Remove redundant type annotations on reassignments

Co-authored-by: bact <128572+bact@users.noreply.github.com>

Author: Copilot

pythainlp/transliterate/w2p.py

@@ -219,8 +219,8 @@ def _encode(self, word: str) -> "np.ndarray":
     def _short_word(self, word: str) -> Optional[str]:
         self.word: str = word
         if self.word.endswith("."):
-            self.word: str = self.word.replace(".", "")
-            self.word: str = "-".join([i + "อ" for i in list(self.word)])
+            self.word = self.word.replace(".", "")
+            self.word = "-".join([i + "อ" for i in list(self.word)])
             return self.word
         return None
 

pythainlp/wangchanberta/core.py

@@ -113,13 +113,13 @@ def get_ner(
                 for i in self.json_ner
             ]
         elif self.dataset_name == "thainer":
-            self.sent_ner: list[tuple[str, str]] = [
+            self.sent_ner = [
                 (i["word"].replace("<_>", " ").replace("▁", ""), i["entity"])
                 for i in self.json_ner
                 if i["word"] != "▁"
             ]
         else:
-            self.sent_ner: list[tuple[str, str]] = [
+            self.sent_ner = [
                 (
                     i["word"].replace("<_>", " ").replace("▁", ""),
                     i["entity"].replace("_", "-").replace("E-", "I-"),

pythainlp/wsd/core.py

@@ -46,8 +46,8 @@ def __init__(
     def change_device(self, device: str) -> None:
         from sentence_transformers import SentenceTransformer
 
-        self.device: str = device
-        self.model: "SentenceTransformer" = SentenceTransformer(self.model_name, device=self.device)
+        self.device = device
+        self.model = SentenceTransformer(self.model_name, device=self.device)
 
     def get_score(self, sentences1: str, sentences2: str) -> float:
         from sentence_transformers import util