Machine learning models/Production/Arabic Wikipedia article topic

Model card
Model card
This page is an on-wiki machine learning model card.
	A model card is a document about a machine learning model that seeks to answer basic questions about the model.
Model Information Hub
Model creator(s)	Aaron Halfaker (User:EpochFail) and Amir Sarabadani
Model owner(s)	WMF Machine Learning Team (ml@wikimediafoundation.org)
Model interface	Ores homepage
Code	drafttopic Github, ORES training data, and ORES model binaries
Uses PII	No
In production?	Yes
Which projects?	Arabic Wikipedia
	This model uses article text to predict the likelihood that the article belongs to a set of topics.
	v; t; e;

Motivation[edit]

How can we predict what general topic an article is in? Answering this question is useful for various analyses of Wikipedia dynamics. However, it is difficult to group a very diverse range of Wikipedia articles into coherent, consistent topics manually.

This model, part of the ORES suite of models, analyzes an article to predict its likelihood of belonging to a set of topics. Similar models (though not necessarily with the same performance level or topics, are deployed across about a dozen other projects. There is also a language agnostic article topic model.

This model may be useful for high-level analyses of Wikipedia dynamics (pageviews, article quality, edit trends) and filtering articles.

Users and uses[edit]

Use this model for

high-level analyses of Wikipedia dynamics such as pageview, article quality, or edit trends — e.g. How are pageview dynamics different between the physics and biology categories?
filtering to relevant articles — e.g. filter articles only to those in the music category.

Don't use this model for

definitively establishing what topic an article pertains to
automated editing of articles or topics without a human in the loop

Current uses

This model is a part of ORES, and generally accessible via API. It is used for high-level analysis of Wikipedia, platform research, and other on-wiki tasks.

Example API call:

https://ores.wikimedia.org/v3/scores/arwiki/1234/articletopic

Ethical considerations, caveats, and recommendations[edit]

This model was trained on data that is now several years old (from mid-2020). Underlying data drift may skew model outputs.
This model uses word2vec as a training feature. Word2vec, like other natural language embeddings, encodes the linguistic biases of underlying datasets — along the lines of gender, race, ethnicity, religion etc. Since Wikipedia has known biases in its text, this model may encode and at times reproduce those biases.
This model has highly variable performance across different topics — consult the test statistics below to get a sense of inter-topic performance.

Model[edit]

Performance[edit]

Test data confusion matrix:

