def prepare_sequence(seq, to_ix):
    idxs = [to_ix[w] for w in seq]
    return torch.tensor(idxs, dtype=torch.long)


training_data = dataset[0:900]

word_to_ix = {}
for sent, tags in dataset:
    for word in sent:
        if word not in word_to_ix:
            word_to_ix[word] = len(word_to_ix)

tag_to_ix = {}

for ix, tag in enumerate(unique_tags):
    tag_to_ix[tag] = ix


EMBEDDING_DIM = 64
HIDDEN_DIM = 64