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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))
# add placehold for the [Q] marker
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']
# postprocess for the [Q] marker and the [MASK] augmentation
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
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