使用 huggingface 库会报错：KeyError: 'logits'

Posted 2023-03-29

【中文标题】使用 huggingface 库会报错：KeyError: \'logits\'

【英文标题】：Using huggingface library gives an error: KeyError: 'logits'使用 huggingface 库会报错：KeyError: 'logits'

【发布时间】：2022-01-13 14:21:32

【问题描述】：

我是 huggingface 库的新手，正在尝试运行一个模型来执行掩码语言（“填充掩码”任务）：

from transformers import BertTokenizer, BertForMaskedLM
import torch
from transformers import pipeline, AutoTokenizer, AutoModel

# Initialize MLM pipeline
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")

print(len(tokenizer.vocab))
>>> 28996

但是当我尝试获取令牌的概率时，我得到了一个错误：

classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer)
results = classifier("Paris is the [MASK] of France.")

>>>KeyError                                  Traceback (most recent call last)
<ipython-input-15-30c429f29424> in <module>()
      1 classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer)
----> 2 results = classifier("Paris is the [MASK] of France.")

4 frames
/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py in __getitem__(self, k)
   2041         if isinstance(k, str):
   2042             inner_dict = k: v for (k, v) in self.items()
-> 2043             return inner_dict[k]
   2044         else:
   2045             return self.to_tuple()[k]

KeyError: 'logits'

我还尝试了其他教程中的以下内容并得到了相同的错误：

mlm = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Get mask token
mask = mlm.tokenizer.mask_token

# Get result for particular masked phrase
phrase = f'Paris is the [MASK] of France.'
result = mlm(phrase, top_k=10000)

# Print result
print(result)

【参考方案1】：

您以错误的方式使用 管道。您应该只将 model_name 提供给模型参数。 应该是这样的：

model_name = "emilyalsentzer/Bio_ClinicalBERT"
classifier = pipeline("fill-mask", model=model_name, tokenizer=tokenizer)
results = classifier("Paris is the [MASK] of France.")

如果你想查看结果

for i in range(len(results)):
  print(f"the i-th result=results[i]['token_str'] has score results[i]['score']")

这将是

the 0-th result=cause has score 0.1672661453485489
the 1-th result=site has score 0.14680784940719604
the 2-th result=source has score 0.12052636593580246
the 3-th result=area has score 0.07053395360708237
the 4-th result=sign has score 0.05601896718144417

所以我不确定您使用的模型是否是预测 [MASK] 部分的好选择。