by hamelsmu

Code For Medium Article "How To Create Data Products That Are Magical Using Sequence-to-Sequence Mod...

131 Stars 43 Forks Last release: Not found Apache License 2.0 32 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

GitHub license

Sequence-to-Sequence Tutorial with Github Issues Data

Code For Medium Article: "How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"


pip install -r requirements.txt

If you are using the AWS Deep Learning Ubuntu AMI, many of the required dependencies will already be installed, so you only need to run:

source activate tensorflow_p36
pip install ktext annoy nltk pydot

See #4 below if you wish to run this tutorial using Docker.


  1. Tutorial Notebook: The Jupyter notebook that coincides with the Medium post.

  2. convenience functions that are used in the tutorial notebook to make predictions.

  3. ktext: this library is used in the tutorial to clean data. This library can be installed with

  4. Nvidia Docker Container: contains all libraries that are required to run the tutorial. This container is built with Nvidia-Docker v1.0. You can install Nvidia-Docker and run this container like so:

curl -s -L |   sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L$distribution/nvidia-docker.list |   sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install nvidia-docker

sudo nvidia-docker run hamelsmu/seq2seq_tutorial

This should work with both Nvidia-Docker v1.0 and v2.0.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.