---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
datasets:
- gyr66/privacy_detection
language:
- zh
model-index:
- name: RoBERTa-ext-large-crf-chinese-finetuned-ner
  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: 0.6813
    - name: Recall
      type: recall
      value: 0.7573
    - name: F1
      type: f1
      value: 0.7173
    - name: Accuracy
      type: accuracy
      value: 0.9639
---

# RoBERTa-ext-large-crf-chinese-finetuned-ner

This model is a fine-tuned version of [chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on the [gyr66/privacy_detection](https://huggingface.co/datasets/gyr66/privacy_detection) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7186
- Precision: 0.6813
- Recall: 0.7573
- F1: 0.7173
- Accuracy: 0.9639

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0197        | 1.0   | 503  | 0.6375          | 0.6663    | 0.7314 | 0.6973 | 0.9621   |
| 0.0251        | 2.0   | 1006 | 0.6048          | 0.6494    | 0.7435 | 0.6933 | 0.9611   |
| 0.0176        | 3.0   | 1509 | 0.6196          | 0.6669    | 0.7389 | 0.7011 | 0.9618   |
| 0.0116        | 4.0   | 2012 | 0.6361          | 0.6511    | 0.7560 | 0.6997 | 0.9624   |
| 0.0082        | 5.0   | 2515 | 0.6682          | 0.6746    | 0.7387 | 0.7052 | 0.9622   |
| 0.0067        | 6.0   | 3018 | 0.6587          | 0.6715    | 0.7409 | 0.7045 | 0.9635   |
| 0.0046        | 7.0   | 3521 | 0.6846          | 0.6770    | 0.7613 | 0.7167 | 0.9636   |
| 0.0019        | 8.0   | 4024 | 0.7081          | 0.6766    | 0.7510 | 0.7118 | 0.9630   |
| 0.0014        | 9.0   | 4527 | 0.7064          | 0.6812    | 0.7553 | 0.7163 | 0.9641   |
| 0.001         | 10.0  | 5030 | 0.7186          | 0.6813    | 0.7573 | 0.7173 | 0.9639   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0