bert-base-chinese-finetuned-ner_0301_J_DATA

This model is a fine-tuned version of ckiplab/bert-base-chinese-ner on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0764
• Precision: 0.9663
• Recall: 0.9708
• F1: 0.9685
• Accuracy: 0.9925

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3872	1.0	705	0.1222	0.9088	0.9311	0.9198	0.9781
0.0732	2.0	1410	0.0642	0.9303	0.9509	0.9405	0.9900
0.034	3.0	2115	0.0588	0.9616	0.9661	0.9639	0.9909
0.0267	4.0	2820	0.0631	0.9639	0.9673	0.9656	0.9925
0.0232	5.0	3525	0.0617	0.9630	0.9720	0.9674	0.9924
0.017	6.0	4230	0.0652	0.9674	0.9708	0.9691	0.9926
0.0123	7.0	4935	0.0573	0.9618	0.9720	0.9669	0.9923
0.009	8.0	5640	0.0667	0.9651	0.9696	0.9674	0.9922
0.0055	9.0	6345	0.0768	0.9640	0.9696	0.9668	0.9925
0.0045	10.0	7050	0.0775	0.9662	0.9696	0.9679	0.9925
0.004	11.0	7755	0.0753	0.9606	0.9685	0.9645	0.9923
0.0018	12.0	8460	0.0735	0.9629	0.9696	0.9662	0.9925
0.0019	13.0	9165	0.0754	0.9663	0.9708	0.9685	0.9927
0.0019	14.0	9870	0.0760	0.9651	0.9696	0.9674	0.9925
0.0013	15.0	10575	0.0764	0.9663	0.9708	0.9685	0.9925

Framework versions

• Transformers 4.20.1
• Pytorch 1.13.0+cu117
• Datasets 2.8.0
• Tokenizers 0.12.1