https://github.com/tandembank/data-science.dataset-labeller

Web-based tool for labelling datasets
https://github.com/tandembank/data-science.dataset-labeller

datascience dataset-generation datasets django javascript python react

Last synced: about 2 months ago
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Web-based tool for labelling datasets

• Host: GitHub
• URL: https://github.com/tandembank/data-science.dataset-labeller
• Owner: tandembank
• License: mit
• Created: 2018-10-23T16:22:35.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-24T17:44:26.000Z (over 5 years ago)
• Last Synced: 2024-07-09T15:56:55.035Z (2 months ago)
• Topics: datascience, dataset-generation, datasets, django, javascript, python, react
• Language: JavaScript
• Size: 414 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Dataset labeller

[![](https://img.shields.io/travis/tandembank/data-science.dataset-labeller.svg)](https://travis-ci.org/tandembank/data-science.dataset-labeller) [![](https://img.shields.io/codecov/c/gh/tandembank/data-science.dataset-labeller.svg)](https://codecov.io/gh/tandembank/data-science.dataset-labeller) [![](https://img.shields.io/github/license/tandembank/data-science.dataset-labeller.svg)](https://github.com/tandembank/data-science.dataset-labeller/blob/master/LICENSE)

This is a web-based tool we developed to label datasets quickly at [Tandem](https://tandem.co.uk). It is based on Python, Django, React and run through Docker Compose.

![Creating a new dataset and starting to label](https://epixstudios.co.uk/uploads/filer_public/7f/62/7f62f0ad-9cf3-47ba-9ad6-79282f456c7f/dataset_labeller_demo.gif)

## Installation and Running

The easiest way to get this application running is via [Docker Compose](https://docs.docker.com/compose/install/). Once you have this working, run the following commands to install.

    git clone https://github.com/tandembank/data-science.dataset-labeller.git

    cd data-science.dataset-labeller

    cp docker-compose.example.yml docker-compose.yml

    docker-compose build

You should now have the Docker image built. To run it, along with it's database server, run this from the same loaction:

    docker-compose up

After a few seconds you should be able to access it via http://localhost:8080/ in your browser.

## Usage

This is the process for labelling a new dataset:

  1. Upload a CSV file containing rows that you want to label.

  2. Give it a name.

  3. Select the columns that should be displayed to a person labelling.

  2. Define the possible category labels and keyboard shortcuts to make things faster.

  3. Decide how many people need to label each row datapoint – this is useful if you want to get a consensus.

  4. Save dataset.

  5. Get your team to login and label it.

  6. View job progress on the dashboard.

  7. Download the labelled dataset as a CSV – it'll have an extra column with the labels

## Features

  * Import and export data in the format that you're comfortable with – no need to pre-process data, just select the columns to display for labelling.

  * Each user has their own account so you can see who labelled what.

  * Labellers can access the tool remotely or within a corporate network using just their web browser.

  * Slick and quick user interface while you're labelling – the next few datapoints are already loaded in your browsers so they're ready to show as soon as you've labelled the current one.

  * Multiple users can be labelling at once as we use locks to avoid collisions.

  * If some datapoints are tricky to label or your team are going at break-neck speed you can choose to get a consensus from an odd number of users, say 3 or 5.

  * Database included and configured in the Docker Compose file.

  * Cell content such as JSON lists gets displayed nicely formatted. We aim to extend this to identify other formats and image URLs.