Home Projects DeepQA
DeepQA
Python

DeepQA

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

by Conchylicultor · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
chatbotdeep-learningseq2seqApache-2.0Python
View on GitHub
In plain words

Run a chatbot that can have conversations by predicting sentences based on a trained model using TensorFlow.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
DeepQA — GitHub preview card
📈 Star history
2 9122 911
2026-07-072026-08-31
📈 Track DeepQA

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

DeepQA has 2.9k stars on GitHub. It has been forked 1.2k times. DeepQA is written mainly in Python. It has been in active development since 2016. DeepQA is available under the Apache-2.0 license. Its main topics are chatbot, deep-learning, seq2seq, tensorflow.

Frequently asked questions

What is DeepQA?

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

Is DeepQA open source?

DeepQA is an open-source project. It is released under the Apache-2.0 license.

Is DeepQA free?

Yes. DeepQA is free and open source — you can use, modify and self-host it.

What license does DeepQA use?

DeepQA is available under the Apache-2.0 license.

What language is DeepQA written in?

DeepQA is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — DeepQA

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=conchylicultor-deepqa)](https://olud.ai/project/conchylicultor-deepqa.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.