def convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer):
        """Convert a set of `InputExample`s to a list of `InputFeatures`."""

        for (ex_index, example) in enumerate(examples):
            label_map = {}
            for (i, label) in enumerate(label_list):
                label_map[label] = i

            tokens_a = tokenizer.tokenize(example.text_a)
            tokens_b = None
            if example.text_b:
                tokens_b = tokenizer.tokenize(example.text_b)

            if tokens_b:
                # Modifies `tokens_a` and `tokens_b` in place so that the total
                # length is less than the specified length.
                # Account for [CLS], [SEP], [SEP] with "- 3"
                self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
            else:
                # Account for [CLS] and [SEP] with "- 2"
                if len(tokens_a) > max_seq_length - 2:
                    tokens_a = tokens_a[0:(max_seq_length - 2)]

            # The convention in BERT is:
            # (a) For sequence pairs:
            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
            #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
            # (b) For single sequences:
            #  tokens:   [CLS] the dog is hairy . [SEP]
            #  type_ids: 0     0   0   0  0     0 0
            #
            # Where "type_ids" are used to indicate whether this is the first
            # sequence or the second sequence. The embedding vectors for `type=0` and
            # `type=1` were learned during pre-training and are added to the wordpiece
            # embedding vector (and position vector). This is not *strictly* necessary
            # since the [SEP] token unambiguously separates the sequences, but it makes
            # it easier for the model to learn the concept of sequences.
            #
            # For classification tasks, the first vector (corresponding to [CLS]) is
            # used as as the "sentence vector". Note that this only makes sense because
            # the entire model is fine-tuned.
            tokens = []
            segment_ids = []
            tokens.append("[CLS]")
            segment_ids.append(0)
            for token in tokens_a:
                tokens.append(token)
                segment_ids.append(0)
            tokens.append("[SEP]")
            segment_ids.append(0)

            if tokens_b:
                for token in tokens_b:
                    tokens.append(token)
                    segment_ids.append(1)
                tokens.append("[SEP]")
                segment_ids.append(1)

            input_ids = tokenizer.convert_tokens_to_ids(tokens)

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            input_mask = [1] * len(input_ids)

            # Zero-pad up to the sequence length.
            while len(input_ids) < max_seq_length:
                input_ids.append(0)
                input_mask.append(0)
                segment_ids.append(0)

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length

            label_id = label_map[example.label]
            if ex_index < 5:
                tf.logging.info("*** Example ***")
                tf.logging.info("guid: %s" % (example.guid))
                tf.logging.info("tokens: %s" % " ".join(
                    [tokenization.printable_text(x) for x in tokens]))
                tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
                tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

            feature = InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_id=label_id)

            yield feature
Convert a set of `InputExample`s to a list of `InputFeatures`.
similarity.py