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 # setfit-integrated-gradients
 
-Hacking SetFit so that it works with [integrated gradients](https://www.tensorflow.org/tutorials/interpretability/integrated_gradients). See demo.ipynb for an example.
+Hacking SetFit so that it works with [integrated gradients](https://www.tensorflow.org/tutorials/interpretability/integrated_gradients). See **demo.ipynb** for an example.
 
-I wrote this mini-library before SetFit 0.6.0. At the time, there was no SetFitHead class yet, so I just took the sklearn LogisticRegression and passed its parameters to an equivalent Torch class. I did my best to break the forward pass of SetFit into pieces so that I can push gradients through the head and up to the token embeddings.
-
-Attributions from integrated gradients are computed per token and then averaged to get word-level attributions.
-
-I'm leaving this here for posterity and in case it is useful to others for further hacking.
+Integrated gradients is a way to explain the decisions of the model by scoring what parts of the input influenced a particular decision. 
 
 ## Installation
 
 ```
 pip install -e . 
 ```
+
+The "-e" switch installs the package in develop mode. 
+
+## Notes
+
+I wrote this mini-library before SetFit 0.6.0. At the time, there was no SetFitHead class yet, so I just took the sklearn LogisticRegression and passed its parameters to an equivalent Torch class. I did my best to break the forward pass of SetFit into pieces so that I can push gradients through the head and up to the token embeddings.
+
+Attributions from integrated gradients are computed per token and then averaged to get word-level attributions.
+
+I'm leaving this here for posterity and in case it is useful to others for further hacking.