Update docs

Author: wannaphong

pythainlp/spell/core.py

@@ -22,7 +22,7 @@ def spell(word: str, engine: str = "pn") -> List[str]:
         * *pn* - Peter Norvig's algorithm [#norvig_spellchecker]_ (default)
         * *phunspell* - A spell checker utilizing spylls a port of Hunspell.
         * *symspellpy* - symspellpy is a Python port of SymSpell v6.5.
-        * *tltk* - wrapper for `TLTK <https://pypi.org/project/tltk/>`_.,
+        * *tltk* - wrapper for `TLTK <https://pypi.org/project/tltk/>`_.
 
     :return: list of possible correct words within 1 or 2 edit distance and
              sorted by frequency of word occurrences in the spelling dictionary

pythainlp/tag/pos_tag.py

@@ -17,8 +17,7 @@ def pos_tag(
             it will convert list word to a string.
         * *tltk* - TLTK: Thai Language Toolkit (support TNC corpus only.\
             if you choose other corpus, It's change to TNC corpus.)
-    :param str corpus:
-        the corpus that used to create the language model for tagger
+    :param str corpus: the corpus that used to create the language model for tagger
         * *lst20* - `LST20 <https://aiforthai.in.th/corpus.php>`_ corpus \
             by National Electronics and Computer Technology Center, Thailand
         * *lst20_ud* - LST20 text, with tags mapped to Universal POS tag \
@@ -132,8 +131,7 @@ def pos_tag_sents(
         * *wangchanberta*  - wangchanberta model (support lst20 corpus only)
         * *tltk* - TLTK: Thai Language Toolkit (support TNC corpus only.\
             if you choose other corpus, It's change to TNC corpus.)
-    :param str corpus:
-        the corpus that used to create the language model for tagger
+    :param str corpus: the corpus that used to create the language model for tagger
         * *lst20* - `LST20 <https://aiforthai.in.th/corpus.php>`_ corpus \
             by National Electronics and Computer Technology Center, Thailand
         * *lst20_ud* - LST20 text, with tags mapped to Universal POS tags \

pythainlp/tokenize/sefr_cut.py

@@ -21,4 +21,4 @@ def segment(text: str, engine: str = 'ws1000') -> List[str]:
     if engine != DEFAULT_ENGINE:
         DEFAULT_ENGINE = engine
         sefr_cut.load_model(engine=DEFAULT_ENGINE)
-    return sefr_cut.tokenize(text)[0]
+    return sefr_cut.tokenize(text)[0]