add _load_model in pipeline

optimum/intel/pipelines/__init__.py

@@ -141,6 +141,11 @@ def clean_custom_task(task_info):
     return task_info, None
 
 
+def _load_model(task, model, **kwargs):
+    if task == "text-generation":
+        return TSModelForCausalLM.from_pretrained(model, **kwargs)
+
+
 def pipeline(
     task: str = None,
     model: Optional[Union[str, "PreTrainedModel"]] = None,
@@ -262,8 +267,8 @@ def pipeline(
             "Please provide a task class or a model"
         )
 
-    if task != "text-generation":
-        raise ValueError("Optimum-intel ipex optimization only supports text-generation task for now.")
+    if task not in SUPPORTED_TASKS.keys():
+        raise ValueError(f"Optimum-intel ipex optimization only supports {SUPPORTED_TASKS.keys()} task for now.")
 
     if model is None and tokenizer is not None:
         raise RuntimeError(
@@ -358,7 +363,7 @@ def pipeline(
     # Load the correct model if possible
     # Infer the framework from the model if not already defined
     if isinstance(model, str):
-        model = TSModelForCausalLM.from_pretrained(model, config=config, export=True, **model_kwargs)
+        model = _load_model(task, model, config=config, export=True, **model_kwargs)
 
     model_config = model.config
     hub_kwargs["_commit_hash"] = model.config._commit_hash