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医疗领域中文命名实体识别

项目地址:https://github.com/iioSnail/chinese_medical_ner

使用方法:

from transformers import AutoModelForTokenClassification, BertTokenizerFast

tokenizer = BertTokenizerFast.from_pretrained('iioSnail/bert-base-chinese-medical-ner')
model = AutoModelForTokenClassification.from_pretrained("iioSnail/bert-base-chinese-medical-ner")

sentences = ["瘦脸针、水光针和玻尿酸详解!", "半月板钙化的病因有哪些?"]
inputs = tokenizer(sentences, return_tensors="pt", padding=True, add_special_tokens=False)
outputs = model(**inputs)
outputs = outputs.logits.argmax(-1) * inputs['attention_mask']

print(outputs)

输出结果:

tensor([[1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 4, 4],
        [1, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0]])

其中 1=B, 2=I, 3=E, 4=O1, 3表示一个二字医疗实体,1,2,3表示一个3字医疗实体, 1,2,2,3表示一个4字医疗实体,依次类推。

可以使用项目中的MedicalNerModel.format_outputs(sentences, outputs)来将输出进行转换。

效果如下:

[
  [
    {'start': 0, 'end': 3, 'word': '瘦脸针'},
    {'start': 4, 'end': 7, 'word': '水光针'},
    {'start': 8, 'end': 11, 'word': '玻尿酸'}、
  ],
  [
    {'start': 0, 'end': 5, 'word': '半月板钙化'}
  ]
]

更多信息请参考项目:https://github.com/iioSnail/chinese_medical_ner

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