Machine learning models/Production/Ukrainian Wikipedia article topic

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?	Ukrainian 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/ukwiki/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* 15166 13791 1375 861 45800 Culture.Biography.Women 3789 2889 900 359 57679 Culture.Food and drink 1445 1030 415 86 60296 Culture.Internet culture 3148 2586 562 213 58466 Culture.Linguistics 1672 1192 480 107 60048 Culture.Literature 5363 4083 1280 516 55948 Culture.Media.Books 1832 1430 402 147 59848 Culture.Media.Entertainment 2162 930 1232 283 59382 Culture.Media.Films 2933 2522 411 121 58773 Culture.Media.Media* 13712 11856 1856 1274 46841 Culture.Media.Music 2739 2204 535 207 58881 Culture.Media.Radio 468 186 282 50 61309 Culture.Media.Software 2119 1568 551 357 59351 Culture.Media.Television 1940 1427 513 128 59759 Culture.Media.Video games 2071 1868 203 55 59701 Culture.Performing arts 1433 809 624 131 60263 Culture.Philosophy and religion 3515 1802 1713 370 57942 Culture.Sports 5614 4968 646 182 56031 Culture.Visual arts.Architecture 2163 1497 666 246 59418 Culture.Visual arts.Comics and Anime 1640 1351 289 74 60113 Culture.Visual arts.Fashion 1229 884 345 87 60511 Culture.Visual arts.Visual arts* 5742 4125 1617 466 55619 Geography.Geographical 4540 3096 1444 605 56682 Geography.Regions.Africa.Africa* 5072 3795 1277 443 56312 Geography.Regions.Africa.Central Africa 1164 835 329 86 60577 Geography.Regions.Africa.Eastern Africa 727 541 186 44 61056 Geography.Regions.Africa.Northern Africa 1422 882 540 128 60277 Geography.Regions.Africa.Southern Africa 905 541 364 57 60865 Geography.Regions.Africa.Western Africa 370 215 155 46 61411 Geography.Regions.Americas.Central America 1329 788 541 77 60421 Geography.Regions.Americas.North America 5433 3333 2100 774 55620 Geography.Regions.Americas.South America 1517 998 519 91 60219 Geography.Regions.Asia.Asia* 12648 10108 2540 1153 48026 Geography.Regions.Asia.Central Asia 1310 880 430 98 60419 Geography.Regions.Asia.East Asia 2929 2187 742 242 58656 Geography.Regions.Asia.North Asia 3840 2607 1233 493 57494 Geography.Regions.Asia.South Asia 1742 1282 460 68 60017 Geography.Regions.Asia.Southeast Asia 1528 1029 499 79 60220 Geography.Regions.Asia.West Asia 2514 1823 691 176 59137 Geography.Regions.Europe.Eastern Europe 6703 5086 1617 706 54418 Geography.Regions.Europe.Europe* 17567 13864 3703 2331 41929 Geography.Regions.Europe.Northern Europe 3575 2180 1395 387 57865 Geography.Regions.Europe.Southern Europe 3636 2526 1110 335 57856 Geography.Regions.Europe.Western Europe 4437 3195 1242 473 56917 Geography.Regions.Oceania 1810 1267 543 119 59898 History and Society.Business and economics 3639 2090 1549 466 57722 History and Society.Education 1730 892 838 151 59946 History and Society.History 5140 2835 2305 718 55969 History and Society.Military and warfare 4576 3054 1522 543 56708 History and Society.Politics and government 4178 2243 1935 473 57176 History and Society.Society 3965 1044 2921 253 57609 History and Society.Transportation 3659 3233 426 159 58009 STEM.Biology 3859 3183 676 189 57779 STEM.Chemistry 1465 1023 442 190 60172 STEM.Computing 2683 2099 584 433 58711 STEM.Earth and environment 1966 1214 752 170 59691 STEM.Engineering 3036 2298 738 226 58565 STEM.Libraries & Information 1052 700 352 69 60706 STEM.Mathematics 1217 897 320 91 60519 STEM.Medicine & Health 1842 1218 624 173 59812 STEM.Physics 1449 937 512 205 60173 STEM.STEM* 18828 16748 2080 1108 41891 STEM.Space 1841 1627 214 62 59924 STEM.Technology 4780 3320 1460 779 56268

