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docs: fix reference links #1250

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2 changes: 1 addition & 1 deletion docs/source/offline-evaluation.md
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Expand Up @@ -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.

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2 changes: 1 addition & 1 deletion docs/source/using-the-python-api.mdx
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Expand Up @@ -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.
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