Add staticmethod decorator

flair/models/sequence_tagger_model.py

@@ -217,6 +217,7 @@ def _get_state_dict(self):
         }
         return model_state
 
+    @staticmethod
     def _init_model_with_state_dict(state):
 
         rnn_type = "LSTM" if not "rnn_type" in state.keys() else state["rnn_type"]
@@ -833,6 +834,7 @@ def _filter_empty_string(texts: List[str]) -> List[str]:
             )
         return filtered_texts
 
+    @staticmethod
     def _fetch_model(model_name) -> str:
 
         model_map = {}

flair/models/similarity_learning_model.py

@@ -375,6 +375,7 @@ def _get_state_dict(self):
         }
         return model_state
 
+    @staticmethod
     def _init_model_with_state_dict(state):
         # The conversion from old model's constructor interface
         if "input_embeddings" in state:

flair/models/text_classification_model.py

@@ -99,6 +99,7 @@ def _get_state_dict(self):
         }
         return model_state
 
+    @staticmethod
     def _init_model_with_state_dict(state):
 
         model = TextClassifier(
@@ -417,6 +418,7 @@ def _labels_to_indices(self, sentences: List[Sentence]):
 
         return vec
 
+    @staticmethod
     def _fetch_model(model_name) -> str:
 
         model_map = {}

flair/models/text_regression_model.py

@@ -169,6 +169,7 @@ def _get_state_dict(self):
         }
         return model_state
 
+    @staticmethod
     def _init_model_with_state_dict(state):
 
         model = TextRegressor(document_embeddings=state["document_embeddings"])

flair/nn.py

@@ -48,12 +48,14 @@ def _get_state_dict(self):
         functionality."""
         pass
 
+    @staticmethod
     @abstractmethod
     def _init_model_with_state_dict(state):
         """Initialize the model from a state dictionary. Implementing this enables the load() and load_checkpoint()
         functionality."""
         pass
 
+    @staticmethod
     @abstractmethod
     def _fetch_model(model_name) -> str:
         return model_name
@@ -71,7 +73,7 @@ def save(self, model_file: Union[str, Path]):
     def load(cls, model: Union[str, Path]):
         """
         Loads the model from the given file.
-        :param model_file: the model file
+        :param model: the model file
         :return: the loaded text classifier model
         """
         model_file = cls._fetch_model(str(model))