---
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 and when people visit your space on huggingface.co, the app
   will use your token to automatically store new HITs in your dataset. NOTE:
   if you push something to your dataset manually, you need to reboot your space
   or it could get merge conflicts when trying to push HIT data.

*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.

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

*Tips and Tricks*
- If you are developing and running this space locally to test it out, try
deleting the data directory that the app clones before running the app again.
Otherwise, the app could get merge conflicts when storing new HITs on the hub.
When you redeploy your app on Hugging Face spaces, the data directory is deleted
automatically.
- huggingface spaces have limited computational resources and memory. If you
run too many HITs and/or assignments at once, then you could encounter issues.
You could also encounter issues if you are trying to create a dataset that is
very large. Check the log of your space for any errors that could be happening.