---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:5749
- loss:BinaryCrossEntropyLoss
base_model: distilbert/distilroberta-base
datasets:
- sentence-transformers/stsb
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- pearson
- spearman
model-index:
- name: CrossEncoder based on distilbert/distilroberta-base
  results:
  - task:
      type: cross-encoder-correlation
      name: Cross Encoder Correlation
    dataset:
      name: stsb validation
      type: stsb-validation
    metrics:
    - type: pearson
      value: 0.8864227817727027
      name: Pearson
    - type: spearman
      value: 0.8837678149208236
      name: Spearman
  - task:
      type: cross-encoder-correlation
      name: Cross Encoder Correlation
    dataset:
      name: stsb test
      type: stsb-test
    metrics:
    - type: pearson
      value: 0.8503521391700528
      name: Pearson
    - type: spearman
      value: 0.8403655772346184
      name: Spearman
---

# CrossEncoder based on distilbert/distilroberta-base

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
- **Supported Modality:** Text
- **Training Dataset:**
    - [stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
- **Language:** en
<!-- - **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)

### Full Model Architecture

```
CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'RobertaForSequenceClassification'})
)
```

## 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("omkar334/reranker-distilroberta-base-stsb")
# Get scores for pairs of inputs
pairs = [
    ['A man with a hard hat is dancing.', 'A man wearing a hard hat is dancing.'],
    ['A young child is riding a horse.', 'A child is riding a horse.'],
    ['A man is feeding a mouse to a snake.', 'The man is feeding a mouse to the snake.'],
    ['A woman is playing the guitar.', 'A man is playing guitar.'],
    ['A woman is playing the flute.', 'A man is playing a flute.'],
]
scores = model.predict(pairs)
print(scores)
# [0.9598 0.9533 0.9566 0.3766 0.4535]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'A man with a hard hat is dancing.',
    [
        'A man wearing a hard hat is dancing.',
        'A child is riding a horse.',
        'The man is feeding a mouse to the snake.',
        'A man is playing guitar.',
        'A man is playing a flute.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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## Evaluation

### Metrics

#### Cross Encoder Correlation

* Datasets: `stsb-validation` and `stsb-test`
* Evaluated with [<code>CrossEncoderCorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderCorrelationEvaluator)

| Metric       | stsb-validation | stsb-test  |
|:-------------|:----------------|:-----------|
| pearson      | 0.8864          | 0.8504     |
| **spearman** | **0.8838**      | **0.8404** |

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## Training Details

### Training Dataset

#### stsb

* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 100 samples:
  |          | sentence1                                                                        | sentence2                                                                        | score                                                          |
  |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type     | string                                                                           | string                                                                           | float                                                          |
  | modality | text                                                                             | text                                                                             |                                                                |
  | details  | <ul><li>min: 7 tokens</li><li>mean: 9.49 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 9.61 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.66</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                  | sentence2                                                             | score             |
  |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
  | <code>A plane is taking off.</code>                        | <code>An air plane is taking off.</code>                              | <code>1.0</code>  |
  | <code>A man is playing a large flute.</code>               | <code>A man is playing a flute.</code>                                | <code>0.76</code> |
  | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</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

#### stsb

* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 100 samples:
  |          | sentence1                                                                         | sentence2                                                                        | score                                                          |
  |:---------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type     | string                                                                            | string                                                                           | float                                                          |
  | modality | text                                                                              | text                                                                             |                                                                |
  | details  | <ul><li>min: 7 tokens</li><li>mean: 10.04 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 9.98 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</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
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 64
- `num_train_epochs`: 4
- `warmup_steps`: 0.1
- `bf16`: True
- `per_device_eval_batch_size`: 64

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

- `per_device_train_batch_size`: 64
- `num_train_epochs`: 4
- `max_steps`: -1
- `learning_rate`: 5e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `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`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 64
- `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`: False
- `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_static_graph`: None
- `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 | stsb-validation_spearman | stsb-test_spearman |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------:|
| -1     | -1   | -             | -               | -0.0362                  | -                  |
| 0.2222 | 20   | 0.6909        | -               | -                        | -                  |
| 0.4444 | 40   | 0.6506        | -               | -                        | -                  |
| 0.6667 | 60   | 0.5969        | -               | -                        | -                  |
| 0.8889 | 80   | 0.5680        | 0.5461          | 0.8552                   | -                  |
| 1.1111 | 100  | 0.5551        | -               | -                        | -                  |
| 1.3333 | 120  | 0.5379        | -               | -                        | -                  |
| 1.5556 | 140  | 0.5449        | -               | -                        | -                  |
| 1.7778 | 160  | 0.5443        | 0.5342          | 0.8777                   | -                  |
| 2.0    | 180  | 0.5373        | -               | -                        | -                  |
| 2.2222 | 200  | 0.5287        | -               | -                        | -                  |
| 2.4444 | 220  | 0.5248        | -               | -                        | -                  |
| 2.6667 | 240  | 0.5283        | 0.5383          | 0.8785                   | -                  |
| 2.8889 | 260  | 0.5251        | -               | -                        | -                  |
| 3.1111 | 280  | 0.5156        | -               | -                        | -                  |
| 3.3333 | 300  | 0.5093        | -               | -                        | -                  |
| 3.5556 | 320  | 0.5164        | 0.5369          | 0.8824                   | -                  |
| 3.7778 | 340  | 0.5152        | -               | -                        | -                  |
| 4.0    | 360  | 0.5208        | 0.5331          | 0.8838                   | -                  |
| -1     | -1   | -             | -               | -                        | 0.8404             |


### Training Time
- **Training**: 3.2 minutes
- **Evaluation**: 15.8 seconds
- **Total**: 3.5 minutes

### Framework Versions
- Python: 3.11.14
- Sentence Transformers: 5.6.0.dev0
- Transformers: 5.9.0
- PyTorch: 2.12.0
- Accelerate: 1.13.0
- Datasets: 4.8.5
- Tokenizers: 0.22.2

## Additional Resources

- [Training and Finetuning Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-reranker): the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
- [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video reranker models through the same API.
- [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): training multimodal Cross Encoders.

## 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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