Our Mission

The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables.

We believe this is best done together with the community, supported by NLP and ML.

All content on this website is openly licenced under CC-BY-SA (same as Wikipedia) and everyone can contribute - look for the "Edit" buttons!

We also operate specialized portals for papers with code in astronomy, physics, computer sciences, mathematics and statistics.

Joining the community

Join our community of thousands of contributors across academia and industry!

You can also follow us and get in touch on Twitter and GitHub .


Anyone can contribute - look for the "Edit" buttons!

Want to submit a new code implementation? Search for the paper title, and then add the implementation on the paper page.

Want to add an evaluation table or a task? You'll see edit buttons on the paper and task pages - just go ahead and edit! We found this a fun way to learn about new areas of machine learning and staying in tune with research.

If you are running a competition, you can mirror the competition results on Papers with Code.

Please note that any contribution you make (i.e. linking code or submitting results) will be licensed under the free CC BY-SA licence.

Inclusion policy

To ensure high quality of data, all edits are monitored on Slack on the #recentchanges channel. This is an open channel and everyone is invited to follow and review contributions.

For a result to be included as a benchmark result we require that the paper is published as pre-print, in a conference or a journal. Having code is strongly encouraged but not required so we can capture the latest published results even before the code has been released.

Downloading the data

All data is licenced under the CC BY-SA licence, same as Wikipedia.

Download links:

Additional data sources

The vast majority of the data is either annotated by the community or ourselves. However, we also included data from other resources that are published under a compatible licence, such as NLP-progress, EFF AI metrics, SQuAD and RedditSota.

More information on what has been included and how, please see the paperswithcode/sota-extractor repository.


The core Papers with Code team is based in Meta AI Research.

Papers with Code is a community project. No data is shared with any Meta Platforms product.

All contributions are welcome!