Wikimore

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?	English Wikipedia
	This model uses article text to predict the likelihood that the article belongs to a set of topics.

Motivation

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

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/enwiki/1234/articletopic

Ethical considerations, caveats, and recommendations

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

Performance

Test data confusion matrix:

Test data confusion matrix

Label	n	True positive	False positive	False negative	True Negative
Culture.Biography.Biography*	16539	14663	1876	1218	46123
Culture.Biography.Women	4162	3180	982	812	58906
Culture.Food and drink	1301	916	385	69	62510
Culture.Internet culture	2969	2307	662	200	60711
Culture.Linguistics	1367	1027	340	104	62409
Culture.Literature	5298	3939	1359	433	58149
Culture.Media.Books	1906	1355	551	198	61776
Culture.Media.Entertainment	1825	917	908	173	61882
Culture.Media.Films	2347	1940	407	145	61388
Culture.Media.Media*	14457	12411	2046	1445	47978
Culture.Media.Music	2665	2162	503	292	60923
Culture.Media.Radio	1191	963	228	28	62661
Culture.Media.Software	1784	1028	756	349	61747
Culture.Media.Television	2224	1694	530	179	61477
Culture.Media.Video games	2114	1904	210	36	61730
Culture.Performing arts	1324	854	470	101	62455
Culture.Philosophy and religion	2712	1579	1133	339	60829
Culture.Sports	5901	5302	599	306	57673
Culture.Visual arts.Architecture	2571	1906	665	250	61059
Culture.Visual arts.Comics and Anime	1509	1231	278	91	62280
Culture.Visual arts.Fashion	1173	805	368	46	62661
Culture.Visual arts.Visual arts*	5989	4575	1414	521	57370
Geography.Geographical	3518	2284	1234	278	60084
Geography.Regions.Africa.Africa*	6484	5678	806	359	57037
Geography.Regions.Africa.Central Africa	1155	887	268	35	62690
Geography.Regions.Africa.Eastern Africa	1100	901	199	36	62744
Geography.Regions.Africa.Northern Africa	1309	999	310	94	62477
Geography.Regions.Africa.Southern Africa	1260	1011	249	43	62577
Geography.Regions.Africa.Western Africa	1151	958	193	65	62664
Geography.Regions.Americas.Central America	1302	931	371	88	62490
Geography.Regions.Americas.North America	7482	5303	2179	1251	55147
Geography.Regions.Americas.South America	1575	1156	419	134	62171
Geography.Regions.Asia.Asia*	11206	9648	1558	824	51850
Geography.Regions.Asia.Central Asia	1133	889	244	45	62702
Geography.Regions.Asia.East Asia	2749	2089	660	256	60875
Geography.Regions.Asia.North Asia	1369	929	440	183	62328
Geography.Regions.Asia.South Asia	2428	2104	324	118	61334
Geography.Regions.Asia.Southeast Asia	1726	1358	368	103	62051
Geography.Regions.Asia.West Asia	2301	1897	404	124	61455
Geography.Regions.Europe.Eastern Europe	3088	2460	628	292	60500
Geography.Regions.Europe.Europe*	12265	9544	2721	1743	49872
Geography.Regions.Europe.Northern Europe	4099	2867	1232	637	59144
Geography.Regions.Europe.Southern Europe	2397	1720	677	297	61186
Geography.Regions.Europe.Western Europe	3062	2119	943	444	60374
Geography.Regions.Oceania	2535	2103	432	146	61199
History and Society.Business and economics	3458	1651	1807	561	59861
History and Society.Education	2204	1073	1131	244	61432
History and Society.History	3307	1380	1927	513	60060
History and Society.Military and warfare	4048	2928	1120	380	59452
History and Society.Politics and government	4604	2919	1685	468	58808
History and Society.Society	4009	1667	2342	407	59464
History and Society.Transportation	3601	3115	486	196	60083
STEM.Biology	2951	2421	530	146	60783
STEM.Chemistry	1319	933	386	152	62409
STEM.Computing	2102	1394	708	427	61351
STEM.Earth and environment	1619	1156	463	123	62138
STEM.Engineering	2361	1704	657	184	61335
STEM.Libraries & Information	1165	691	474	80	62635
STEM.Mathematics	1136	766	370	69	62675
STEM.Medicine & Health	1784	1200	584	167	61929
STEM.Physics	1173	766	407	135	62572
STEM.STEM*	16613	14497	2116	1062	46205
STEM.Space	1412	1219	193	50	62418
STEM.Technology	3691	2310	1381	611	59578

