Update README.md

README.md

@@ -26,7 +26,7 @@ pipeline_tag: text-generation
 This model is intended to be used for generating conversational responses in a variety of contexts, such as chatbots, virtual assistants, and customer service applications. It is designed to provide natural and engaging responses to user input, with a focus on maintaining a consistent tone and style throughout the conversation. The model is suitable for use in both text-based and voice-based interfaces, and can be easily integrated into existing applications using the PyTorch and Transformers frameworks.
 
 ## Training Data
-The model is trained on a large dataset of conversational data, consisting of interactions between users and an AI assistant. The data is preprocessed to remove any sensitive information and is formatted in a way that is suitable for training a language model. The training data is split into a training set and a validation set, with the training set used to update the model parameters and the validation set used to evaluate the model performance. The model was trained on 225,000 examples over 1,125,000 steps, it achieved decent metrics.
+The model is trained on a large dataset of conversational data, consisting of interactions between users and an AI assistant. The data is preprocessed to remove any sensitive information and is formatted in a way that is suitable for training a language model. The training data is split into a training set and a validation set, with the training set used to update the model parameters and the validation set used to evaluate the model performance. The model was trained on 245,000 examples over 1,225,000 steps, it achieved decent metrics.
 This model outperformed the base GPT-2 model significantly on a new conversational dataset during a fine-tuning session. Here is a side-by-side comparison of the two models during the first steps of training 
 ```python
 # Base GPT-2
@@ -73,7 +73,7 @@ The model architecture used in this model is GPT-2, a transformer-based language
 The model is evaluated based on several metrics, including loss, reward, penalty, BLEU score, and perplexity. The loss metric is calculated during training and reflects the difference between the predicted output and the actual output. The reward metric is based on the number of correct words generated by the model, while the penalty metric penalizes the model for repeating words consecutively. The BLEU score measures the similarity between the generated text and the ground truth text, while the perplexity metric measures how well the model is able to predict the next word in a sequence. During validation, the model achieved the following metrics:
 
 - BLEU Score: 9
-- Perplexity: 22
+- Perplexity: 19
 - Loss: 1.7
 
 Although these metrics seem mediocre, it's actually better because that way the model is able to make open-ended responses, but is still coherent to the user's input.