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NYT-Based News Tagger

A labeller for news articles trained on the NYT annotated corpus by Jasmin Rubinovitz as part of the MIT Media Lab SuperGlue project. Give it the clean text of a story (i.e. no html content), and it returns various descriptors and taxonomic classifiers based on models trained on the tagging in the NYT corpus.

Note - we have not formally assessed these models for embedded bias. Surely they have many, because they are based on the Google News word2vec model and New York Times historical tagging. Be aware as you use results that they likely reflect historical American cultural biases in news reporting.

We use it in the Media Cloud project to automatically tag all news stories with the themes we think they are about.

Running Via DockerHub

The quickest path to running this is to fetch the latest release from DockerHub:

docker pull rahulbot/nyt-news-labeler:latest
docker run -p 8000:8000 -m 8G -d rahulbot/nyt-news-labeler:latest

Then just hit a http://localhost:8080/ to test it out.

Local Dev Installation

This is an old set of code, so it can be hard to install and run locally. The serialization of the models is tied to a specific version of ternsorflow, which can make this hard to install as well.

  1. Install Python 3.7 (See scripts/setup-mac-os.sh for tips)
  2. Install python requirements: pip install -r requirements.txt
  3. Install brotli: brew install brotli (on MacOS)
  4. Download the models: download_models.py (this will take 10+ minutes, depending on your internet speed)

Run ./run.sh. Note: this consumes about 8 GB of memory while running, to keep all the models loaded up.

Web Test Harness

This exposes a simple web UI to make testing easier. Visit localhost:8000/ to try it out. You can paste any raw text in, and click "Get Labels". In a second you will see the top 30 labels from each model below the input.

API

For batch processing this exposes a simple API. You can make a request like this:

curl -X POST http://localhost:8000/predict.json -H "Content-Type: application/json" -d '{"text": "Federal agents show stronger force at Portland protests despite order to withdraw" }'

You will get back results like this:

{
   "milliseconds":77.39500000000001,
   "predictions":{
      "allDescriptors":[
         {
            "label":"demonstrations and riots",
            "score":"0.28221"
         },
         {
            "label":"politics and government",
            "score":"0.03751"
         },
         ...
      ],
      "descriptors3000":[
         {
            "label":"company reports",
            "score":"0.74512"
         },
         {
            "label":"demonstrations and riots",
            "score":"0.64673"
         },
         ...
      ],
      "descriptors600":[
         {
            "label":"demonstrations and riots",
            "score":"0.65299"
         },
         {
            "label":"politics and government",
            "score":"0.09620"
         },
         ...
      ],
      "descriptorsAndTaxonomies":[
         {
            "label":"demonstrations and riots",
            "score":"0.43143"
         },
         {
            "label":"top/news",
            "score":"0.27492"
         },
         ...
      ],
      "taxonomies":[
         {
            "label":"Top/Features/Travel/Guides/Destinations/North America/United States/Oregon",
            "score":"0.35107"
         },
         {
            "label":"Top/News",
            "score":"0.18331"
         },
         ...
      ]
   },
   "status":"OK",
   "version":"1.1.0"
}

Releasing to Docker Hub

When you creating a new release, be sure to increment the VERSION constant in app.py. Then tag the repo with the same number.

I build and release this to DockerHub for easier deployment on your server. To release the latest code I run:

docker build -t rahulbot/nyt-news-labeler .
docker push rahulbot/nyt-news-labeler

To release a tagged version, I something like this run:

docker build -t rahulbot/nyt-news-labeler:1.1.0 .
docker push rahulbot/nyt-news-labeler:1.1.0

To run a container I've built locally I do:

docker run -p 8000:8000 -m 8G rahulbot/nyt-news-labeler