sentence-transformers safetensors roberta cross-encoder text-classification generated_from_trainer dataset_size:404290 loss:BinaryCrossEntropyLoss en dataset:sentence-transformers/quora-duplicates arxiv:1908.10084 base_model:distilbert/distilroberta-base base_model:finetune:distilbert/distilroberta-base model-index co2_eq_emissions region:us
---
base_model: distilbert/distilroberta-base
datasets:
- sentence-transformers/quora-duplicates
language:
- en
library_name: sentence-transformers
metrics:
- accuracy
- accuracy_threshold
- f1
- f1_threshold
- precision
- recall
- average_precision
pipeline_tag: text-classification
tags:
- sentence-transformers
- cross-encoder
- text-classification
- generated_from_trainer
- dataset_size:404290
- loss:BinaryCrossEntropyLoss
co2_eq_emissions:
  emissions: 26.889480385249758
  energy_consumed: 0.06917762292257246
  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: 0.214
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CrossEncoder based on distilbert/distilroberta-base
  results:
  - task:
      type: cross-encoder-classification
      name: Cross Encoder Classification
    dataset:
      name: quora duplicates dev
      type: quora-duplicates-dev
    metrics:
    - type: accuracy
      value: 0.8938
      name: Accuracy
    - type: accuracy_threshold
      value: 0.5088549852371216
      name: Accuracy Threshold
    - type: f1
      value: 0.8612281373675477
      name: F1
    - type: f1_threshold
      value: 0.3856155276298523
      name: F1 Threshold
    - type: precision
      value: 0.8182920912178554
      name: Precision
    - type: recall
      value: 0.908919428725411
      name: Recall
    - type: average_precision
      value: 0.920292628179356
      name: Average Precision
  - task:
      type: cross-encoder-classification
      name: Cross Encoder Classification
    dataset:
      name: quora duplicates test
      type: quora-duplicates-test
    metrics:
    - type: accuracy
      value: 0.8938
      name: Accuracy
    - type: accuracy_threshold
      value: 0.5091445446014404
      name: Accuracy Threshold
    - type: f1
      value: 0.8612281373675477
      name: F1
    - type: f1_threshold
      value: 0.38580775260925293
      name: F1 Threshold
    - type: precision
      value: 0.8182920912178554
      name: Precision
    - type: recall
      value: 0.908919428725411
      name: Recall
    - type: average_precision
      value: 0.92029239602284
      name: Average Precision
---

# 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 [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) 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:**
    - [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **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("sentence_transformers_model_id")
# Get scores for pairs...
pairs = [
    ['What is the step by step guide to invest in share market in india?', 'What is the step by step guide to invest in share market?'],
    ['What is the story of Kohinoor (Koh-i-Noor) Diamond?', 'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?'],
    ['How can I increase the speed of my internet connection while using a VPN?', 'How can Internet speed be increased by hacking through DNS?'],
    ['Why am I mentally very lonely? How can I solve it?', 'Find the remainder when [math]23^{24}[/math] is divided by 24,23?'],
    ['Which one dissolve in water quikly sugar, salt, methane and carbon di oxide?', 'Which fish would survive in salt water?'],
]
scores = model.predict(pairs)
print(scores.shape)
# [5]

# ... or rank different texts based on similarity to a single text
ranks = model.rank(
    'What is the step by step guide to invest in share market in india?',
    [
        'What is the step by step guide to invest in share market?',
        'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?',
        'How can Internet speed be increased by hacking through DNS?',
        'Find the remainder when [math]23^{24}[/math] is divided by 24,23?',
        'Which fish would survive in salt water?',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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## Evaluation

### Metrics

#### Cross Encoder Classification

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

| Metric                | quora-duplicates-dev | quora-duplicates-test |
|:----------------------|:---------------------|:----------------------|
| accuracy              | 0.8938               | 0.8938                |
| accuracy_threshold    | 0.5089               | 0.5091                |
| f1                    | 0.8612               | 0.8612                |
| f1_threshold          | 0.3856               | 0.3858                |
| precision             | 0.8183               | 0.8183                |
| recall                | 0.9089               | 0.9089                |
| **average_precision** | **0.9203**           | **0.9203**            |

