使用 BERT 迭代多个文件以进行 QA returns 无

Question

我想减轻我的工作。我需要对 BERT 为我提供的数千个文件的答案进行一些分析。我的主要 objective 是遍历每个文件并提出问题。

我一直在尝试使用以下代码使其自动化

import os

directory = '/content/dva/'

for filename in os.listdir(directory):
with open(directory + filename) as infile:
    try:

      nlp({
    'question': 'How is artificial intelligence being used in real time health delivery?',
    'context': data
})
    except:
        print(filename + ' is throwing an error')

上面的代码returns没什么。但是，如果我一个一个地做。它工作正常。所以我试着改变它。

x = ["How is artificial intelligence being used in real time health delivery?",\
     "What adjunctive or supportive methods can help patients?",\
     "How does hypertension affect patients?",\
      "What does the computer do?"]

y = [item.strip() for item in x]

def testing(theList):
  nlp = pipeline('question-answering')

  for each_element in theList:
    nlp({'question': each_element,'context': data})


  

testing(y) # returns nothing
print(testing(y)) # returns None

有没有人有任何见解？上面的代码非常适用于艾伦的 ELMo。

Answer 1

出于某种原因，当遍历所有文件时，print() 实际上会 return 给出答案。这很奇怪，因为通常你不需要调用 print 来让它工作。

工作代码：

import os

directory = '/content/dva/'

for filename in os.listdir(directory):
with open(directory + filename) as infile:
try:

  print(nlp({
'question': 'How is artificial intelligence being used in real time health delivery?',
'context': data
}))
    except:
        print(filename + ' is throwing an error')

使用 BERT 迭代多个文件以进行 QA returns 无

Iterating through multiple files with BERT for QA returns nothing

python

nlp

bert-language-model

elmo