This example shows how the sequences_scores are computed from transition_scores :
Look at the reconstructed loss
# If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
# Tip: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
# use case, you might want to recompute it with `normalize_logits=True`.
output_length = input_length + np.sum(transition_scores.numpy() < 0, axis=1)
length_penalty = model.generation_config.length_penalty
reconstructed_scores = transition_scores.sum(axis=1) / (output_length**length_penalty)
print(np.allclose(outputs.sequences_scores, reconstructed_scores))