---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:1122
- loss:BinaryCrossEntropyLoss
base_model: cross-encoder/stsb-roberta-large
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/stsb-roberta-large
  results:
  - task:
      type: cross-encoder-classification
      name: Cross Encoder Classification
    dataset:
      name: validation
      type: validation
    metrics:
    - type: accuracy
      value: 0.95
      name: Accuracy
    - type: accuracy_threshold
      value: 0.4996068775653839
      name: Accuracy Threshold
    - type: f1
      value: 0.9517241379310345
      name: F1
    - type: f1_threshold
      value: 0.3665258288383484
      name: F1 Threshold
    - type: precision
      value: 0.9452054794520548
      name: Precision
    - type: recall
      value: 0.9583333333333334
      name: Recall
    - type: average_precision
      value: 0.9752684979366919
      name: Average Precision
---

# CrossEncoder based on cross-encoder/stsb-roberta-large

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/stsb-roberta-large](https://huggingface.co/cross-encoder/stsb-roberta-large) 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/stsb-roberta-large](https://huggingface.co/cross-encoder/stsb-roberta-large) <!-- at revision 2b12c2c0088918e76151fd5937b7bba986ef1f98 -->
- **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/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/address-crossencoder-stsb-roberta-large-finetuned")
# Get scores for pairs of texts
pairs = [
    ['C/O Rakesh Tower C Sector 137 Gurgaon', 'C Tower Sec-137 Gurugram'],
    ['Tellapur Hyderabad', 'Telapur Hyderabad'],
    ['Flat 703 Electronic City Bangalore', 'Flat 703 Electronic City Mumbai'],
    ['B-12 Malviya Nagar Delhi', 'B-22 Malviya Nagar Delhi'],
    ['Flat 1203 Lower Parel Mumbai', 'Flat 1203 Lower Parel Chennai'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'C/O Rakesh Tower C Sector 137 Gurgaon',
    [
        'C Tower Sec-137 Gurugram',
        'Telapur Hyderabad',
        'Flat 703 Electronic City Mumbai',
        'B-22 Malviya Nagar Delhi',
        'Flat 1203 Lower Parel Chennai',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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## Evaluation

### Metrics

#### Cross Encoder Classification

* Dataset: `validation`
* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)

| Metric                | Value      |
|:----------------------|:-----------|
| accuracy              | 0.95       |
| accuracy_threshold    | 0.4996     |
| f1                    | 0.9517     |
| f1_threshold          | 0.3665     |
| precision             | 0.9452     |
| recall                | 0.9583     |
| **average_precision** | **0.9753** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 1,122 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                                                                                        | float                                                          |
  | details | <ul><li>min: 8 characters</li><li>mean: 27.83 characters</li><li>max: 55 characters</li></ul> | <ul><li>min: 8 characters</li><li>mean: 27.83 characters</li><li>max: 61 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.55</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                            | sentence2                                                        | label            |
  |:---------------------------------------------------------------------|:-----------------------------------------------------------------|:-----------------|
  | <code>Eighty Eight 8th Cross HSR Layout Bengaluru</code>             | <code>52 Fifty Two D Second Lane Marathahalli Bengaluru</code>   | <code>0.0</code> |
  | <code>Flat 301 C/O Sharma Kondapur Near Hitech City Hyderabad</code> | <code>Flat 301 C/O Sharma Kondapoor Near Hi Tech City Hyd</code> | <code>1.0</code> |
  | <code>Anna Nagar 12B Chennai 600040</code>                           | <code>12B Anna Nagar Chennai</code>                              | <code>1.0</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
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 140 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 140 samples:
  |         | sentence1                                                                                      | sentence2                                                                                      | label                                                          |
  |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                                         | string                                                                                         | float                                                          |
  | details | <ul><li>min: 13 characters</li><li>mean: 29.04 characters</li><li>max: 74 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 29.13 characters</li><li>max: 62 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                          | sentence2                                    | label            |
  |:---------------------------------------------------|:---------------------------------------------|:-----------------|
  | <code>C/O Rakesh Tower C Sector 137 Gurgaon</code> | <code>C Tower Sec-137 Gurugram</code>        | <code>1.0</code> |
  | <code>Tellapur Hyderabad</code>                    | <code>Telapur Hyderabad</code>               | <code>1.0</code> |
  | <code>Flat 703 Electronic City Bangalore</code>    | <code>Flat 703 Electronic City Mumbai</code> | <code>0.0</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

- `num_train_epochs`: 6
- `learning_rate`: 1.5e-05
- `warmup_steps`: 0.1
- `weight_decay`: 0.01
- `gradient_accumulation_steps`: 4
- `disable_tqdm`: True
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `load_best_model_at_end`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `per_device_train_batch_size`: 8
- `num_train_epochs`: 6
- `max_steps`: -1
- `learning_rate`: 1.5e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 4
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: True
- `project`: huggingface
- `trackio_space_id`: trackio
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `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
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `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 | validation_average_precision |
|:-------:|:-------:|:-------------:|:---------------:|:----------------------------:|
| 0.2837  | 10      | 0.4552        | -               | -                            |
| 0.5674  | 20      | 0.4294        | -               | -                            |
| 0.8511  | 30      | 0.4078        | -               | -                            |
| 1.0     | 36      | -             | 0.2787          | 0.9570                       |
| 1.1135  | 40      | 0.3982        | -               | -                            |
| 1.3972  | 50      | 0.3678        | -               | -                            |
| 1.6809  | 60      | 0.3367        | -               | -                            |
| 1.9645  | 70      | 0.4198        | -               | -                            |
| 2.0     | 72      | -             | 0.2252          | 0.9702                       |
| 2.2270  | 80      | 0.3148        | -               | -                            |
| 2.5106  | 90      | 0.3862        | -               | -                            |
| 2.7943  | 100     | 0.3374        | -               | -                            |
| **3.0** | **108** | **-**         | **0.1974**      | **0.9725**                   |
| 3.0567  | 110     | 0.3272        | -               | -                            |
| 3.3404  | 120     | 0.2932        | -               | -                            |
| 3.6241  | 130     | 0.3010        | -               | -                            |
| 3.9078  | 140     | 0.3119        | -               | -                            |
| 4.0     | 144     | -             | 0.1829          | 0.9736                       |
| 4.1702  | 150     | 0.3005        | -               | -                            |
| 4.4539  | 160     | 0.3292        | -               | -                            |
| 4.7376  | 170     | 0.2207        | -               | -                            |
| 5.0     | 180     | 0.2954        | 0.1745          | 0.9750                       |
| 5.2837  | 190     | 0.2853        | -               | -                            |
| 5.5674  | 200     | 0.2969        | -               | -                            |
| 5.8511  | 210     | 0.2600        | -               | -                            |
| 6.0     | 216     | -             | 0.1719          | 0.9753                       |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.11
- Sentence Transformers: 5.3.0
- Transformers: 5.3.0
- PyTorch: 2.11.0+cpu
- 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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