ruff fix

Author: riapush

autointent/modules/scoring/_lora/lora.py

@@ -87,13 +87,13 @@ def fit(
         model_name = self.model_config.model_name
         self._tokenizer = AutoTokenizer.from_pretrained(model_name)
         self._model = AutoModelForSequenceClassification.from_pretrained(
-            model_name, 
+            model_name,
             num_labels=self._n_classes,
             problem_type="multi_label_classification" if self._multilabel else "single_label_classification"
             )
         self._model = get_peft_model(self._model, self._lora_config)
 
-        device = torch.device(self.model_config.device if self.model_config.device else 'cpu')
+        device = torch.device(self.model_config.device if self.model_config.device else "cpu")
         self._model = self._model.to(device)
 
         use_cpu = self.model_config.device == "cpu"
@@ -136,8 +136,8 @@ def predict(self, utterances: list[str]) -> npt.NDArray[Any]:
         if not hasattr(self, "_model") or not hasattr(self, "_tokenizer"):
             msg = "Model is not trained. Call fit() first."
             raise RuntimeError(msg)
-        
-        device = torch.device(self.model_config.device if self.model_config.device else 'cpu')
+
+        device = torch.device(self.model_config.device if self.model_config.device else "cpu")
         self._model = self._model.to(device)
 
         all_predictions = []

tests/modules/scoring/test_lora.py

@@ -53,4 +53,4 @@ def test_bert_cache_clearing(dataset):
     assert not hasattr(scorer, "_tokenizer") or scorer._tokenizer is None
 
     with pytest.raises(RuntimeError):
-        scorer.predict(test_data)
+        scorer.predict(test_data)