fix black ruff and mypy

flair/data.py

@@ -599,21 +599,21 @@ def __len__(self) -> int:
     def __repr__(self) -> str:
         return self.__str__()
 
-    def add_label(self, typename: str, value: str, score: float = 1.0):
+    def add_label(self, typename: str, value: str, score: float = 1.0, **metadata):
         # The Token is a special _PartOfSentence in that it may be initialized without a Sentence.
         # therefore, labels get added only to the Sentence if it exists
         if self.sentence:
-            super().add_label(typename=typename, value=value, score=score)
+            super().add_label(typename=typename, value=value, score=score, **metadata)
         else:
-            DataPoint.add_label(self, typename=typename, value=value, score=score)
+            DataPoint.add_label(self, typename=typename, value=value, score=score, **metadata)
 
-    def set_label(self, typename: str, value: str, score: float = 1.0):
+    def set_label(self, typename: str, value: str, score: float = 1.0, **metadata):
         # The Token is a special _PartOfSentence in that it may be initialized without a Sentence.
         # Therefore, labels get set only to the Sentence if it exists
         if self.sentence:
-            super().set_label(typename=typename, value=value, score=score)
+            super().set_label(typename=typename, value=value, score=score, **metadata)
         else:
-            DataPoint.set_label(self, typename=typename, value=value, score=score)
+            DataPoint.set_label(self, typename=typename, value=value, score=score, **metadata)
 
     def to_dict(self, tag_type: Optional[str] = None):
         return {

flair/models/entity_mention_linking.py

@@ -443,9 +443,11 @@ def _get_state(self) -> Dict[str, Any]:
     @classmethod
     def _from_state(cls, state_dict: Dict[str, Any]) -> "EntityPreprocessor":
         return cls(
-            preprocessor=None
-            if state_dict["preprocessor"] is None
-            else EntityPreprocessor._from_state(state_dict["preprocessor"]),
+            preprocessor=(
+                None
+                if state_dict["preprocessor"] is None
+                else EntityPreprocessor._from_state(state_dict["preprocessor"])
+            ),
         )
 
 
@@ -601,11 +603,7 @@ def bi_encoder(
                 ngram_range=(1, 2),
             )
 
-        sparse_weight = (
-            sparse_weight
-            if model_name_or_path not in HYBRID_MODELS_SPARSE_WEIGHT
-            else HYBRID_MODELS_SPARSE_WEIGHT[model_name_or_path]
-        )
+        sparse_weight = HYBRID_MODELS_SPARSE_WEIGHT.get(model_name_or_path, sparse_weight)
 
         return cls(
             embeddings,
@@ -903,9 +901,11 @@ def predict(
             for entity in entities_mentions:
                 data_points.append(entity.data_point)
                 mentions.append(
-                    self.preprocessor.process_mention(entity.data_point.text, sentence)
-                    if self.preprocessor is not None
-                    else entity.data_point.text,
+                    (
+                        self.preprocessor.process_mention(entity.data_point.text, sentence)
+                        if self.preprocessor is not None
+                        else entity.data_point.text
+                    ),
                 )
 
         # Retrieve top-k concept / entity candidates
@@ -915,7 +915,9 @@ def predict(
             # Add a label annotation for each candidate
             for data_point, mention_candidates in zip(data_points[i : i + batch_size], candidates):
                 for candidate_id, confidence in mention_candidates:
-                    data_point.add_label(pred_label_type, candidate_id, confidence, name=self.dictionary[candidate_id].concept_name)
+                    data_point.add_label(
+                        pred_label_type, candidate_id, confidence, name=self.dictionary[candidate_id].concept_name
+                    )
 
     @staticmethod
     def _fetch_model(model_name: str) -> str: