def convert_examples_to_features(examples,
                                 label_list,
                                 max_seq_length,
                                 tokenizer,
                                 cls_token_at_end=False,
                                 cls_token="[CLS]",
                                 cls_token_segment_id=1,
                                 sep_token="[SEP]",
                                 sep_token_extra=False,
                                 pad_on_left=False,
                                 pad_token=0,
                                 pad_token_segment_id=0,
                                 pad_token_label_id=-1,
                                 sequence_a_segment_id=0,
                                 mask_padding_with_zero=True,
                                 lang='en',
                                 languages_without_spaces=None,
                                 word_dropout_rate=0):
    """ Loads a data file into a list of `InputBatch`s
      `cls_token_at_end` define the location of the CLS token:
        - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
        - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
      `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    """

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 1000 == 0:
            logger.info("Writing example %d of %d", ex_index, len(examples))

        tokens = []
        label_ids = []
        pooling_ids = []
        for i, (word, label) in enumerate(zip(example.words, example.labels)):
            if isinstance(tokenizer, XLMTokenizer):
                word_tokens = tokenizer.tokenize(word, lang=lang)
            else:
                if languages_without_spaces is not None and lang in languages_without_spaces and i > 0:
                    word_tokens = tokenizer.tokenize(word, remove_space=True)
                else:
                    word_tokens = tokenizer.tokenize(word)
            if len(word) != 0 and len(word_tokens) == 0:
                word_tokens = [tokenizer.unk_token]

            if len(word_tokens) == 0:
                continue

            tokens.extend(word_tokens)
            # Use the real label id for the first token of the word, and padding ids for the remaining tokens
            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))
            pooling_ids.extend([len(pooling_ids) + 1] * len(word_tokens))

        # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        special_tokens_count = 3 if sep_token_extra else 2
        if len(tokens) > max_seq_length - special_tokens_count:
            print('truncate token', len(tokens), max_seq_length, special_tokens_count)
            tokens = tokens[:(max_seq_length - special_tokens_count)]
            label_ids = label_ids[:(max_seq_length - special_tokens_count)]
            pooling_ids = pooling_ids[:(max_seq_length - special_tokens_count)]

            # 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.

        if word_dropout_rate > 0:
            for i in range(len(tokens)):
                if random.random() < word_dropout_rate:
                    tokens[i] = tokenizer.unk_token

        tokens += [sep_token]
        label_ids += [pad_token_label_id]
        pooling_ids += [0]
        if sep_token_extra:
            # roberta uses an extra separator b/w pairs of sentences
            tokens += [sep_token]
            label_ids += [pad_token_label_id]
            pooling_ids += [0]
        segment_ids = [sequence_a_segment_id] * len(tokens)

        if cls_token_at_end:
            tokens += [cls_token]
            label_ids += [pad_token_label_id]
            segment_ids += [cls_token_segment_id]
            pooling_ids += [0]
        else:
            tokens = [cls_token] + tokens
            label_ids = [pad_token_label_id] + label_ids
            segment_ids = [cls_token_segment_id] + segment_ids
            pooling_ids = [0] + pooling_ids

        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 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_seq_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
            label_ids = ([pad_token_label_id] * padding_length) + label_ids
            pooling_ids = ([0] * padding_length) + pooling_ids
        else:
            input_ids += ([pad_token] * padding_length)
            input_mask += ([0 if mask_padding_with_zero else 1] * padding_length)
            segment_ids += ([pad_token_segment_id] * padding_length)
            label_ids += ([pad_token_label_id] * padding_length)
            pooling_ids += ([0] * padding_length)

        if example.langs and len(example.langs) > 0:
            langs = [example.langs[0]] * max_seq_length
        else:
            print('example.langs', example.langs, example.words, len(example.langs))
            print('ex_index', ex_index, len(examples))
            langs = None

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length
        assert len(label_ids) == max_seq_length
        assert len(pooling_ids) == max_seq_length
        assert len(langs) == max_seq_length

        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s", example.guid)
            logger.info("tokens: %s", " ".join([str(x) for x in tokens]))
            logger.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
            logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
            logger.info("label_ids: %s", " ".join([str(x) for x in label_ids]))
            logger.info("pooling_ids: %s", " ".join([str(x) for x in pooling_ids]))
            logger.info("langs: {}".format(langs))

        features.append(
            InputFeatures(input_ids=input_ids,
                          input_mask=input_mask,
                          segment_ids=segment_ids,
                          label_ids=label_ids,
                          pooling_ids=pooling_ids,
                          langs=langs))
    return features
 Loads a data file into a list of `InputBatch`s
      `cls_token_at_end` define the location of the CLS token:
        - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
        - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
      `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    
xtune/src/utils_tag.py