bert-base-spanish-wwm-cased-finetuned-squad2-es-finetuned-sqac

# Ejemplo de uso
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
import torch

model_name = 'MMG/bert-base-spanish-wwm-cased-finetuned-squad2-es-finetuned-sqac'
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def answer_question(question, context):
    # Tokenización de entrada
    inputs = tokenizer.encode_plus(question, context, return_tensors='pt')
    input_ids = inputs['input_ids'].tolist()[0]
    # Obtener puntuaciones de respuesta
    outputs = model(**inputs)
    answer_start_scores = outputs.start_logits
    answer_end_scores = outputs.end_logits
    # Obtener índices de tokens de respuesta
    answer_start = torch.argmax(answer_start_scores)
    answer_end = torch.argmax(answer_end_scores) + 1
    # Convertir tokens a la respuesta final
    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
    return answer

context = 'Tu contexto aquí'
question = 'Tu pregunta aquí'
print(answer_question(question, context))