BERT for Chinese Named Entity Recognition(bert4ner) Model
中文实体识别模型
bert4ner-base-chinese evaluate PEOPLE(人民日报) test data：
The overall performance of BERT on people test:
| Accuracy | Recall | F1 | |
|---|---|---|---|
| BertSoftmax | 0.9425 | 0.9627 | 0.9525 |
在PEOPLE的测试集上达到接近SOTA水平。
BertSoftmax的网络结构(原生BERT)：
Usage
本项目开源在实体识别项目：nerpy，可支持bert4ner模型，通过如下命令调用：
>>> from nerpy import NERModel
>>> model = NERModel("bert", "shibing624/bert4ner-base-chinese")
>>> predictions, raw_outputs, entities = model.predict(["常建良，男，1963年出生，工科学士，高级工程师"], split_on_space=False)
entities: [('常建良', 'PER'), ('1963年', 'TIME')]
模型文件组成：
bert4ner-base-chinese
├── config.json
├── model_args.json
├── pytorch_model.bin
├── special_tokens_map.json
├── tokenizer_config.json
└── vocab.txt
Usage (HuggingFace Transformers)
Without nerpy, you can use the model like this:
First, you pass your input through the transformer model, then you have to apply the bio tag to get the entity words.
Install package:
pip install transformers seqeval
import os
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification
from seqeval.metrics.sequence_labeling import get_entities
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("shibing624/bert4ner-base-chinese")
model = AutoModelForTokenClassification.from_pretrained("shibing624/bert4ner-base-chinese")
label_list = ['I-ORG', 'B-LOC', 'O', 'B-ORG', 'I-LOC', 'I-PER', 'B-TIME', 'I-TIME', 'B-PER']
sentence = "王宏伟来自北京，是个警察，喜欢去王府井游玩儿。"
def get_entity(sentence):
tokens = tokenizer.tokenize(sentence)
inputs = tokenizer.encode(sentence, return_tensors="pt")
with torch.no_grad():
outputs = model(inputs).logits
predictions = torch.argmax(outputs, dim=2)
char_tags = [(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].numpy())][1:-1]
print(sentence)
print(char_tags)
pred_labels = [i[1] for i in char_tags]
entities = []
line_entities = get_entities(pred_labels)
for i in line_entities:
word = sentence[i[1]: i[2] + 1]
entity_type = i[0]
entities.append((word, entity_type))
print("Sentence entity:")
print(entities)
get_entity(sentence)
output:
王宏伟来自北京，是个警察，喜欢去王府井游玩儿。
[('王', 'B-PER'), ('宏', 'I-PER'), ('伟', 'I-PER'), ('来', 'O'), ('自', 'O'), ('北', 'B-LOC'), ('京', 'I-LOC'), ('，', 'O'), ('是', 'O'), ('个', 'O'), ('警', 'O'), ('察', 'O'), ('，', 'O'), ('喜', 'O'), ('欢', 'O'), ('去', 'O'), ('王', 'B-LOC'), ('府', 'I-LOC'), ('井', 'I-LOC'), ('游', 'O'), ('玩', 'O'), ('儿', 'O'), ('。', 'O')]
Sentence entity:
[('王宏伟', 'PER'), ('北京', 'LOC'), ('王府井', 'LOC')]
训练数据集
中文实体识别数据集
| 数据集 | 语料 | 下载链接 | 文件大小 |
|---|---|---|---|
CNER中文实体识别数据集 | CNER(12万字) | CNER github | 1.1MB |
PEOPLE中文实体识别数据集 | 人民日报数据集（200万字） | PEOPLE github | 12.8MB |
CNER中文实体识别数据集，数据格式：
美 B-LOC
国 I-LOC
的 O
华 B-PER
莱 I-PER
士 I-PER
我 O
跟 O
他 O
如果需要训练bert4ner，请参考https://github.com/shibing624/nerpy/tree/main/examples
Citation
@software{nerpy,
author = {Xu Ming},
title = {nerpy: Named Entity Recognition toolkit},
year = {2022},
url = {https://github.com/shibing624/nerpy},
}