indexer.index_dataset(sentences)
train_sentences = sentences[:-1000]
dev_sentences = sentences[-1000:]
train_dataset = RuPosDataset(train_sentences, indexer, device)
train_sampler = RandomSampler(train_dataset)
train_iterator = DataLoader(train_dataset,
batch_size=256,
sampler=train_sampler,
collate_fn=train_dataset.collate_fn)
dev_dataset = RuPosDataset(dev_sentences, indexer, device)
dev_sampler = SequentialSampler(dev_dataset)
dev_iterator = DataLoader(dev_dataset,
batch_size=256,
sampler=dev_sampler,
collate_fn=dev_dataset.collate_fn)
embeddings = load_embeddings(indexer.token_vocab, 'data/cc.ru.300.vec')
model = SimpleTagger(output_dim=len(indexer.pos_vocab),
embedding_matrix=embeddings)
model.to(device)
trainer = Trainer(model, train_iterator, dev_iterator)
for i in range(20):
print('Epoch: %d' % (i + 1))
trainer.train_epoch()
trainer.test_epoch()
