https://github.com/nicolas-ivanov/tf_seq2seq_chatbot

[unmaintained]
https://github.com/nicolas-ivanov/tf_seq2seq_chatbot

chatbot seq2seq tensorflow

Last synced: 8 days ago
JSON representation

[unmaintained]

• Host: GitHub
• URL: https://github.com/nicolas-ivanov/tf_seq2seq_chatbot
• Owner: nicolas-ivanov
• Created: 2015-12-07T16:52:57.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2020-04-14T15:18:49.000Z (over 4 years ago)
• Last Synced: 2024-08-02T08:09:53.256Z (3 months ago)
• Topics: chatbot, seq2seq, tensorflow
• Language: Python
• Homepage:
• Size: 18.3 MB
• Stars: 422
• Watchers: 48
• Forks: 205
• Open Issues: 17
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## tensorflow seq2seq chatbot

> *Note: the repository is not maintained. Feel free to PM me if you'd like to take up the maintainance.*

Build a general-purpose conversational chatbot based on a hot 

seq2seq approach implemented in [tensorflow](https://www.tensorflow.org/versions/master/tutorials/seq2seq/index.html#sequence-to-sequence_basics).

Since it doesn't produce good results so far, also consider other implementations of [seq2seq](https://github.com/nicolas-ivanov/seq2seq_chatbot_links).

The current results are pretty lousy:

    hello baby	        - 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 .

    

Disclaimer: 

* the answers are hand-picked (it looks cooler that way)

* chatbot has no power to follow the conversation line so far; in the example above it's a just a coincidence (hand-picked one)

Everyone is welcome to investigate the code and suggest the improvements.

**Actual deeds**

* realise how to diversify chatbot answers (currently the most probable one is picked and it's dull)

**Papers**

* [Sequence to Sequence Learning with Neural Networks](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)

* [A Neural Conversational Model](http://arxiv.org/pdf/1506.05869v1.pdf)

**Nice picture**

[![seq2seq](https://4.bp.blogspot.com/-aArS0l1pjHQ/Vjj71pKAaEI/AAAAAAAAAxE/Nvy1FSbD_Vs/s640/2TFstaticgraphic_alt-01.png)](http://4.bp.blogspot.com/-aArS0l1pjHQ/Vjj71pKAaEI/AAAAAAAAAxE/Nvy1FSbD_Vs/s1600/2TFstaticgraphic_alt-01.png)

Curtesy of [this](http://googleresearch.blogspot.ru/2015/11/computer-respond-to-this-email.html) article.

**Setup**

    git clone [email protected]:nicolas-ivanov/tf_seq2seq_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](https://www.tensorflow.org/versions/master/get_started/os_setup.html)