百度实体抽取模型
Project description
pyUnit-NER ![](https://pypi-camo.freetls.fastly.net/768e8c094523f7049aa6b8b037ff4dbadf3cb8f5/68747470733a2f2f67697465652e636f6d2f74796f75692f6c6f676f2f7261772f6d61737465722f6c6f676f2f70686f746f6c6f672e706e67)
NER模块集合
安装
pip install pyunit-ner
推荐使用Docker部署
docker pull jtyoui/pyunit-ner
docker run -d -P jtyoui/pyunit-ner
默认官方数据集训练的模型(只能识别:人名、地名、机构名)
默认的参数和映射表
import pprint
from pyunit_ner import ernie_st, ernie_match, parseNER
def test():
# 默认的模型参数和映射表
model = '/home/jtyoui/Documents/model'
s = ernie_st(new_model_path=model)
data = ernie_match('刘万光对李伟说:在贵阳市南明村永乐乡发生了一件恐怖的事情', s)
result = parseNER(data)
return result
if __name__ == '__main__':
pprint.pprint(test())
抽取实体接口文档
http://ip:port/docs
请求报文
参数名 | 类型 | NULL | 说明 |
---|---|---|---|
data | string | Yes | 数据 |
请求示例
import requests
url = "http://127.0.0.1:9000/pyunit/ner?data=我在贵州贵阳观山湖"
headers = {'Content-Type': "application/x-www-form-urlencoded"}
response = requests.get(url).json()
print(response)
返回报文
参数名 | 类型 | NULL | 说明 |
---|---|---|---|
msg | string | Yes | 返回消息 |
data | list | Yes | 标注数据类型 |
address | list | Yes | 地址 |
person | list | Yes | 人名 |
org | list | Yes | 机构名 |
{
"code": 200,
"entity": {
"address": [
"贵州贵阳观山湖"
],
"number": [
"6",
"6",
"4",
"5",
"4",
"5",
"4",
"5",
"5"
],
"organization": [],
"person": [],
"word": [
"我",
"在",
"贵",
"州",
"贵",
"阳",
"观",
"山",
"湖"
]
},
"msg": "success"
}
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