Machine learning models/Production/Vietnamese 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?	Vietnamese 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/viwiki/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* 14139 12651 1488 789 45836 Culture.Biography.Women 5153 4127 1026 774 54837 Culture.Food and drink 1399 1015 384 122 59243 Culture.Internet culture 3531 2917 614 233 57000 Culture.Linguistics 1509 1165 344 69 59186 Culture.Literature 5659 4444 1215 505 54600 Culture.Media.Books 1467 1089 378 111 59186 Culture.Media.Entertainment 1984 1041 943 194 58586 Culture.Media.Films 2612 2073 539 173 57979 Culture.Media.Media* 13270 11633 1637 1282 46212 Culture.Media.Music 2908 2509 399 163 57693 Culture.Media.Radio 285 178 107 32 60447 Culture.Media.Software 2291 1910 381 288 58185 Culture.Media.Television 1983 1438 545 133 58648 Culture.Media.Video games 2139 1969 170 54 58571 Culture.Performing arts 1374 960 414 105 59285 Culture.Philosophy and religion 3040 1731 1309 295 57429 Culture.Sports 3871 3411 460 104 56789 Culture.Visual arts.Architecture 1854 1334 520 167 58743 Culture.Visual arts.Comics and Anime 2239 2029 210 78 58447 Culture.Visual arts.Fashion 1501 1213 288 105 59158 Culture.Visual arts.Visual arts* 6119 4741 1378 400 54245 Geography.Geographical 3991 2584 1407 552 56221 Geography.Regions.Africa.Africa* 5744 4467 1277 485 54535 Geography.Regions.Africa.Central Africa 1214 811 403 123 59427 Geography.Regions.Africa.Eastern Africa 451 216 235 40 60273 Geography.Regions.Africa.Northern Africa 1474 1036 438 95 59195 Geography.Regions.Africa.Southern Africa 1178 798 380 78 59508 Geography.Regions.Africa.Western Africa 672 537 135 41 60051 Geography.Regions.Americas.Central America 1588 992 596 116 59060 Geography.Regions.Americas.North America 5438 3677 1761 651 54675 Geography.Regions.Americas.South America 2210 1706 504 183 58371 Geography.Regions.Asia.Asia* 13867 11650 2217 1216 45681 Geography.Regions.Asia.Central Asia 1211 914 297 84 59469 Geography.Regions.Asia.East Asia 5571 4489 1082 445 54748 Geography.Regions.Asia.North Asia 1693 1283 410 235 58836 Geography.Regions.Asia.South Asia 2038 1624 414 108 58618 Geography.Regions.Asia.Southeast Asia 2632 1945 687 190 57942 Geography.Regions.Asia.West Asia 2178 1753 425 105 58481 Geography.Regions.Europe.Eastern Europe 3590 2868 722 277 56897 Geography.Regions.Europe.Europe* 12682 10215 2467 1220 46862 Geography.Regions.Europe.Northern Europe 2896 1842 1054 227 57641 Geography.Regions.Europe.Southern Europe 2854 2126 728 203 57707 Geography.Regions.Europe.Western Europe 4043 3153 890 237 56484 Geography.Regions.Oceania 2238 1660 578 126 58400 History and Society.Business and economics 3404 2099 1305 350 57010 History and Society.Education 1589 926 663 132 59043 History and Society.History 4595 2468 2127 569 55600 History and Society.Military and warfare 5048 3942 1106 446 55270 History and Society.Politics and government 