Test data sample rates:

Test data sample rates
Label Sample Population Culture.Biography.Biography* 0.245 0.12 Culture.Biography.Women 0.061 0.015 Culture.Food and drink 0.023 0.003 Culture.Internet culture 0.051 0.004 Culture.Linguistics 0.027 0.008 Culture.Literature 0.087 0.015 Culture.Media.Books 0.03 0.004 Culture.Media.Entertainment 0.035 0.004 Culture.Media.Films 0.047 0.012 Culture.Media.Media* 0.222 0.055 Culture.Media.Music 0.044 0.021 Culture.Media.Radio 0.008 0.002 Culture.Media.Software 0.034 0.001 Culture.Media.Television 0.031 0.009 Culture.Media.Video games 0.033 0.003 Culture.Performing arts 0.023 0.003 Culture.Philosophy and religion 0.057 0.01 Culture.Sports 0.091 0.06 Culture.Visual arts.Architecture 0.035 0.011 Culture.Visual arts.Comics and Anime 0.027 0.002 Culture.Visual arts.Fashion 0.02 0.001 Culture.Visual arts.Visual arts* 0.093 0.018 Geography.Geographical 0.073 0.021 Geography.Regions.Africa.Africa* 0.082 0.008 Geography.Regions.Africa.Central Africa 0.019 0.001 Geography.Regions.Africa.Eastern Africa 0.012 0.001 Geography.Regions.Africa.Northern Africa 0.023 0.001 Geography.Regions.Africa.Southern Africa 0.015 0.001 Geography.Regions.Africa.Western Africa 0.006 0.001 Geography.Regions.Americas.Central America 0.021 0.003 Geography.Regions.Americas.North America 0.088 0.063 Geography.Regions.Americas.South America 0.025 0.007 Geography.Regions.Asia.Asia* 0.205 0.052 Geography.Regions.Asia.Central Asia 0.021 0.001 Geography.Regions.Asia.East Asia 0.047 0.012 Geography.Regions.Asia.North Asia 0.062 0.006 Geography.Regions.Asia.South Asia 0.028 0.016 Geography.Regions.Asia.Southeast Asia 0.025 0.006 Geography.Regions.Asia.West Asia 0.041 0.012 Geography.Regions.Europe.Eastern Europe 0.108 0.018 Geography.Regions.Europe.Europe* 0.284 0.081 Geography.Regions.Europe.Northern Europe 0.058 0.029 Geography.Regions.Europe.Southern Europe 0.059 0.014 Geography.Regions.Europe.Western Europe 0.072 0.02 Geography.Regions.Oceania 0.029 0.016 History and Society.Business and economics 0.059 0.01 History and Society.Education 0.028 0.008 History and Society.History 0.083 0.011 History and Society.Military and warfare 0.074 0.015 History and Society.Politics and government 0.068 0.028 History and Society.Society 0.064 0.008 History and Society.Transportation 0.059 0.016 STEM.Biology 0.062 0.034 STEM.Chemistry 0.024 0.002 STEM.Computing 0.043 0.003 STEM.Earth and environment 0.032 0.005 STEM.Engineering 0.049 0.006 STEM.Libraries & Information 0.017 0.001 STEM.Mathematics 0.02 0 STEM.Medicine & Health 0.03 0.006 STEM.Physics 0.023 0.001 STEM.STEM* 0.305 0.065 STEM.Space 0.03 0.004 STEM.Technology 0.077 0.005

Test data performance:

