Update README.md

README.md

@@ -61,9 +61,9 @@ ds = load_dataset("akhauriyash/GraphArch-Regression")
 
 Use the code below as reference for evaluating a basic RegressLM model ( better, more models to come! :) )
 
-Note that the best practice is to fine-tune this base model on more NAS ONNX graph data, and few-shot transfer to the target search space (Say NASNet, etc.). 
+**Note that the best practice is to fine-tune this base model on more NAS ONNX graph data**, and few-shot transfer to the target search space (Say NASNet, etc.). 
 If we want to finetune on 16 examples from say, ENAS, the optimal strategy we found was to construct a small NAS dataset of e.g., DARTS, NASNet, Amoeba, ENAS and use ~(1024, 1024, 1024, 16) samples from each, and up-sample (repeat) the 16 ENAS samples 8 times. Random-shuffle the dataset and fine-tune the RLM with 1e-4 LR (cosine decay) to avoid catastrophic forgetting.
-The code below is just illustrative to demonstrate non-trivial NAS performance
+The code below is just illustrative to demonstrate non-trivial NAS performance. The model training corpus was only 1% NAS data, the rest was code.
 
 ```
 import torch