albert-base-chinese-0407-ner

yihsuan/albert-base-chinese-0407-ner

albert-base-chinese-0407-ner is a token classification model from yihsuan. Use it when you need labels on individual words, such as names. It is set up for transformers.

--- license: gpl-3.0 tags: - generatedfromtrainer metrics: - precision - recall - f1 - accuracy model-index: - name: albert-base-chinese-0407-ner results: [] ---

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At a glance

Task: Token Classification
Library: transformers
Model type: albert
Access: Public
Created: Apr 7, 2022
Updated: Apr 7, 2022
SHA: 7113e82d

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Model card

From the Hugging Face model README

license: gpl-3.0 tags:

generated_from_trainer metrics:
precision
recall
f1
accuracy model-index:
name: albert-base-chinese-0407-ner results: []

albert-base-chinese-0407-ner

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

Loss: 0.0948
Precision: 0.8603
Recall: 0.8871
F1: 0.8735
Accuracy: 0.9704

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.3484	0.05	500	0.5395	0.1841	0.1976	0.1906	0.8465
0.3948	0.09	1000	0.2910	0.6138	0.7113	0.6590	0.9263
0.2388	0.14	1500	0.2030	0.6628	0.7797	0.7165	0.9414
0.1864	0.18	2000	0.1729	0.7490	0.7935	0.7706	0.9498
0.1754	0.23	2500	0.1641	0.7415	0.7869	0.7635	0.9505
0.1558	0.28	3000	0.1532	0.7680	0.8002	0.7838	0.9530
0.1497	0.32	3500	0.1424	0.7865	0.8282	0.8068	0.9555
0.1488	0.37	4000	0.1373	0.7887	0.8111	0.7997	0.9553
0.1361	0.42	4500	0.1311	0.7942	0.8382	0.8156	0.9590
0.1335	0.46	5000	0.1264	0.7948	0.8423	0.8179	0.9596
0.1296	0.51	5500	0.1242	0.8129	0.8416	0.8270	0.9603
0.1338	0.55	6000	0.1315	0.7910	0.8588	0.8235	0.9586
0.1267	0.6	6500	0.1193	0.8092	0.8399	0.8243	0.9609
0.1207	0.65	7000	0.1205	0.8021	0.8469	0.8239	0.9601
0.1214	0.69	7500	0.1201	0.7969	0.8489	0.8220	0.9605
0.1168	0.74	8000	0.1134	0.8087	0.8607	0.8339	0.9620
0.1162	0.78	8500	0.1127	0.8177	0.8492	0.8331	0.9625
0.1202	0.83	9000	0.1283	0.7986	0.8550	0.8259	0.9580
0.1135	0.88	9500	0.1101	0.8213	0.8572	0.8389	0.9638
0.1121	0.92	10000	0.1097	0.8190	0.8588	0.8384	0.9635
0.1091	0.97	10500	0.1088	0.8180	0.8521	0.8347	0.9632
0.1058	1.02	11000	0.1085	0.8136	0.8716	0.8416	0.9630
0.0919	1.06	11500	0.1079	0.8309	0.8566	0.8436	0.9646
0.0914	1.11	12000	0.1079	0.8423	0.8542	0.8482	0.9656
0.0921	1.15	12500	0.1109	0.8312	0.8647	0.8476	0.9646
0.0926	1.2	13000	0.1240	0.8413	0.8488	0.8451	0.9637
0.0914	1.25	13500	0.1040	0.8336	0.8666	0.8498	0.9652
0.0917	1.29	14000	0.1032	0.8352	0.8707	0.8526	0.9662
0.0928	1.34	14500	0.1052	0.8347	0.8656	0.8498	0.9651
0.0906	1.38	15000	0.1032	0.8399	0.8619	0.8507	0.9662
0.0903	1.43	15500	0.1074	0.8180	0.8708	0.8436	0.9651
0.0889	1.48	16000	0.0990	0.8367	0.8713	0.8537	0.9670
0.0914	1.52	16500	0.1055	0.8508	0.8506	0.8507	0.9661
0.0934	1.57	17000	0.0979	0.8326	0.8740	0.8528	0.9669
0.0898	1.62	17500	0.1022	0.8393	0.8615	0.8502	0.9668
0.0869	1.66	18000	0.0962	0.8484	0.8762	0.8621	0.9682
0.089	1.71	18500	0.1008	0.8447	0.8714	0.8579	0.9674
0.0927	1.75	19000	0.0986	0.8379	0.8749	0.8560	0.9673
0.0883	1.8	19500	0.0965	0.8518	0.8749	0.8632	0.9688
0.0965	1.85	20000	0.0937	0.8412	0.8766	0.8585	0.9682
0.0834	1.89	20500	0.0920	0.8451	0.8862	0.8652	0.9687
0.0817	1.94	21000	0.0943	0.8439	0.8800	0.8616	0.9686
0.088	1.99	21500	0.0927	0.8483	0.8762	0.8620	0.9683
0.0705	2.03	22000	0.0993	0.8525	0.8783	0.8652	0.9690
0.0709	2.08	22500	0.0976	0.8610	0.8697	0.8653	0.9689
0.0655	2.12	23000	0.0997	0.8585	0.8665	0.8625	0.9683
0.0656	2.17	23500	0.0966	0.8569	0.8822	0.8694	0.9695
0.0698	2.22	24000	0.0955	0.8604	0.8775	0.8689	0.9696
0.065	2.26	24500	0.0971	0.8614	0.8780	0.8696	0.9697
0.0653	2.31	25000	0.0959	0.8600	0.8787	0.8692	0.9698
0.0685	2.35	25500	0.1001	0.8610	0.8710	0.8659	0.9690
0.0684	2.4	26000	0.0969	0.8490	0.8877	0.8679	0.9690
0.0657	2.45	26500	0.0954	0.8532	0.8832	0.8680	0.9696
0.0668	2.49	27000	0.0947	0.8604	0.8793	0.8698	0.9695
0.0644	2.54	27500	0.0989	0.8527	0.8790	0.8656	0.9696
0.0685	2.59	28000	0.0955	0.8596	0.8772	0.8683	0.9700
0.0702	2.63	28500	0.0937	0.8585	0.8837	0.8709	0.9700
0.0644	2.68	29000	0.0946	0.8605	0.8830	0.8716	0.9702
0.065	2.72	29500	0.0953	0.8617	0.8822	0.8719	0.9701
0.063	2.77	30000	0.0943	0.8597	0.8848	0.8721	0.9701
0.0638	2.82	30500	0.0941	0.8619	0.8846	0.8731	0.9702
0.066	2.86	31000	0.0942	0.8608	0.8847	0.8726	0.9701
0.0589	2.91	31500	0.0952	0.8632	0.8836	0.8733	0.9704
0.0568	2.95	32000	0.0948	0.8603	0.8871	0.8735	0.9704

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: albert
Created: Apr 7, 2022
Updated: Apr 7, 2022