refactor: prompts

Signed-off-by: Meow <[email protected]>

modeling_lora.py

@@ -165,7 +165,6 @@ class LoRAParametrization(nn.Module):
     ):
         """
         Registering LoRA adapters to all embedding and linear layers.
-
         Additionally, we implement a custom forward function for LoRA parametrization.
         This function modifies the layer's forward pass to optionally use task-specific
         parameters. When a `task_id` is provided, it employs a LoRA parametrization
@@ -373,7 +372,6 @@ class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
     ) -> 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`):
@@ -394,6 +392,10 @@ class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
             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
+            else:
+                sentences = [self._task_instructions[task_type] + sentence for sentence in sentences]
         return self.roberta.encode(
             sentences, *args, adapter_mask=adapter_mask, **kwargs
         )