generate_answers.py

from transformers import BertTokenizer, BertForQuestionAnswering
import torch

model_path = "D:/code/bert_easy/bert-large-uncased-whole-word-masking-finetuned-squad"
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForQuestionAnswering.from_pretrained(model_path)

def get_answer(question, context):
    """Answers a question using BERT on given context."""
    inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    start = torch.argmax(outputs.start_logits)
    end = torch.argmax(outputs.end_logits) + 1
    return tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start:end]))