Conversation Model Training
#Project Overview
This repository contains the code for training a sequence-to-sequence (seq2seq) model using conversational data. The model is designed to understand and generate responses based on previous exchanges, which is fundamental for applications such as chatbots, customer service automation, and more. The model leverages TensorFlow and a LSTM-based architecture to handle sequence data effectively.
#Features
. Seq2Seq Model: Uses an LSTM-based encoder-decoder architecture to process and generate text sequences. . Data Preprocessing: Includes tokenization and sequence padding to prepare text data for training. . Incremental Learning: Supports training the model incrementally with new data batches, simulating an online learning environment.
#Prerequisites Before you begin, ensure you have met the following requirements:
Python 3.6+ TensorFlow 2.x NumPy Other dependencies listed in requirements.txt
#Installation To install the necessary Python packages and libraries, run the following command:
bash Copy code pip install -r requirements.txt Usage Here’s a quick guide on how to train the model with your data:
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Prepare your data: Your data should be formatted as a list of conversations, with each conversation containing sequential exchanges between a client and an agent.
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Modify the data paths: Adjust the data loading paths and preprocessing steps as per your dataset structure.
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Train the model:
#Contributing Contributions to the project are welcome! If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
#License Distributed under the MIT License. See LICENSE for more information.
#Contact Tun Hein - [email protected]