---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- text-classification
- generated_from_trainer
- dataset_size:5749
- loss:BinaryCrossEntropyLoss
base_model: distilbert/distilroberta-base
datasets:
- sentence-transformers/stsb
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
- pearson
- spearman
co2_eq_emissions:
  emissions: 2.6550346776830636
  energy_consumed: 0.006830514578476734
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.031
  hardware_used: 1 x NVIDIA GeForce RTX 3090
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: 0.877295960646044
      name: Pearson
    - type: spearman
      value: 0.8754151440157509
      name: Spearman
  - task:
      type: cross-encoder-correlation
      name: Cross Encoder Correlation
    dataset:
      name: stsb test
      type: stsb-test
    metrics:
    - type: pearson
      value: 0.8503341584157813
      name: Pearson
    - type: spearman
      value: 0.8388642249054395
      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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## 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:** 514 tokens
- **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/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("tomaarsen/reranker-distilroberta-base-stsb")
# Get scores for pairs...
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.shape)
# [5]

# ... 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>CECorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CECorrelationEvaluator)

| Metric       | stsb-validation | stsb-test  |
|:-------------|:----------------|:-----------|
| pearson      | 0.8773          | 0.8503     |
| **spearman** | **0.8754**      | **0.8389** |

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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 1000 samples:
  |         | sentence1                                                                                       | sentence2                                                                                      | score                                                          |
  |:--------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                                          | string                                                                                         | float                                                          |
  | details | <ul><li>min: 16 characters</li><li>mean: 31.92 characters</li><li>max: 113 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 31.51 characters</li><li>max: 94 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</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/sentence_transformer/losses.html#binarycrossentropyloss)

### 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 1000 samples:
  |         | sentence1                                                                                       | sentence2                                                                                       | score                                                          |
  |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                                          | string                                                                                          | float                                                          |
  | details | <ul><li>min: 12 characters</li><li>mean: 57.37 characters</li><li>max: 144 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 56.84 characters</li><li>max: 141 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</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/sentence_transformer/losses.html#binarycrossentropyloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `bf16`: 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`: 4
- `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`: 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`: False
- `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
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | stsb-validation_spearman | stsb-test_spearman |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------:|
| -1     | -1   | -             | -               | -0.0150                  | -                  |
| 0.2222 | 20   | 0.6905        | -               | -                        | -                  |
| 0.4444 | 40   | 0.6548        | -               | -                        | -                  |
| 0.6667 | 60   | 0.5906        | -               | -                        | -                  |
| 0.8889 | 80   | 0.5631        | 0.5475          | 0.8589                   | -                  |
| 1.1111 | 100  | 0.5517        | -               | -                        | -                  |
| 1.3333 | 120  | 0.5473        | -               | -                        | -                  |
| 1.5556 | 140  | 0.5454        | -               | -                        | -                  |
| 1.7778 | 160  | 0.5402        | 0.5346          | 0.8760                   | -                  |
| 2.0    | 180  | 0.542         | -               | -                        | -                  |
| 2.2222 | 200  | 0.5229        | -               | -                        | -                  |
| 2.4444 | 220  | 0.524         | -               | -                        | -                  |
| 2.6667 | 240  | 0.5286        | 0.5373          | 0.8744                   | -                  |
| 2.8889 | 260  | 0.5236        | -               | -                        | -                  |
| 3.1111 | 280  | 0.5269        | -               | -                        | -                  |
| 3.3333 | 300  | 0.5209        | -               | -                        | -                  |
| 3.5556 | 320  | 0.5115        | 0.5409          | 0.8754                   | -                  |
| 3.7778 | 340  | 0.5149        | -               | -                        | -                  |
| 4.0    | 360  | 0.5084        | -               | -                        | -                  |
| -1     | -1   | -             | -               | -                        | 0.8389             |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.007 kWh
- **Carbon Emitted**: 0.003 kg of CO2
- **Hours Used**: 0.031 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0.dev0
- PyTorch: 2.5.0+cu121
- Accelerate: 1.3.0
- Datasets: 2.20.0
- Tokenizers: 0.21.0

## 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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