4595 2624 1971 485 55684 History and Society.Society 6148 2979 3169 513 54103 History and Society.Transportation 3573 3290 283 82 57109 STEM.Biology 7137 6573 564 165 53462 STEM.Chemistry 1506 1175 331 170 59088 STEM.Computing 2452 2053 399 305 58007 STEM.Earth and environment 1649 1118 531 123 58992 STEM.Engineering 3027 2529 498 129 57608 STEM.Libraries & Information 489 359 130 32 60243 STEM.Mathematics 942 782 160 56 59766 STEM.Medicine & Health 1774 1261 513 144 58846 STEM.Physics 1374 1025 349 139 59251 STEM.STEM* 20595 18875 1720 892 39277 STEM.Space 1637 1485 152 39 59088 STEM.Technology 4233 3133 1100 585 55946

Test data sample rates:

Test data sample rates
Label Sample Population Culture.Biography.Biography* 0.233 0.123 Culture.Biography.Women 0.085 0.015 Culture.Food and drink 0.023 0.002 Culture.Internet culture 0.058 0.003 Culture.Linguistics 0.025 0.007 Culture.Literature 0.093 0.015 Culture.Media.Books 0.024 0.004 Culture.Media.Entertainment 0.033 0.004 Culture.Media.Films 0.043 0.011 Culture.Media.Media* 0.218 0.058 Culture.Media.Music 0.048 0.024 Culture.Media.Radio 0.005 0.002 Culture.Media.Software 0.038 0.001 Culture.Media.Television 0.033 0.009 Culture.Media.Video games 0.035 0.003 Culture.Performing arts 0.023 0.003 Culture.Philosophy and religion 0.05 0.011 Culture.Sports 0.064 0.071 Culture.Visual arts.Architecture 0.031 0.011 Culture.Visual arts.Comics and Anime 0.037 0.002 Culture.Visual arts.Fashion 0.025 0.001 Culture.Visual arts.Visual arts* 0.101 0.018 Geography.Geographical 0.066 0.024 Geography.Regions.Africa.Africa* 0.095 0.008 Geography.Regions.Africa.Central Africa 0.02 0.001 Geography.Regions.Africa.Eastern Africa 0.007 0 Geography.Regions.Africa.Northern Africa 0.024 0.001 Geography.Regions.Africa.Southern Africa 0.019 0.001 Geography.Regions.Africa.Western Africa 0.011 0.001 Geography.Regions.Americas.Central America 0.026 0.003 Geography.Regions.Americas.North America 0.089 0.064 Geography.Regions.Americas.South America 0.036 0.006 Geography.Regions.Asia.Asia* 0.228 0.045 Geography.Regions.Asia.Central Asia 0.02 0.001 Geography.Regions.Asia.East Asia 0.092 0.011 Geography.Regions.Asia.North Asia 0.028 0.001 Geography.Regions.Asia.South Asia 0.034 0.015 Geography.Regions.Asia.Southeast Asia 0.043 0.006 Geography.Regions.Asia.West Asia 0.036 0.011 Geography.Regions.Europe.Eastern Europe 0.059 0.013 Geography.Regions.Europe.Europe* 0.209 0.076 Geography.Regions.Europe.Northern Europe 0.048 0.031 Geography.Regions.Europe.Southern Europe 0.047 0.013 Geography.Regions.Europe.Western Europe 0.067 0.019 Geography.Regions.Oceania 0.037 0.015 History and Society.Business and economics 0.056 0.01 History and Society.Education 0.026 0.007 History and Society.History 0.076 0.011 History and Society.Military and warfare 0.083 0.014 History and Society.Politics and government 0.076 0.028 History and Society.Society 0.101 0.013 History and Society.Transportation 0.059 0.015 