Test data performance
Label Match rate Filter rate Recall Precision f1 Accuracy ROC AUC PR AUC Culture.Biography.Biography* 0.125 0.875 0.909 0.871 0.89 0.973 0.981 0.944 Culture.Biography.Women 0.017 0.983 0.762 0.651 0.703 0.99 0.982 0.715 Culture.Food and drink 0.003 0.997 0.713 0.561 0.628 0.998 0.981 0.636 Culture.Internet culture 0.007 0.993 0.821 0.458 0.588 0.996 0.984 0.749 Culture.Linguistics 0.007 0.993 0.713 0.764 0.738 0.996 0.976 0.759 Culture.Literature 0.02 0.98 0.761 0.556 0.643 0.987 0.977 0.707 Culture.Media.Books 0.006 0.994 0.781 0.58 0.665 0.997 0.986 0.733 Culture.Media.Entertainment 0.006 0.994 0.43 0.264 0.327 0.993 0.966 0.256 Culture.Media.Films 0.012 0.988 0.86 0.83 0.845 0.996 0.985 0.888 Culture.Media.Media* 0.072 0.928 0.865 0.654 0.745 0.968 0.979 0.854 Culture.Media.Music 0.02 0.98 0.805 0.831 0.818 0.992 0.982 0.841 Culture.Media.Radio 0.002 0.998 0.397 0.531 0.455 0.998 0.959 0.284 Culture.Media.Software 0.007 0.993 0.74 0.139 0.234 0.994 0.985 0.243 Culture.Media.Television 0.009 0.991 0.736 0.755 0.745 0.996 0.981 0.772 Culture.Media.Video games 0.004 0.996 0.902 0.74 0.813 0.999 0.991 0.876 Culture.Performing arts 0.004 0.996 0.565 0.443 0.496 0.997 0.973 0.473 Culture.Philosophy and religion 0.012 0.988 0.513 0.458 0.484 0.989 0.952 0.427 Culture.Sports 0.056 0.944 0.885 0.946 0.914 0.99 0.981 0.948 Culture.Visual arts.Architecture 0.012 0.988 0.692 0.646 0.668 0.993 0.976 0.665 Culture.Visual arts.Comics and Anime 0.003 0.997 0.824 0.616 0.705 0.998 0.987 0.7 Culture.Visual arts.Fashion 0.002 0.998 0.719 0.31 0.433 0.998 0.979 0.372 Culture.Visual arts.Visual arts* 0.021 0.979 0.718 0.614 0.662 0.987 0.97 0.703 Geography.Geographical 0.025 0.975 0.682 0.582 0.628 0.983 0.971 0.669 Geography.Regions.Africa.Africa* 0.014 0.986 0.748 0.45 0.562 0.99 0.974 0.614 Geography.Regions.Africa.Central Africa 0.002 0.998 0.717 0.262 0.384 0.998 0.984 0.315 Geography.Regions.Africa.Eastern Africa 0.001 0.999 0.744 0.341 0.468 0.999 0.976 0.487 Geography.Regions.Africa.Northern Africa 0.003 0.997 0.62 0.283 0.389 0.997 0.975 0.327 Geography.Regions.Africa.Southern Africa 0.002 0.998 0.598 0.455 0.517 0.999 0.967 0.39 Geography.Regions.Africa.Western Africa 0.001 0.999 0.581 0.366 0.449 0.999 0.964 0.281 Geography.Regions.Americas.Central America 0.003 0.997 0.593 0.617 0.605 0.997 0.974 0.592 Geography.Regions.Americas.North America 0.051 0.949 0.613 0.75 0.675 0.963 0.962 0.744 Geography.Regions.Americas.South