metadata

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 on the 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