Machine learning models/Production/Hungarian Wikipedia article topic

Model card
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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?	Hungarian 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/huwiki/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* 15526 14154 1372 779 43039 Culture.Biography.Women 3586 2498 1088 353 55405 Culture.Food and drink 1513 985 528 131 57700 Culture.Internet culture 3266 2754 512 198 55880 Culture.Linguistics 1643 1170 473 97 57604 Culture.Literature 5570 4162 1408 514 53260 Culture.Media.Books 1732 1384 348 105 57507 Culture.Media.Entertainment 2340 1064 1276 284 56720 Culture.Media.Films 2888 2403 485 145 56311 Culture.Media.Media* 14392 12706 1686 1219 43733 Culture.Media.Music 3309 2800 509 246 55789 Culture.Media.Radio 330 161 169 15 58999 Culture.Media.Software 2170 1752 418 274 56900 Culture.Media.Television 2469 1986 483 136 56739 Culture.Media.Video games 2087 1941 146 48 57209 Culture.Performing arts 1491 948 543 114 57739 Culture.Philosophy and religion 4003 2356 1647 417 54924 Culture.Sports 6230 5587 643 179 52935 Culture.Visual arts.Architecture 2030 1416 614 250 57064 Culture.Visual arts.Comics and Anime 1854 1526 328 95 57395 Culture.Visual arts.Fashion 806 485 321 54 58484 Culture.Visual arts.Visual arts* 5379 3710 1669 421 53544 Geography.Geographical 3887 2579 1308 537 54920 Geography.Regions.Africa.Africa* 4049 2762 1287 331 54964 Geography.Regions.Africa.Central Africa 880 570 310 72 58392 Geography.Regions.Africa.Eastern Africa 369 233 136 41 58934 Geography.Regions.Africa.Northern Africa 1503 940 563 126 57715 Geography.Regions.Africa.Southern Africa 695 515 180 47 58602 Geography.Regions.Africa.Western Africa 155 64 91 29 59160 Geography.Regions.Americas.Central America 1381 789 592 102 57861 Geography.Regions.Americas.North America 5628 3631 1997 930 52786 Geography.Regions.Americas.South America 1610 1097 513 126 57608 Geography.Regions.Asia.Asia* 10749 8632 2117 945 47650 Geography.Regions.Asia.Central Asia 1349 938 411 103 57892 Geography.Regions.Asia.East Asia 3424 2703 721 219 55701 Geography.Regions.Asia.North Asia 1888 1385 503 229 57227 Geography.Regions.Asia.South Asia 1826 1268 558 110 57408 Geography.Regions.Asia.Southeast Asia 1601 974 627 116 57627 Geography.Regions.Asia.West Asia 2495 1794 701 200 56649 Geography.Regions.Europe.Eastern Europe 4951 3665 1286 556 53837 Geography.Regions.Europe.Europe* 16846 13610 3236 2259 40239 Geography.Regions.Europe.Northern Europe 3617 2386 1231 378 55349 Geography.Regions.Europe.Southern Europe 4056 2998 1058 359 54929 Geography.Regions.Europe.Western Europe 5217 4004 1213 551 53576 Geography.Regions.Oceania 1853 1310 543 121 57370 History and Society.Business and economics 3192 1728 1464 324 55828 History and Society.Education 1612 735 877 165 57567 History and Society.History 6143 3880 2263 739 52462 History and Society.Military and warfare 5039 3575 1464 471 53834 History and Society.Politics and government 4453 2632 1821 436 54455 History and Society.Society 6223 2897 3326 552 52569 History and Society.Transportation 4094 3714 380 159 55091 STEM.Biology 4751 4126 625 137 54456 STEM.Chemistry 1620 1312 308 166 57558 STEM.Computing 2449 1986 463 283 56612 STEM.Earth and environment 1882 1335 547 131 57331 STEM.Engineering 2505 1786 719 215 56624 STEM.Libraries & Information 510 289 221 38 58796 STEM.Mathematics 990 785 205 63 58291 STEM.Medicine & Health 1939 1405 534 160 57245 STEM.Physics 1419 958 461 166 57759 STEM.STEM* 18523 16670 1853 917 39904 STEM.Space 2372 2227 145 42 56930 STEM.Technology 4874 3669 1205 597 53873

