Seq2seq-Chatbot-for-Keras
Basic Information
This repository contains a generative chatbot model based on sequence-to-sequence (seq2seq) modeling implemented with Keras. It is intended as a reference implementation for people who want to see how an encoder–decoder architecture can be applied to conversational response generation. The repo's main purpose is to demonstrate a Keras-based approach to building a generative dialogue agent, providing a starting point for experimentation, adaptation to different conversation datasets, and education about seq2seq workflows. It targets developers, students, and researchers who need a concrete example of a neural conversational model implemented in a popular deep learning framework.
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App Details
Features
Implements a seq2seq-style generative chatbot architecture using the Keras framework. Focuses on encoder–decoder modeling for producing conversational responses. Serves as a compact, framework-native example suitable for learning and experimentation. Designed to be adaptable and extended to custom dialog datasets or modified decoding strategies. Emphasizes core model structure rather than production integrations. Useful as a baseline implementation to compare modeling choices and to reproduce basic generative chatbot behavior with Keras.
Use Cases
Provides a concise Keras-based example of how to build a generative conversational agent using seq2seq techniques, which helps users learn encoder–decoder modeling and experiment with dialog generation. Acts as a practical starting point for prototyping chatbots, testing training and inference ideas, and adapting models to new conversational data. Useful for educators to illustrate seq2seq concepts and for researchers or developers who need a simple baseline to extend, fine-tune, or benchmark their own improvements in Keras.
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