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https://github.com/danvk/localturk
Mechanical Turk on your own machine.
https://github.com/danvk/localturk
Last synced: about 18 hours ago
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Mechanical Turk on your own machine.
- Host: GitHub
- URL: https://github.com/danvk/localturk
- Owner: danvk
- License: mit
- Created: 2013-02-24T21:44:23.000Z (over 11 years ago)
- Default Branch: main
- Last Pushed: 2024-09-22T20:39:41.000Z (about 2 months ago)
- Last Synced: 2024-10-18T21:58:47.515Z (29 days ago)
- Language: TypeScript
- Size: 241 KB
- Stars: 206
- Watchers: 9
- Forks: 33
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# 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.csvThen 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 tasksFor 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.pngThen you'd make an HTML template for the task:
```html
Has a red ball
Does not have a red ball
```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`.Alternatively, you can also pass in a text file containing the paths to images.
In this case, the images can be URLs, rather than paths to local files.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:
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!_If you'd like to reference sensitive data that you'd prefer not to commit to your repo
(an API key, say), you can pass it via a command-line parameter:localturk --var API_KEY=123456abcd
Then you can reference this as `${API_KEY}` in your template file.
## 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 testTo publish a new version on npm, run:
yarn tsc
yarn publish