def rouge_decoding(ids, model_params): """ To convert the IDs to text using the vocab and SP encoder. :param ids: [B,T] tensor of ids in vocab :param model_params: the defined model parameters :return: decoded text removed from graph (stop grad) """ decode_text_tensor = public_parsing_ops.decode(ids, model_params.vocab_filename, model_params.encoder_type) decode_text = tf.stop_gradient(decode_text_tensor[0]) return decode_text
def decode_host_call(tensor_dict): for key in decode_keys: predictions[key] = public_parsing_ops.decode( tensor_dict[key], model_params.vocab_filename, model_params.encoder_type) return tensor_dict
def test_tf_decode(self, encoder_type): string = tf.constant(["the quick brown fox.", "the quick brown\n"]) ids = parsing_ops.encode(string, 10, _SPM_VOCAB, encoder_type) self.assertAllEqual( parsing_ops.decode(ids, _SPM_VOCAB, encoder_type), public_parsing_ops.decode(ids, _SPM_VOCAB, encoder_type))