Lift Wing API/Reference/Get articletopic outlink prediction

POST	/service/lw/inference/v1/models/outlink-topic-model:predict

Get article topics and related scores from the Language agnostic link-based article topic model for a given wiki page. Check the model card for more information.

Examples

curl

Anonymous access

# Get topics and related scores from the Article Topic Outlink model for the page of Douglas Adams on English Wikipedia.
$ curl https://api.wikimedia.org/service/lw/inference/v1/models/outlink-topic-model:predict -X POST -d '{"page_title": "Douglas_Adams", "lang": "en"}' -H "Content-type: application/json"

Logged in access

# Get topics and related scores from the Article Topic Outlink model for the page of Douglas Adams on English Wikipedia.
$ curl https://api.wikimedia.org/service/lw/inference/v1/models/outlink-topic-model:predict -X POST -d '{"page_title": "Douglas_Adams", "lang": "en"}' -H "Authorization: Bearer YOUR_ACCESS_TOKEN" -H "Content-type: application/json"

Python

# Python 3
# Get topics and related scores from the Article Topic Outlink model for the page of Douglas Adams on English Wikipedia.

import json
import requests

use_auth = False
inference_url = 'https://api.wikimedia.org/service/lw/inference/v1/models/outlink-topic-model: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 = {"page_title": "Douglas_Adams", "lang": "en"}
response = requests.post(inference_url, headers=headers, data=json.dumps(data))
print(response.json())

JavaScript

/*
Get topics and related scores from the Article Topic Outlink model for the page of Douglas Adams on English Wikipedia.
*/

const inferenceUrl = "https://api.wikimedia.org/service/lw/inference/v1/models/outlink-topic-model: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 = {"page_title": "Douglas_Adams", "lang": "en"};

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

POST Parameters

lang required	A string representing the language code related to the target wiki. Example: "en" for English Wikipedia.
page_title required	A string representing the title of the Wiki article to score. Example: "Douglas_Adams" for the Wiki page of the author Douglas Adams.
threshold	A float representing a custom threshold value to use when evaluating the scores. Default: empty.
features_str	A string representing custom features to pass to the model. Default: empty.
debug	A boolean indicating whether or not to enable debug output to return all predicted topics and scores, equivalent to setting the threshold to 0. Default: false.

Responses

200	Success: Returns an Article Topic Outlink scores object. Example { "prediction": { "article": "https://en.wikipedia.org/wiki/Douglas_Adams", "results": [ { "topic": "Culture.Media.Media*", "score": 0.6723417043685913 }, { "topic": "Culture.Biography.Biography*", "score": 0.5156299471855164 } ] } }