Test data confusion matrix

Label	n	True positive	False positive	False negative	True Negative
Culture.Biography.Biography*	27484	26431	1053	865	35015
Culture.Biography.Women	6106	4346	1760	917	56341
Culture.Food and drink	1375	870	505	102	61887
Culture.Internet culture	3354	2676	678	226	59784
Culture.Linguistics	1435	911	524	91	61838
Culture.Literature	5701	4106	1595	596	57067
Culture.Media.Books	1581	1176	405	129	61654
Culture.Media.Entertainment	2112	980	1132	223	61029
Culture.Media.Films	2053	1574	479	115	61196
Culture.Media.Media*	14001	11521	2480	1662	47701
Culture.Media.Music	2678	1991	687	329	60357
Culture.Media.Radio	1123	463	660	96	62145
Culture.Media.Software	1989	1402	587	319	61056
Culture.Media.Television	2317	1324	993	259	60788
Culture.Media.Video games	2203	1973	230	62	61099
Culture.Performing arts	1539	735	804	155	61670
Culture.Philosophy and religion	3317	1609	1708	357	59690
Culture.Sports	8914	8162	752	385	54065
Culture.Visual arts.Architecture	1813	1141	672	186	61365
Culture.Visual arts.Comics and Anime	1876	1456	420	101	61387
Culture.Visual arts.Fashion	1360	1005	355	109	61895
Culture.Visual arts.Visual arts*	5891	4148	1743	443	57030
Geography.Geographical	3687	2614	1073	421	59256
Geography.Regions.Africa.Africa*	6437	5143	1294	431	56496
Geography.Regions.Africa.Central Africa	1126	837	289	50	62188
Geography.Regions.Africa.Eastern Africa	968	660	308	60	62336
Geography.Regions.Africa.Northern Africa	2008	1465	543	213	61143
Geography.Regions.Africa.Southern Africa	1196	912	284	49	62119
Geography.Regions.Africa.Western Africa	721	495	226	44	62599
Geography.Regions.Americas.Central America	1356	837	519	75	61933
Geography.Regions.Americas.North America	8321	5547	2774	2239	52804
Geography.Regions.Americas.South America	1643	1230	413	103	61618
Geography.Regions.Asia.Asia*	12468	10325	2143	902	49994
Geography.Regions.Asia.Central Asia	1166	808	358	69	62129
Geography.Regions.Asia.East Asia	2811	2022	789	268	60285
Geography.Regions.Asia.North Asia	2444	1725	719	217	60703
Geography.Regions.Asia.South Asia	1839	1402	437	60	61465
Geography.Regions.Asia.Southeast Asia	1594	1142	452	65	61705
Geography.Regions.Asia.West Asia	3729	2967	762	296	59339
Geography.Regions.Europe.Eastern Europe	3523	2569	954	241	59600
Geography.Regions.Europe.Europe*	13759	10696	3063	2025	47580
Geography.Regions.Europe.Northern Europe	4058	2640	1418	635	58671
Geography.Regions.Europe.Southern Europe	2814	2020	794	294	60256
Geography.Regions.Europe.Western Europe	3997	2921	1076	584	58783
Geography.Regions.Oceania	2313	1864	449	81	60970
History and Society.Business and economics	3556	1924	1632	473	59335
History and Society.Education	1988	854	1134	188	61188
History and Society.History	4321	1808	2513	642	58401
History and Society.Military and warfare	3987	2540	1447	412	58965
History and Society.Politics and government	5373	3181	2192	663	57328
History and Society.Society	4393	1070	3323	363	58608
History and Society.Transportation	3249	2777	472	153	59962
STEM.Biology	3007	2282	725	212	60145
STEM.Chemistry	1472	1003	469	159	61733
