The goal of this task is to get a working, query-able KubeFlow instance that replicates the functionality of our article topic models.
For example, if you provide a revision ID to ORES, it gives you a topic prediction. https://en.wikipedia.org/w/index.php?title=Ann_Bishop_(biologist)&oldid=937084701 links to a specific version of the article about Ann Bishop. We can provide that ID (937084701) to ORES and ask for a topic prediction with this query: https://ores.wikimedia.org/v3/scores/enwiki/937084701/articletopic
We get a result that looks like this:
[...]
"prediction": [
"Culture.Biography.Biography*",
"Culture.Biography.Women",
"History and Society.History",
"STEM.Medicine & Health",
"STEM.STEM*"
],
"probability": {
"Culture.Biography.Biography*": 0.9897817848322134,
"Culture.Biography.Women": 0.9723014590702798,
"Culture.Food and drink": 0.00035026153227330815,
"Culture.Internet culture": 0.00018265332725578013,
"Culture.Linguistics": 0.000538261749894609,
"Culture.Literature": 0.03619774139697521,
"Culture.Media.Books": 0.002291345684133623,
"Culture.Media.Entertainment": 0.0006008800036119385,
"Culture.Media.Films": 0.000301603482794946,
"Culture.Media.Media*": 0.006322510257768613,
"Culture.Media.Music": 0.0003659734842755226,
"Culture.Media.Radio": 9.197231374768e-05,
"Culture.Media.Software": 0.00015020779276450603,
"Culture.Media.Television": 0.00030685931095892125,
"Culture.Media.Video games": 3.5082704979932084e-05,
"Culture.Performing arts": 0.0010131663084430853,
"Culture.Philosophy and religion": 0.07665653521788206,
"Culture.Sports": 0.0047852147462091885,
"Culture.Visual arts.Architecture": 0.0016610955992546084,
[...]Our pipeline looks roughly like this:
- Model
- Extracted features and labels