Welcome to our demo on building a Gemma chatbot with Keras!

In this demo, we will walk you through the process of creating a chatbot using the Gemma dataset and the Keras deep learning framework. Gemma is a dataset of dialogues between a human and a robot, which can be used for training chatbots.

Step 1: Preprocessing the data

First, we need to preprocess the Gemma dataset to prepare it for training our chatbot. This involves tokenizing the text, removing special characters, and converting the text to lowercase. We will also split the dataset into training and testing sets.

Step 2: Building the model

Next, we will build a deep learning model using Keras. We will use a recurrent neural network (RNN) with LSTM cells to learn patterns in the dialogues and generate responses to user inputs. We will train the model on the preprocessed Gemma dataset.

Step 3: Testing the chatbot

Once the model is trained, we can test the chatbot by interacting with it using sample inputs. We can evaluate the performance of the chatbot by comparing its responses to the ground truth responses in the Gemma dataset.

Conclusion

Building a chatbot with Gemma and Keras is a fun and challenging project that can help you learn more about deep learning and natural language processing. We hope this demo has inspired you to create your own chatbot using these tools.