Use google BERT to do SQuAD !
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
- python3
- pip3 install -r requirements.txt
Pretrained model download from here
unzip and move files to model directory
from bert import QA
model = QA('model')
doc = "Victoria has a written constitution enacted in 1975, but based on the 1855 colonial constitution, passed by the United Kingdom Parliament as the Victoria Constitution Act 1855, which establishes the Parliament as the state's law-making body for matters coming under state responsibility. The Victorian Constitution can be amended by the Parliament of Victoria, except for certain 'entrenched' provisions that require either an absolute majority in both houses, a three-fifths majority in both houses, or the approval of the Victorian people in a referendum, depending on the provision."
q = 'When did Victoria enact its constitution?'
answer = model.predict(doc,q)
print(answer['answer'])
# 1975
print(ans.keys())
# dict_keys(['answer', 'start', 'end', 'confidence', 'document']))model.predict(doc,q) return dict
{
"answer" : "answer text",
"start" : "start index",
"end" : "end index",
"confiednce" : "confidence of answer",
"document" : "tokenzied document , list"
}