refactor-task-type-to-task

modeling_lora.py

@@ -367,35 +367,35 @@ class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
         self,
         sentences: Union[str, List[str]],
         *args,
-        task_type: Optional[str] = None,
+        task: Optional[str] = None,
         **kwargs,
     ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
         """
         Computes sentence embeddings.
         sentences(`str` or `List[str]`):
             Sentence or sentences to be encoded
-        task_type(`str`, *optional*, defaults to `None`):
-            Specifies the task for which the encoding is intended. If `task_type` is not provided,
+        task(`str`, *optional*, defaults to `None`):
+            Specifies the task for which the encoding is intended. If `task` is not provided,
             all LoRA adapters are disabled, and the model reverts to its original,
             general-purpose weights.
         """
-        if task_type and task_type not in self._lora_adaptations:
+        if task and task not in self._lora_adaptations:
             raise ValueError(
-                f"Unsupported task '{task_type}'. "
+                f"Unsupported task '{task}'. "
                 f"Supported tasks are: {', '.join(self.config.lora_adaptations)}."
-                f"Alternatively, don't pass the `task_type` argument to disable LoRA."
+                f"Alternatively, don't pass the `task` argument to disable LoRA."
             )
         adapter_mask = None
-        if task_type:
-            task_id = self._adaptation_map[task_type]
+        if task:
+            task_id = self._adaptation_map[task]
             num_examples = 1 if isinstance(sentences, str) else len(sentences)
             adapter_mask = torch.full(
                 (num_examples,), task_id, dtype=torch.int32, device=self.device
             )
             if isinstance(sentences, str):
-                sentences = self._task_instructions[task_type] + sentences
+                sentences = self._task_instructions[task] + sentences
             else:
-                sentences = [self._task_instructions[task_type] + sentence for sentence in sentences]
+                sentences = [self._task_instructions[task] + sentence for sentence in sentences]
         return self.roberta.encode(
             sentences, *args, adapter_mask=adapter_mask, **kwargs
         )

modeling_xlm_roberta.py

@@ -473,7 +473,7 @@ class XLMRobertaModel(XLMRobertaPreTrainedModel):
         normalize_embeddings: bool = True,
         truncate_dim: Optional[int] = None,
         adapter_mask: Optional[torch.Tensor] = None,
-        task_type: Optional[str] = None,
+        task: Optional[str] = None,
         **tokenizer_kwargs,
     ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
         """