Test data sample rates:

Test data sample rates

Label	Sample	Population
Culture.Biography.Biography*	0.259	0.123
Culture.Biography.Women	0.065	0.015
Culture.Food and drink	0.02	0.002
Culture.Internet culture	0.046	0.003
Culture.Linguistics	0.021	0.007
Culture.Literature	0.083	0.015
Culture.Media.Books	0.03	0.004
Culture.Media.Entertainment	0.029	0.004
Culture.Media.Films	0.037	0.011
Culture.Media.Media*	0.226	0.058
Culture.Media.Music	0.042	0.024
Culture.Media.Radio	0.019	0.002
Culture.Media.Software	0.028	0.001
Culture.Media.Television	0.035	0.009
Culture.Media.Video games	0.033	0.003
Culture.Performing arts	0.021	0.003
Culture.Philosophy and religion	0.042	0.011
Culture.Sports	0.092	0.071
Culture.Visual arts.Architecture	0.04	0.011
Culture.Visual arts.Comics and Anime	0.024	0.002
Culture.Visual arts.Fashion	0.018	0.001
Culture.Visual arts.Visual arts*	0.094	0.018
Geography.Geographical	0.055	0.024
Geography.Regions.Africa.Africa*	0.102	0.008
Geography.Regions.Africa.Central Africa	0.018	0.001
Geography.Regions.Africa.Eastern Africa	0.017	0
Geography.Regions.Africa.Northern Africa	0.02	0.001
Geography.Regions.Africa.Southern Africa	0.02	0.001
Geography.Regions.Africa.Western Africa	0.018	0.001
Geography.Regions.Americas.Central America	0.02	0.003
Geography.Regions.Americas.North America	0.117	0.064
Geography.Regions.Americas.South America	0.025	0.006
Geography.Regions.Asia.Asia*	0.175	0.045
Geography.Regions.Asia.Central Asia	0.018	0.001
Geography.Regions.Asia.East Asia	0.043	0.011
Geography.Regions.Asia.North Asia	0.021	0.001
Geography.Regions.Asia.South Asia	0.038	0.015
Geography.Regions.Asia.Southeast Asia	0.027	0.006
Geography.Regions.Asia.West Asia	0.036	0.011
Geography.Regions.Europe.Eastern Europe	0.048	0.013
Geography.Regions.Europe.Europe*	0.192	0.076
Geography.Regions.Europe.Northern Europe	0.064	0.031
Geography.Regions.Europe.Southern Europe	0.038	0.013
Geography.Regions.Europe.Western Europe	0.048	0.019
Geography.Regions.Oceania	0.04	0.015
History and Society.Business and economics	0.054	0.01
History and Society.Education	0.035	0.007
History and Society.History	0.052	0.011
History and Society.Military and warfare	0.063	0.014
History and Society.Politics and government	0.072	0.028
History and Society.Society	0.063	0.013
History and Society.Transportation	0.056	0.015
STEM.Biology	0.046	0.034
STEM.Chemistry	0.021	0.002
STEM.Computing	0.033	0.003
STEM.Earth and environment	0.025	0.005
STEM.Engineering	0.037	0.005
STEM.Libraries & Information	0.018	0.001
STEM.Mathematics	0.018	0
STEM.Medicine & Health	0.028	0.006
STEM.Physics	0.018	0.001
STEM.STEM*	0.26	0.069
STEM.Space	0.022	0.006
STEM.Technology	0.058	0.005

Test data performance:

