README.md

metadata

title: Dadc
emoji: 🏢
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 3.0.17
app_file: app.py
pinned: false
license: bigscience-bloom-rail-1.0

A basic example of dynamic adversarial data collection with a Gradio app.

Instructions for someone to use for their own project:

Setting up the Space

1. Clone this repo and deploy it on your own Hugging Face space.
2. Add one of your Hugging Face tokens to the secrets for your space, with the name HF_TOKEN. Now, create an empty Hugging Face dataset on the hub. Put the url of this dataset in the secrets for your space, with the name DATASET_REPO_URL. It can be a private or public dataset. When you run this space on mturk in the following lines, the app will use your token to automatically store new hits to your dataset.

Running Data Collection

1. On your local repo that you pulled, create a copy of config.py.example, just called config.py. Now, put keys from your AWS account in config.py. These keys should be for an AWS account that has the AmazonMechanicalTurkFullAccess permission. You also need to create an mturk requestor account associated with your AWS account.
2. Run python collect.py locally. If you run it with the --live_mode flag, it launches HITs on mturk, using the app you deployed on the space as the data collection UI and backend. NOTE: this means that you will need to pay real workers. If you don't use the --live_mode flag, then it will run the HITs on mturk sandbox, which is identical to the normal mturk, but just for testing. You can create a worker account and go to the sandbox version to test your HIT.

Profit Now, you should be watching hits come into your Hugging Face dataset automatically!