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使用预训练语言模型BERT做中文NER
| Date | Stars |
|---|---|
| 2026-07-24 | 973 |
| 2026-07-25 | 973 |
| 2026-07-28 | 973 |
| 2026-07-30 | 973 |
| 2026-08-06 | 973 |
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# bert-chinese-ner ## 前言 使用预训练语言模型BERT做中文NER尝试，fine - tune BERT模型 PS: 移步最新[**albert fine-tune ner**](https://github.com/ProHiryu/albert-chinese-ner)模型 ## 代码参考 - [BERT-NER](https://github.com/kyzhouhzau/BERT-NER) - [BERT-TF](https://github.com/google-research/bert) ## 使用方法 从[BERT-TF](https://github.com/google-research/bert)下载bert源代码，存放在路径下bert文件夹中 从[BERT-Base Chinese](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)下载模型，存放在checkpoint文件夹下 使用BIO数据标注模式，使用人民日报经典数据 train： `python BERT_NER.py --data_dir=data/ --bert_config_file=checkpoint/bert_config.json --init_checkpoint=checkpoint/bert_model.ckpt --vocab_file=vocab.txt --output_dir=./output/result_dir/` ## 结果 经过100个epoch跑出来的结果 ``` eval_f = 0.9662649 eval_precision = 0.9668882 eval_recall = 0.9656949 global_step = 135181 loss = 40.160034 ``` 测试结果第一句： 
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matched fp:ce4a4c047fd7956e, topic:tensorflow