def unwrap_sentence(self, sentences, transitions):
# Build Tokens
x = sentences
# Build Transitions
t = transitions
example = Example()
example.tokens = to_gpu(Variable(torch.from_numpy(x), volatile=not self.training))
example.transitions = t
return example
def build_example(self, sentences, transitions):
batch_size = sentences.shape[0]
# Build Tokens
x = sentences
# Build Transitions
t = transitions
example = Example()
example.tokens = to_gpu(Variable(torch.from_numpy(x), volatile=not self.training))
example.transitions = t
return example
def unwrap_sentence_pair(self, sentences, transitions):
# Build Tokens
x_prem = sentences[:, :, 0]
x_hyp = sentences[:, :, 1]
x = np.concatenate([x_prem, x_hyp], axis=0)
# Build Transitions
t_prem = transitions[:, :, 0]
t_hyp = transitions[:, :, 1]
t = np.concatenate([t_prem, t_hyp], axis=0)
example = Example()
example.tokens = to_gpu(Variable(torch.from_numpy(x), volatile=not self.training))
example.transitions = t
return example
def build_example(self, sentences, transitions):
batch_size = sentences.shape[0]
# sentences: (#batches, #feature, #2)
# Build Tokens
x_prem = sentences[:,:,0]
x_hyp = sentences[:,:,1]
x = np.concatenate([x_prem, x_hyp], axis=0)
# Build Transitions
t_prem = transitions[:,:,0]
t_hyp = transitions[:,:,1]
t = np.concatenate([t_prem, t_hyp], axis=0)
example = Example()
example.tokens = to_gpu(Variable(torch.from_numpy(x), volatile=not self.training))
example.transitions = t
return example