Tokenizers for NLP tasks.
pip install -U naive-tokenizers>>> from naive_tokenizers import JiebaTokenizer, TransformerTokenizer, BertTokenizer
>>> from naive_tokenizers import DefaultIntervener
>>> intervener = DefaultIntervener()
>>> jieba = JiebaTokenizer(vocab_file='testdata/vocab_chinese.txt', intervener=intervener)
Building prefix dict from the default dictionary ...
Loading model from cache /var/folders/vl/s79n468900g83fqc558bkkz80000gn/T/jieba.cache
Loading model cost 0.564 seconds.
Prefix dict has been built successfully.
>>> print(jieba.tokenize('高级javadeveloper'))
['高级', 'javadeveloper']
>>>
>>>
>>> intervener.add_split_token('javadeveloper', 'java developer')
>>> print(jieba.tokenize('高级javadeveloper'))
['高级', 'java', 'developer']
>>>
>>> transformer = TransformerTokenizer(vocab_file='testdata/vocab_chinese.txt')
>>> print(transformer.tokenize('高级javadeveloper'))
['高', '级', 'java', '##de', '##vel', '##oper']
>>>
>>> bert = BertTokenizer(vocab_file='testdata/vocab_chinese.txt')
>>> print(bert.tokenize('高级javadeveloper'))
['高', '级', 'java', '##de', '##vel', '##oper']
>>>
>>>