Brief summary
The Research team is working on improving the Machine Learning based tools to support Wikipedia Patrollers. As part of this effort we are developing a new model to detect revisions that require patrollers' attention (T314385).
The current model is based on implicit users’ feedback (revisions that have been reverted), and gives recommendations on which revisions are likely to be reverted. To improve this model and to make it easier to use its output, we want to build a web app that allows users to 1) rate the quality of the recommendation and 2) directly revert edits based on these recommendations.
Inspired by the SpeedPatrolling Tool, we want to build an app that allows users to give explicit feedback on our recommendations as well as allow them to directly revert revisions when needed. The app should be able to connect with our API to pull the recommendations and show them to the users. Also it should save the users feedback allowing to retrain/finetune our existing model.
Skills required
- Intermediate JavaScript
- HTML & CSS
- Familiarity with Flask
- Design/UX skills welcome but not required
- Experience with Sklearn welcome but not required
Mentor(s)
Microtasks
- Make sure that you can login to the PAWS service with your wiki account: https://paws.wmflabs.org/paws/hub
- Using this notebook as a starting point, create your own notebook (see these instructions for forking the notebook to start with) and complete the functions / analyses. All PAWS notebooks have the option of generating a public link, which can be shared back so that we can evaluate what you did. Use a mixture of code cells and markdown to document what you find and your thoughts.
- As you have questions, feel free to add comments to this task (and please don't hesitate to answer other applicant's questions if you can help)
- If you feel you have completed your notebook, you may request feedback and we will provide high-level feedback on what is good and what is missing. To do so, send an email to your mentor with the link to your public PAWS notebook. We will try to make time to give this feedback at least once to anyone who would like it.
- When you feel you are happy with your notebook, you should include the public link in your final Outreachy project application as a recorded contribution. You may record contributions as you go as well to track progress.