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README.md

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 This repo contains the template we would like to use for the SAE Bench project. The `template.ipynb` is a good starting point explaining the input to your custom eval (SAEs hosted on SAELens) and the output (a standardized results file).
 
-The `sparse_probing` folder contains a full example implementation of a custom eval. In `sparse_probing_eval.py`, we have a function that takes a list of SAELens SAE names (defined in `eval_config.py`) and an sae release and returns a dictionary of results in a standard format. This folder contains some helper functions, like pre-computing model activations in `activation_collection.py`, that might be useful for you, too! We try to reuse functions as much as possible across evals to reduce bugs. Let Adam and Can know if you've implemented a helper function that might be useful for other evals as well (like autointerp, feauture scoring). 
+The `sparse_probing` folder contains a full example implementation of a custom eval. In `sparse_probing_eval.py`, we have a function that takes a list of SAELens SAE names (defined in `eval_config.py`) and an sae release and returns a dictionary of results in a standard format. This folder contains some helper functions, like pre-computing model activations in `activation_collection.py`, that might be useful for you, too! We try to reuse functions as much as possible across evals to reduce bugs. Let Adam and Can know if you've implemented a helper function that might be useful for other evals as well (like autointerp, feature scoring). 
 
 `python3 sparse_probing_eval.py` should run as is and demonstrate how to use our SAE Bench SAEs with Transformer Lens and SAE Lens. It will also generate a results file which can be graphed using `graph_sae_results.ipynb`.