STEM.Computing	2390	1775	615	390	60584
STEM.Earth and environment	1693	962	731	178	61493
STEM.Engineering	2891	2054	837	240	60233
STEM.Libraries & Information	1184	748	436	60	62120
STEM.Mathematics	1144	739	405	63	62157
STEM.Medicine & Health	2210	1493	717	226	60928
STEM.Physics	1446	885	561	206	61712
STEM.STEM*	17887	15502	2385	1108	44369
STEM.Space	1720	1499	221	56	61588
STEM.Technology	4341	2892	1449	607	58416

Test data sample rates:

Test data sample rates

Label	Sample	Population
Culture.Biography.Biography*	0.434	0.12
Culture.Biography.Women	0.096	0.015
Culture.Food and drink	0.022	0.003
Culture.Internet culture	0.053	0.004
Culture.Linguistics	0.023	0.008
Culture.Literature	0.09	0.015
Culture.Media.Books	0.025	0.004
Culture.Media.Entertainment	0.033	0.004
Culture.Media.Films	0.032	0.012
Culture.Media.Media*	0.221	0.055
Culture.Media.Music	0.042	0.021
Culture.Media.Radio	0.018	0.002
Culture.Media.Software	0.031	0.001
Culture.Media.Television	0.037	0.009
Culture.Media.Video games	0.035	0.003
Culture.Performing arts	0.024	0.003
Culture.Philosophy and religion	0.052	0.01
Culture.Sports	0.141	0.06
Culture.Visual arts.Architecture	0.029	0.011
Culture.Visual arts.Comics and Anime	0.03	0.002
Culture.Visual arts.Fashion	0.021	0.001
Culture.Visual arts.Visual arts*	0.093	0.018
Geography.Geographical	0.058	0.021
Geography.Regions.Africa.Africa*	0.102	0.008
Geography.Regions.Africa.Central Africa	0.018	0.001
Geography.Regions.Africa.Eastern Africa	0.015	0.001
Geography.Regions.Africa.Northern Africa	0.032	0.001
Geography.Regions.Africa.Southern Africa	0.019	0.001
Geography.Regions.Africa.Western Africa	0.011	0.001
Geography.Regions.Americas.Central America	0.021	0.003
Geography.Regions.Americas.North America	0.131	0.063
Geography.Regions.Americas.South America	0.026	0.007
Geography.Regions.Asia.Asia*	0.197	0.052
Geography.Regions.Asia.Central Asia	0.018	0.001
Geography.Regions.Asia.East Asia	0.044	0.012
Geography.Regions.Asia.North Asia	0.039	0.006
Geography.Regions.Asia.South Asia	0.029	0.016
Geography.Regions.Asia.Southeast Asia	0.025	0.006
Geography.Regions.Asia.West Asia	0.059	0.012
Geography.Regions.Europe.Eastern Europe	0.056	0.018
Geography.Regions.Europe.Europe*	0.217	0.081
Geography.Regions.Europe.Northern Europe	0.064	0.029
Geography.Regions.Europe.Southern Europe	0.044	0.014
Geography.Regions.Europe.Western Europe	0.063	0.02
Geography.Regions.Oceania	0.037	0.016
History and Society.Business and economics	0.056	0.01
History and Society.Education	0.031	0.008
History and Society.History	0.068	0.011
History and Society.Military and warfare	0.063	0.015
History and Society.Politics and government	0.085	0.028
History and Society.Society	0.069	0.008
History and Society.Transportation	0.051	0.016
STEM.Biology	0.047	0.034
STEM.Chemistry	0.023	0.002
STEM.Computing	0.038	0.003
STEM.Earth and environment	0.027	0.005
STEM.Engineering	0.046	0.006
STEM.Libraries & Information	0.019	0.001
STEM.Mathematics	0.018	0
STEM.Medicine & Health	0.035	0.006
STEM.Physics	0.023	0.001
STEM.STEM*	0.282	0.065
STEM.Space	0.027	0.004
STEM.Technology	0.069	0.005

