Update README.md

README.md

@@ -27,7 +27,7 @@ How is SHP different from [Anthropic's HH-RLHF dataset](https://huggingface.co/d
 
 | Dataset              | Input                      | Output                       | No. Domains               | Data Format                           |
 | -------------------- | -------------------------- | ---------------------------- | ------------------------- | ------------------------------------- |
-| SHP                  | Reddit post and comments   | Aggregate Preference Label   | 18 (cooking, cars, ...)   | Question/Answer + Assertion/Response  |
+| SHP                  | Reddit post and comments   | Aggregate Preference Label with Scores  | 18 (cooking, cars, ...)   | Question/Answer + Assertion/Response  |
 | Anthropic/HH-RLHF    | Dialogue history with LLM  | Individual Preference Label  | 2 (harmful, helpful)      | Multi-turn Dialogue                   |
 
 
@@ -169,7 +169,9 @@ If you want to finetune a model to predict human preferences (e.g., for NLG eval
 ### Evaluating
 
 Since it is easier to predict stronger preferences than weaker ones (e.g., preferences with a big difference in comment score), we recommend reporting a performance curve instead of a single number.
-For example, here is the accuracy curve for a FLAN-T5-xl model trained using the suggestions above, on only preferences with a 2+ score ratio and using no more than 5 preferences from each post to prevent overfitting:
+For example, here is the accuracy curve for a FLAN-T5-xl model trained on the askculinary ddata using the suggestions above.
+
+The orange line is without filtering the training data and the blue line is with training only on preferences with a 2+ score ratio and using no more than 5 preferences from each post to prevent overfitting: