def __call__(self, batch):
inputs, targets, domains = self.get_inputs_and_targets(batch)
inputs = self.pad(inputs)
inputs = mktensor(inputs, device=self.device, dtype=torch.long)
targets = mktensor(targets, device=self.device, dtype=torch.long)
domains = mktensor(domains, device=self.device, dtype=torch.long)
return inputs, targets.to(self.device), domains.to(self.device)
def __call__(self, batch):
inputs, targets = self.get_inputs_and_targets(batch)
inputs, _, segments, attention_masks = self.pad_and_mask(inputs)
inputs = mktensor(inputs, device=self.device, dtype=torch.long)
targets = mktensor(targets, device=self.device, dtype=torch.long)
segments = mktensor(segments, device=self.device, dtype=torch.long)
attention_masks = mktensor(attention_masks,
device=self.device,
dtype=torch.long)
return inputs, targets.to(self.device), segments.to(
self.device), attention_masks.to(self.device)
def __call__(self, batch):
inputs, targets = map(list, zip(*batch))
lengths = torch.tensor([len(s) for s in inputs], device=self.device)
# Pad and convert to tensor
inputs = (pad_sequence(inputs,
batch_first=True,
padding_value=self.pad_indx).to(self.device))
targets = mktensor(targets, device=self.device, dtype=torch.long)
return inputs, targets.to(self.device), lengths
def __call__(self, batch):
inputs, titles, targets = map(list, zip(*batch))
number_of_sentences = torch.tensor([len(s) for s in inputs], device=self.device)
length_of_sentences = ([torch.tensor([len(s) for s in inp]) for inp in inputs])
inputs = [pad_sequence1(i, padding_len=150, batch_first=True, padding_value=0) for i in inputs]
# Pad and convert to tensor
inputs = (pad_sequence(inputs,
batch_first=True,
padding_value=self.pad_indx)
.to(self.device))
length_of_sentences = pad_sequence1(length_of_sentences, padding_len=inputs.shape[1], batch_first=True, padding_value=1)
lengths_title = torch.tensor([len(t) for t in titles], device=self.device)
titles = (pad_sequence(titles,
batch_first=True,
padding_value=self.pad_indx)
.to(self.device))
targets = mktensor(targets, device=self.device, dtype=torch.long)
return inputs, titles, targets.to(self.device), number_of_sentences, length_of_sentences, lengths_title
def __call__(self, x):
return mktensor(x, device=self.device, dtype=self.dtype)
