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--- |
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title: Dadc |
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emoji: 🏢 |
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colorFrom: red |
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colorTo: gray |
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sdk: gradio |
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sdk_version: 3.0.17 |
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app_file: app.py |
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pinned: false |
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license: bigscience-bloom-rail-1.0 |
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--- |
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A basic example of dynamic adversarial data collection with a Gradio app. |
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*Instructions for someone to use for their own project:* |
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**Setting up the Space** |
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1. Clone this repo and deploy it on your own Hugging Face space. |
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2. Add one of your Hugging Face tokens to the secrets for your space, with the |
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name `HF_TOKEN`. Now, create an empty Hugging Face dataset on the hub. Put |
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the url of this dataset in the secrets for your space, with the name |
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`DATASET_REPO_URL`. It can be a private or public dataset. When you run this |
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space on mturk in the following lines, the app will use your token to |
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automatically store new hits to your dataset. |
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**Running Data Collection** |
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1. On your local repo that you pulled, create a copy of `config.py.example`, |
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just called `config.py`. Now, put keys from your AWS account in `config.py`. |
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These keys should be for an AWS account that has the |
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AmazonMechanicalTurkFullAccess permission. You also need to |
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create an mturk requestor account associated with your AWS account. |
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2. Run `python collect.py` locally. If you run it with the `--live_mode` flag, |
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it launches HITs on mturk, using the app you deployed on the space as the |
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data collection UI and backend. NOTE: this means that you will need to pay |
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real workers. If you don't use the `--live_mode` flag, then it will run the |
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HITs on mturk sandbox, which is identical to the normal mturk, but just for |
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testing. You can create a worker account and go to the sandbox version to |
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test your HIT. |
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**Profit** |
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Now, you should be watching hits come into your Hugging Face dataset |
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automatically! |
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