sjrhuschlee/deberta-v3-base-squad2-ext-v1

import torch
from transformers import(
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    pipeline
)
model_name = "sjrhuschlee/deberta-v3-base-squad2-ext-v1"

# a) Usando pipelines
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
qa_input = {
    'question': '¿Dónde vivo?',
    'context': 'Mi nombre es Sarah y vivo en Londres'
}
res = nlp(qa_input)
# {'score': 0.984, 'start': 30, 'end': 37, 'answer': 'Londres'}

# b) Cargar modelo y tokenizador
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

question = '¿Dónde vivo?'
context = 'Mi nombre es Sarah y vivo en Londres'
encoding = tokenizer(question, context, return_tensors="pt")
start_scores, end_scores = model(
    encoding["input_ids"],
    attention_mask=encoding["attention_mask"],
    return_dict=False
)

all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
# 'Londres'