def main():
train, dev = datasets.imdb()
train = train[:5000]
dev = dev[:5000]
train_X, train_y = zip(*train)
dev_X, dev_y = zip(*dev)
model = LinearModel(2)
train_y = to_categorical(train_y, nb_classes=2)
After Change
for doc in nlp.pipe(train_X)]
dev_X = [model.ops.asarray([tok.orth for tok in doc], dtype="uint64")
for doc in nlp.pipe(dev_X)]
dev_X = preprocess(model.ops, dev_X)
with model.begin_training(train_X, train_y, L2=1e-6) as (trainer, optimizer):
trainer.dropout = 0.0
trainer.batch_size = 512
trainer.nb_epoch = 3