STEM.Biology 0.117 0.034 STEM.Chemistry 0.025 0.002 STEM.Computing 0.04 0.003 STEM.Earth and environment 0.027 0.005 STEM.Engineering 0.05 0.005 STEM.Libraries & Information 0.008 0.001 STEM.Mathematics 0.016 0 STEM.Medicine & Health 0.029 0.006 STEM.Physics 0.023 0.001 STEM.STEM* 0.339 0.069 STEM.Space 0.027 0.006 STEM.Technology 0.07 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 0.895 0.881 0.888 0.972 0.981 0.945 Culture.Biography.Women 0.025 0.975 0.801 0.459 0.584 0.983 0.983 0.57 Culture.Food and drink 0.004 0.996 0.726 0.466 0.567 0.997 0.982 0.594 Culture.Internet culture 0.007 0.993 0.826 0.416 0.553 0.995 0.987 0.736 Culture.Linguistics 0.007 0.993 0.772 0.83 0.8 0.997 0.978 0.821 Culture.Literature 0.021 0.979 0.785 0.574 0.663 0.988 0.98 0.727 Culture.Media.Books 0.005 0.995 0.742 0.616 0.673 0.997 0.985 0.708 Culture.Media.Entertainment 0.005 0.995 0.525 0.364 0.43 0.995 0.97 0.415 Culture.Media.Films 0.011 0.989 0.794 0.739 0.765 0.995 0.984 0.812 Culture.Media.Media* 0.077 0.923 0.877 0.669 0.759 0.967 0.981 0.85 Culture.Media.Music 0.023 0.977 0.863 0.882 0.872 0.994 0.986 0.913 Culture.Media.Radio 0.002 0.998 0.625 0.718 0.668 0.999 0.946 0.506 Culture.Media.Software 0.006 0.994 0.834 0.184 0.301 0.995 0.987 0.359 Culture.Media.Television 0.009 0.991 0.725 0.74 0.733 0.995 0.982 0.737 Culture.Media.Video games 0.003 0.997 0.921 0.723 0.81 0.999 0.992 0.913 Culture.Performing arts 0.004 0.996 0.699 0.534 0.605 0.997 0.98 0.594 Culture.Philosophy and religion 0.011 0.989 0.569 0.545 0.557 0.99 0.962 0.561 Culture.Sports 0.064 0.936 0.881 0.974 0.925 0.99 0.982 0.959 Culture.Visual arts.Architecture 0.01 0.99 0.72 0.73 0.725 0.994 0.981 0.717 Culture.Visual arts.Comics and Anime 0.003 0.997 0.906 0.599 0.722 0.998 0.989 0.805 Culture.Visual arts.Fashion 0.002 0.998 0.808 0.27 0.404 0.998 0.987 0.346 Culture.Visual arts.Visual arts* 0.021 0.979 0.775 0.664 0.715 0.989 0.977 0.764 Geography.Geographical 0.025 0.975 0.647 0.617 0.632 0.982 0.97 0.679 Geography.Regions.Africa.Africa* 0.015 0.985 0.778 0.409 0.536 0.99 0.978 0.611 Geography.Regions.Africa.Central Africa 0.002 0.998 0.668 0.17 0.271 0.998 0.983 0.243 Geography.Regions.Africa.Eastern Africa 0.001 0.999 0.479 0.247 0.326 0.999 0.966 0.139 Geography.Regions.Africa.Northern Africa 0.002 0.998 0.703 0.35 0.467 0.998 0.98 0.37 Geography.Regions.Africa.Southern Africa 0.002 0.998 0.677 0.378 0.486 0.998 0.974 0.354 Geography.Regions.Africa.Western Africa 0.001 0.999 0.799 0.445 0.571 0.999 0.982 0.49 Geography.Regions.Americas.Central America 0.004 0.996 0.625 0.513 0.564 0.997 0.975 0.549 Geography.Regions.Americas.North America 0.054 0.946 0.676 0.798 0.732 0.968 0.968 0.807 Geography.Regions.Americas.South America 0.008 0.992 0.772 0.611 0.682 0.995 0.982 0.64 Geography.Regions.Asia.Asia* 0.063 0.937 0.84 0.607 0.705 0.968 0.974 0.79 Geography.Regions.Asia.Central Asia 0.002 0.998 0.755 0.317 0.446 0.998 0.985 0.405 