def _train_model(self, model: Word2Vec, texts):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
texts_seq = tokenizer.sequences_to_texts(
tokenizer.texts_to_sequences(texts))
texts_seq = [f.split(" ") for f in texts_seq]
print("Adding to word2vec vocabulary...")
model.min_count = 2
model.build_vocab(texts_seq, update=True)
print("Training word2vec ...")
model.train(texts_seq,
total_examples=len(texts_seq),
epochs=model.epochs)
def train_model_w2vec(
sentences: Iterable,
model: Word2Vec,
trigram_model_path: Path,
save_model_path: Path,
epochs: int,
):
"""Entrena Word2Vec."""
# TODO: Agregar kwargs para pasarlo a los metodos de gensim
trigram = Phraser.load(str(trigram_model_path))
sentences = trigram[sentences]
model.build_vocab(sentences, progress_per=10000)
model.train(
sentences, total_examples=model.corpus_count, epochs=epochs
)
model.init_sims(replace=True)
model.save(str(save_model_path))
def train(model: Word2Vec, word_lists, num_epoches: int = None):
model.build_vocab(word_lists, progress_per=10000)
model.train(word_lists, total_examples=len(word_lists), epochs=num_epoches)
return model
