CUDA-Dockerized Implementation of Hybrid (Generative and Retrieval) Based Conversational ChatBot Model in TensorFlow.

The current results are pretty lousy:

hello   	        - hello
how old are you ?   - twenty .
i am lonely	        - i am not
nice                - you ' re not going to be okay .
so rude	            - i ' m sorry .

picture

Curtesy of this article.

Setup

git clone [email protected]:AhmedAbdalazeem/ChatBot.git
cd tf_seq2seq_chatbot
bash setup.sh

Run

Train a seq2seq model on a small (17 MB) corpus of movie subtitles:

python train.py

(this command will run the training on a CPU... GPU instructions are coming)

Test trained trained model on a set of common questions:

python test.py

Chat with trained model in console:

python chat.py

All configuration params are stored at tf_seq2seq_chatbot/configs/config.py

GPU usage

If you are lucky to have a proper gpu configuration for tensorflow already, this should do the job:

python train.py

Otherwise you may need to build tensorflow from source and run the code as follows:

cd tensorflow  # cd to the tensorflow source folder
cp -r ~/tf_seq2seq_chatbot ./  # copy project's code to tensorflow root
bazel build -c opt --config=cuda tf_seq2seq_chatbot:train  # build with gpu-enable option
./bazel-bin/tf_seq2seq_chatbot/train  # run the built code

Requirements

• tensorflow

References

• https://github.com/nicolas-ivanov/tf_seq2seq_chatbot
• https://github.com/suriyadeepan/easy_seq2seq
• https://github.com/gunthercox/ChatterBot
• https://people.mpi-sws.org/~cristian/Cornell_Movie-Dialogs_Corpus.html