Test data performance

Label	Match rate	Filter rate	Recall	Precision	f1	Accuracy	ROC AUC	PR AUC
Culture.Biography.Biography*	0.132	0.868	0.887	0.829	0.857	0.963	0.978	0.909
Culture.Biography.Women	0.025	0.975	0.764	0.454	0.569	0.983	0.981	0.567
Culture.Food and drink	0.003	0.997	0.704	0.612	0.655	0.998	0.982	0.625
Culture.Internet culture	0.006	0.994	0.777	0.454	0.573	0.996	0.986	0.739
Culture.Linguistics	0.007	0.993	0.751	0.769	0.76	0.997	0.978	0.785
Culture.Literature	0.019	0.981	0.743	0.613	0.672	0.989	0.977	0.742
Culture.Media.Books	0.006	0.994	0.711	0.474	0.568	0.996	0.981	0.597
Culture.Media.Entertainment	0.005	0.995	0.502	0.393	0.441	0.995	0.968	0.415
Culture.Media.Films	0.011	0.989	0.827	0.788	0.807	0.996	0.983	0.844
Culture.Media.Media*	0.078	0.922	0.858	0.646	0.737	0.964	0.978	0.847
Culture.Media.Music	0.024	0.976	0.811	0.806	0.809	0.991	0.985	0.853
Culture.Media.Radio	0.002	0.998	0.809	0.796	0.802	0.999	0.987	0.85
Culture.Media.Software	0.006	0.994	0.576	0.12	0.199	0.994	0.98	0.187
Culture.Media.Television	0.01	0.99	0.762	0.7	0.73	0.995	0.981	0.77
Culture.Media.Video games	0.003	0.997	0.901	0.802	0.848	0.999	0.993	0.902
Culture.Performing arts	0.003	0.997	0.645	0.536	0.586	0.997	0.981	0.593
Culture.Philosophy and religion	0.012	0.988	0.582	0.531	0.555	0.99	0.964	0.538
Culture.Sports	0.069	0.931	0.898	0.929	0.913	0.988	0.984	0.947
Culture.Visual arts.Architecture	0.012	0.988	0.741	0.66	0.698	0.993	0.983	0.749
Culture.Visual arts.Comics and Anime	0.003	0.997	0.816	0.552	0.658	0.998	0.986	0.74
Culture.Visual arts.Fashion	0.001	0.999	0.686	0.431	0.529	0.999	0.983	0.513
Culture.Visual arts.Visual arts*	0.023	0.977	0.764	0.613	0.68	0.987	0.976	0.756
Geography.Geographical	0.02	0.98	0.649	0.773	0.706	0.987	0.97	0.757
Geography.Regions.Africa.Africa*	0.013	0.987	0.876	0.524	0.656	0.993	0.985	0.718
Geography.Regions.Africa.Central Africa	0.001	0.999	0.768	0.465	0.579	0.999	0.988	0.627
Geography.Regions.Africa.Eastern Africa	0.001	0.999	0.819	0.394	0.532	0.999	0.984	0.459
Geography.Regions.Africa.Northern Africa	0.002	0.998	0.763	0.384	0.511	0.998	0.981	0.422
Geography.Regions.Africa.Southern Africa	0.002	0.998	0.802	0.579	0.673	0.999	0.985	0.654
Geography.Regions.Africa.Western Africa	0.002	0.998	0.832	0.355	0.497	0.999	0.983	0.446
Geography.Regions.Americas.Central America	0.004	0.996	0.715	0.627	0.668	0.998	0.982	0.672
Geography.Regions.Americas.North America	0.066	0.934	0.709	0.687	0.698	0.961	0.966	0.767
