Update README.md

README.md

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 ## Model Description
 
-This model consists of a fine-tuned version of google-bert/bert-base-cased for a propaganda detection task. It is effectively a binary classifier, determining wether propaganda is present in the output string.
-This model was created by [`Identrics`](https://identrics.ai/), in the scope of the WASPer project. The detailed taxonomy could be found [here](https://github.com/Identrics/wasper/).
+This model consists of a fine-tuned version of google-bert/bert-base-cased for a propaganda detection task. It is effectively a binary classifier, determining whether propaganda is present in the output string.
+This model was created by [`Identrics`](https://identrics.ai/), in the scope of the WASPer project. The detailed taxonomy of the full pipeline could be found [here](https://github.com/Identrics/wasper/).
 
 
 ## Uses
 
-To be used as a binary classifier to identify if propaganda is present in a string containing a comment from a social media site
+Designed as a binary classifier to determine whether a traditional or social media comment contains propaganda.
 
 ### Example
 
@@ -49,17 +49,16 @@ output = model(**tokens)
 print(output.logits)
 ```
 
-
 ## Training Details
 
-The training datasets for the model consist of a balanced set totaling 840 English examples that include both propaganda and non-propaganda content. These examples are collected from a variety of traditional media and social media sources, ensuring a diverse range of content. Aditionally, the training dataset is enriched with AI-generated samples. The total distribution of the training data is shown in the table below: 
-
+The training dataset for the model consists of a balanced collection of English examples, including both propaganda and non-propaganda content. These examples were sourced from a variety of traditional media and social media platforms and manually annotated by domain experts. Additionally, the dataset is enriched with AI-generated samples.
 
+The model achieved an F1 score of **0.807** during evaluation.
 
-The model was then tested on a smaller evaluation dataset, achieving an f1 score of 0.807.
+## Compute Infrastructure
+The model was fine-tuned using a **GPU / 2xNVIDIA Tesla V100 32GB**.
 
-
-## Citation
+## Citation [this section is to be updated soon]
 
 If you find our work useful, please consider citing WASPer:
 
@@ -72,3 +71,4 @@ If you find our work useful, please consider citing WASPer:
 }
 ```
 
+