Update README.md

README.md

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 #### 3.3.3 Training
 
-The training process involved feeding the cleaned and prepared dataset into the GPT-2 model. We used a combination of supervised learning and transfer learning techniques to fine-tune the model effectively.
+The training process involved feeding the cleaned and prepared dataset into the GPT-2 model. We used a combination of supervised learning techniques to fine-tune the model effectively.
+We trained the model using the Hugging Face Trainer, which takes the parameters as
+input. We opted for this because it is optimized for transformer and also comes from
+the same framework. During training, we used the WANDB API to track the training
+of each model of the respective parties and obtain metrics. We ran the training of the
+models via Kaggle, as Kaggle provides two T4 GPUs and so we got good hardware
+without paying anything. Another advantage was that we could run the training via
+CUDA. The training took between 2 and 10 hours, depending on the number of tweets
+from each party. We will go into this in more detail in the evaluation.
 
 #### 3.3.4 Generation and Deployment