<!--
## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Training Dataset

#### quora-duplicates

* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 404,290 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: 1 characters</li><li>mean: 59.15 characters</li><li>max: 354 characters</li></ul> | <ul><li>min: 6 characters</li><li>mean: 60.74 characters</li><li>max: 399 characters</li></ul> | <ul><li>0: ~64.20%</li><li>1: ~35.80%</li></ul> |
* Samples:
  | sentence1                                                                          | sentence2                                                                                                          | label          |
  |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>What are the features of the Indian caste system?</code>                     | <code>What triggers you the most when you play video games?</code>                                                 | <code>0</code> |
  | <code>What is the best place to learn Mandarin Chinese in Singapore?</code>        | <code>What is the best place in Singapore for durian in December?</code>                                           | <code>0</code> |
  | <code>What will be Hillary Clinton's India policy if she wins the election?</code> | <code>How would the bilateral relationship between India and the USA be under Hillary Clinton's presidency?</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#binarycrossentropyloss)

### Evaluation Dataset

#### quora-duplicates

* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 404,290 evaluation 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: 11 characters</li><li>mean: 57.9 characters</li><li>max: 244 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 59.33 characters</li><li>max: 221 characters</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> |
* Samples:
  | sentence1                                                                              | sentence2                                                                                             | label          |
  |:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------|
  | <code>What is the step by step guide to invest in share market in india?</code>        | <code>What is the step by step guide to invest in share market?</code>                                | <code>0</code> |
  | <code>What is the story of Kohinoor (Koh-i-Noor) Diamond?</code>                       | <code>What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?</code> | <code>0</code> |
  | <code>How can I increase the speed of my internet connection while using a VPN?</code> | <code>How can Internet speed be increased by hacking through DNS?</code>                              | <code>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`: 1
- `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`: 1
- `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 | quora-duplicates-dev_average_precision | quora-duplicates-test_average_precision |
|:------:|:----:|:-------------:|:---------------:|:--------------------------------------:|:---------------------------------------:|
| -1     | -1   | -             | -               | 0.3711                                 | -                                       |
| 0.0167 | 100  | 0.6574        | -               | -                                      | -                                       |
| 0.0333 | 200  | 0.4804        | -               | -                                      | -                                       |
| 0.0500 | 300  | 0.4406        | -               | -                                      | -                                       |
| 0.0666 | 400  | 0.4208        | -               | -                                      | -                                       |
| 0.0833 | 500  | 0.3929        | 0.3958          | 0.8210                                 | -                                       |
| 0.0999 | 600  | 0.3986        | -               | -                                      | -                                       |
| 0.1166 | 700  | 0.3743        | -               | -                                      | -                                       |
| 0.1332 | 800  | 0.3938        | -               | -                                      | -                                       |
| 0.1499 | 900  | 0.3602        | -               | -                                      | -                                       |
| 0.1665 | 1000 | 0.3714        | 0.3437          | 0.8565                                 | -                                       |
| 0.1832 | 1100 | 0.3486        | -               | -                                      | -                                       |
| 0.1998 | 1200 | 0.3479        | -               | -                                      | -                                       |
| 0.2165 | 1300 | 0.3417        | -               | -                                      | -                                       |
| 0.2331 | 1400 | 0.3425        | -               | -                                      | -                                       |
| 0.2498 | 1500 | 0.3353        | 0.3264          | 0.8742                                 | -                                       |
| 0.2664 | 1600 | 0.3335        | -               | -                                      | -                                       |
| 0.2831 | 1700 | 0.3274        | -               | -                                      | -                                       |
| 0.2998 | 1800 | 0.3284        | -               | -                                      | -                                       |
| 0.3164 | 1900 | 0.3118        | -               | -                                      | -                                       |
