def create_keyedvector_from_matrix(self, embedding_matrix, word2id):
"""
Imports the necessary attributes for the Embedding object from an embedding matrix and a word2id vocabulary. Can be used for custom pre-trained embeddings.
Parameters
----------
embedding_matrix: numpy.ndarray
Embedding matrix as a numpy object
word2id: dict
Word vocabulary (key: word, value: word_index)
"""
vocab = {
word: word2id[word]
for word in sorted(word2id, key=word2id.__getitem__, reverse=False)
}
embedding_matrix = embedding_matrix
vector_size = embedding_matrix.shape[1]
kv = KeyedVectors(vector_size)
kv.vector_size = vector_size
kv.vectors = embedding_matrix
kv.index2word = list(vocab.keys())
kv.vocab = {
word: Vocab(index=word_id, count=0)
for word, word_id in vocab.items()
}
self.embedding = kv
def _set_keyedvector(self, attrname, keys, dim, vec=None):
keyed_vec = KeyedVectors(dim)
dummy_max_count = len(keys) + 1
for i, key in enumerate(keys):
key = str(key)
keyed_vec.vocab[key] = Vocab(index=i, count=dummy_max_count - i) # dummy count
keyed_vec.index2word.append(key)
if vec is not None:
keyed_vec.vectors = vec
keyed_vec.init_sims()
setattr(self, attrname, keyed_vec)
