removed comments

tasks/text.py

@@ -74,9 +74,6 @@ async def evaluate_text(request: TextEvaluationRequest):
     # Make random predictions (placeholder for actual model inference)
     true_labels = test_dataset["label"]
 
-    # load the xgboost model
-    # with open("models/stella_400_xgb_500.pkl",'rb') as f:
-    #    xgbclassifier = pickle.load(f)
     from torch import nn, optim
 
     class SimpleNN2(nn.Module):
@@ -103,9 +100,6 @@ async def evaluate_text(request: TextEvaluationRequest):
     # Set the model to evaluation mode
     model_nn.eval()
 
-    # make inference
-    # predictions = xgbclassifier.predict(embeddings)
-
     # Make predictions
     with torch.no_grad():
         outputs = model_nn(embeddings)