npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@huggingface/tasks

v0.13.13

Published

List of ML tasks for huggingface.co/tasks

Downloads

391,385

Readme

Tasks

This package contains the definition files (written in Typescript) for the huggingface.co hub's:

  • pipeline types (a.k.a. task types) - used to determine which widget to display on the model page, and which inference API to run.
  • default widget inputs - when they aren't provided in the model card.
  • definitions and UI elements for model and dataset libraries.

Please add any missing ones to these definitions by opening a PR. Thanks 🔥

⚠️ The hub's definitive doc is at https://huggingface.co/docs/hub.

Definition of Tasks

This package also contains data used to define https://huggingface.co/tasks.

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.

How to Contribute

You can open a pull request to contribute a new documentation about a new task. Under src/tasks 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.

Anatomy of a Task Page

We have a dataset that contains data used in the inference widget. 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.

Libraries of a Task

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.

Feedback (feature requests, bugs, etc.) is super welcome 💙💚💛💜♥️🧡