Reference

Execution procedure (Question Answering on BERT-large)

1. Install required python package.
2. If you want to try out the model and tokenizer specifications, or visualize the start and end scores for every word, run

python demo.py

3. To do QA,　Pass the question string and reference string to the answer_question function in predict.py and execute it. The answer comes back. The model and tokenizer used here are automatically downloaded at the first startup (Click here for model details).

ex.)

import textwrap
from bert.predict import answer_question


wrapper = textwrap.TextWrapper(width=80)

bert_abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
print(f"bert_abstract(reference): \n{wrapper.fill(bert_abstract)}\n")

questions = [
    "What does the 'B' in BERT stand for?",
    "What are some example applications of BERT?",
    "What are some example applications of BEßRT?"
]
for question in questions:
    print(f"question: {question}")
    answer = answer_question(question, bert_abstract)
    print(f"answer: {answer}\n")

If you want to change the model or tokenizer, refer to here and pass each name as an argument and execute it.