Add custom tokenizer for zh and ja

pytorch_transformers/tokenization_xlm.py

@@ -23,7 +23,11 @@ import re
 import unicodedata
 from io import open
 
+import jieba
+import Mykytea
 import sacremoses as sm
+from nltk.tokenize.stanford_segmenter import StanfordSegmenter
+from pythainlp.tokenize import word_tokenize as th_word_tokenize
 
 from .tokenization_utils import PreTrainedTokenizer
 from .tokenization_bert import BasicTokenizer
@@ -83,21 +87,6 @@ def get_pairs(word):
         prev_char = char
     return pairs
 
-def text_standardize(text):
-    """
-    fixes some issues the spacy tokenizer had on books corpus
-    also does some whitespace standardization
-    """
-    text = text.replace('—', '-')
-    text = text.replace('–', '-')
-    text = text.replace('―', '-')
-    text = text.replace('…', '...')
-    text = text.replace('´', "'")
-    text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
-    text = re.sub(r'\s*\n\s*', ' \n ', text)
-    text = re.sub(r'[^\S\n]+', ' ', text)
-    return text.strip()
-
 
 def lowercase_and_remove_accent(text):
     """
@@ -120,7 +109,7 @@ def replace_unicode_punct(text):
     Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
     '''
     text = text.replace('，', ',')
-    text = text.replace('。 *', '. ')
+    text = re.sub(r'。\s*', '. ', text)
     text = text.replace('、', ',')
     text = text.replace('”', '"')
     text = text.replace('“', '"')
@@ -220,6 +209,8 @@ class XLMTokenizer(PreTrainedTokenizer):
         self.lang_with_custom_tokenizer = set(['zh', 'th', 'ja'])
         # True for current supported model (v1.2.0), False for XLM-17 & 100
         self.do_lowercase_and_remove_accent = True
+        self.ja_word_tokenizer = None
+        self.zh_word_tokenizer = None
 
         self.encoder = json.load(open(vocab_file, encoding="utf-8"))
         self.decoder = {v:k for k,v in self.encoder.items()}
@@ -250,6 +241,33 @@ class XLMTokenizer(PreTrainedTokenizer):
         text = remove_non_printing_char(text)
         return text
 
+    def ja_tokenize(self, text):
+        if self.ja_word_tokenizer is None:
+            try:
+                self.ja_word_tokenizer = Mykytea.Mykytea('-model %s/local/share/kytea/model.bin' % os.path.expanduser('~'))
+            except RuntimeError:
+                logger.error("Make sure you install KyTea (https://github.com/neubig/kytea) with the following steps")
+                logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea")
+                logger.error("2. autoreconf -i")
+                logger.error("3. ./configure --prefix=$HOME/local")
+                logger.error("4. make && make install")
+                import sys; sys.exit()
+        return list(self.ja_word_tokenizer.getWS(text))
+
+    def zh_tokenize(self, text):
+        if self.zh_word_tokenizer is None:
+            try:
+                self.zh_word_tokenizer = StanfordSegmenter()
+                self.zh_word_tokenizer.default_config('zh')
+            except LookupError:
+                logger.error("Make sure you download stanford-segmenter (https://nlp.stanford.edu/software/stanford-segmenter-2018-10-16.zip) with the following steps")
+                logger.error("1. wget https://nlp.stanford.edu/software/stanford-segmenter-2018-10-16.zip -O /path/to/stanford-segmenter-2018-10-16.zip")
+                logger.error("2. cd /path/to && unzip stanford-segmenter-2018-10-16.zip")
+                logger.error("3. cd stanford-segmenter-2018-10-16 && cp stanford-segmenter-3.9.2.jar stanford-segmenter.jar")
+                logger.error("4. set env variable STANFORD_SEGMENTER=/path/to/stanford-segmenter-2018-10-16")
+                import sys; sys.exit()
+        return self.zh_word_tokenizer.segment(text)
+
     @property
     def vocab_size(self):
         return len(self.encoder)
@@ -299,7 +317,6 @@ class XLMTokenizer(PreTrainedTokenizer):
 
     def _tokenize(self, text, lang='en'):
         """ Tokenize a string. """
-        split_tokens = []
         if self.do_lowercase_and_remove_accent:
             text = lowercase_and_remove_accent(text)
         if lang not in self.lang_with_custom_tokenizer:
@@ -308,10 +325,24 @@ class XLMTokenizer(PreTrainedTokenizer):
             if lang == 'ro':
                 text = romanian_preprocessing(text)
             text = self.moses_tokenize(text, lang=lang)
+        elif lang == 'th':
+            text = self.moses_pipeline(text, lang=lang)
+            text = th_word_tokenize(text)
+        elif lang == 'zh':
+            # text = self.zh_tokenize(text)
+            text = ' '.join(jieba.cut(text))
+            text = self.moses_pipeline(text, lang=lang)
+            text = text.split()
+        elif lang == 'ja':
+            text = self.moses_pipeline(text, lang=lang)
+            text = self.ja_tokenize(text)
+        else:
+            raise ValueError('It should not reach here')
+
+        split_tokens = []
         for token in text:
             split_tokens.extend([t for t in self.bpe(token).split(' ')])
-        else:
+
-            raise ValueError
         return split_tokens
 
     def _convert_token_to_id(self, token):

requirements.txt

@@ -12,3 +12,7 @@ regex
 sentencepiece
 # For XLM
 sacremoses
+pythainlp
+kytea
+nltk
+jieba

setup.py

@@ -56,7 +56,11 @@ setup(
                       'tqdm',
                       'regex',
                       'sentencepiece',
-                      'sacremoses'],
+                      'sacremoses',
+                      'pythainlp',
+                      'kytea',
+                      'nltk',
+                      'jieba'],
     entry_points={
       'console_scripts': [
         "pytorch_transformers=pytorch_transformers.__main__:main",