def get_data(self, datasets):
x_lst = []
y_lst = []
for dataset in datasets:
for batch in dataset:
x_lst.append(self.transform(batch.text))
y_lst.append(batch.label.values)
X = torch.cat(x_lst)
Y = torch.cat(y_lst)
return X, Y
def forward(self, batch):
batch = batch.transpose(0, 1)
embeddings = self.dropout(self.embedding(batch))
_, (hidden, _) = self.rnn(embeddings)
hidden = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)
return self.out(self.dropout(hidden).squeeze(0))
def transform(self, batch):
individual_results = [model(batch) for model in self.pretrained_models]
x = torch.stack(individual_results, dim=1)
batch = batch.values.transpose(0, 1)
if self.include_bag_of_words:
bags_of_words = []
for sent in batch:
bags_of_words.append(
torch.bincount(sent, minlength=len(TEXT.vocab)))
bags_of_words = torch.stack(bags_of_words).float()
x = torch.cat((x, bags_of_words), dim=1)
if self.include_cbow:
cbows = []
for sent in batch:
cbows.append(EMBEDDINGS[sent].sum(dim=0))
cbows = torch.stack(cbows)
x = torch.cat((x, cbows), dim=1)
return x
def get_data(self, datasets):
x_lst = []
y_lst = []
for dataset in datasets:
for batch in dataset:
sentences = batch.text.transpose('batch',
'seqlen').values.clone()
y_lst.append(batch.label.values.float())
for sent in sentences:
x_lst.append(self.transform(sent))
X = torch.stack(x_lst)
Y = torch.cat(y_lst)
return (X, Y)
def formatBatch(self, batch):
samples_list = []
results_list = []
num_words = 0
for i in range(len(batch)):
sent = batch.text[{"batch": i}].values.data
targets = batch.target.get("batch", i).values.data
num_words += len(sent)
padded_sent = torch.nn.functional.pad(
sent, (self.n - 2, BATCHSIZE - len(sent)), value=0)
nextwords = torch.nn.functional.pad(targets,
(0, BATCHSIZE - len(targets)),
value=0)
samples_list.append(padded_sent)
results_list.append(nextwords)
allsamples = torch.stack(samples_list)
allresults = torch.cat(results_list)
return (allsamples, allresults)