bert-base-chinese-finetuned-ner-v1

leonadase/bert-base-chinese-finetuned-ner-v1

bert-base-chinese-finetuned-ner-v1 is a token classification model from leonadase. Use it when you need labels on individual words, such as names. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
Model type: bert
Access: Public
Created: Apr 8, 2022
Updated: Apr 8, 2022
SHA: 55a98392

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Datasets

fdner

Model card

From the Hugging Face model README

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

Task: Token Classification
Library: transformers
Type: bert
Created: Apr 8, 2022
Updated: Apr 8, 2022