GitHub license Python 3.6 GitHub issues

Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"

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The techniques presented here are old and have been significantly refined in a subsequent project called CodeSearchNet, with an associated paper.

I recommend looking at the aforementioned project for a more modern approach to this topic, as in retrospect this blog post is somewhat of an ugly hack.

You can use these container to reproduce the environment the authors used for this tutorial. Incase it is helpful, I have provided a requirements.txt file, however, we highly recommend using the docker containers provided below as the dependencies can be complicated to build yourself.

• hamelsmu/ml-gpu: Use this container for any gpu bound parts of the tutorial. We recommend running the entire tutorial on an aws p3.8xlarge and using this image.

• hamelsmu/ml-cpu: Use this container for any cpu bound parts of this tutorial.

The notebooks folder contains 5 Jupyter notebooks that correspond to Parts 1-5 of the tutorial.

This tutorial assumes knowledge of the material presented in a previous tutorial on sequence-to-sequence models.

We have made best attempts to make sure running this tutorial is as painless as possible. If you think something can be improved, please submit a PR!