Geography.Regions.Americas.South America	0.007	0.993	0.734	0.684	0.708	0.996	0.983	0.745
Geography.Regions.Asia.Asia*	0.054	0.946	0.861	0.724	0.787	0.979	0.98	0.841
Geography.Regions.Asia.Central Asia	0.001	0.999	0.785	0.487	0.601	0.999	0.987	0.714
Geography.Regions.Asia.East Asia	0.013	0.987	0.76	0.677	0.716	0.993	0.981	0.739
Geography.Regions.Asia.North Asia	0.004	0.996	0.679	0.176	0.28	0.997	0.985	0.223
Geography.Regions.Asia.South Asia	0.015	0.985	0.867	0.874	0.87	0.996	0.985	0.896
Geography.Regions.Asia.Southeast Asia	0.006	0.994	0.787	0.742	0.763	0.997	0.982	0.74
Geography.Regions.Asia.West Asia	0.011	0.989	0.824	0.819	0.822	0.996	0.986	0.875
Geography.Regions.Europe.Eastern Europe	0.015	0.985	0.797	0.683	0.735	0.993	0.984	0.775
Geography.Regions.Europe.Europe*	0.09	0.91	0.778	0.655	0.711	0.952	0.964	0.768
Geography.Regions.Europe.Northern Europe	0.032	0.968	0.699	0.674	0.687	0.98	0.973	0.713
Geography.Regions.Europe.Southern Europe	0.014	0.986	0.718	0.662	0.689	0.992	0.977	0.711
Geography.Regions.Europe.Western Europe	0.02	0.98	0.692	0.649	0.67	0.987	0.976	0.684
Geography.Regions.Oceania	0.015	0.985	0.83	0.843	0.836	0.995	0.985	0.86
History and Society.Business and economics	0.014	0.986	0.477	0.344	0.4	0.986	0.958	0.345
History and Society.Education	0.008	0.992	0.487	0.477	0.482	0.992	0.96	0.436
History and Society.History	0.013	0.987	0.417	0.351	0.381	0.985	0.944	0.319
History and Society.Military and warfare	0.016	0.984	0.723	0.619	0.667	0.99	0.978	0.706
History and Society.Politics and government	0.026	0.974	0.634	0.7	0.665	0.982	0.965	0.709
History and Society.Society	0.012	0.988	0.416	0.439	0.427	0.986	0.929	0.398
History and Society.Transportation	0.016	0.984	0.865	0.803	0.833	0.995	0.986	0.877
STEM.Biology	0.03	0.97	0.82	0.923	0.868	0.992	0.981	0.915
STEM.Chemistry	0.004	0.996	0.707	0.312	0.433	0.997	0.984	0.459
STEM.Computing	0.009	0.991	0.663	0.206	0.314	0.992	0.981	0.301
STEM.Earth and environment	0.005	0.995	0.714	0.622	0.665	0.997	0.978	0.691
STEM.Engineering	0.007	0.993	0.722	0.559	0.63	0.996	0.98	0.652
STEM.Libraries & Information	0.002	0.998	0.593	0.224	0.325	0.998	0.975	0.356
STEM.Mathematics	0.001	0.999	0.674	0.204	0.313	0.999	0.981	0.385
STEM.Medicine & Health	0.007	0.993	0.673	0.617	0.644	0.995	0.978	0.655
STEM.Physics	0.003	0.997	0.653	0.205	0.312	0.998	0.981	0.325
STEM.STEM*	0.081	0.919	0.873	0.743	0.802	0.97	0.977	0.897
STEM.Space	0.006	0.994	0.863	0.867	0.865	0.998	0.987	0.904
STEM.Technology	0.013	0.987	0.626	0.241	0.348	0.988	0.969	0.365

