https://github.com/taishan1994/w2ner_predict
[Unofficial] Predict code for AAAI 2022 paper: Unified Named Entity Recognition as Word-Word Relation Classification
https://github.com/taishan1994/w2ner_predict
Last synced: 3 months ago
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[Unofficial] Predict code for AAAI 2022 paper: Unified Named Entity Recognition as Word-Word Relation Classification
- Host: GitHub
- URL: https://github.com/taishan1994/w2ner_predict
- Owner: taishan1994
- Created: 2022-03-21T03:43:23.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-09-05T06:05:52.000Z (about 3 years ago)
- Last Synced: 2023-03-04T13:53:24.570Z (over 2 years ago)
- Language: Python
- Homepage:
- Size: 282 KB
- Stars: 31
- Watchers: 1
- Forks: 6
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
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README
# W2NER_predict
\[Unofficial\] Predict code for AAAI 2022 paper: Unified Named Entity Recognition as Word-Word Relation Classification
# 说明
该项目是对上述论文的一个复现，并添加了预测代码。以及部分注释。
源论文：https://arxiv.org/pdf/2112.10070.pdf
源代码：https://github.com/ljynlp/w2ner
# 步骤
## 1、安装以下依赖
```
- numpy (1.21.4)
- torch (1.10.0)
- gensim (4.1.2)
- transformers (4.13.0)
- pandas (1.3.4)
- scikit-learn (1.0.1)
- prettytable (2.4.0)
```
## 2、下载数据
这里以resume-zh为例，数据下载地址：link。需要翻墙。
下载好resume-zh将其放置在data文件夹下。
## 3、训练、验证及测试
执行训练语句：
```python
python main.py --config ./config/resume-zh.json
```
训练好后会在主目录下生成model.pt文件。结果：
```
......
2022-03-21 04:06:44 - INFO: Epoch: 9
2022-03-21 04:08:37 - INFO:
+---------+--------+--------+-----------+--------+
| Train 9 | Loss | F1 | Precision | Recall |
+---------+--------+--------+-----------+--------+
| Label | 0.0005 | 0.9762 | 0.9791 | 0.9735 |
+---------+--------+--------+-----------+--------+
2022-03-21 04:08:41 - INFO: EVAL Label F1 [0.99977967 0.98848258 0.99099099 1. 1. 0.95695839
0.97695853 0.94339623 0.92307692 1. ]
2022-03-21 04:08:41 - INFO:
+--------+--------+-----------+--------+
| EVAL 9 | F1 | Precision | Recall |
+--------+--------+-----------+--------+
| Label | 0.9780 | 0.9661 | 0.9911 |
| Entity | 0.9577 | 0.9542 | 0.9613 |
+--------+--------+-----------+--------+
2022-03-21 04:08:46 - INFO: TEST Label F1 [0.99978522 0.98747049 0.99115044 1. 1. 0.95752896
0.98666667 0.94429708 0.90410959 1. ]
2022-03-21 04:08:46 - INFO:
+--------+--------+-----------+--------+
| TEST 9 | F1 | Precision | Recall |
+--------+--------+-----------+--------+
| Label | 0.9771 | 0.9655 | 0.9905 |
| Entity | 0.9617 | 0.9602 | 0.9632 |
+--------+--------+-----------+--------+
2022-03-21 04:08:46 - INFO: Best DEV F1: 0.9597
2022-03-21 04:08:46 - INFO: Best TEST F1: 0.9621
2022-03-21 04:08:51 - INFO: TEST Label F1 [0.99978775 0.98756339 0.97391304 1. 1. 0.95696853
0.98666667 0.95169946 0.89189189 1. ]
2022-03-21 04:08:51 - INFO:
+------------+--------+-----------+--------+
| TEST Final | F1 | Precision | Recall |
+------------+--------+-----------+--------+
| Label | 0.9748 | 0.9614 | 0.9906 |
| Entity | 0.9621 | 0.9591 | 0.9650 |
+------------+--------+-----------+--------+
```
## 4、预测
预测的数据处理代码在data_loader.py里面，主运行代码在predict.py里面。执行指令：
```python
python predict.py --config ./config/resume-zh.json
```
预测结果：
```
texts = [
"高勇，男，中国国籍，无境外居留权。",
"常见量，男。"
]
[
('高勇，男，中国国籍，无境外居留权。', ('中国国籍', 'cont', 5, 8), ('高勇', 'name', 0, 1)),
('常见量，男。', ('常见量', 'name', 0, 2))
]
```
最后需要注意的是源代码默认是使用gpu的，如果要切换的cpu，那么相关的地方还要进行修改。
# Acknowledgement
> https://github.com/ljynlp/w2ner
# 补充
[信息抽取三剑客：实体抽取、关系抽取、事件抽取](https://github.com/taishan1994/chinese_information_extraction)
[基于机器阅读理解的命名实体识别](https://github.com/taishan1994/BERT_MRC_NER_chinese)
[pytorch_bert_bilstm_crf命名实体识别](https://github.com/taishan1994/pytorch_bert_bilstm_crf_ner)