Update README.md

README.md

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 ## Description
 
-Generative transformer that defuses sarcasm by translating sarcastic comment into "neutral" ones.
+
+GPT-2 model (small 0.1B parameters) fine-tuned to defues sarcasm. Example:
+
+```
+Prompt: So glad investment bankers and hedge funds make so much on the low wages these guys get.<|BOS|>
+Generated after prompt: It's concerning that investment bankers and hedge funds are making so much on the low wages these workers receive.
+```
+
+(The model use the special `<|BOS|>` token as a marker for where the generated, defuse comment should start).
+
+
+## Training and Evaluation 
+
+The model has been trained on ~4500 sarcastic comments from the [Sarcasm on Reddit](https://www.kaggle.com/datasets/danofer/sarcasm/data) Kaggle dataset. The dataset includes
+a selection of comments from Reddit that were marked as sarcastic by the author of the comment.
+Another ~500 comments have been used to test the trained model's performance.
+
+In order to teach the model what a defused, not sarcastic comment looks like, we used a more powerful LLM to generate defused comments for the Kaggle dataset.
+We used the [gemma-3-12b-it](https://ai.google.dev/gemma/docs/core) model with 12B parameters and we queried via the Google API with the following prompt for each comment:
+
+```
+given this sarcastic comment: <SARCASTIC_COMMENT>,
+which is a response to this other comment: <CONTEXT>,
+remove all the sarcasm from it while keeping the original meaning. Don't output anything else, and don't try to describe the comment in the third person",
+```
+
+where `<SARCASTIC_COMMENT>` is the sarcastic comment from the Kaggle dataset and `<CONTEXT>` is the comment that preceded the sarcstic comment (this comment was also 
+available as part of the Kaggle dataset). This gives the LLM additional information on how to "translate" the sarcastic comment into a "normal" one.
+
 
 ## How to use