The training dataset for this model is available here.

Implementation

Model architecture

{
    "type": "GradientBoosting",
    "params": {
        "min_samples_split": 2,
        "random_state": null,
        "presort": "deprecated",
        "criterion": "friedman_mse",
        "init": null,
        "n_estimators": 150,
        "label_weights": {},
        "min_weight_fraction_leaf": 0.0,
        "min_impurity_split": null,
        "scale": false,
        "max_leaf_nodes": null,
        "tol": 0.0001,
        "min_impurity_decrease": 0.0,
        "learning_rate": 0.1,
        "ccp_alpha": 0.0,
        "loss": "deviance",
        "warm_start": 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"
        ],
        "n_iter_no_change": null,
        "min_samples_leaf": 1,
        "subsample": 1.0,
        "population_rates": null,
        "validation_fraction": 0.1,
        "max_features": "log2",
        "max_depth": 5,
        "multilabel": true,
        "center": false,
        "verbose": 0
    }
}

Output schema

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

Example input and output

Input:

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

Output:

Example output

{
    "enwiki": {
        "models": {
            "articletopic": {
                "version": "1.3.0"
            }
        },
        "scores": {
            "1234": {
                "articletopic": {
                    "score": {
                        "prediction": [
                            "STEM.STEM*"
                        ],
                        "probability": {
                            "Culture.Biography.Biography*": 0.007324216272400619,
                            "Culture.Biography.Women": 0.0012902608239981099,
                            "Culture.Food and drink": 0.012590959877795965,
                            "Culture.Internet culture": 0.0005287782922645555,
                            "Culture.Linguistics": 0.0023009688839875403,
                            "Culture.Literature": 0.003750485897413451,
                            "Culture.Media.Books": 0.0006460359265654311,
                            "Culture.Media.Entertainment": 0.001019549745209333,
                            "Culture.Media.Films": 0.0008073367081496864,
                            "Culture.Media.Media*": 0.012011986684036512,
                            "Culture.Media.Music": 0.0008537381008416275,
                            "Culture.Media.Radio": 0.0002869665819413362,
                            "Culture.Media.Software": 0.001089289634412607,
                            "Culture.Media.Television": 0.0008907233279284757,
                            "Culture.Media.Video games": 0.0001200262230068454,
                            "Culture.Performing arts": 0.0019581135216171887,
                            "Culture.Philosophy and religion": 0.006759709347651477,
                            "Culture.Sports": 0.002708191532469151,
                            "Culture.Visual arts.Architecture": 0.006677867414092196,
                            "Culture.Visual arts.Comics and Anime": 0.00012020261708547758,
                            "Culture.Visual arts.Fashion": 0.0008955508074615747,
                            "Culture.Visual arts.Visual arts*": 0.01634491597811793,
                            "Geography.Geographical": 0.021850841219839115,
                            "Geography.Regions.Africa.Africa*": 0.01403461063487551,
                            "Geography.Regions.Africa.Central Africa": 0.0001865675770278535,
                            "Geography.Regions.Africa.Eastern Africa": 0.0299178497643664,
                            "Geography.Regions.Africa.Northern Africa": 0.0002661363607017024,
                            "Geography.Regions.Africa.Southern Africa": 0.0002798146633784188,
                            "Geography.Regions.Africa.Western Africa": 0.00034452672014203926,
                            "Geography.Regions.Americas.Central America": 0.0008260548714261792,
                            "Geography.Regions.Americas.North America": 0.264295864207147,
                            "Geography.Regions.Americas.South America": 0.00015273918706570618,
                            "Geography.Regions.Asia.Asia*": 0.005740602435312139,
                            "Geography.Regions.Asia.Central Asia": 9.711333852867366e-05,
                            "Geography.Regions.Asia.East Asia": 0.0008691503787446779,
                            "Geography.Regions.Asia.North Asia": 0.0001781509472634297,
                            "Geography.Regions.Asia.South Asia": 0.0002519598342558709,
                            "Geography.Regions.Asia.Southeast Asia": 0.0007149941847729443,
                            "Geography.Regions.Asia.West Asia": 0.0007647733764823995,
                            "Geography.Regions.Europe.Eastern Europe": 0.0021314421964480305,
                            "Geography.Regions.Europe.Europe*": 0.04348112583641243,
                            "Geography.Regions.Europe.Northern Europe": 0.03873409183096865,
                            "Geography.Regions.Europe.Southern Europe": 0.0017978097009254826,
                            "Geography.Regions.Europe.Western Europe": 0.001196308436445433,
                            "Geography.Regions.Oceania": 0.0010145084950130057,
                            "History and Society.Business and economics": 0.014571914809621094,
                            "History and Society.Education": 0.022647190342216877,
                            "History and Society.History": 0.006181778255649838,
                            "History and Society.Military and warfare": 0.00431305074559507,
                            "History and Society.Politics and government": 0.017814417716100546,
                            "History and Society.Society": 0.022076257425149896,
                            "History and Society.Transportation": 0.0011766622074844352,
                            "STEM.Biology": 0.009271860405065028,
                            "STEM.Chemistry": 0.0011815942896098593,
                            "STEM.Computing": 0.0007807185930526152,
                            "STEM.Earth and environment": 0.0036473015518475203,
                            "STEM.Engineering": 0.003285589480897292,
                            "STEM.Libraries & Information": 0.0037889231177539767,
                            "STEM.Mathematics": 0.010840473137394606,
                            "STEM.Medicine & Health": 0.004612666090909239,
                            "STEM.Physics": 0.0007788391574850387,
                            "STEM.STEM*": 0.7655342414491919,
                            "STEM.Space": 8.220588424577983e-05,
                            "STEM.Technology": 0.01811368106322167
                        }
                    }
                }
            }
        }
    }
}

Data

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

If available, the training dataset can be found here. 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

Code: MIT license
Model: MIT license

Citation

Cite this model card as:

@misc{
  Triedman_Bazira_2023_English_Wikipedia_article_topic,
  title={ English Wikipedia article topic model card },
  author={ Triedman, Harold and Bazira, Kevin },
  year={ 2023 },
  url={ https://meta.wikimedia.org/wiki/Machine_learning_models/Production/English_Wikipedia_article_topic }
}

Category:Machine learning models Category:English Wikipedia