bert-base-chinese-finetuned-ner_0220_J_ORIDATA_FULL_NOMOD

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: 0.0522
• Precision: 0.9728
• Recall: 0.9739
• F1: 0.9733
• Accuracy: 0.9954

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: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3616	1.0	705	0.0914	0.8789	0.9239	0.9008	0.9821
0.0643	2.0	1410	0.0602	0.9242	0.9420	0.9330	0.9912
0.0339	3.0	2115	0.0533	0.9385	0.9545	0.9465	0.9910
0.024	4.0	2820	0.0558	0.9595	0.9693	0.9644	0.9932
0.0145	5.0	3525	0.0584	0.9484	0.9614	0.9549	0.9921
0.007	6.0	4230	0.0535	0.9637	0.9648	0.9642	0.9940
0.0145	7.0	4935	0.0492	0.9573	0.9682	0.9627	0.9942
0.0091	8.0	5640	0.0486	0.9694	0.9716	0.9705	0.9957
0.0049	9.0	6345	0.0526	0.9727	0.9727	0.9727	0.9950
0.0033	10.0	7050	0.0515	0.9661	0.9727	0.9694	0.9949
0.0023	11.0	7755	0.0523	0.9661	0.9716	0.9688	0.9950
0.0019	12.0	8460	0.0522	0.9728	0.9739	0.9733	0.9954

Framework versions

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