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import re | |
class TokenAlignPreprocessor: | |
def __init__(self, tokenizer, pre_tokenizer, outside_label_id): | |
self.tokenizer = tokenizer | |
self.pre_tokenizer = pre_tokenizer | |
self.outside_label_id = outside_label_id | |
def align_label(self, word, word_tokens, char_labels): | |
i = j = 0 | |
token_labels = [] | |
while i < len(word) and j < len(word_tokens): | |
step = len(word_tokens[j].replace('##', '')) | |
token_labels.append(min(char_labels[i:i+step])) | |
i += step | |
j += 1 | |
return token_labels | |
def convert_example(self, example): | |
tokens = ['[CLS]'] | |
labels = [self.outside_label_id] | |
text = ''.join(example['tokens']) | |
pretokens = self.pre_tokenizer.pre_tokenize_str(text) | |
for word, (begin, end) in pretokens: | |
word_tokens = self.tokenizer.tokenize(word) | |
if '[UNK]' in word_tokens: | |
token_labels = [self.outside_label_id] * len(word_tokens) | |
else: | |
char_labels = example['ner_tags'][begin:end] | |
token_labels = self.align_label(word, word_tokens, char_labels) | |
tokens += word_tokens | |
labels += token_labels | |
tokens.append('[SEP]') | |
input_ids = self.tokenizer.convert_tokens_to_ids(tokens) | |
n = len(input_ids) | |
token_type_ids = [0] * n | |
attention_mask = [1] * n | |
labels.append(self.outside_label_id) | |
return {'input_ids': input_ids, | |
'token_type_ids': token_type_ids, | |
'attention_mask': attention_mask, | |
'labels': labels} | |
def ner_tokenize(sentence, label2id): | |
"""<LC>문막휴게소</LC>와 같이 태깅된 데이터를 KLUE NER 데이터셋과 같은 형태로 토큰화한다""" | |
tag_list = '|'.join({ | |
label[2:] for label in label2id if label != 0 | |
}) | |
start_tags = f'<({tag_list})>' | |
end_tags = f'</({tag_list})>' | |
tokens = [] | |
ner_tags = [] | |
start = None | |
n = len(sentence) | |
raw_idx = 0 | |
token_idx = 0 | |
while raw_idx < n: | |
token = sentence[raw_idx] | |
if token == '<' and (m := re.match(start_tags, sentence[raw_idx:])): | |
start = token_idx | |
raw_idx += len(m.group(0)) | |
elif token == '<' and (m := re.match(end_tags, sentence[raw_idx:])): | |
tag = m.group(1) | |
ner_tags[start] = label2id[f'B-{tag}'] | |
for j in range(start+1, token_idx): | |
ner_tags[j] = label2id[f'I-{tag}'] | |
raw_idx += len(m.group(0)) | |
else: | |
tokens.append(token) | |
ner_tags.append(label2id['O']) | |
raw_idx += 1 | |
token_idx += 1 | |
return {'tokens': tokens, 'ner_tags': ner_tags} | |
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