def test_compare_word2vec_glove(self):
token2idx_word2vec, embedding_matrix_word2vec = load_word2vec(
path="tests/data/word2vec.wv")
token2idx_glove, embedding_matrix_glove = load_glove(
path="tests/data/glove.txt")
self.assertDictEqual(token2idx_word2vec, token2idx_glove)
self.assertTrue(
embedding_matrix_word2vec.shape == embedding_matrix_glove.shape)
def test_load_glove_without_pad_unk(self):
token2idx, embedding_matrix = load_glove(
path="tests/data/glove.txt",
add_pad=False,
add_unk=False,
)
self.assertEqual(len(token2idx), 8)
self.assertEqual(len(token2idx), embedding_matrix.shape[0])
self.assertEqual(embedding_matrix.shape[-1], 100)
self.assertTrue("<PAD>" not in token2idx)
self.assertTrue("<UNK>" not in token2idx)
def test_load_glove_without_unk(self):
token2idx, embedding_matrix = load_glove(
path="tests/data/glove.txt",
add_unk=False,
)
self.assertEqual(len(token2idx), 9)
self.assertEqual(len(token2idx), embedding_matrix.shape[0])
self.assertEqual(embedding_matrix.shape[-1], 100)
self.assertEqual(token2idx["<PAD>"], 0)
self.assertTrue("<UNK>" not in token2idx)
self.assertTrue(
np.allclose(embedding_matrix[0],
np.zeros_like(embedding_matrix[0])), )
def test_load_glove(self):
token2idx, embedding_matrix = load_glove(path="tests/data/glove.txt")
self.assertEqual(len(token2idx), 10)
self.assertEqual(len(token2idx), embedding_matrix.shape[0])
self.assertEqual(embedding_matrix.shape[-1], 100)
self.assertEqual(token2idx["<PAD>"], 0)
self.assertEqual(token2idx["<UNK>"], 1)
self.assertTrue(
np.allclose(embedding_matrix[0],
np.zeros_like(embedding_matrix[0])), )
self.assertTrue(
np.allclose(embedding_matrix[1],
embedding_matrix[2:].mean(axis=0)), )
Embedding,
EmbeddingPreTrained,
EmbeddingWithDropout,
load_glove,
load_word2vec,
)
from pytorch_ner.prepare_data import prepare_conll_data_format
token_seq, _ = prepare_conll_data_format(path="tests/data/conll.txt",
sep=" ",
verbose=False)
tokens = list(set(token for sentence in token_seq for token in sentence))
_, word2vec_embeddings = load_word2vec(
path="tests/data/word2vec.wv") # type: ignore
_, glove_embeddings = load_glove(path="tests/data/glove.txt") # type: ignore
embedding_w2v_freeze = EmbeddingPreTrained(word2vec_embeddings)
embedding_w2v_fine_tune = EmbeddingPreTrained(word2vec_embeddings,
freeze=False)
embedding_glove_freeze = EmbeddingPreTrained(glove_embeddings)
embedding_glove_fine_tune = EmbeddingPreTrained(glove_embeddings, freeze=False)
random_embedding_with_spatial_dropout = EmbeddingWithDropout(
embedding_layer=Embedding(num_embeddings=2000, embedding_dim=128),
dropout=SpatialDropout1d(p=0.5),
)
emb = random_embedding_with_spatial_dropout(
torch.randint(low=0, high=2000, size=(10, 20)), )