当想要找到具有最高 `start` 分数的标记时，torch.argmax() 中的 TypeError

Posted 2023-03-29

【中文标题】当想要找到具有最高 `start` 分数的标记时，torch.argmax() 中的 TypeError

【英文标题】：TypeError in torch.argmax() when want to find the tokens with the highest `start` score

【发布时间】：2021-11-13 07:20:25

【问题描述】：

我想运行此代码以使用拥抱面部转换器回答问题。

import torch
from transformers import BertForQuestionAnswering
from transformers import BertTokenizer

#Model
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')

#Tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')

question = '''Why was the student group called "the Methodists?"'''

paragraph = ''' The movement which would become The United Methodist Church began in the mid-18th century within the Church of England.
            A small group of students, including John Wesley, Charles Wesley and George Whitefield, met on the Oxford University campus.
            They focused on Bible study, methodical study of scripture and living a holy life.
            Other students mocked them, saying they were the "Holy Club" and "the Methodists", being methodical and exceptionally detailed in their Bible study, opinions and disciplined lifestyle.
            Eventually, the so-called Methodists started individual societies or classes for members of the Church of England who wanted to live a more religious life. '''
            
encoding = tokenizer.encode_plus(text=question,text_pair=paragraph)

inputs = encoding['input_ids']  #Token embeddings
sentence_embedding = encoding['token_type_ids']  #Segment embeddings
tokens = tokenizer.convert_ids_to_tokens(inputs) #input tokens

start_scores, end_scores = model(input_ids=torch.tensor([inputs]), token_type_ids=torch.tensor([sentence_embedding]))

start_index = torch.argmax(start_scores)

但我在最后一行收到此错误：

Exception has occurred: TypeError
argmax(): argument 'input' (position 1) must be Tensor, not str
  File "D:\bert\QuestionAnswering.py", line 33, in <module>
    start_index = torch.argmax(start_scores)

我不知道怎么了。谁能帮帮我？

【参考方案1】：

BertForQuestionAnswering 返回一个QuestionAnsweringModelOutput 对象。

由于您将BertForQuestionAnswering 的输出设置为start_scores, end_scores，因此返回的QuestionAnsweringModelOutput 对象被强制转换为字符串元组('start_logits', 'end_logits')，从而导致类型不匹配错误。

以下应该有效：

outputs = model(input_ids=torch.tensor([inputs]), token_type_ids=torch.tensor([sentence_embedding]))

start_index = torch.argmax(outputs.start_logits)

【参考方案2】：

Huggingface 转换器提供了一种运行模型的简单高级方法，如guide 所示：

from transformers import pipeline

nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
print(nlp(question=question, context=paragraph, topk=5))

topk 允许选择几个得分最高的答案。