update README

README.md

@@ -12,9 +12,9 @@ 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:*
+**Instructions for someone to use for their own project:**
 
-**Setting up the Space**
+*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
@@ -25,7 +25,7 @@ A basic example of dynamic adversarial data collection with a Gradio app.
    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**
+*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
@@ -33,11 +33,11 @@ A basic example of dynamic adversarial data collection with a Gradio app.
    create an mturk requestor account associated with your AWS account.
 2. Run `python collect.py` locally.
 
-**Profit**
+*Profit*
 Now, you should be watching hits come into your Hugging Face dataset
 automatically!
 
-**Tips and Tricks**
+*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.