America 0.006 0.994 0.658 0.75 0.701 0.996 0.974 0.73 Geography.Regions.Asia.Asia* 0.064 0.936 0.799 0.654 0.719 0.967 0.966 0.794 Geography.Regions.Asia.Central Asia 0.002 0.998 0.672 0.248 0.362 0.998 0.982 0.33 Geography.Regions.Asia.East Asia 0.013 0.987 0.747 0.691 0.718 0.993 0.978 0.771 Geography.Regions.Asia.North Asia 0.012 0.988 0.679 0.31 0.426 0.99 0.971 0.436 Geography.Regions.Asia.South Asia 0.013 0.987 0.736 0.916 0.816 0.995 0.981 0.872 Geography.Regions.Asia.Southeast Asia 0.005 0.995 0.673 0.763 0.715 0.997 0.974 0.705 Geography.Regions.Asia.West Asia 0.011 0.989 0.725 0.743 0.734 0.994 0.979 0.769 Geography.Regions.Europe.Eastern Europe 0.026 0.974 0.759 0.524 0.62 0.983 0.971 0.677 Geography.Regions.Europe.Europe* 0.112 0.888 0.789 0.568 0.661 0.935 0.952 0.758 Geography.Regions.Europe.Northern Europe 0.024 0.976 0.61 0.732 0.665 0.982 0.964 0.714 Geography.Regions.Europe.Southern Europe 0.015 0.985 0.695 0.628 0.659 0.99 0.976 0.708 Geography.Regions.Europe.Western Europe 0.023 0.977 0.72 0.646 0.681 0.986 0.973 0.723 Geography.Regions.Oceania 0.013 0.987 0.7 0.855 0.77 0.993 0.979 0.82 History and Society.Business and economics 0.014 0.986 0.574 0.413 0.48 0.988 0.963 0.458 History and Society.Education 0.007 0.993 0.516 0.621 0.563 0.994 0.965 0.558 History and Society.History 0.019 0.981 0.552 0.324 0.408 0.983 0.951 0.375 History and Society.Military and warfare 0.02 0.98 0.667 0.522 0.586 0.986 0.968 0.626 History and Society.Politics and government 0.023 0.977 0.537 0.653 0.589 0.979 0.951 0.61 History and Society.Society 0.007 0.993 0.263 0.334 0.294 0.99 0.9 0.238 History and Society.Transportation 0.017 0.983 0.884 0.842 0.862 0.995 0.986 0.898 STEM.Biology 0.032 0.968 0.825 0.9 0.861 0.991 0.979 0.9 STEM.Chemistry 0.004 0.996 0.698 0.273 0.392 0.996 0.982 0.429 STEM.Computing 0.01 0.99 0.782 0.232 0.358 0.992 0.987 0.357 STEM.Earth and environment 0.006 0.994 0.617 0.508 0.557 0.995 0.969 0.545 STEM.Engineering 0.008 0.992 0.757 0.532 0.625 0.995 0.98 0.713 STEM.Libraries & Information 0.002 0.998 0.665 0.286 0.4 0.999 0.978 0.343 STEM.Mathematics 0.002 0.998 0.737 0.185 0.295 0.998 0.986 0.338 STEM.Medicine & Health 0.007 0.993 0.661 0.597 0.627 0.995 0.972 0.596 STEM.Physics 0.004 0.996 0.647 0.151 0.245 0.996 0.982 0.182 STEM.STEM* 0.082 0.918 0.89 0.705 0.787 0.969 0.977 0.882 STEM.Space 0.005 0.995 0.884 0.785 0.831 0.998 0.991 0.904 STEM.Technology 0.017 0.983 0.695 0.208 0.32 0.985 0.972 0.379

