如何应用来自 huggingface 的预训练变压器模型?

How to apply a pretrained transformer model from huggingface?

我有兴趣使用 Huggingface 的预训练模型进行命名实体识别 (NER) 任务,而无需进一步训练或测试模型。

model page of HuggingFace上,重复使用模型的唯一信息如下:

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")

我尝试了以下代码,但我得到的是张量输出,而不是每个命名实体的 class 标签。

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")

text = "my text for named entity recognition here."

input_ids = torch.tensor(tokenizer.encode(text, padding=True, truncation=True,max_length=50, add_special_tokens = True)).unsqueeze(0)

with torch.no_grad():
  output = model(input_ids, output_attentions=True)

关于如何将模型应用于 NER 文本的任何建议?

您要找的命名实体识别pipeline(token分类):

from transformers import AutoTokenizer, pipeline,  AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModelForTokenClassification.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
nerpipeline = pipeline('ner', model=model, tokenizer=tokenizer)
text = "my text for named entity recognition here."
nerpipeline(text)

输出:

[{'word': 'my',
  'score': 0.5209763050079346,
  'entity': 'LABEL_0',
  'index': 1,
  'start': 0,
  'end': 2},
 {'word': 'text',
  'score': 0.5161970257759094,
  'entity': 'LABEL_0',
  'index': 2,
  'start': 3,
  'end': 7},
 {'word': 'for',
  'score': 0.5297629237174988,
  'entity': 'LABEL_1',
  'index': 3,
  'start': 8,
  'end': 11},
 {'word': 'named',
  'score': 0.5258920788764954,
  'entity': 'LABEL_1',
  'index': 4,
  'start': 12,
  'end': 17},
 {'word': 'entity',
  'score': 0.5415489673614502,
  'entity': 'LABEL_1',
  'index': 5,
  'start': 18,
  'end': 24},
 {'word': 'recognition',
  'score': 0.5396601557731628,
  'entity': 'LABEL_1',
  'index': 6,
  'start': 25,
  'end': 36},
 {'word': 'here',
  'score': 0.5165827870368958,
  'entity': 'LABEL_0',
  'index': 7,
  'start': 37,
  'end': 41},
 {'word': '.',
  'score': 0.5266348123550415,
  'entity': 'LABEL_0',
  'index': 8,
  'start': 41,
  'end': 42}]

请注意,您需要使用 AutoModelForTokenClassification 而不是 AutoModel,并且并非所有模型都有经过训练的标记分类头(即,您将获得标记分类头的随机权重)。