| import torch
|
|
|
| from transformers import BertTokenizerFast
|
| from colbert.modeling.tokenization.utils import _split_into_batches
|
|
|
|
|
| class QueryTokenizer():
|
| def __init__(self, query_maxlen):
|
| self.tok = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
|
| self.query_maxlen = query_maxlen
|
|
|
| self.Q_marker_token, self.Q_marker_token_id = '[Q]', self.tok.convert_tokens_to_ids('[unused0]')
|
| self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
|
| self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id
|
| self.mask_token, self.mask_token_id = self.tok.mask_token, self.tok.mask_token_id
|
|
|
| assert self.Q_marker_token_id == 100 and self.mask_token_id == 103
|
|
|
| def tokenize(self, batch_text, add_special_tokens=False):
|
| assert type(batch_text) in [list, tuple], (type(batch_text))
|
|
|
| tokens = [self.tok.tokenize(x, add_special_tokens=False) for x in batch_text]
|
|
|
| if not add_special_tokens:
|
| return tokens
|
|
|
| prefix, suffix = [self.cls_token, self.Q_marker_token], [self.sep_token]
|
| tokens = [prefix + lst + suffix + [self.mask_token] * (self.query_maxlen - (len(lst)+3)) for lst in tokens]
|
|
|
| return tokens
|
|
|
| def encode(self, batch_text, add_special_tokens=False):
|
| assert type(batch_text) in [list, tuple], (type(batch_text))
|
|
|
| ids = self.tok(batch_text, add_special_tokens=False)['input_ids']
|
|
|
| if not add_special_tokens:
|
| return ids
|
|
|
| prefix, suffix = [self.cls_token_id, self.Q_marker_token_id], [self.sep_token_id]
|
| ids = [prefix + lst + suffix + [self.mask_token_id] * (self.query_maxlen - (len(lst)+3)) for lst in ids]
|
|
|
| return ids
|
|
|
| def tensorize(self, batch_text, bsize=None):
|
| assert type(batch_text) in [list, tuple], (type(batch_text))
|
|
|
|
|
| batch_text = ['. ' + x for x in batch_text]
|
|
|
| obj = self.tok(batch_text, padding='max_length', truncation=True,
|
| return_tensors='pt', max_length=self.query_maxlen)
|
|
|
| ids, mask = obj['input_ids'], obj['attention_mask']
|
|
|
|
|
| ids[:, 1] = self.Q_marker_token_id
|
| ids[ids == 0] = self.mask_token_id
|
|
|
| if bsize:
|
| batches = _split_into_batches(ids, mask, bsize)
|
| return batches
|
|
|
| return ids, mask
|
|
|
