README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - fdner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-chinese-finetuned-ner-v1
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: fdner
          type: fdner
          args: fdner
        metrics:
          - name: Precision
            type: precision
            value: 0.981203007518797
          - name: Recall
            type: recall
            value: 0.9886363636363636
          - name: F1
            type: f1
            value: 0.9849056603773584
          - name: Accuracy
            type: accuracy
            value: 0.9909536373916321

bert-base-chinese-finetuned-ner-v1

This model is a fine-tuned version of bert-base-chinese on the fdner dataset. It achieves the following results on the evaluation set:

• Loss: 0.0413
• Precision: 0.9812
• Recall: 0.9886
• F1: 0.9849
• Accuracy: 0.9910

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: 10
• eval_batch_size: 10
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	8	2.0640	0.0	0.0	0.0	0.4323
No log	2.0	16	1.7416	0.0204	0.0227	0.0215	0.5123
No log	3.0	24	1.5228	0.0306	0.0265	0.0284	0.5456
No log	4.0	32	1.2597	0.0961	0.1591	0.1198	0.6491
No log	5.0	40	1.0273	0.1588	0.2159	0.1830	0.7450
No log	6.0	48	0.8026	0.2713	0.3258	0.2960	0.8208
No log	7.0	56	0.6547	0.36	0.4091	0.3830	0.8513
No log	8.0	64	0.5180	0.4650	0.5038	0.4836	0.8873
No log	9.0	72	0.4318	0.5139	0.5606	0.5362	0.9067
No log	10.0	80	0.3511	0.6169	0.6894	0.6512	0.9291
No log	11.0	88	0.2887	0.6691	0.6894	0.6791	0.9414
No log	12.0	96	0.2396	0.7042	0.7576	0.7299	0.9516
No log	13.0	104	0.2052	0.7568	0.8371	0.7950	0.9587
No log	14.0	112	0.1751	0.8303	0.8712	0.8503	0.9610
No log	15.0	120	0.1512	0.8464	0.8977	0.8713	0.9668
No log	16.0	128	0.1338	0.8759	0.9091	0.8922	0.9710
No log	17.0	136	0.1147	0.8959	0.9129	0.9043	0.9746
No log	18.0	144	0.1011	0.9326	0.9432	0.9379	0.9761
No log	19.0	152	0.0902	0.9251	0.9356	0.9303	0.9795
No log	20.0	160	0.0806	0.9440	0.9583	0.9511	0.9804
No log	21.0	168	0.0743	0.9586	0.9659	0.9623	0.9812
No log	22.0	176	0.0649	0.9511	0.9583	0.9547	0.9851
No log	23.0	184	0.0595	0.9591	0.9773	0.9681	0.9876
No log	24.0	192	0.0537	0.9625	0.9735	0.9680	0.9883
No log	25.0	200	0.0505	0.9701	0.9848	0.9774	0.9894
No log	26.0	208	0.0464	0.9737	0.9811	0.9774	0.9904
No log	27.0	216	0.0439	0.9737	0.9811	0.9774	0.9906
No log	28.0	224	0.0428	0.9812	0.9886	0.9849	0.9910
No log	29.0	232	0.0417	0.9812	0.9886	0.9849	0.9910
No log	30.0	240	0.0413	0.9812	0.9886	0.9849	0.9910

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6