def __init__(self,
path: str = None,
vocab: Vocab = None,
normalize: bool = False,
load_all=True,
mask_zero=True,
trainable=False,
name=None,
dtype=None,
dynamic=False,
**kwargs):
super().__init__(trainable, name, dtype, dynamic, **kwargs)
if load_all and vocab and vocab.locked:
vocab.unlock()
self.vocab, self.array_np = self._load(path, vocab, normalize)
self.vocab.lock()
self.array_ks = tf.keras.layers.Embedding(
input_dim=len(self.vocab),
output_dim=self.dim,
trainable=trainable,
embeddings_initializer=tf.keras.initializers.Constant(
self.array_np),
mask_zero=mask_zero)
self.mask_zero = mask_zero
self.supports_masking = mask_zero
def index_word2vec_with_vocab(filepath: str,
vocab: Vocab,
extend_vocab=True,
unk=None,
lowercase=False,
init='uniform',
normalize=None) -> torch.Tensor:
"""
Args:
filepath: The path to pretrained embedding.
vocab: The vocabulary from training set.
extend_vocab: Unlock vocabulary of training set to add those tokens in pretrained embedding file.
unk: UNK token.
lowercase: Convert words in pretrained embeddings into lowercase.
init: Indicate which initialization to use for oov tokens.
normalize: ``True`` or a method to normalize the embedding matrix.
Returns:
An embedding matrix.
"""
pret_vocab, pret_matrix = load_word2vec_as_vocab_tensor(filepath)
if unk and unk in pret_vocab:
pret_vocab[vocab.safe_unk_token] = pret_vocab.pop(unk)
if extend_vocab:
vocab.unlock()
for word in pret_vocab:
vocab.get_idx(word.lower() if lowercase else word)
vocab.lock()
ids = []
unk_id_offset = 0
for word, idx in vocab.token_to_idx.items():
word_id = pret_vocab.get(word, None)
# Retry lower case
if word_id is None:
word_id = pret_vocab.get(word.lower(), None)
if word_id is None:
word_id = len(pret_vocab) + unk_id_offset
unk_id_offset += 1
ids.append(word_id)
if unk_id_offset:
unk_embeds = torch.zeros(unk_id_offset, pret_matrix.size(1))
if init and init != 'zeros':
if init == 'uniform':
init = embedding_uniform
else:
raise ValueError(f'Unsupported init {init}')
unk_embeds = init(unk_embeds)
pret_matrix = torch.cat([pret_matrix, unk_embeds])
ids = torch.LongTensor(ids)
embedding = pret_matrix.index_select(0, ids)
if normalize == 'norm':
embedding /= (torch.norm(embedding, dim=1, keepdim=True) + 1e-12)
elif normalize == 'std':
embedding /= torch.std(embedding)
return embedding
