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A Machine Learning Law Match Maker
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static enh: results sponsors are text #3 Mar 29, 2018
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README.md

LOC Challenge AI

a Machine Learning Law Match Maker

This application takes any text input and returns the most similar congressional bills using the machine learning WMD Similarity Model and Google's 2013 pretrained word embeddings

Application Installation

⏵ pip3 install --user --upgrade flask
⏵ git clone git@github.com:whs2k/GPO-AI.git

Running

⏵ cd loc_challenge_aim
⏵ env FLASK_APP=challenge.py flask run

Or shortcut:

⏵ make run

Recreate This Work:

  1. Scrape GPO data (notebooks/1.0-whs-xmlExtract.ipynb)
  2. Save Data as CSV (data/1.3-billTitleSponsors.csv)
  3. Build and Train WMD Model (notebooks/1.0-whs-xmlExtract.ipynb)
  4. Save Model and Similarity Instance; three files (model/-gitignored-)
  5. Install and Run Application
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