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.index_to_key = list(vocab.keys())
kv.key_to_index = {
word: Vocab(index=word_id, count=0)
for word, word_id in vocab.items()
}
self.embedding = kv
