Socket
Socket
Sign inDemoInstall

localturk

Package Overview
Dependencies
88
Maintainers
1
Versions
16
Alerts
File Explorer

Advanced tools

Install Socket

Detect and block malicious and high-risk dependencies

Install

    localturk

Run Mechanical Turk-like tasks on your own.


Version published
Weekly downloads
32
increased by52.38%
Maintainers
1
Install size
3.69 MB
Created
Weekly downloads
 

Readme

Source

CircleCI

localturk

Local Turk implements Amazon's Mechanical Turk API on your own machine.

It's handy if you want to:

  1. Develop a Mechanical Turk template
  2. Do some repetitive tasks on your own, without involving Turkers.

You could use it, for instance, to generate test and training data for a Machine Learning algorithm.

Quick Start

Install:

npm install -g localturk

Run:

cd localturk/sample
localturk transcribe.html tasks.csv outputs.csv

Then visit http://localhost:4321/ to start Turking.

Templates and Tasks

Using Local Turk is just like using Amazon's Mechanical Turk. You create:

  1. An HTML template file with a <form>
  2. A CSV file of tasks

For example, say you wanted to record whether some images contained a red ball. You would make a CSV file containing the URLs for each image:

image_url
http://example.com/image_with_red_ball.png
http://example.com/image_without_red_ball.png

Then you'd make an HTML template for the task:

<img src="${image_url}" />
<input type=radio name=has_button value="yes" /> Has a red ball<br/>
<input type=radio name=has_button value="no" /> Does not have a red ball<br/>

Finally, you'd start up the Local Turk server:

$ localturk path/to/template.html path/to/tasks.csv path/to/output.csv

Now you can visit http://localhost:4321/ to complete each task. When you're done, the output.csv file will contain

image_url,has_button
http://example.com/image_with_red_ball.png,yes
http://example.com/image_without_red_ball.png,no

Image Classification

The use case described above (classifying images) is an extremely common one.

To expedite this, localturk provides a separate script for doing image classification. The example above could be written as:

classify-images --labels 'Has a red ball,Does not have a red ball' *.png

This will bring up a web server with a UI for assigning one of those two labels to each image on your local file system. The results will go in output.csv.

For more details, run classify-images --help.

Tips & Tricks

It can be hard to remember the exact format for template files. localturk can help! Run it with the --write-template argument to generate a template file for your input that you can edit:

localturk --write-template tasks.csv > template.html

When you're going through many tasks, keyboard shortcuts can speed things up tremendously. localturk supports these via the data-key attribute on form elements. For example, make yourer submit button look like this:

<input type="submit" name="result" value="Good" data-key="d">

Now, when you press d, it'll automatically click the "Good" button for you. Note that this feature is not available on mechanical turk itself!

Development

To make changes to localturk, clone it and set it up using yarn:

yarn

You can run localturk.ts or classify-images.ts directly using ts-node:

ts-node localturk.ts path/to/template.html path/to/tasks.csv path/to/output.csv

To type check and run the tests:

yarn tsc
yarn test

To publish a new version on npm, run:

yarn tsc
yarn publish

FAQs

Last updated on 08 Feb 2018

Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc