Welcome to the contribution guide to Hugging Face Tasks and thank you for considering contributing to the community!
The Task pages are made to lower the barrier of entry to understand a task that can be solved with machine learning and use or train a model to accomplish it. It's a collaborative documentation effort made to help out software developers, social scientists, or anyone with no background in machine learning that is interested in understanding how machine learning models can be used to solve a problem.
The task pages avoid jargon to let everyone understand the documentation, and if specific terminology is needed, it is explained on the most basic level possible. This is important to understand before contributing to Tasks: at the end of every task page, the user is expected to be able to find and pull a model from the Hub and use it on their data and see if it works for their use case to come up with a proof of concept.
You can open a pull request to hub-docs repository to contribute a new documentation about a new task. Under tasks/src we have a folder for every task that contains two files, about.md and data.ts. about.md contains the markdown part of the page, use cases, resources and minimal code block to infer a model that belongs to the task. data.ts contains redirections to canonical models and datasets, metrics, the schema of the task and the information the inference widget needs.
We have tasks/assets that contains data used in the inference widget and images used in the markdown file. The last file is const.ts, which has the task to library mapping (e.g. spacy to token-classification) where you can add a library. They will look in the top right corner like below.
This might seem overwhelming, but you don't necessarily need to add all of these in one pull request or on your own, you can simply contribute one section. Feel free to ask for help whenever you need.