Get revscoring articletopic prediction

POST	/service/lw/inference/v1/models/{wiki}-articletopic:predict

Get a score from the Revscoring Article Topic model (previously hosted on ORES) for a given Wiki revision id.

Note: For historical reasons, the articletopic model that is used for wikidata items uses a different URL scheme (item instead of article), thus the URI for wikidata is /service/lw/inference/v1/models/wikidatawiki-itemtopic:predict.

Examples

curl

Anonymous access

# Get a score from the Revscoring Article Topic model for the revision 12345 of English Wikipedia.
$ curl https://api.wikimedia.org/service/lw/inference/v1/models/enwiki-articletopic:predict -X POST -d '{"rev_id": 12345}' -H "Content-type: application/json"

Logged in access

# Get a score from the Revscoring Article Topic model for the revision 12345 of English Wikipedia.
$ curl https://api.wikimedia.org/service/lw/inference/v1/models/enwiki-articletopic:predict -X POST -d '{"rev_id": 12345}' -H "Authorization: Bearer YOUR_ACCESS_TOKEN" -H "Content-type: application/json"

Python

# Python 3
# Get a score from the Revscoring Article Topic model for the revision 12345 of English Wikipedia.

import json
import requests

use_auth = False
inference_url = 'https://api.wikimedia.org/service/lw/inference/v1/models/enwiki-articletopic:predict'

if use_auth:
  headers: {
      'Authorization': 'Bearer YOUR_ACCESS_TOKEN',
      'User-Agent': 'YOUR_APP_NAME (YOUR_EMAIL_OR_CONTACT_PAGE)',
      'Content-type': 'application/json'
  }
else:
  headers = {}
data = {"rev_id": 12345 }
response = requests.post(inference_url, headers=headers, data=json.dumps(data))
print(response.json())

JavaScript

/*
Get a score from the Revscoring Article Topic model for the revision 12345 of English Wikipedia.
*/

const inferenceUrl = "https://api.wikimedia.org/service/lw/inference/v1/models/enwiki-articletopic:predict";
const accessToken = "YOUR_ACCESS_TOKEN";
const appName = "YOUR_APP_NAME";
const email = "YOUR_EMAIL_OR_CONTACT_PAGE";
let headers = new Headers({
    "Content-Type": "application/json",
    "Authorization": "Bearer " + accessToken,
    "User-Agent": appName + " ( " + email + " )"
});
let data = {"rev_id": 12345 };

fetch(inferenceUrl, {
    method: "POST",
    headers: headers,
    body: JSON.stringify(data)
})
.then(response => response.json())
.then(inferenceData => console.log(inferenceData));

URI Parameters

wiki required

Wiki code:arwiki (Arabic), cswiki (Czech), enwiki (English), euwiki (Basque), huwiki (Hungarian), hywiki (Armenian), kowiki (Korean), srwiki (Serbian), ukwiki (Ukrainian), viwiki (Vietnamese), wikidatawiki (Wikidata, see note above).

POST Parameters

rev_id required	Wiki Revision id: integer related to a certain revision id for the Wiki set in the URI parameter.
extended_output	Whether or not the response should include the extended output of the model (like the list of features used etc..). Either true or false. Default: false

Responses

200

Success: Revision id found. Returns a Revscoring score object. Example { "enwiki": { "models": { "articletopic": { "version": "1.3.0" } }, "scores": { "12345": { "articletopic": { "score": { "prediction": [ "STEM.STEM*" ], "probability": { "Culture.Biography.Biography*": 0.0037221493203970753, "Culture.Biography.Women": 0.0016274082204131065, "Culture.Food and drink": 0.003869384114515742, "Culture.Internet culture": 0.0027448342044452587, "Culture.Linguistics": 0.0004704196841241876, "Culture.Literature": 0.0029036128875354625, "Culture.Media.Books": 0.000742678990345212, "Culture.Media.Entertainment": 0.0989755577969651, "Culture.Media.Films": 0.0031771755584005376, "Culture.Media.Media*": 0.21201751150971165, "Culture.Media.Music": 0.0009582260980479466, "Culture.Media.Radio": 6.200606120337714e-05, "Culture.Media.Software": 0.006069833295704291, "Culture.Media.Television": 0.0015207772089172447, "Culture.Media.Video games": 0.0005615093206567188, "Culture.Performing arts": 0.0016136571753909768, "Culture.Philosophy and religion": 0.005162001475415081, "Culture.Sports": 0.006003766168026103, "Culture.Visual arts.Architecture": 0.02932200170357891, "Culture.Visual arts.Comics and Anime": 0.000367749742655391, "Culture.Visual arts.Fashion": 0.0073053897003234405, "Culture.Visual arts.Visual arts*": 0.045704381965926126, "Geography.Geographical": 0.0035315512289540245, "Geography.Regions.Africa.Africa*": 0.00483994316149668, "Geography.Regions.Africa.Central Africa": 0.0004058831032942602, "Geography.Regions.Africa.Eastern Africa": 8.202888604849657e-05, "Geography.Regions.Africa.Northern Africa": 0.00014172395987364393, "Geography.Regions.Africa.Southern Africa": 0.0008028449272043562, "Geography.Regions.Africa.Western Africa": 0.000272816147365302, "Geography.Regions.Americas.Central America": 0.00023176185775056676, "Geography.Regions.Americas.North America": 0.0031703002857622485, "Geography.Regions.Americas.South America": 0.000188786651678672, "Geography.Regions.Asia.Asia*": 0.005622487337808649, "Geography.Regions.Asia.Central Asia": 0.00035000790929257494, "Geography.Regions.Asia.East Asia": 0.0033450611384150454, "Geography.Regions.Asia.North Asia": 0.0004353985298481421, "Geography.Regions.Asia.South Asia": 0.00020663669814920222, "Geography.Regions.Asia.Southeast Asia": 0.00032506406741737574, "Geography.Regions.Asia.West Asia": 0.001011685969040056, "Geography.Regions.Europe.Eastern Europe": 0.0032436576651452653, "Geography.Regions.Europe.Europe*": 0.004678418783680385, "Geography.Regions.Europe.Northern Europe": 0.0003478676128337494, "Geography.Regions.Europe.Southern Europe": 0.0023790437464152685, "Geography.Regions.Europe.Western Europe": 0.001866188163881686, "Geography.Regions.Oceania": 0.020499546152686617, "History and Society.Business and economics": 0.006809005223678666, "History and Society.Education": 0.0004626665847193515, "History and Society.History": 0.003483860446308053, "History and Society.Military and warfare": 0.0007483799946402382, "History and Society.Politics and government": 0.04064941446684307, "History and Society.Society": 0.0039896466301302305, "History and Society.Transportation": 0.0021103679224344224, "STEM.Biology": 0.0063757590084982845, "STEM.Chemistry": 0.002592859482757056, "STEM.Computing": 0.025968220954150065, "STEM.Earth and environment": 0.004485463793376359, "STEM.Engineering": 0.0007893097092528008, "STEM.Libraries & Information": 0.0005542340017675382, "STEM.Mathematics": 0.39109499414712584, "STEM.Medicine & Health": 0.08689195662186826, "STEM.Physics": 0.004884195584942447, "STEM.STEM*": 0.9049354239597514, "STEM.Space": 0.0002139344401835148, "STEM.Technology": 0.07492699172495908 } } } } } } }