def file_based_convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer, output_file):
        """Convert a set of `InputExample`s to a TFRecord file."""

        writer = tf.python_io.TFRecordWriter(output_file)

        for (ex_index, example) in enumerate(examples):
            if ex_index % 10000 == 0:
                tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

            feature = self.convert_single_example(ex_index, example, label_list,
                                                  max_seq_length, tokenizer)

            def create_int_feature(values):
                f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
                return f

            features = collections.OrderedDict()
            features["input_ids"] = create_int_feature(feature.input_ids)
            features["input_mask"] = create_int_feature(feature.input_mask)
            features["segment_ids"] = create_int_feature(feature.segment_ids)
            features["label_ids"] = create_int_feature([feature.label_id])

            tf_example = tf.train.Example(features=tf.train.Features(feature=features))
            writer.write(tf_example.SerializeToString())
Convert a set of `InputExample`s to a TFRecord file.
similarity.py