README.md

metadata

language:
  - zh
tags:
  - generated_from_trainer
datasets:
  - gyr66/privacy_detection
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-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: 0.65322
          - name: Recall
            type: recall
            value: 0.74169
          - name: F1
            type: f1
            value: 0.69465
          - name: Accuracy
            type: accuracy
            value: 0.90517

bert-base-chinese-finetuned-ner

This model is a fine-tuned version of Danielwei0214/bert-base-chinese-finetuned-ner on the gyr66/privacy_detection dataset. It achieves the following results on the evaluation set:

• Loss: 0.7929
• Precision: 0.6532
• Recall: 0.7417
• F1: 0.6947
• Accuracy: 0.9052

Model description

The model is used for competition: "https://www.datafountain.cn/competitions/472"

Training and evaluation data

The training and evaluation data is from gyr66/privacy_detection dataset.

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

Framework versions

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