def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch,
encode_config):
embed_config["width"] = width
encode_config["width"] = width
docs = get_batch(3)
tok2vec = build_Tok2Vec_model(embed_arch(**embed_config),
encode_arch(**encode_config))
tok2vec.initialize(docs)
vectors, backprop = tok2vec.begin_update(docs)
assert len(vectors) == len(docs)
assert vectors[0].shape == (len(docs[0]), width)
backprop(vectors)
def test_tok2vec_batch_sizes(batch_size, width, embed_size):
batch = get_batch(batch_size)
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=width,
rows=[embed_size] * 4,
include_static_vectors=False,
attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
),
MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
)
tok2vec.initialize()
vectors, backprop = tok2vec.begin_update(batch)
assert len(vectors) == len(batch)
for doc_vec, doc in zip(vectors, batch):
assert doc_vec.shape == (len(doc), width)
def test_empty_doc():
width = 128
embed_size = 2000
vocab = Vocab()
doc = Doc(vocab, words=[])
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=width,
rows=[embed_size, embed_size, embed_size, embed_size],
include_static_vectors=False,
attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
),
MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
)
tok2vec.initialize()
vectors, backprop = tok2vec.begin_update([doc])
assert len(vectors) == 1
assert vectors[0].shape == (0, width)
