Machine learning models/Production/Korean Wikipedia article topic

Model card
This page is an on-wiki machine learning 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?	Korean 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/kowiki/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* 15801 14235 1566 827 44113 Culture.Biography.Women 4424 3274 1150 430 55887 Culture.Food and drink 1776 1367 409 88 58877 Culture.Internet culture 3463 2729 734 221 57057 Culture.Linguistics 1688 1152 536 100 58953 Culture.Literature 5858 4323 1535 505 54378 Culture.Media.Books 1614 1169 445 121 59006 Culture.Media.Entertainment 2343 1229 1114 239 58159 Culture.Media.Films 2950 2446 504 109 57682 Culture.Media.Media* 15181 13011 2170 1546 44014 Culture.Media.Music 3247 2609 638 275 57219 Culture.Media.Radio 702 429 273 66 59973 Culture.Media.Software 2364 1802 562 377 58000 Culture.Media.Television 2480 1803 677 162 58099 Culture.Media.Video games 2305 2021 284 85 58351 Culture.Performing arts 1527 871 656 142 59072 Culture.Philosophy and religion 3856 2050 1806 334 56551 Culture.Sports 5207 4500 707 171 55363 Culture.Visual arts.Architecture 2141 1419 722 218 58382 Culture.Visual arts.Comics and Anime 2404 2041 363 153 58184 Culture.Visual arts.Fashion 1355 948 407 76 59310 Culture.Visual arts.Visual arts* 6401 4521 1880 538 53802 Geography.Geographical 3907 2541 1366 538 56296 Geography.Regions.Africa.Africa* 4310 3234 1076 243 56188 Geography.Regions.Africa.Central Africa 847 614 233 42 59852 Geography.Regions.Africa.Eastern Africa 475 336 139 77 60189 Geography.Regions.Africa.Northern Africa 1517 1089 428 111 59113 Geography.Regions.Africa.Southern Africa 690 496 194 58 59993 Geography.Regions.Africa.Western Africa 280 195 85 34 60427 Geography.Regions.Americas.Central America 1359 860 499 68 59314 Geography.Regions.Americas.North America 6585 4572 2013 1182 52974 Geography.Regions.Americas.South America 1532 1117 415 90 59119 Geography.Regions.Asia.Asia* 15107 12516 2591 1540 44094 Geography.Regions.Asia.Central Asia 1328 938 390 86 59327 Geography.Regions.Asia.East Asia 7562 6091 1471 823 52356 Geography.Regions.Asia.North Asia 1823 1372 451 197 58721 Geography.Regions.Asia.South Asia 1867 1404 463 83 58791 Geography.Regions.Asia.Southeast Asia 1913 1355 558 91 58737 Geography.Regions.Asia.West Asia 2393 1749 644 182 58166 Geography.Regions.Europe.Eastern Europe 3409 2597 812 316 57016 Geography.Regions.Europe.Europe* 13405 10882 2523 1627 45709 Geography.Regions.Europe.Northern Europe 3936 2782 1154 405 56400 Geography.Regions.Europe.Southern Europe 3303 2386 917 284 57154 Geography.Regions.Europe.Western Europe 4084 3070 1014 427 56230 Geography.Regions.Oceania 1829 1341 488 62 58850 History and Society.Business and economics 3915 2140 1775 403 56423 History and Society.Education 1907 1131 776 127 58707 History and Society.History 5526 3175 2351 660 54555 History and Society.Military and warfare 5276 3611 1665 477 54988 History and Society.Politics and government 5159 2870 2289 534 55048 History and Society.Society 6675 3126 3549 588 53478 History and Society.Transportation 3768 3259 509 167 56806 STEM.Biology 3464 2662 802 192 57085 STEM.Chemistry 1495 1150 345 135 59111 STEM.Computing 2753 2123 630 459 57529 STEM.Earth and environment 1903 1247 656 136 58702 STEM.Engineering 2710 1879 831 184 57847 STEM.Libraries & Information 753 508 245 39 59949 STEM.Mathematics 1179 875 304 97 59465 STEM.Medicine & Health 1993 1265 728 173 58575 STEM.Physics 1467 937 530 183 59091 STEM.STEM* 17902 15664 2238 1087 41752 STEM.Space 1703 1499 204 44 58994 STEM.Technology 4764 3350 1414 669 55308

Test data sample rates:

Test data sample rates
Label Sample Population Culture.Biography.Biography* 0.26 0.123 Culture.Biography.Women 0.073 0.015 Culture.Food and drink 0.029 0.002 Culture.Internet culture 0.057 0.003 Culture.Linguistics 0.028 0.007 Culture.Literature 0.096 0.015 Culture.Media.Books 0.027 0.004 Culture.Media.Entertainment 0.039 0.004 Culture.Media.Films 0.049 0.011 Culture.Media.Media* 0.25 0.058 Culture.Media.Music 0.053 0.024 Culture.Media.Radio 0.012 0.002 Culture.Media.Software 0.039 0.001 Culture.Media.Television 0.041 0.009 Culture.Media.Video games 0.038 0.003 Culture.Performing arts 0.025 0.003 Culture.Philosophy and religion 0.063 0.011 Culture.Sports 0.086 0.071 Culture.Visual arts.Architecture 0.035 0.011 Culture.Visual arts.Comics and Anime 0.04 0.002 Culture.Visual arts.Fashion 0.022 0.001 Culture.Visual arts.Visual arts* 0.105 0.018 Geography.Geographical 0.064 0.024 Geography.Regions.Africa.Africa* 0.071 0.008 Geography.Regions.Africa.Central Africa 0.014 0.001 Geography.Regions.Africa.Eastern Africa 0.008 0 Geography.Regions.Africa.Northern Africa 0.025 0.001 Geography.Regions.Africa.Southern Africa 0.011 0.001 Geography.Regions.Africa.Western Africa 0.005 0.001 Geography.Regions.Americas.Central America 0.022 0.003 Geography.Regions.Americas.North America 0.108 0.064 Geography.Regions.Americas.South America 0.025 0.006 Geography.Regions.Asia.Asia* 0.249 0.045 Geography.Regions.Asia.Central Asia 0.022 0.001 Geography.Regions.Asia.East Asia 0.124 0.011 Geography.Regions.Asia.North Asia 0.03 0.001 Geography.Regions.Asia.South Asia 0.031 0.015 Geography.Regions.Asia.Southeast Asia 0.031 0.006 Geography.Regions.Asia.West Asia 0.039 0.011 Geography.Regions.Europe.Eastern Europe 0.056 0.013 Geography.Regions.Europe.Europe* 0.221 0.076 Geography.Regions.Europe.Northern Europe 0.065 0.031 Geography.Regions.Europe.Southern Europe 0.054 0.013 Geography.Regions.Europe.Western Europe 0.067 0.019 Geography.Regions.Oceania 0.03 0.015 History and Society.Business and economics 0.064 0.01 History and Society.Education 0.031 0.007 History and Society.History 0.091 0.011 History and Society.Military and warfare 0.087 0.014 History and Society.Politics and government 0.085 0.028 History and Society.Society 0.11 0.013 History and Society.Transportation 0.062 0.015 STEM.Biology 0.057 0.034 STEM.Chemistry 0.025 0.002 STEM.Computing 0.045 0.003 STEM.Earth and environment 0.031 0.005 STEM.Engineering 0.045 0.005 STEM.Libraries & Information 0.012 0.001 STEM.Mathematics 0.019 0 STEM.Medicine & Health 0.033 0.006 STEM.Physics 0.024 0.001 STEM.STEM* 0.295 0.069 STEM.Space 0.028 0.006 STEM.Technology 0.078 0.005

Test data performance:

Test data performance
Label Match rate Filter rate Recall Precision f1 Accuracy ROC AUC PR AUC Culture.Biography.Biography* 0.127 0.873 0.901 0.873 0.887 0.972 0.981 0.949 Culture.Biography.Women 0.018 0.982 0.74 0.589 0.656 0.989 0.978 0.646 Culture.Food and drink 0.003 0.997 0.77 0.56 0.648 0.998 0.984 0.668 Culture.Internet culture 0.007 0.993 0.788 0.418 0.546 0.995 0.983 0.629 Culture.Linguistics 0.007 0.993 0.682 0.748 0.714 0.996 0.976 0.734 Culture.Literature 0.02 0.98 0.738 0.558 0.635 0.987 0.975 0.708 Culture.Media.Books 0.005 0.995 0.724 0.589 0.649 0.997 0.98 0.701 Culture.Media.Entertainment 0.006 0.994 0.525 0.315 0.394 0.994 0.966 0.37 Culture.Media.Films 0.011 0.989 0.829 0.824 0.826 0.996 0.982 0.804 Culture.Media.Media* 0.082 0.918 0.857 0.611 0.713 0.96 0.975 0.832 Culture.Media.Music 0.024 0.976 0.804 0.804 0.804 0.991 0.982 0.825 Culture.Media.Radio 0.002 0.998 0.611 0.546 0.576 0.998 0.964 0.424 Culture.Media.Software 0.007 0.993 0.762 0.136 0.23 0.993 0.984 0.295 Culture.Media.Television 0.009 0.991 0.727 0.699 0.713 0.995 0.979 0.716 Culture.Media.Video games 0.004 0.996 0.877 0.612 0.721 0.998 0.988 0.817 Culture.Performing arts 0.004 0.996 0.57 0.408 0.476 0.996 0.966 0.4 Culture.Philosophy and religion 0.011 0.989 0.532 0.494 0.512 0.989 0.946 0.481 Culture.Sports 0.064 0.936 0.864 0.956 0.908 0.987 0.978 0.942 Culture.Visual arts.Architecture 0.011 0.989 0.663 0.655 0.659 0.993 0.976 0.698 Culture.Visual arts.Comics and Anime 0.004 0.996 0.849 0.416 0.558 0.997 0.985 0.608 Culture.Visual arts.Fashion 0.002 0.998 0.7 0.307 0.426 0.998 0.979 0.372 Culture.Visual arts.Visual arts* 0.023 0.977 0.706 0.571 0.631 0.985 0.968 0.692 Geography.Geographical 0.025 0.975 0.65 0.624 0.637 0.983 0.971 0.662 Geography.Regions.Africa.Africa* 0.01 0.99 0.75 0.578 0.653 0.994 0.975 0.662 Geography.Regions.Africa.Central Africa 0.001 0.999 0.725 0.395 0.511 0.999 0.982 0.425 Geography.Regions.Africa.Eastern Africa 0.002 0.998 0.707 0.201 0.313 0.999 0.97 0.22 Geography.Regions.Africa.Northern Africa 0.003 0.997 0.718 0.32 0.443 0.998 0.978 0.327 Geography.Regions.Africa.Southern Africa 0.002 0.998 0.719 0.467 0.566 0.999 0.963 0.472 Geography.Regions.Africa.Western Africa 0.001 0.999 0.696 0.459 0.553 0.999 0.951 0.378 Geography.Regions.Americas.Central America 0.003 0.997 0.633 0.647 0.64 0.998 0.973 0.622 Geography.Regions.Americas.North America 0.065 0.935 0.694 0.686 0.69 0.96 0.964 0.733 Geography.Regions.Americas.South America 0.006 0.994 0.729 0.753 0.741 0.997 0.978 0.759 Geography.Regions.Asia.Asia* 0.07 0.93 0.828 0.539 0.653 0.96 0.966 0.75 Geography.Regions.Asia.Central Asia 0.002 0.998 0.706 0.297 0.418 0.998 0.979 0.336 Geography.Regions.Asia.East Asia 0.024 0.976 0.805 0.375 0.512 0.982 0.977 0.568 Geography.Regions.Asia.North Asia 0.004 0.996 0.753 0.172 0.28 0.996 0.984 0.336 Geography.Regions.Asia.South Asia 0.013 0.987 0.752 0.892 0.816 0.995 0.98 0.867 Geography.Regions.Asia.Southeast Asia 0.006 0.994 0.708 0.735 0.721 0.997 0.975 0.742 Geography.Regions.Asia.West Asia 0.011 0.989 0.731 0.721 0.726 0.994 0.978 0.726 Geography.Regions.Europe.Eastern Europe 0.015 0.985 0.762 0.642 0.697 0.992 0.98 0.738 Geography.Regions.Europe.Europe* 0.094 0.906 0.812 0.661 0.728 0.954 0.966 0.799 Geography.Regions.Europe.Northern Europe 0.029 0.971 0.707 0.758 0.731 0.984 0.974 0.787 Geography.Regions.Europe.Southern Europe 0.014 0.986 0.722 0.659 0.689 0.992 0.975 0.724 Geography.Regions.Europe.Western Europe 0.022 0.978 0.752 0.661 0.703 0.988 0.979 0.737 Geography.Regions.Oceania 0.012 0.988 0.733 0.914 0.814 0.995 0.979 0.864 History and Society.Business and economics 0.013 0.987 0.547 0.44 0.487 0.988 0.96 0.446 History and Society.Education 0.007 0.993 0.593 0.671 0.629 0.995 0.972 0.635 History and Society.History 0.018 0.982 0.575 0.345 0.431 0.984 0.953 0.438 History and Society.Military and warfare 0.018 0.982 0.684 0.532 0.598 0.987 0.971 0.665 History and Society.Politics and government 0.025 0.975 0.556 0.627 0.589 0.978 0.95 0.617 History and Society.Society 0.017 0.983 0.468 0.355 0.404 0.983 0.926 0.372 History and Society.Transportation 0.016 0.984 0.865 0.819 0.841 0.995 0.984 0.876 STEM.Biology 0.029 0.971 0.768 0.889 0.824 0.989 0.977 0.881 STEM.Chemistry 0.003 0.997 0.769 0.345 0.476 0.997 0.986 0.567 STEM.Computing 0.01 0.99 0.771 0.208 0.328 0.991 0.985 0.349 STEM.Earth and environment 0.005 0.995 0.655 0.564 0.606 0.996 0.973 0.591 STEM.Engineering 0.007 0.993 0.693 0.535 0.604 0.995 0.976 0.626 STEM.Libraries & Information 0.001 0.999 0.675 0.392 0.496 0.999 0.972 0.437 STEM.Mathematics 0.002 0.998 0.742 0.16 0.263 0.998 0.981 0.301 STEM.Medicine & Health 0.007 0.993 0.635 0.581 0.607 0.995 0.971 0.569 STEM.Physics 0.004 0.996 0.639 0.15 0.242 0.997 0.98 0.222 STEM.STEM* 0.084 0.916 0.875 0.719 0.789 0.968 0.974 0.88 STEM.Space 0.006 0.994 0.88 0.877 0.879 0.999 0.988 0.906 STEM.Technology 0.015 0.985 0.703 0.233 0.35 0.987 0.971 0.398