Test data performance:

Test data performance

Label	Match rate	Filter rate	Recall	Precision	f1	Accuracy	ROC AUC	PR AUC
Culture.Biography.Biography*	0.137	0.863	0.962	0.845	0.899	0.974	0.986	0.951
Culture.Biography.Women	0.026	0.974	0.712	0.403	0.514	0.98	0.978	0.492
Culture.Food and drink	0.003	0.997	0.633	0.495	0.556	0.997	0.975	0.542
Culture.Internet culture	0.007	0.993	0.798	0.442	0.569	0.995	0.983	0.663
Culture.Linguistics	0.007	0.993	0.635	0.777	0.699	0.996	0.97	0.712
Culture.Literature	0.021	0.979	0.72	0.512	0.598	0.986	0.973	0.685
Culture.Media.Books	0.005	0.995	0.744	0.607	0.668	0.997	0.98	0.651
Culture.Media.Entertainment	0.005	0.995	0.464	0.335	0.389	0.994	0.964	0.369
Culture.Media.Films	0.011	0.989	0.767	0.826	0.795	0.995	0.981	0.821
Culture.Media.Media*	0.077	0.923	0.823	0.586	0.685	0.958	0.971	0.787
Culture.Media.Music	0.021	0.979	0.743	0.746	0.745	0.989	0.979	0.767
Culture.Media.Radio	0.002	0.998	0.412	0.383	0.397	0.997	0.966	0.32
Culture.Media.Software	0.006	0.994	0.705	0.151	0.248	0.994	0.981	0.222
Culture.Media.Television	0.009	0.991	0.571	0.546	0.559	0.992	0.974	0.593
Culture.Media.Video games	0.004	0.996	0.896	0.72	0.798	0.999	0.992	0.881
Culture.Performing arts	0.004	0.996	0.478	0.368	0.416	0.996	0.967	0.335
Culture.Philosophy and religion	0.011	0.989	0.485	0.46	0.472	0.989	0.942	0.435
Culture.Sports	0.062	0.938	0.916	0.892	0.904	0.988	0.981	0.922
Culture.Visual arts.Architecture	0.01	0.99	0.629	0.694	0.66	0.993	0.976	0.662
Culture.Visual arts.Comics and Anime	0.003	0.997	0.776	0.531	0.63	0.998	0.984	0.651
Culture.Visual arts.Fashion	0.002	0.998	0.739	0.273	0.399	0.998	0.98	0.381
Culture.Visual arts.Visual arts*	0.02	0.98	0.704	0.627	0.663	0.987	0.968	0.699
Geography.Geographical	0.022	0.978	0.709	0.684	0.696	0.987	0.975	0.746
Geography.Regions.Africa.Africa*	0.014	0.986	0.799	0.474	0.595	0.991	0.977	0.607
Geography.Regions.Africa.Central Africa	0.001	0.999	0.743	0.394	0.515	0.999	0.979	0.462
Geography.Regions.Africa.Eastern Africa	0.001	0.999	0.682	0.262	0.379	0.999	0.977	0.313
Geography.Regions.Africa.Northern Africa	0.004	0.996	0.73	0.221	0.339	0.996	0.98	0.271
Geography.Regions.Africa.Southern Africa	0.002	0.998	0.763	0.558	0.645	0.999	0.977	0.514
Geography.Regions.Africa.Western Africa	0.001	0.999	0.687	0.421	0.522	0.999	0.979	0.388
Geography.Regions.Americas.Central America	0.003	0.997	0.617	0.638	0.628	0.997	0.973	0.594
Geography.Regions.Americas.North America	0.08	0.92	0.667	0.524	0.587	0.941	0.953	0.609
Geography.Regions.Americas.South America	0.007	0.993	0.749	0.755	0.752	0.997	0.978	0.728
Geography.Regions.Asia.Asia*	0.06	0.94	0.828	0.721	0.771	0.974	0.971	0.841
Geography.Regions.Asia.Central Asia	0.002	0.998	0.693	0.332	0.449	0.999	0.977	0.354
Geography.Regions.Asia.East Asia	0.013	0.987	0.719	0.666	0.692	0.992	0.977	0.715
Geography.Regions.Asia.North Asia	0.007	0.993	0.706	0.527	0.604	0.995	0.975	0.654
Geography.Regions.Asia.South Asia	0.014	0.986	0.762	0.929	0.837	0.995	0.983	0.878
Geography.Regions.Asia.Southeast Asia	0.006	0.994	0.716	0.81	0.76	0.997	0.977	0.778
Geography.Regions.Asia.West Asia	0.014	0.986	0.796	0.654	0.718	0.993	0.981	0.743
Geography.Regions.Europe.Eastern Europe	0.017	0.983	0.729	0.771	0.75	0.991	0.975	0.796
Geography.Regions.Europe.Europe*	0.1	0.9	0.777	0.626	0.693	0.945	0.958	0.774
Geography.Regions.Europe.Northern Europe	0.029	0.971	0.651	0.644	0.647	0.979	0.966	0.696
Geography.Regions.Europe.Southern Europe	0.015	0.985	0.718	0.674	0.695	0.991	0.977	0.742
Geography.Regions.Europe.Western Europe	0.025	0.975	0.731	0.608	0.664	0.985	0.975	0.686
Geography.Regions.Oceania	0.015	0.985	0.806	0.91	0.855	0.996	0.98	0.872
History and Society.Business and economics	0.013	0.987	0.541	0.401	0.461	0.988	0.957	0.416
History and Society.Education	0.006	0.994	0.43	0.528	0.474	0.992	0.956	0.471
History and Society.History	0.015	0.985	0.418	0.297	0.348	0.983	0.936	0.273
History and Society.Military and warfare	0.017	0.983	0.637	0.587	0.611	0.988	0.967	0.632
History and Society.Politics and government	0.028	0.972	0.592	0.598	0.595	0.977	0.951	0.624
History and Society.Society	0.008	0.992	0.244	0.248	0.246	0.988	0.893	0.162
History and Society.Transportation	0.016	0.984	0.855	0.847	0.851	0.995	0.982	0.897
STEM.Biology	0.03	0.97	0.759	0.885	0.817	0.988	0.974	0.86
STEM.Chemistry	0.004	0.996	0.681	0.31	0.426	0.997	0.984	0.418
STEM.Computing	0.008	0.992	0.743	0.247	0.371	0.993	0.984	0.38
STEM.Earth and environment	0.006	0.994	0.568	0.483	0.522	0.995	0.969	0.48
STEM.Engineering	0.008	0.992	0.71	0.508	0.592	0.994	0.976	0.657
STEM.Libraries & Information	0.001	0.999	0.632	0.309	0.415	0.999	0.972	0.323
STEM.Mathematics	0.001	0.999	0.646	0.227	0.336	0.999	0.978	0.375
STEM.Medicine & Health	0.008	0.992	0.676	0.542	0.601	0.994	0.973	0.659
STEM.Physics	0.004	0.996	0.612	0.147	0.237	0.996	0.982	0.18
STEM.STEM*	0.079	0.921	0.867	0.712	0.782	0.969	0.974	0.883
STEM.Space	0.005	0.995	0.872	0.804	0.836	0.999	0.991	0.877
STEM.Technology	0.014	0.986	0.666	0.251	0.364	0.988	0.97	0.351

Implementation[edit]

Model architecture

{
    "type": "GradientBoosting",
    "params": {
        "scale": false,
        "center": false,
        "labels": [
            "Culture.Biography.Biography*",
            "Culture.Biography.Women",
            "Culture.Food and drink",
            "Culture.Internet culture",
            "Culture.Linguistics",
            "Culture.Literature",
            "Culture.Media.Books",
            "Culture.Media.Entertainment",
            "Culture.Media.Films",
            "Culture.Media.Media*",
            "Culture.Media.Music",
            "Culture.Media.Radio",
            "Culture.Media.Software",
            "Culture.Media.Television",
            "Culture.Media.Video games",
            "Culture.Performing arts",
            "Culture.Philosophy and religion",
            "Culture.Sports",
            "Culture.Visual arts.Architecture",
            "Culture.Visual arts.Comics and Anime",
            "Culture.Visual arts.Fashion",
            "Culture.Visual arts.Visual arts*",
            "Geography.Geographical",
            "Geography.Regions.Africa.Africa*",
            "Geography.Regions.Africa.Central Africa",
            "Geography.Regions.Africa.Eastern Africa",
            "Geography.Regions.Africa.Northern Africa",
            "Geography.Regions.Africa.Southern Africa",
            "Geography.Regions.Africa.Western Africa",
            "Geography.Regions.Americas.Central America",
            "Geography.Regions.Americas.North America",
            "Geography.Regions.Americas.South America",
            "Geography.Regions.Asia.Asia*",
            "Geography.Regions.Asia.Central Asia",
            "Geography.Regions.Asia.East Asia",
            "Geography.Regions.Asia.North Asia",
            "Geography.Regions.Asia.South Asia",
            "Geography.Regions.Asia.Southeast Asia",
            "Geography.Regions.Asia.West Asia",
            "Geography.Regions.Europe.Eastern Europe",
            "Geography.Regions.Europe.Europe*",
            "Geography.Regions.Europe.Northern Europe",
            "Geography.Regions.Europe.Southern Europe",
            "Geography.Regions.Europe.Western Europe",
            "Geography.Regions.Oceania",
            "History and Society.Business and economics",
            "History and Society.Education",
            "History and Society.History",
            "History and Society.Military and warfare",
            "History and Society.Politics and government",
            "History and Society.Society",
            "History and Society.Transportation",
            "STEM.Biology",
            "STEM.Chemistry",
            "STEM.Computing",
            "STEM.Earth and environment",
            "STEM.Engineering",
            "STEM.Libraries & Information",
            "STEM.Mathematics",
            "STEM.Medicine & Health",
            "STEM.Physics",
            "STEM.STEM*",
            "STEM.Space",
            "STEM.Technology"
        ],
        "multilabel": true,
        "population_rates": null,
        "ccp_alpha": 0.0,
        "criterion": "friedman_mse",
        "init": null,
        "learning_rate": 0.1,
        "loss": "deviance",
        "max_depth": 5,
        "max_features": "log2",
        "max_leaf_nodes": null,
        "min_impurity_decrease": 0.0,
        "min_impurity_split": null,
        "min_samples_leaf": 1,
        "min_samples_split": 2,
        "min_weight_fraction_leaf": 0.0,
        "n_estimators": 150,
        "n_iter_no_change": null,
        "presort": "deprecated",
        "random_state": null,
        "subsample": 1.0,
        "tol": 0.0001,
        "validation_fraction": 0.1,
        "verbose": 0,
        "warm_start": false,
        "label_weights": {}
    }
}