Test data sample rates:

Test data sample rates
Label Sample Population Culture.Biography.Biography* 0.262 0.123 Culture.Biography.Women 0.06 0.015 Culture.Food and drink 0.025 0.002 Culture.Internet culture 0.055 0.003 Culture.Linguistics 0.028 0.007 Culture.Literature 0.094 0.015 Culture.Media.Books 0.029 0.004 Culture.Media.Entertainment 0.039 0.004 Culture.Media.Films 0.049 0.011 Culture.Media.Media* 0.243 0.058 Culture.Media.Music 0.056 0.024 Culture.Media.Radio 0.006 0.002 Culture.Media.Software 0.037 0.001 Culture.Media.Television 0.042 0.009 Culture.Media.Video games 0.035 0.003 Culture.Performing arts 0.025 0.003 Culture.Philosophy and religion 0.067 0.011 Culture.Sports 0.105 0.071 Culture.Visual arts.Architecture 0.034 0.011 Culture.Visual arts.Comics and Anime 0.031 0.002 Culture.Visual arts.Fashion 0.014 0.001 Culture.Visual arts.Visual arts* 0.091 0.018 Geography.Geographical 0.065 0.024 Geography.Regions.Africa.Africa* 0.068 0.008 Geography.Regions.Africa.Central Africa 0.015 0.001 Geography.Regions.Africa.Eastern Africa 0.006 0 Geography.Regions.Africa.Northern Africa 0.025 0.001 Geography.Regions.Africa.Southern Africa 0.012 0.001 Geography.Regions.Africa.Western Africa 0.003 0.001 Geography.Regions.Americas.Central America 0.023 0.003 Geography.Regions.Americas.North America 0.095 0.064 Geography.Regions.Americas.South America 0.027 0.006 Geography.Regions.Asia.Asia* 0.181 0.045 Geography.Regions.Asia.Central Asia 0.023 0.001 Geography.Regions.Asia.East Asia 0.058 0.011 Geography.Regions.Asia.North Asia 0.032 0.001 Geography.Regions.Asia.South Asia 0.031 0.015 Geography.Regions.Asia.Southeast Asia 0.027 0.006 Geography.Regions.Asia.West Asia 0.042 0.011 Geography.Regions.Europe.Eastern Europe 0.083 0.013 Geography.Regions.Europe.Europe* 0.284 0.076 Geography.Regions.Europe.Northern Europe 0.061 0.031 Geography.Regions.Europe.Southern Europe 0.068 0.013 Geography.Regions.Europe.Western Europe 0.088 0.019 Geography.Regions.Oceania 0.031 0.015 History and Society.Business and economics 0.054 0.01 History and Society.Education 0.027 0.007 History and Society.History 0.104 0.011 History and Society.Military and warfare 0.085 0.014 History and Society.Politics and government 0.075 0.028 History and Society.Society 0.105 0.013 History and Society.Transportation 0.069 0.015 STEM.Biology 0.08 0.034 STEM.Chemistry 0.027 0.002 STEM.Computing 0.041 0.003 STEM.Earth and environment 0.032 0.005 STEM.Engineering 0.042 0.005 STEM.Libraries & Information 0.009 0.001 STEM.Mathematics 0.017 0 STEM.Medicine & Health 0.033 0.006 STEM.Physics 0.024 0.001 STEM.STEM* 0.312 0.069 STEM.Space 0.04 0.006 STEM.Technology 0.082 0.005

Test data performance:

