Update README.md

Author: kohpangwei

README.md

@@ -113,7 +113,7 @@ While the `camelyon17` dataset is small and fast to train on, we advise against
 
 The image datasets (`iwildcam`, `camelyon17`, `fmow`, and `poverty`) tend to have high disk I/O usage. If training time is much slower for you than the approximate times listed above, consider checking if I/O is a bottleneck (e.g., by moving to a local disk if you are using a network drive, or by increasing the number of data loader workers). To speed up training, you could also disable evaluation at each epoch or for all splits by toggling `--evaluate_all_splits` and related arguments.
 
-We have an [executable version](https://wilds.stanford.edu/codalab) of our paper on CodaLab that contains the exact commands, code, and data used for the experiments reported in our paper. Trained model weights for all datasets can also be found there.
+We have an [executable version](https://wilds.stanford.edu/codalab) of our paper on CodaLab that contains the exact commands, code, and data for the experiments reported in our paper, which rely on these scripts. Trained model weights for all datasets can also be found there.
 
 
 ## Using the WILDS package
@@ -203,6 +203,9 @@ Invoking the `eval` method of each dataset yields all metrics reported in the pa
 ```
 Most `eval` methods take in predicted labels for `all_y_pred` by default, but the default inputs vary across datasets and are documented in the `eval` docstrings of the corresponding dataset class.
 
+## Leaderboard
+If you are developing new training algorithms and/or models on WILDS, please consider submitting them to our [public leaderboard](https://wilds.stanford.edu/leaderboard/).
+
 ## Citing WILDS
 If you use WILDS datasets in your work, please cite [our paper](https://arxiv.org/abs/2012.07421) ([Bibtex](https://wilds.stanford.edu/assets/files/bibtex.md)):