add README in template repo

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+# SUPERB Submission Template
+
+This repository can be used to generate a template so you can submit your pretrained model for evaluation on [the leaderboard](https://huggingface.co/spaces/superb/superb-leaderboard) in the [SUPERB Challenge](https://superbbenchmark.org/challenge).
+
+## Quickstart
+
+### 1. Create an account and organisation on the Hugging Face Hub
+
+First create an account on the Hugging Face Hub and you can sign up [here](https://huggingface.co/join) if you haven't already! Next, create a new organization and invite the SUPERB Hidden Set Committee to join. You will upload your model to a repository under this organization so that members inside it can access the model.
+
+* [superb-hidden-set](https://huggingface.co/superb-hidden-set)
+
+### 2. Create a template repository on your machine
+
+The next step is to create a template repository on your local machine that contains various files and a CLI to help you validate and submit your pretrained models. The Hugging Face Hub uses [Git Large File Storage (LFS)](https://git-lfs.github.com) to manage large files, so first install it if you don't have it already. For example, on macOS you can run:
+
+```bash
+brew install git-lfs
+git lfs install
+```
+
+Next, run the following commands to create the repository. We recommend creating a Python virtual environment for the project, e.g. with Anaconda:
+
+```bash
+# Create and activate a virtual environment
+conda create -n superb-submit python=3.8 && conda activate superb-submit
+# Install the following libraries
+pip install cookiecutter huggingface-hub==0.0.16
+# Create the template repository
+cookiecutter git+https://huggingface.co/superb/superb-submission
+```
+
+This will ask you to specify your Hugging Face Hub username, password, organisation, and the name of the repository:
+
+```
+hf_hub_username [<huggingface>]:
+hf_hub_password [<password>]:
+hf_hub_organisation [superb-submissions]:
+repo_name [<my-superb-submissions>]:
+```
+
+This will trigger the following steps:
+
+1. Create a private dataset repository on the Hugging Face Hub under `{hf_hub_organisation}/{repo_name}`
+2. Clone the repository to your local machine
+3. Add various template files, commit them locally to the repository, and push them to the Hub
+
+The resulting repository should have the following structure:
+
+```
+my-superb-submission
+├── LICENSE
+├── README.md               <- The README with submission instructions
+├── cli.py                  <- The CLI for validating predictions etc
+└── requirements.txt        <- The requirements packages for the submissions
+├── expert.py               <- Your model definition
+└── model.pt                <- Your model weights
+```
+
+### 3. Install the dependencies
+
+The final step is to install the project's dependencies:
+
+```bash
+# Navigate to the template repository
+cd my-superb-submission
+# Install dependencies
+python -m pip install -r requirements.txt
+```
+
+That's it! You're now all set to start pretraining your speech models - see the instructions below on how to submit them to the Hub.
+
+
+## Submitting to the leaderboard
+
+To make a submission to the [leaderboard](https://superbbenchmark.org/leaderboard), there are 4 main steps:
+
+1. Modify `expert.py` and `model.py` so we can initialize an upstream model following the [policy](https://superbbenchmark.org/challenge) by:
+
+    ```python
+    upstream = UpstreamExpert(ckpt="./model.pt")
+    ```
+
+2. Validate the upstream model meets the requirements in the [policy](https://superbbenchmark.org/challenge). If everything is correct, you should see the following message: "All submission files validated! Now you can make a submission."
+
+    ```
+    python cli.py validate
+    ```
+
+3. Push the predictions to the Hub! If there are no errors, you should see the following message: "Upload successful!"
+
+    ```
+    python cli.py upload "commit message: my best model"
+    ```
+
+4. [Make a submission at SUPERB website](https://superbbenchmark.org/submit) by uniquely indentifying this submission/model with the following information, which will be shown by:
+
+    ```
+    python cli.py info
+    ```
+
+    - Organization Name
+    - Repository Name
+    - Commit Hash (full 40 characters)
+
+After you finish the above 4 steps. Please stay tuned and wait for us to get the finetuned results on the hidden set!