Test data performance
Label Match rate Filter rate Recall Precision f1 Accuracy ROC AUC PR AUC Culture.Biography.Biography* 0.128 0.872 0.912 0.878 0.895 0.974 0.982 0.953 Culture.Biography.Women 0.016 0.984 0.697 0.619 0.656 0.989 0.978 0.687 Culture.Food and drink 0.004 0.996 0.651 0.415 0.507 0.997 0.976 0.477 Culture.Internet culture 0.006 0.994 0.843 0.456 0.592 0.996 0.985 0.678 Culture.Linguistics 0.007 0.993 0.712 0.758 0.734 0.996 0.973 0.719 Culture.Literature 0.021 0.979 0.747 0.552 0.635 0.987 0.977 0.722 Culture.Media.Books 0.005 0.995 0.799 0.639 0.71 0.997 0.984 0.798 Culture.Media.Entertainment 0.007 0.993 0.455 0.247 0.32 0.993 0.965 0.226 Culture.Media.Films 0.011 0.989 0.832 0.775 0.802 0.996 0.984 0.844 Culture.Media.Media* 0.077 0.923 0.883 0.669 0.761 0.968 0.98 0.866 Culture.Media.Music 0.025 0.975 0.846 0.825 0.836 0.992 0.985 0.88 Culture.Media.Radio 0.001 0.999 0.488 0.806 0.608 0.999 0.938 0.438 Culture.Media.Software 0.006 0.994 0.807 0.183 0.298 0.995 0.987 0.347 Culture.Media.Television 0.009 0.991 0.804 0.749 0.776 0.996 0.985 0.807 Culture.Media.Video games 0.003 0.997 0.93 0.744 0.827 0.999 0.99 0.857 Culture.Performing arts 0.004 0.996 0.636 0.483 0.549 0.997 0.976 0.575 Culture.Philosophy and religion 0.014 0.986 0.589 0.457 0.514 0.988 0.958 0.495 Culture.Sports 0.067 0.933 0.897 0.953 0.924 0.99 0.981 0.952 Culture.Visual arts.Architecture 0.012 0.988 0.698 0.631 0.662 0.992 0.979 0.683 Culture.Visual arts.Comics and Anime 0.003 0.997 0.823 0.523 0.64 0.998 0.987 0.701 Culture.Visual arts.Fashion 0.001 0.999 0.602 0.346 0.439 0.999 0.97 0.303 Culture.Visual arts.Visual arts* 0.02 0.98 0.69 0.622 0.654 0.987 0.969 0.667 Geography.Geographical 0.025 0.975 0.663 0.623 0.643 0.983 0.971 0.66 Geography.Regions.Africa.Africa* 0.011 0.989 0.682 0.472 0.558 0.992 0.971 0.534 Geography.Regions.Africa.Central Africa 0.002 0.998 0.648 0.249 0.36 0.999 0.981 0.266 Geography.Regions.Africa.Eastern Africa 0.001 0.999 0.631 0.292 0.4 0.999 0.96 0.195 Geography.Regions.Africa.Northern Africa 0.003 0.997 0.625 0.261 0.368 0.997 0.975 0.336 Geography.Regions.Africa.Southern Africa 0.002 0.998 0.741 0.521 0.612 0.999 0.976 0.51 Geography.Regions.Africa.Western Africa 0.001 0.999 0.413 0.366 0.388 0.999 0.881 0.24 Geography.Regions.Americas.Central America 0.004 0.996 0.571 0.518 0.543 0.997 0.969 0.442 Geography.Regions.Americas.North America 0.058 0.942 0.645 0.719 0.68 0.961 0.963 0.752 Geography.Regions.Americas.South