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License: Apache License 2.0
Methods of training NLP models to ignored biased strategies
License: Apache License 2.0
Thanks for a great repository!
The HANS download token seems to be broken. Can you please fix it?
This is the exception I'm getting:
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce
Hi,
I had a question regarding the PyTorch implementation of LearnedMixin.
def forward(self, hidden, logits, bias, labels):
logits = logits.float() # In case we were in fp16 mode
logits = F.log_softmax(logits, 1)
factor = self.bias_lin.forward(hidden)
factor = factor.float()
factor = F.softplus(factor)
bias = bias * factor
bias_lp = F.log_softmax(bias, 1)
entropy = -(torch.exp(bias_lp) * bias_lp).sum(1).mean(0)
loss = F.cross_entropy(logits + bias, labels) + self.penalty*entropy
return loss
The forward function adds logits
and bias
variables, however, logits
has been log-softmaxed whereas bias
is not (bias
seems to be raw logits from bias-only model). Should we really apply log-softmax to logits
before sending into cross_entropy
loss? Could you explain the reasoning behind this?
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