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