jihoonkimharu/bert-base-klue-mrc-finetuned

Cómo usar el modelo

from transformers import BertTokenizer, BertForQuestionAnswering
import torch

# Cargar el modelo preentrenado y el tokenizador
model_name = 'jihoonkimharu/bert-base-klue-mrc-finetuned'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForQuestionAnswering.from_pretrained(model_name)

# Ejemplo de texto y pregunta
text = "\uC5F0\uC548\uC9C0 \uC804\uBB38\uB300\uD559\uC758 \uC774\uB984\uC740 \uBB34\uC5C7\uC778\uC9C0?"
question = "\uC5F0\uC548\uC9C0\uC758 \uD559\uAD50\uC774\uB984\uC740 \uBB34\uC5C7\uC778\uAC00?"
input_ids = tokenizer.encode(question, text)

# Realizar la inferencia
outputs = model(torch.tensor([input_ids]))
start_scores = outputs.start_logits
ebd_scores = outputs.end_logits

# Decodificar la respuesta
all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
print(f'Respuesta: {answer}')