Simple chatbot implementation with PyTorch.
The implementation should be easy to follow for beginners and provide a basic understanding of chatbots. The implementation is straightforward with a Feed Forward Neural net with 2 hidden layers. Customization for your use case is super easy. Just modify intents.json with possible patterns and responses and re-run the training (see below for more info). The approach is inspired by this article and ported to PyTorch: https://chatbotsmagazine.com/contextual-chat-bots-with-tensorflow-4391749d0077.
The training data is stored in the intents.json file. You can customize the chatbot by modifying this file and re-running the training.
The chatbot uses a Feed Forward Neural Net with 2 hidden layers. The input and hidden layers use the ReLU activation function, while the output layer does not use any activation function.
The chatbot makes predictions for new sentences by tokenizing and stemming the input, converting it into a bag-of-words representation, and feeding it into the neural network to produce a probability distribution over the different intents. The chatbot selects the intent with the highest probability and uses the associated response to generate a message back to the user.
This project is licensed under the MIT License - see the LICENSE file for details.