Implementation

Model architecture

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

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.Media.Books": { "type": "number" }, "Geography.Regions.Asia.North Asia": { "type": "number" }, "Culture.Media.Films": { "type": "number" }, "History and Society.Business and economics": { "type": "number" }, "Culture.Visual arts.Architecture": { "type": "number" }, "STEM.STEM*": { "type": "number" }, "Culture.Visual arts.Visual arts*": { "type": "number" }, "Geography.Regions.Asia.Southeast Asia": { "type": "number" }, "STEM.Computing": { "type": "number" }, "STEM.Mathematics": { "type": "number" }, "History and Society.Transportation": { "type": "number" }, "STEM.Space": { "type": "number" }, "Culture.Media.Music": { "type": "number" }, "Culture.Media.Video games": { "type": "number" }, "History and Society.Politics and government": { "type": "number" }, "Geography.Regions.Africa.Eastern Africa": { "type": "number" }, "Geography.Regions.Europe.Western Europe": { "type": "number" }, "Culture.Sports": { "type": "number" }, "Geography.Regions.Africa.Africa*": { "type": "number" }, "Culture.Media.Software": { "type": "number" }, "STEM.Chemistry": { "type": "number" }, "Culture.Linguistics": { "type": "number" }, "STEM.Libraries & Information": { "type": "number" }, "Culture.Biography.Women": { "type": "number" }, "Geography.Regions.Americas.South America": { "type": "number" }, "Geography.Regions.Asia.West Asia": { "type": "number" }, "STEM.Physics": { "type": "number" }, "Culture.Visual arts.Fashion": { "type": "number" }, "Geography.Regions.Africa.Northern Africa": { "type": "number" }, "History and Society.Education": { "type": "number" }, "Geography.Regions.Americas.North America": { "type": "number" }, "Geography.Regions.Europe.Eastern Europe": { "type": "number" }, "Geography.Regions.Americas.Central America": { "type": "number" }, "Geography.Regions.Asia.Central Asia": { "type": "number" }, "Culture.Biography.Biography*": { "type": "number" }, "Culture.Media.Entertainment": { "type": "number" }, "Geography.Regions.Asia.Asia*": { "type": "number" }, "Geography.Geographical": { "type": "number" }, "Culture.Internet culture": { "type": "number" }, "Geography.Regions.Asia.East Asia": { "type": "number" }, "Geography.Regions.Europe.Southern Europe": { "type": "number" }, "Geography.Regions.Africa.Western Africa": { "type": "number" }, "History and Society.Society": { "type": "number" }, "Geography.Regions.Asia.South Asia": { "type": "number" }, "Geography.Regions.Africa.Southern Africa": { "type": "number" }, "STEM.Engineering": { "type": "number" }, "Culture.Performing arts": { "type": "number" }, "Culture.Media.Media*": { "type": "number" }, "STEM.Biology": { "type": "number" }, "Geography.Regions.Europe.Northern Europe": { "type": "number" }, "Culture.Media.Radio": { "type": "number" }, "Geography.Regions.Europe.Europe*": { "type": "number" }, "Culture.Media.Television": { "type": "number" }, "History and Society.Military and warfare": { "type": "number" }, "Geography.Regions.Oceania": { "type": "number" }, "Culture.Food and drink": { "type": "number" }, "STEM.Technology": { "type": "number" }, "Culture.Philosophy and religion": { "type": "number" }, "Geography.Regions.Africa.Central Africa": { "type": "number" }, "STEM.Medicine & Health": { "type": "number" }, "History and Society.History": { "type": "number" }, "STEM.Earth and environment": { "type": "number" }, "Culture.Visual arts.Comics and Anime": { "type": "number" }, "Culture.Literature": { "type": "number" } } } } }

