heres the full code of the repo changed the dataset first
https://github.com/Abdullaah-Ali/chatbot
- Setting Up the Environment: Before diving into the development process, it's essential to set up the environment properly. First, ensure you have the necessary libraries installed. You'll need Keras, Numpy, and other dependencies. You can install them using pip:
pip install keras numpy
- Understanding the Dataset: A well-prepared training dataset is crucial for the success of your chatbot. Ensure your dataset is formatted correctly and preprocessed for training.The quality of your dataset will significantly impact the performance of the chatbot.
3 The Keras Functional API : provides a flexible way to define complex models. Designing the architecture of your chatbot model involves understanding the structure of the neural network. You'll need to decide on the number of layers, the type of layers, and the connections between them. Each layer serves a specific purpose, such as processing input data, extracting features, and generating output responses.
4 With the architecture defined :, it's time to implement the m4 odel using the Keras Functional API. Start by coding the layers according to the design. Then, compile the model by specifying the appropriate loss function and optimizer. Split the dataset into training and validation sets to evaluate the model's performance during training.
output = Dense(len(label_encoder.classes_), activation='softmax')(pooled_output)
# Define the model
model = Model(inputs=input_text, outputs=output)
# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(padded_sequences, encoded_answers, epochs=600, batch_size=200)
as the architecture is definned run the bot on the machine and your model would be trained
heres the full code
https://github.com/Abdullaah-Ali/chatbot
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