我有:
from transformers import XLNetTokenizer, XLNetForQuestionAnswering
import torch
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetForQuestionAnswering.from_pretrained('xlnet-base-cased')
input_ids = torch.tensor(tokenizer.encode("What is my name?", add_special_tokens=True)).unsqueeze(0) # Batch size 1
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
loss = outputs[0]
print(outputs)
print(loss)
根据文件。这有助于:
(tensor(2.3008, grad_fn=<DivBackward0>),)
tensor(2.3008, grad_fn=<DivBackward0>)
然而,如果可能的话,我想要一个实际的答案
感谢Joe Davison提供了答案on Twitter:
答复如下:
不太对,但至少分数很低
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