| 0.3331 | 2000 | 0.3073        | 0.3282          | 0.8826                                 | -                                       |
| 0.3497 | 2100 | 0.3233        | -               | -                                      | -                                       |
| 0.3664 | 2200 | 0.3072        | -               | -                                      | -                                       |
| 0.3830 | 2300 | 0.314         | -               | -                                      | -                                       |
| 0.3997 | 2400 | 0.3065        | -               | -                                      | -                                       |
| 0.4163 | 2500 | 0.3046        | 0.2877          | 0.8930                                 | -                                       |
| 0.4330 | 2600 | 0.2857        | -               | -                                      | -                                       |
| 0.4496 | 2700 | 0.285         | -               | -                                      | -                                       |
| 0.4663 | 2800 | 0.2957        | -               | -                                      | -                                       |
| 0.4829 | 2900 | 0.2965        | -               | -                                      | -                                       |
| 0.4996 | 3000 | 0.2824        | 0.2842          | 0.8998                                 | -                                       |
| 0.5162 | 3100 | 0.3019        | -               | -                                      | -                                       |
| 0.5329 | 3200 | 0.2841        | -               | -                                      | -                                       |
| 0.5495 | 3300 | 0.2981        | -               | -                                      | -                                       |
| 0.5662 | 3400 | 0.2878        | -               | -                                      | -                                       |
| 0.5828 | 3500 | 0.278         | 0.2803          | 0.9061                                 | -                                       |
| 0.5995 | 3600 | 0.2841        | -               | -                                      | -                                       |
| 0.6162 | 3700 | 0.2794        | -               | -                                      | -                                       |
| 0.6328 | 3800 | 0.2808        | -               | -                                      | -                                       |
| 0.6495 | 3900 | 0.27          | -               | -                                      | -                                       |
| 0.6661 | 4000 | 0.2719        | 0.2697          | 0.9091                                 | -                                       |
| 0.6828 | 4100 | 0.2792        | -               | -                                      | -                                       |
| 0.6994 | 4200 | 0.2669        | -               | -                                      | -                                       |
| 0.7161 | 4300 | 0.2696        | -               | -                                      | -                                       |
| 0.7327 | 4400 | 0.2642        | -               | -                                      | -                                       |
| 0.7494 | 4500 | 0.2684        | 0.2591          | 0.9140                                 | -                                       |
| 0.7660 | 4600 | 0.2593        | -               | -                                      | -                                       |
| 0.7827 | 4700 | 0.2756        | -               | -                                      | -                                       |
| 0.7993 | 4800 | 0.2584        | -               | -                                      | -                                       |
| 0.8160 | 4900 | 0.2525        | -               | -                                      | -                                       |
| 0.8326 | 5000 | 0.267         | 0.2540          | 0.9168                                 | -                                       |
| 0.8493 | 5100 | 0.2612        | -               | -                                      | -                                       |
| 0.8659 | 5200 | 0.2607        | -               | -                                      | -                                       |
| 0.8826 | 5300 | 0.2565        | -               | -                                      | -                                       |
| 0.8993 | 5400 | 0.2432        | -               | -                                      | -                                       |
| 0.9159 | 5500 | 0.2568        | 0.2489          | 0.9198                                 | -                                       |
| 0.9326 | 5600 | 0.2572        | -               | -                                      | -                                       |
| 0.9492 | 5700 | 0.2658        | -               | -                                      | -                                       |
| 0.9659 | 5800 | 0.2568        | -               | -                                      | -                                       |
| 0.9825 | 5900 | 0.2539        | -               | -                                      | -                                       |
| 0.9992 | 6000 | 0.2458        | 0.2503          | 0.9203                                 | -                                       |
| -1     | -1   | -             | -               | -                                      | 0.9203                                  |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.069 kWh
- **Carbon Emitted**: 0.027 kg of CO2
- **Hours Used**: 0.214 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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