metadata

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

RoBERTa-finetuned-privacy-detection

This model is a fine-tuned version of 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