The gensim.models.Word2vec class implements the SG and CBOW architectures introduced previously. The Word2vec notebook contains additional implementation detail.

To facilitate memory-efficient text ingestion, the LineSentence class creates a generator from individual sentences contained in the provided text file:

sentence_path = Path('data', 'ngrams', f'ngrams_2.txt')
sentences = LineSentence(sentence_path)

The Word2vec class offers the configuration options previously introduced:

model = Word2vec(sentences,
                 sg=1,    # 1=skip-gram; otherwise CBOW
                 hs=0,    # hier. softmax if 1, neg. sampling if 0
                 size=300,      # Vector dimensionality
                 window=3,      # Max dist. btw target and context word
                 min_count=50,  # Ignore words with lower frequency
                 negative=10,  # noise word count for negative sampling
                 workers=8,     # no threads 
                 iter=1,        # no epochs = iterations over corpus
                 alpha=0.025,   # initial learning rate
                 min_alpha=0.0001 # final learning rate
                )

The notebook shows how to persist and reload models to continue training, or how to store the embedding vectors separately, for example, for use in ML models.