Implementation

Model architecture

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

Output schema

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

Example input and output

Input:

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

Output:

Example output
{ "ukwiki": { "models": { "articletopic": { "version": "1.3.0" } }, "scores": { "1234": { "articletopic": { "score": { "prediction": [ "Culture.Media.Entertainment", "Culture.Media.Films", "Culture.Media.Media*", "STEM.STEM*" ], "probability": { "Culture.Biography.Biography*": 0.06498980629164289, "Culture.Biography.Women": 0.09735683417742311, "Culture.Food and drink": 0.000523699699397982, "Culture.Internet culture": 0.0020378622899029177, "Culture.Linguistics": 0.0016311998268999513, "Culture.Literature": 0.012105139018247661, "Culture.Media.Books": 0.003154971196247378, "Culture.Media.Entertainment": 0.5970285574411643, "Culture.Media.Films": 0.6098774286112809, "Culture.Media.Media*": 0.6813965808444392, "Culture.Media.Music": 0.00819183087138225, "Culture.Media.Radio": 0.033328047128739664, "Culture.Media.Software": 0.0036038682836095704, "Culture.Media.Television": 0.015975115428127188, "Culture.Media.Video games": 0.0002729242274025432, "Culture.Performing arts": 0.010021068609200184, "Culture.Philosophy and religion": 0.08107947551534797, "Culture.Sports": 0.01126554934508554, "Culture.Visual arts.Architecture": 0.007503543006049268, "Culture.Visual arts.Comics and Anime": 0.010089193188944407, "Culture.Visual arts.Fashion": 0.0003908147416162821, "Culture.Visual arts.Visual arts*": 0.06032534559763305, "Geography.Geographical": 0.012344109368572971, "Geography.Regions.Africa.Africa*": 0.012560789546965578, "Geography.Regions.Africa.Central Africa": 0.0010803105638953752, "Geography.Regions.Africa.Eastern Africa": 0.00011246776562541031, "Geography.Regions.Africa.Northern Africa": 0.009624298316990852, "Geography.Regions.Africa.Southern Africa": 0.0004266187714493931, "Geography.Regions.Africa.Western Africa": 9.969668607552113e-06, "Geography.Regions.Americas.Central America": 0.06118223784802867, "Geography.Regions.Americas.North America": 0.03925429350174892, "Geography.Regions.Americas.South America": 0.05219719485708542, "Geography.Regions.Asia.Asia*": 0.008542578261190403, "Geography.Regions.Asia.Central Asia": 0.00026849369255479776, "Geography.Regions.Asia.East Asia": 0.0002861807464576771, "Geography.Regions.Asia.North Asia": 0.0011599087617587605, "Geography.Regions.Asia.South Asia": 0.0015128573492686713, "Geography.Regions.Asia.Southeast Asia": 0.002553083994766201, "Geography.Regions.Asia.West Asia": 0.0005711962419297393, "Geography.Regions.Europe.Eastern Europe": 0.0037089892492006117, "Geography.Regions.Europe.Europe*": 0.27058500017040055, "Geography.Regions.Europe.Northern Europe": 0.008797312001829935, "Geography.Regions.Europe.Southern Europe": 0.0068022456360417195, "Geography.Regions.Europe.Western Europe": 0.08573292886089152, "Geography.Regions.Oceania": 0.010721904050560125, "History and Society.Business and economics": 0.0022774846472204424, "History and Society.Education": 0.023723447768514965, "History and Society.History": 0.016452406006402392, "History and Society.Military and warfare": 0.003573186866768048, "History and Society.Politics and government": 0.007725262244322175, "History and Society.Society": 0.01170722296370807, "History and Society.Transportation": 0.0008191284679530242, "STEM.Biology": 0.020667912361522288, "STEM.Chemistry": 0.000310701475054592, "STEM.Computing": 0.0026236938213711316, "STEM.Earth and environment": 0.16856390174271477, "STEM.Engineering": 0.0014354375119114869, "STEM.Libraries & Information": 0.0040652121528858476, "STEM.Mathematics": 0.0007447907192789808, "STEM.Medicine & Health": 0.0025102595896690613, "STEM.Physics": 0.001325294456067261, "STEM.STEM*": 0.8558854466981729, "STEM.Space": 0.0004964062174690077, "STEM.Technology": 0.008370450540204109 } } } } } } }

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