Comments (2)
Thank you! I'm glad you find this helpful.
Like we discussed in #16 , those numbers are the logits. And they are also averaged over all the examples, I printed it out to get some sense of what are the choice scores like. If you want to know the probability the model assign to each answer choice, you can save the choice_scores
tensor before pred_score, prediction = choices_scores.min(dim=1)
in the predict(self, batch)
function of EncoderDecoder.py. And then multiply by -1 and do softmax with dim=1.
(JIC, don't forget to .detach() and .cpu() )
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Thank you so much for your quick reply! I just asked because in some cases I detected the obtained probability was slightly above 1 (when naively using the torch.exp(-score)
). I figured it could be because we used half-precision (16 bits). I'll use softmax instead! :)
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