Output schema

{
    "title": "Scikit learn-based classifier score with probability",
    "type": "object",
    "properties": {
        "prediction": {
            "description": "The most likely labels predicted by the estimator",
            "type": "array",
            "items": {
                "type": "string"
            }
        },
        "probability": {
            "description": "A mapping of probabilities onto each of the potential output labels",
            "type": "object",
            "properties": {
                "Culture.Biography.Biography*": {
                    "type": "number"
                },
                "Culture.Biography.Women": {
                    "type": "number"
                },
                "Culture.Food and drink": {
                    "type": "number"
                },
                "Culture.Internet culture": {
                    "type": "number"
                },
                "Culture.Linguistics": {
                    "type": "number"
                },
                "Culture.Literature": {
                    "type": "number"
                },
                "Culture.Media.Books": {
                    "type": "number"
                },
                "Culture.Media.Entertainment": {
                    "type": "number"
                },
                "Culture.Media.Films": {
                    "type": "number"
                },
                "Culture.Media.Media*": {
                    "type": "number"
                },
                "Culture.Media.Music": {
                    "type": "number"
                },
                "Culture.Media.Radio": {
                    "type": "number"
                },
                "Culture.Media.Software": {
                    "type": "number"
                },
                "Culture.Media.Television": {
                    "type": "number"
                },
                "Culture.Media.Video games": {
                    "type": "number"
                },
                "Culture.Performing arts": {
                    "type": "number"
                },
                "Culture.Philosophy and religion": {
                    "type": "number"
                },
                "Culture.Sports": {
                    "type": "number"
                },
                "Culture.Visual arts.Architecture": {
                    "type": "number"
                },
                "Culture.Visual arts.Comics and Anime": {
                    "type": "number"
                },
                "Culture.Visual arts.Fashion": {
                    "type": "number"
                },
                "Culture.Visual arts.Visual arts*": {
                    "type": "number"
                },
                "Geography.Geographical": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Africa*": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Central Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Eastern Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Northern Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Southern Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Western Africa": {
                    "type": "number"
                },
                "Geography.Regions.Americas.Central America": {
                    "type": "number"
                },
                "Geography.Regions.Americas.North America": {
                    "type": "number"
                },
                "Geography.Regions.Americas.South America": {
                    "type": "number"
                },
                "Geography.Regions.Asia.Asia*": {
                    "type": "number"
                },
                "Geography.Regions.Asia.Central Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.East Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.North Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.South Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.Southeast Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.West Asia": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Eastern Europe": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Europe*": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Northern Europe": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Southern Europe": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Western Europe": {
                    "type": "number"
                },
                "Geography.Regions.Oceania": {
                    "type": "number"
                },
                "History and Society.Business and economics": {
                    "type": "number"
                },
                "History and Society.Education": {
                    "type": "number"
                },
                "History and Society.History": {
                    "type": "number"
                },
                "History and Society.Military and warfare": {
                    "type": "number"
                },
                "History and Society.Politics and government": {
                    "type": "number"
                },
                "History and Society.Society": {
                    "type": "number"
                },
                "History and Society.Transportation": {
                    "type": "number"
                },
                "STEM.Biology": {
                    "type": "number"
                },
                "STEM.Chemistry": {
                    "type": "number"
                },
                "STEM.Computing": {
                    "type": "number"
                },
                "STEM.Earth and environment": {
                    "type": "number"
                },
                "STEM.Engineering": {
                    "type": "number"
                },
                "STEM.Libraries & Information": {
                    "type": "number"
                },
                "STEM.Mathematics": {
                    "type": "number"
                },
                "STEM.Medicine & Health": {
                    "type": "number"
                },
                "STEM.Physics": {
                    "type": "number"
                },
                "STEM.STEM*": {
                    "type": "number"
                },
                "STEM.Space": {
                    "type": "number"
                },
                "STEM.Technology": {
                    "type": "number"
                }
            }
        }
    }
}

Example input and output

Input:

https://ores.wikimedia.org/v3/scores/arwiki/1234/articletopic

Output:

Example output

{
    "arwiki": {
        "models": {
            "articletopic": {
                "version": "1.3.0"
            }
        },
        "scores": {
            "1234": {
                "articletopic": {
                    "score": {
                        "prediction": [
                            "Culture.Philosophy and religion"
                        ],
                        "probability": {
                            "Culture.Biography.Biography*": 0.0018775771968361505,
                            "Culture.Biography.Women": 0.0022559171278080403,
                            "Culture.Food and drink": 0.02911020063825202,
                            "Culture.Internet culture": 0.00013396466832571897,
                            "Culture.Linguistics": 0.0002016623350064864,
                            "Culture.Literature": 0.01630206474244034,
                            "Culture.Media.Books": 0.0019739903099149016,
                            "Culture.Media.Entertainment": 0.005776333050595081,
                            "Culture.Media.Films": 0.0005357906321037945,
                            "Culture.Media.Media*": 0.01873311881808571,
                            "Culture.Media.Music": 0.0006598789501761831,
                            "Culture.Media.Radio": 0.0008711772320624666,
                            "Culture.Media.Software": 0.00045306840360182074,
                            "Culture.Media.Television": 0.004563663906527891,
                            "Culture.Media.Video games": 0.00014238944031148178,
                            "Culture.Performing arts": 0.00031857030709468275,
                            "Culture.Philosophy and religion": 0.9915133947766572,
                            "Culture.Sports": 0.0032536645394025034,
                            "Culture.Visual arts.Architecture": 0.003171696122120992,
                            "Culture.Visual arts.Comics and Anime": 0.0030041266177882604,
                            "Culture.Visual arts.Fashion": 0.008690660472179179,
                            "Culture.Visual arts.Visual arts*": 0.015999157238911713,
                            "Geography.Geographical": 0.0059701879622301715,
                            "Geography.Regions.Africa.Africa*": 0.20140713747028768,
                            "Geography.Regions.Africa.Central Africa": 0.0005659981357134575,
                            "Geography.Regions.Africa.Eastern Africa": 0.004211961235703654,
                            "Geography.Regions.Africa.Northern Africa": 0.012708751878181768,
                            "Geography.Regions.Africa.Southern Africa": 0.0006662932204457024,
                            "Geography.Regions.Africa.Western Africa": 0.0005140083680583202,
                            "Geography.Regions.Americas.Central America": 0.0016675740255396078,
                            "Geography.Regions.Americas.North America": 0.0062834206734540595,
                            "Geography.Regions.Americas.South America": 0.0006909355854571832,
                            "Geography.Regions.Asia.Asia*": 0.01339558309316005,
                            "Geography.Regions.Asia.Central Asia": 0.0006353555918342474,
                            "Geography.Regions.Asia.East Asia": 0.001418334017189674,
                            "Geography.Regions.Asia.North Asia": 0.000575145968948881,
                            "Geography.Regions.Asia.South Asia": 0.003160871781527632,
                            "Geography.Regions.Asia.Southeast Asia": 0.002178204210680102,
                            "Geography.Regions.Asia.West Asia": 0.014274257947124423,
                            "Geography.Regions.Europe.Eastern Europe": 0.0013954670948905467,
                            "Geography.Regions.Europe.Europe*": 0.011614035833480698,
                            "Geography.Regions.Europe.Northern Europe": 0.0018661121742356134,
                            "Geography.Regions.Europe.Southern Europe": 0.007193423419921128,
                            "Geography.Regions.Europe.Western Europe": 0.0034747000735290425,
                            "Geography.Regions.Oceania": 0.0014825584617553515,
                            "History and Society.Business and economics": 0.009040864250628715,
                            "History and Society.Education": 0.0034818655626133728,
                            "History and Society.History": 0.015335759410860367,
                            "History and Society.Military and warfare": 0.0030978850596784535,
                            "History and Society.Politics and government": 0.029165387410026597,
                            "History and Society.Society": 0.031041994032148346,
                            "History and Society.Transportation": 0.00022066416987785007,
                            "STEM.Biology": 0.005349680305325293,
                            "STEM.Chemistry": 0.0027381500340171645,
                            "STEM.Computing": 0.0006550726961037265,
                            "STEM.Earth and environment": 0.0008943785135649393,
                            "STEM.Engineering": 0.0013233564634350919,
                            "STEM.Libraries & Information": 7.807094663224667e-05,
                            "STEM.Mathematics": 0.002929987189174986,
                            "STEM.Medicine & Health": 0.061891530372885306,
                            "STEM.Physics": 0.0026454984695803902,
                            "STEM.STEM*": 0.098436433397069,
                            "STEM.Space": 0.001050419037453933,
                            "STEM.Technology": 0.004580541565463146
                        }
                    }
                }
            }
        }
    }
}

Data[edit]

Data pipeline

The data to train was fetched from a set of revision IDs. Then various pieces of information about the revision were extracted using automated processes, and the revision text was fed into word2vec to get an article embedding. Finally, labels are derived from the mid-level WikiProject categories that the article is associated with.

Training data

Training data was automatically and randomly separated from test data during training using the drafttopic git repository (which trains both drafttopic and articletopic models).

Test data

Test data was automatically and randomly split off from train data using the drafttopic git repository (which trains both drafttopic and articletopic models). The model then makes a prediction on that data, which is compared to the underlying ground truth to calculate performance statistics.

Licenses[edit]

Code: MIT license
Model: MIT license

Citation[edit]

Cite this model card as:

@misc{
  Triedman_Bazira_2023_Arabic_Wikipedia_article_topic,
  title={ Arabic Wikipedia article topic model card },
  author={ Triedman, Harold and Bazira, Kevin },
  year={ 2023 },
  url={ https://meta.wikimedia.org/wiki/Machine_learning_models/Production/Arabic_Wikipedia_article_topic }
}