 0.9345 | |
| | **0.8333** | **10** | **0.3874** | **0.9353** | |
| | 1.0 | 12 | 0.3849 | 0.9357 | |
| |
| * The bold row denotes the saved checkpoint. |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 5.3.0 |
| - Transformers: 4.57.6 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.8.4 |
| - Tokenizers: 0.22.2 |
| |
| ## 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.* |
| --> |