America 0.006 0.994 0.681 0.665 0.673 0.996 0.975 0.663 Geography.Regions.Asia.Asia* 0.055 0.945 0.803 0.663 0.726 0.972 0.971 0.794 Geography.Regions.Asia.Central Asia 0.002 0.998 0.695 0.253 0.371 0.998 0.98 0.305 Geography.Regions.Asia.East Asia 0.013 0.987 0.789 0.699 0.742 0.994 0.98 0.799 Geography.Regions.Asia.North Asia 0.005 0.995 0.734 0.145 0.243 0.996 0.984 0.202 Geography.Regions.Asia.South Asia 0.012 0.988 0.694 0.848 0.764 0.993 0.973 0.796 Geography.Regions.Asia.Southeast Asia 0.006 0.994 0.608 0.647 0.627 0.996 0.972 0.578 Geography.Regions.Asia.West Asia 0.011 0.989 0.719 0.693 0.706 0.993 0.977 0.73 Geography.Regions.Europe.Eastern Europe 0.02 0.98 0.74 0.485 0.586 0.987 0.975 0.616 Geography.Regions.Europe.Europe* 0.111 0.889 0.808 0.556 0.659 0.936 0.958 0.762 Geography.Regions.Europe.Northern Europe 0.027 0.973 0.66 0.754 0.704 0.983 0.971 0.767 Geography.Regions.Europe.Southern Europe 0.016 0.984 0.739 0.601 0.663 0.99 0.974 0.714 Geography.Regions.Europe.Western Europe 0.025 0.975 0.767 0.596 0.671 0.986 0.979 0.755 Geography.Regions.Oceania 0.013 0.987 0.707 0.838 0.767 0.993 0.976 0.803 History and Society.Business and economics 0.011 0.989 0.541 0.489 0.514 0.99 0.958 0.472 History and Society.Education 0.006 0.994 0.456 0.542 0.495 0.993 0.961 0.432 History and Society.History 0.021 0.979 0.632 0.332 0.436 0.982 0.961 0.486 History and Society.Military and warfare 0.019 0.981 0.709 0.539 0.612 0.987 0.973 0.668 History and Society.Politics and government 0.024 0.976 0.591 0.683 0.634 0.981 0.954 0.662 History and Society.Society 0.016 0.984 0.466 0.364 0.409 0.983 0.93 0.37 History and Society.Transportation 0.017 0.983 0.907 0.828 0.866 0.996 0.986 0.905 STEM.Biology 0.032 0.968 0.868 0.923 0.895 0.993 0.982 0.931 STEM.Chemistry 0.004 0.996 0.81 0.305 0.443 0.997 0.984 0.544 STEM.Computing 0.007 0.993 0.811 0.305 0.444 0.995 0.986 0.489 STEM.Earth and environment 0.005 0.995 0.709 0.586 0.642 0.996 0.973 0.648 STEM.Engineering 0.007 0.993 0.713 0.498 0.586 0.995 0.979 0.624 STEM.Libraries & Information 0.001 0.999 0.567 0.353 0.435 0.999 0.963 0.22 STEM.Mathematics 0.001 0.999 0.793 0.234 0.362 0.999 0.981 0.389 STEM.Medicine & Health 0.007 0.993 0.725 0.626 0.672 0.995 0.979 0.648 STEM.Physics 0.003 0.997 0.675 0.167 0.267 0.997 0.981 0.168 STEM.STEM* 0.083 0.917 0.9 0.748 0.817 0.972 0.978 0.896 STEM.Space 0.006 0.994 0.939 0.885 0.911 0.999 0.994 0.963 STEM.Technology 0.015 0.985 0.753 0.262 0.388 0.988 0.977 0.51

