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,
    "Api-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
            }
          }
        }
      }
    }
  }
}