https://github.com/hamelsmu/code_search

Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"
https://github.com/hamelsmu/code_search

code-search data-science deep-learning fastai keras machine-learning machine-learning-on-source-code ml-on-code natural-language-processing nlp python pytorch search search-algorithm searching-algorithms semantic-search semantic-search-engine tensorflow tutorial

Last synced: 3 months ago
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Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"

• Host: GitHub
• URL: https://github.com/hamelsmu/code_search
• Owner: hamelsmu
• License: mit
• Created: 2018-05-07T13:41:00.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T02:10:59.000Z (about 2 years ago)
• Last Synced: 2024-10-16T02:46:07.601Z (4 months ago)
• Topics: code-search, data-science, deep-learning, fastai, keras, machine-learning, machine-learning-on-source-code, ml-on-code, natural-language-processing, nlp, python, pytorch, search, search-algorithm, searching-algorithms, semantic-search, semantic-search-engine, tensorflow, tutorial
• Language: Jupyter Notebook
• Homepage: https://medium.com/@hamelhusain/semantic-code-search-3cd6d244a39c
• Size: 73.6 MB
• Stars: 490
• Watchers: 24
• Forks: 138
• Open Issues: 28
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
[![GitHub license](https://img.shields.io/github/license/hamelsmu/code_search.svg)](https://github.com/hamelsmu/code_search/blob/master/LICENSE)

[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

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## Semantic Code Search

Code For Medium Article: "[How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning](https://medium.com/@hamelhusain/semantic-code-search-3cd6d244a39c)"

![Alt text](./gifs/live_search.gif)

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## Warning - This Project Is Deprecated In Favor Of [CodeSearchNet](https://github.com/github/codesearchnet)

**The techniques presented here are old and have been significantly refined in a subsequent project called [CodeSearchNet](https://github.com/github/codesearchnet), with an associated [paper](https://arxiv.org/abs/1909.09436).**

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.

## Resources

#### Docker Containers

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](./requirements/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](https://hub.docker.com/r/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](https://hub.docker.com/r/hamelsmu/ml-cpu/): Use this container for any *cpu* bound parts of this tutorial.

 #### Notebooks

 The [notebooks](./notebooks) folder contains 5 Jupyter notebooks that correspond to Parts 1-5 of the tutorial.

#### Related Blog Posts

This tutorial assumes knowledge of the material presented in [a previous tutorial on sequence-to-sequence models](https://towardsdatascience.com/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8).

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## PRs And Comments Are Welcome

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!