---
language:
- zh
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- gyr66/privacy_detection
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa-finetuned-privacy-detection
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: gyr66/privacy_detection
      type: gyr66/privacy_detection
      config: privacy_detection
      split: train
      args: privacy_detection
    metrics:
    - name: Precision
      type: precision
      value: 0.6168845082494108
    - name: Recall
      type: recall
      value: 0.7248237663645518
    - name: F1
      type: f1
      value: 0.6665123278157193
    - name: Accuracy
      type: accuracy
      value: 0.9061190926862569
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# RoBERTa-finetuned-privacy-detection

This model is a fine-tuned version of [gyr66/RoBERTa-finetuned-privacy-detection](https://huggingface.co/gyr66/RoBERTa-finetuned-privacy-detection) on the gyr66/privacy_detection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3534
- Precision: 0.6169
- Recall: 0.7248
- F1: 0.6665
- Accuracy: 0.9061

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 56
- eval_batch_size: 56
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2027        | 1.0   | 36   | 0.3485          | 0.5913    | 0.7273 | 0.6523 | 0.9030   |
| 0.1652        | 2.0   | 72   | 0.3534          | 0.6153    | 0.7314 | 0.6684 | 0.9053   |
| 0.143         | 3.0   | 108  | 0.3534          | 0.6169    | 0.7248 | 0.6665 | 0.9061   |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.2