letor-part-1-model-skipgrams.py

def make_skipgrams():
    train_x, all_labels = [], []
    for sequence in sequences:
        pairs, labels = tf.keras.preprocessing.sequence.skipgrams(
            sequence, VOCAB_SIZE, negative_samples=1.0, window_size=1, shuffle=True
        )
        train_x.extend(pairs)
        all_labels.extend(labels)

    train_x = np.array(train_x)
    all_labels = np.array(all_labels, dtype=np.float32)
    
    content_words = train_x[:, 0]
    context_words = train_x[:, 1]
    
    return content_words, context_words, all_labels