Example input and output

Input:

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

Output:

Example output
{ "kowiki": { "models": { "articletopic": { "version": "1.3.0" } }, "scores": { "1234": { "articletopic": { "score": { "prediction": [ "Culture.Media.Software", "STEM.Computing", "STEM.STEM*", "STEM.Technology" ], "probability": { "Culture.Biography.Biography*": 0.0034228232602912688, "Culture.Biography.Women": 0.0009212944875142359, "Culture.Food and drink": 0.00016616038714377659, "Culture.Internet culture": 0.2867922233340401, "Culture.Linguistics": 0.002654510795031825, "Culture.Literature": 0.0012054160435867756, "Culture.Media.Books": 0.00036196456970389446, "Culture.Media.Entertainment": 0.0006637877545336003, "Culture.Media.Films": 0.0005868003736939404, "Culture.Media.Media*": 0.45082914781367006, "Culture.Media.Music": 0.001082295801151109, "Culture.Media.Radio": 0.0011221650601441191, "Culture.Media.Software": 0.6106288410598574, "Culture.Media.Television": 0.0009180753067480871, "Culture.Media.Video games": 0.0005296792414704426, "Culture.Performing arts": 0.0004068675009092034, "Culture.Philosophy and religion": 0.001721456315217673, "Culture.Sports": 0.0014508720045588101, "Culture.Visual arts.Architecture": 0.0027564300681295873, "Culture.Visual arts.Comics and Anime": 0.00011604420903405569, "Culture.Visual arts.Fashion": 0.0004264809451367489, "Culture.Visual arts.Visual arts*": 0.04328520557676202, "Geography.Geographical": 0.0014977017906365317, "Geography.Regions.Africa.Africa*": 0.0010947982572026048, "Geography.Regions.Africa.Central Africa": 0.0001076128906272681, "Geography.Regions.Africa.Eastern Africa": 0.0001031875796218527, "Geography.Regions.Africa.Northern Africa": 0.00011686970514296082, "Geography.Regions.Africa.Southern Africa": 0.00024862773913410214, "Geography.Regions.Africa.Western Africa": 2.762890992972462e-06, "Geography.Regions.Americas.Central America": 0.0007010831066025544, "Geography.Regions.Americas.North America": 0.091460199158989, "Geography.Regions.Americas.South America": 0.00028786083315990665, "Geography.Regions.Asia.Asia*": 0.0058671426735626385, "Geography.Regions.Asia.Central Asia": 0.00015090794615980674, "Geography.Regions.Asia.East Asia": 0.003846219220546423, "Geography.Regions.Asia.North Asia": 0.0003808596131831929, "Geography.Regions.Asia.South Asia": 0.001023733486019144, "Geography.Regions.Asia.Southeast Asia": 0.0008401133079025592, "Geography.Regions.Asia.West Asia": 0.0004181003521755211, "Geography.Regions.Europe.Eastern Europe": 0.0007729484916529774, "Geography.Regions.Europe.Europe*": 0.0076809813869878445, "Geography.Regions.Europe.Northern Europe": 0.0015344738768241725, "Geography.Regions.Europe.Southern Europe": 0.0008720581124561569, "Geography.Regions.Europe.Western Europe": 0.0009963839426511923, "Geography.Regions.Oceania": 0.0006521764424870279, "History and Society.Business and economics": 0.194483737383016, "History and Society.Education": 0.0024200094358826025, "History and Society.History": 0.002813042961808109, "History and Society.Military and warfare": 0.0010156644267182038, "History and Society.Politics and government": 0.0031610030502964293, "History and Society.Society": 0.008022766658664051, "History and Society.Transportation": 0.0009338602686515991, "STEM.Biology": 0.001981476985079679, "STEM.Chemistry": 0.0006977183151301637, "STEM.Computing": 0.8835030881350452, "STEM.Earth and environment": 0.000649715914923719, "STEM.Engineering": 0.0037725577248181254, "STEM.Libraries & Information": 0.00031103568572905425, "STEM.Mathematics": 0.006915553934201882, "STEM.Medicine & Health": 0.0024120927108435735, "STEM.Physics": 0.00022156409216132937, "STEM.STEM*": 0.9830731017933537, "STEM.Space": 0.00013360703642841977, "STEM.Technology": 0.7863607248919928 } } } } } } }

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