---
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': ...}, ...]
```

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## 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** |

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## 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",
}
```

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