def __call__(self, batch):
myid, text_input, is_humor = map(list, zip(*batch))
input_lengths = torch.tensor(
[len(s) for s in text_input], device=self.device)
# attention mask
max_length = max(input_lengths)
inputs_pad_mask = pad_mask(input_lengths, max_length=max_length,
device=self.device)
# Pad inputs and targets
padded_inputs = (
pad_sequence(text_input, batch_first=True,
padding_value=self.pad_indx)
.to(self.device))
return myid, padded_inputs, inputs_pad_mask
def __call__(self, batch):
myid, text_input, humor_rating,humor_contr = map(list, zip(*batch))
input_lengths = torch.tensor(
[len(s) for s in text_input], device=self.device)
# attention mask
max_length = max(input_lengths)
inputs_pad_mask = pad_mask(input_lengths, max_length=max_length,
device=self.device)
# Pad inputs and targets
padded_inputs = (
pad_sequence(text_input, batch_first=True,
padding_value=self.pad_indx)
.to(self.device))
humor_rating = mktensor(humor_rating, dtype=torch.float)
humor_contr = mktensor(humor_contr, dtype=torch.long)
return padded_inputs,inputs_pad_mask, humor_rating, humor_contr