diff --git a/docs/source/offline-evaluation.md b/docs/source/offline-evaluation.md
index 37c921b61..0f7b622b0 100644
--- a/docs/source/offline-evaluation.md
+++ b/docs/source/offline-evaluation.md
@@ -5,7 +5,7 @@ Hub, you can take advantage of the `hf_data_files` argument to point lighteval
 at local JSON/CSV resources. This makes it easy to evaluate datasets that live
 in your repo or that are generated on the fly.
 
-Internally, `hf_data_files` is passed directly to the `data_files` parameter of `datasets.load_dataset` ([docs]((https://huggingface.co/docs/datasets/en/package_reference/loading_methods#datasets.load_dataset))).
+Internally, `hf_data_files` is passed directly to the `data_files` parameter of `datasets.load_dataset` ([docs](https://huggingface.co/docs/datasets/en/package_reference/loading_methods#datasets.load_dataset)).
 
 See [adding a custom task](adding-a-custom-task) for more information on how to create a custom task.
 
diff --git a/docs/source/using-the-python-api.mdx b/docs/source/using-the-python-api.mdx
index c9bbbbebe..1c0ca8190 100644
--- a/docs/source/using-the-python-api.mdx
+++ b/docs/source/using-the-python-api.mdx
@@ -2,7 +2,7 @@
 
 Lighteval can be used from a custom Python script. To evaluate a model, you will need to set up an
 [`~logging.evaluation_tracker.EvaluationTracker`], [`~pipeline.PipelineParameters`],
-a [`model`](package_reference/models) or a [`model_config`](package_reference/model_config),
+a [`model`](package_reference/models) or a [`model_config`](package_reference/models),
 and a [`~pipeline.Pipeline`].
 
 After that, simply run the pipeline and save the results.