Implementation

Model architecture

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

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/huwiki/1234/articletopic

Output:

Example output
{ "huwiki": { "models": { "articletopic": { "version": "1.4.0" } }, "scores": { "1234": { "articletopic": { "score": { "prediction": [ "Geography.Regions.Asia.East Asia", "Geography.Regions.Europe.Europe*", "STEM.STEM*" ], "probability": { "Culture.Biography.Biography*": 0.3423533920926803, "Culture.Biography.Women": 0.14025995403786506, "Culture.Food and drink": 0.06340726339907797, "Culture.Internet culture": 0.011131262002287472, "Culture.Linguistics": 0.04803618594888942, "Culture.Literature": 0.022006683732088216, "Culture.Media.Books": 0.002187089040235113, "Culture.Media.Entertainment": 0.15309575643241796, "Culture.Media.Films": 0.034026455544947425, "Culture.Media.Media*": 0.20341149710773634, "Culture.Media.Music": 0.0497533083246081, "Culture.Media.Radio": 0.32875096388132496, "Culture.Media.Software": 0.013034094135796932, "Culture.Media.Television": 0.0031210534375715336, "Culture.Media.Video games": 0.00030343872173972046, "Culture.Performing arts": 0.005608603834940765, "Culture.Philosophy and religion": 0.08066624396157131, "Culture.Sports": 0.06433894067592255, "Culture.Visual arts.Architecture": 0.03750949389175476, "Culture.Visual arts.Comics and Anime": 0.0064514547243535985, "Culture.Visual arts.Fashion": 0.05307244012171412, "Culture.Visual arts.Visual arts*": 0.11900694660877364, "Geography.Geographical": 0.032374625359398314, "Geography.Regions.Africa.Africa*": 0.15894948721508503, "Geography.Regions.Africa.Central Africa": 0.016011778190990485, "Geography.Regions.Africa.Eastern Africa": 0.004741053756007876, "Geography.Regions.Africa.Northern Africa": 0.0244144581524136, "Geography.Regions.Africa.Southern Africa": 0.0019768365502830605, "Geography.Regions.Africa.Western Africa": 3.962206086248686e-05, "Geography.Regions.Americas.Central America": 0.00522749251093348, "Geography.Regions.Americas.North America": 0.014687908835933414, "Geography.Regions.Americas.South America": 0.002403901278264949, "Geography.Regions.Asia.Asia*": 0.20487191607636618, "Geography.Regions.Asia.Central Asia": 0.09112897244334056, "Geography.Regions.Asia.East Asia": 0.9998386100569907, "Geography.Regions.Asia.North Asia": 0.0027527477451360595, "Geography.Regions.Asia.South Asia": 0.020285604017724872, "Geography.Regions.Asia.Southeast Asia": 0.03250982686466025, "Geography.Regions.Asia.West Asia": 0.05840215484971228, "Geography.Regions.Europe.Eastern Europe": 0.03298542948603228, "Geography.Regions.Europe.Europe*": 0.5315974960652942, "Geography.Regions.Europe.Northern Europe": 0.007914585235952828, "Geography.Regions.Europe.Southern Europe": 0.09252560636319572, "Geography.Regions.Europe.Western Europe": 0.021413181244936737, "Geography.Regions.Oceania": 0.026964542276338526, "History and Society.Business and economics": 0.11345817838571329, "History and Society.Education": 0.018546943483473882, "History and Society.History": 0.16482995511902204, "History and Society.Military and warfare": 0.057955265993041155, "History and Society.Politics and government": 0.0422117254411273, "History and Society.Society": 0.20169635418355597, "History and Society.Transportation": 0.015610557061165244, "STEM.Biology": 0.037673572379875016, "STEM.Chemistry": 0.010794729056374292, "STEM.Computing": 0.04398691739762987, "STEM.Earth and environment": 0.0039011304821686735, "STEM.Engineering": 0.16717936084988483, "STEM.Libraries & Information": 0.022633922980662434, "STEM.Mathematics": 0.08494932357374003, "STEM.Medicine & Health": 0.010577875534037367, "STEM.Physics": 0.0009472592704866106, "STEM.STEM*": 0.7982423641999686, "STEM.Space": 0.003438360400580638, "STEM.Technology": 0.08261264399841732 } } } } } } }

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

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_Hungarian_Wikipedia_article_topic,
  title={ Hungarian Wikipedia article topic model card },
  author={ Triedman, Harold and Bazira, Kevin },
  year={ 2023 },
  url={ https://meta.wikimedia.org/wiki/Machine_learning_models/Production/Hungarian_Wikipedia_article_topic }
}