chinese-address-ner

This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unkown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1080
• Precision: 0.9664
• Recall: 0.9774
• F1: 0.9719
• Accuracy: 0.9758

Model description

输入一串地址中文信息，比如快递单：北京市海淀区西北旺东路10号院(马连洼街道西北旺社区东北方向)，按照行政级别（总有 7 级）抽取地址信息，返回每个 token 的类别。具体类别含义表示如下：

返回类别	BIO 体系	解释
LABEL_0	O	忽略信息
LABEL_1	B-A1	第一级地址（头）
LABEL_2	I-A1	第一级地址（其余部分）
...	...	...

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: 50
• eval_batch_size: 50
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
2.5055	1.0	7	1.6719	0.1977	0.2604	0.2248	0.5649
1.837	2.0	14	1.0719	0.4676	0.6	0.5256	0.7421
1.0661	3.0	21	0.7306	0.6266	0.7472	0.6816	0.8106
0.8373	4.0	28	0.5197	0.6456	0.8113	0.7191	0.8614
0.522	5.0	35	0.3830	0.7667	0.8679	0.8142	0.9001
0.4295	6.0	42	0.3104	0.8138	0.8906	0.8505	0.9178
0.3483	7.0	49	0.2453	0.8462	0.9132	0.8784	0.9404
0.2471	8.0	56	0.2081	0.8403	0.9132	0.8752	0.9428
0.2299	9.0	63	0.1979	0.8419	0.9245	0.8813	0.9420
0.1761	10.0	70	0.1823	0.8830	0.9396	0.9104	0.9500
0.1434	11.0	77	0.1480	0.9036	0.9547	0.9284	0.9629
0.134	12.0	84	0.1341	0.9173	0.9623	0.9392	0.9678
0.128	13.0	91	0.1365	0.9375	0.9623	0.9497	0.9694
0.0824	14.0	98	0.1159	0.9557	0.9774	0.9664	0.9734
0.0744	15.0	105	0.1092	0.9591	0.9736	0.9663	0.9766
0.0569	16.0	112	0.1117	0.9556	0.9736	0.9645	0.9742
0.0559	17.0	119	0.1040	0.9628	0.9774	0.9700	0.9790
0.0456	18.0	126	0.1052	0.9593	0.9774	0.9682	0.9782
0.0405	19.0	133	0.1133	0.9590	0.9698	0.9644	0.9718
0.0315	20.0	140	0.1060	0.9591	0.9736	0.9663	0.9750
0.0262	21.0	147	0.1087	0.9554	0.9698	0.9625	0.9718
0.0338	22.0	154	0.1183	0.9625	0.9698	0.9662	0.9726
0.0225	23.0	161	0.1080	0.9664	0.9774	0.9719	0.9758
0.028	24.0	168	0.1057	0.9591	0.9736	0.9663	0.9742
0.0202	25.0	175	0.1062	0.9628	0.9774	0.9700	0.9766
0.0168	26.0	182	0.1097	0.9664	0.9774	0.9719	0.9758
0.0173	27.0	189	0.1093	0.9628	0.9774	0.9700	0.9774
0.0151	28.0	196	0.1162	0.9628	0.9774	0.9700	0.9766
0.0135	29.0	203	0.1126	0.9483	0.9698	0.9590	0.9758
0.0179	30.0	210	0.1100	0.9449	0.9698	0.9572	0.9774
0.0161	31.0	217	0.1098	0.9449	0.9698	0.9572	0.9766
0.0158	32.0	224	0.1191	0.9483	0.9698	0.9590	0.9734
0.0151	33.0	231	0.1058	0.9483	0.9698	0.9590	0.9750
0.0121	34.0	238	0.0990	0.9593	0.9774	0.9682	0.9790
0.0092	35.0	245	0.1128	0.9519	0.9698	0.9607	0.9774
0.0097	36.0	252	0.1181	0.9627	0.9736	0.9681	0.9766
0.0118	37.0	259	0.1185	0.9591	0.9736	0.9663	0.9782
0.0118	38.0	266	0.1021	0.9557	0.9774	0.9664	0.9823
0.0099	39.0	273	0.1000	0.9559	0.9811	0.9683	0.9815
0.0102	40.0	280	0.1025	0.9559	0.9811	0.9683	0.9815
0.0068	41.0	287	0.1080	0.9522	0.9774	0.9646	0.9807
0.0105	42.0	294	0.1157	0.9449	0.9698	0.9572	0.9766
0.0083	43.0	301	0.1207	0.9380	0.9698	0.9536	0.9766
0.0077	44.0	308	0.1208	0.9483	0.9698	0.9590	0.9766
0.0077	45.0	315	0.1176	0.9483	0.9698	0.9590	0.9774
0.0071	46.0	322	0.1137	0.9483	0.9698	0.9590	0.9790
0.0075	47.0	329	0.1144	0.9483	0.9698	0.9590	0.9782
0.0084	48.0	336	0.1198	0.9483	0.9698	0.9590	0.9766
0.0103	49.0	343	0.1217	0.9519	0.9698	0.9607	0.9766
0.0087	50.0	350	0.1230	0.9519	0.9698	0.9607	0.9766

Framework versions

• Transformers 4.8.2
• Pytorch 1.8.0
• Datasets 1.9.0
• Tokenizers 0.10.3