| | 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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| |
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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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| |
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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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| |
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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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| |
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| | ids, mask = obj['input_ids'], obj['attention_mask']
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| |
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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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| |
|
