app.py

import torch
from transformers import AutoTokenizer, BigBirdForQuestionAnswering
from datasets import load_dataset

tokenizer = AutoTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdForQuestionAnswering.from_pretrained("google/bigbird-roberta-base")
squad_ds = load_dataset("squad_v2", split="train")
# select random article and question
LONG_ARTICLE = squad_ds[81514]["context"]
QUESTION = squad_ds[81514]["question"]
QUESTION

inputs = tokenizer(QUESTION, LONG_ARTICLE, return_tensors="pt")
# long article and question input
list(inputs["input_ids"].shape)

with torch.no_grad():
    outputs = model(**inputs)

answer_start_index = outputs.start_logits.argmax()
answer_end_index = outputs.end_logits.argmax()
predict_answer_token_ids = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
predict_answer_token = tokenizer.decode(predict_answer_token_ids)