bert之tokenizer 详解_bert的tokenizer

tokenizer基本含义
tokenizer就是分词器； 只不过在bert里和我们理解的中文分词不太一样，主要不是分词方法的问题，bert里基本都是最大匹配方法。

最大的不同在于“词”的理解和定义。 比如：中文基本是字为单位。
英文则是subword的概念，例如将"unwanted"分解成[“un”, “##want”, “##ed”] 请仔细理解这个做法的优点。
这是tokenizer的一个要义。

bert里涉及的tokenizer
BasicTokenzer
主要的类是BasicTokenizer，做一些基础的大小写、unicode转换、标点符号分割、小写转换、中文字符分割、去除重音符号等操作，最后返回的是关于词的数组（中文是字的数组）
 

 def tokenize(self, text):
    """Tokenizes a piece of text."""
    text = convert_to_unicode(text)
    text = self._clean_text(text)
 
    # This was added on November 1st, 2018 for the multilingual and Chinese
    # models. This is also applied to the English models now, but it doesn't
    # matter since the English models were not trained on any Chinese data
    # and generally don't have any Chinese data in them (there are Chinese
    # characters in the vocabulary because Wikipedia