classification task-head for transformer

transform_stance.py

def transform_stance(X1):
    # Input transform for classification task-head
    n_batch = len(X1)
    xmb = np.zeros((n_batch, 1, n_ctx, 2), dtype=np.int32)
    mmb = np.zeros((n_batch, 1, n_ctx), dtype=np.float32)
    start = encoder['_start_']
    for i, x1 in enumerate(X1):
        x12 = [start] + x1[:max_len] + [clf_token]
        l12 = len(x12)
        xmb[i, 0, :l12, 0] = x12
        mmb[i, 0, :l12] = 1
    # Position information that is added to the input embeddings in the TransformerModel
    xmb[:, :, :, 1] = np.arange(n_vocab + n_special, n_vocab + n_special + n_ctx)
return xmb, mmb