Link to notebook

src/display/about.py

@@ -46,6 +46,7 @@ To assess the reasoning skill of a given `model`, we carry out the following ste
 
 Each `regime` yields a different _accuracy gain Δ_, and the leaderboard reports (for every `model`/`task`) the best Δ achieved by any regime. All models are evaluated against the same set of regimes.
 
+A notebook with detailed result exploration and visualization is available [here](https://github.com/logikon-ai/cot-eval/blob/main/notebooks/CoT_Leaderboard_Results_Exploration.ipynb).
 
 ## How is it different from other leaderboards?
 
@@ -68,7 +69,7 @@ Unlike these leaderboards, the `/\/` Open CoT Leaderboard assesses a model's abi
 
 ## Test dataset selection (`tasks`)
 
-The test dataset porblems in the CoT Leaderboard can be solved through clear thinking alone, no specific knowledge is required to do so. They are subsets of the [AGIEval benchmark](https://github.com/ruixiangcui/AGIEval) and re-published as [`logikon-bench`](https://huggingface.co/datasets/logikon/logikon-bench). The `logiqa` dataset has been newly translated from Chinese to English.
+The test dataset problems in the CoT Leaderboard can be solved through clear thinking alone, no specific knowledge is required to do so. They are subsets of the [AGIEval benchmark](https://github.com/ruixiangcui/AGIEval) and re-published as [`logikon-bench`](https://huggingface.co/datasets/logikon/logikon-bench). The `logiqa` dataset has been newly translated from Chinese to English.
 
 
 ## Reproducibility