Update README.md

README.md

@@ -53,6 +53,10 @@ model = CrossEncoder('jaspercatapang/R-star', max_length=512)
 scores = model.predict([('Your query here', 'First relevant passage for first query'), ('Your query here', 'Second relevant passage for second query')])
 ```
 
+### Training and Evaluation
+1. For training, the Colab notebook can be found [here](https://colab.research.google.com/drive/1F105XTCchub-flcGB1XqqoaYlJr16YR3).
+2. For evaluation, the Colab notebook can be found [here](https://colab.research.google.com/drive/1H5RppJX9cfRXd8Hls2_Vis5sb6SHB1zf).
+
 ## Limitations
 Based on our evaluation, R* tends to favor longer passages when scoring, which could introduce a bias. This is true for most cross-encoder models. It is advisable to preprocess text to normalize passage lengths for fair comparison. Note that R* is optimized for passage-level comparisons and may not perform well on word- or phrase-level similarity tasks.