Cbert_base_ws-finetuned-ner – AI Model by HYM | AlphaNeural AI

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Cbert_base_ws-finetuned-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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Cbert_base_ws-finetuned-ner

This model is a fine-tuned version of ckiplab/bert-base-chinese-ws on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0582
Precision: 0.9602
Recall: 0.9633
F1: 0.9617
Accuracy: 0.9827

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: 18
eval_batch_size: 18
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
0.0482	0.64	1000	0.0509	0.9601	0.9582	0.9592	0.9817
0.0364	1.28	2000	0.0521	0.9590	0.9615	0.9602	0.9820
0.0341	1.92	3000	0.0548	0.9546	0.9625	0.9585	0.9812
0.0264	2.56	4000	0.0550	0.9593	0.9623	0.9608	0.9822
0.0227	3.19	5000	0.0582	0.9602	0.9633	0.9617	0.9827
0.021	3.83	6000	0.0595	0.9581	0.9624	0.9603	0.9820
0.0162	4.47	7000	0.0686	0.9574	0.9626	0.9600	0.9819
0.0159	5.11	8000	0.0719	0.9596	0.9614	0.9605	0.9822
0.0144	5.75	9000	0.0732	0.9590	0.9620	0.9605	0.9822
0.0109	6.39	10000	0.0782	0.9599	0.9626	0.9612	0.9824
0.0122	7.03	11000	0.0803	0.9605	0.9620	0.9612	0.9825
0.0097	7.67	12000	0.0860	0.9591	0.9620	0.9605	0.9822
0.0087	8.31	13000	0.0877	0.9591	0.9616	0.9603	0.9821
0.0087	8.95	14000	0.0902	0.9585	0.9630	0.9607	0.9823
0.0078	9.58	15000	0.0929	0.9589	0.9621	0.9605	0.9821

Framework versions

Transformers 4.13.0
Pytorch 1.8.0+cu111
Datasets 2.4.0
Tokenizers 0.10.3