Datasets:

DjMel
/

oz-eval

@@ -35,7 +35,7 @@ We then compare likelihoods of each letter (`A, B, C, D, E`) and calculate the f
 GPT-like models were evaluated by taking top 20 probabilities of the first output token, which were further filtered for letters `A` to `E`. Letter with the highest probability was taken as a final answer.
-Exact code for the task can be found as a PR [here](https://github.com/huggingface/lighteval/pull/225).
 You should run the evaluation with the following command (do not forget to add --use_chat_template):
 ```

 GPT-like models were evaluated by taking top 20 probabilities of the first output token, which were further filtered for letters `A` to `E`. Letter with the highest probability was taken as a final answer.
+Exact code for the task can be found [here](https://github.com/Stopwolf/lighteval/blob/oz-eval/community_tasks/oz_evals.py).
 You should run the evaluation with the following command (do not forget to add --use_chat_template):
 ```