Fixed docs

PyThaiNLP · Apr 21, 2024 · 13a6c7c · 13a6c7c
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diff --git a/docs/api/el.rst b/docs/api/el.rst
@@ -46,9 +46,9 @@ Example
 Here's a simple example of how to use the `EntityLinker` class:
 
 ::
-  from pythainlp.el import EntityLinker
+   from pythainlp.el import EntityLinker
   
-  text = "กรุงเทพเป็นเมืองหลวงของประเทศไทย"
-  el = EntityLinker()
-  linked_entities = el.get_el(text)
-  print(linked_entities)
+   text = "กรุงเทพเป็นเมืองหลวงของประเทศไทย"
+   el = EntityLinker()
+   linked_entities = el.get_el(text)
+   print(linked_entities)
diff --git a/pythainlp/corpus/util.py b/pythainlp/corpus/util.py
@@ -90,27 +90,27 @@ def revise_wordset(
 
     :Example::
     ::
-
-    from pythainlp.corpus import thai_words
-    from pythainlp.corpus.util import revise_wordset
-    from pythainlp.tokenize.longest import segment
-
-    base_words = thai_words()
-    more_words = {
-        "ถวิล อุดล", "ทองอินทร์ ภูริพัฒน์", "เตียง ศิริขันธ์", "จำลอง ดาวเรือง"
-    }
-    base_words = base_words.union(more_words)
-    dict_trie = Trie(wordlist)
-
-    tokenize = lambda text: segment(text, dict_trie)
-
-    training_data = [
-        [str, str, str. ...],
-        [str, str, str, str, ...],
-        ...
-    ]
-
-    revised_words = revise_wordset(tokenize, wordlist, training_data)
+    
+        from pythainlp.corpus import thai_words
+        from pythainlp.corpus.util import revise_wordset
+        from pythainlp.tokenize.longest import segment
+
+        base_words = thai_words()
+        more_words = {
+            "ถวิล อุดล", "ทองอินทร์ ภูริพัฒน์", "เตียง ศิริขันธ์", "จำลอง ดาวเรือง"
+        }
+        base_words = base_words.union(more_words)
+        dict_trie = Trie(wordlist)
+
+        tokenize = lambda text: segment(text, dict_trie)
+
+        training_data = [
+            [str, str, str. ...],
+            [str, str, str, str, ...],
+            ...
+        ]
+
+        revised_words = revise_wordset(tokenize, wordlist, training_data)
     """
     bad_words = find_badwords(tokenize, training_data)
     return set(orig_words) - bad_words