Geography.Regions.Asia.East Asia 0.017 0.983 0.806 0.536 0.644 0.99 0.981 0.668 Geography.Regions.Asia.North Asia 0.005 0.995 0.758 0.15 0.25 0.996 0.983 0.23 Geography.Regions.Asia.South Asia 0.014 0.986 0.797 0.87 0.832 0.995 0.982 0.869 Geography.Regions.Asia.Southeast Asia 0.008 0.992 0.739 0.577 0.648 0.995 0.979 0.688 Geography.Regions.Asia.West Asia 0.011 0.989 0.805 0.832 0.818 0.996 0.981 0.839 Geography.Regions.Europe.Eastern Europe 0.015 0.985 0.799 0.682 0.736 0.993 0.983 0.815 Geography.Regions.Europe.Europe* 0.085 0.915 0.805 0.724 0.762 0.962 0.969 0.832 Geography.Regions.Europe.Northern Europe 0.023 0.977 0.636 0.836 0.723 0.985 0.971 0.782 Geography.Regions.Europe.Southern Europe 0.013 0.987 0.745 0.737 0.741 0.993 0.978 0.801 Geography.Regions.Europe.Western Europe 0.019 0.981 0.78 0.785 0.782 0.992 0.98 0.817 Geography.Regions.Oceania 0.013 0.987 0.742 0.841 0.788 0.994 0.98 0.826 History and Society.Business and economics 0.012 0.988 0.617 0.507 0.557 0.99 0.971 0.56 History and Society.Education 0.007 0.993 0.583 0.659 0.619 0.995 0.973 0.613 History and Society.History 0.016 0.984 0.537 0.367 0.436 0.985 0.957 0.443 History and Society.Military and warfare 0.019 0.981 0.781 0.582 0.667 0.989 0.98 0.759 History and Society.Politics and government 0.024 0.976 0.571 0.657 0.611 0.98 0.956 0.647 History and Society.Society 0.015 0.985 0.485 0.397 0.437 0.984 0.94 0.408 History and Society.Transportation 0.015 0.985 0.921 0.908 0.914 0.997 0.989 0.943 STEM.Biology 0.034 0.966 0.921 0.912 0.917 0.994 0.987 0.959 STEM.Chemistry 0.004 0.996 0.78 0.298 0.431 0.997 0.987 0.48 STEM.Computing 0.007 0.993 0.837 0.302 0.443 0.994 0.988 0.475 STEM.Earth and environment 0.005 0.995 0.678 0.598 0.635 0.996 0.977 0.623 STEM.Engineering 0.007 0.993 0.835 0.663 0.739 0.997 0.985 0.847 STEM.Libraries & Information 0.001 0.999 0.734 0.462 0.567 0.999 0.972 0.53 STEM.Mathematics 0.001 0.999 0.83 0.27 0.407 0.999 0.984 0.51 STEM.Medicine & Health 0.007 0.993 0.711 0.652 0.68 0.996 0.974 0.718 STEM.Physics 0.003 0.997 0.746 0.213 0.332 0.997 0.986 0.322 STEM.STEM* 0.084 0.916 0.916 0.754 0.827 0.974 0.981 0.908 STEM.Space 0.006 0.994 0.907 0.893 0.9 0.999 0.994 0.952 STEM.Technology 0.014 0.986 0.74 0.269 0.395 0.988 0.977 0.446

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/viwiki/1234/articletopic

Output:

Example output
{ "viwiki": { "models": { "articletopic": { "version": "1.4.0" } }, "scores": { "1234": { "articletopic": { "score": { "prediction": [ "Culture.Linguistics", "Geography.Geographical", "Geography.Regions.Africa.Africa*", "STEM.STEM*" ], "probability": { "Culture.Biography.Biography*": 0.010688680396213181, "Culture.Biography.Women": 0.008195720460488087, "Culture.Food and drink": 0.00012907532555688536, "Culture.Internet culture": 0.013800940060468312, "Culture.Linguistics": 0.5191168929941612, "Culture.Literature": 0.0025178108493059317, "Culture.Media.Books": 0.000812132490359537, "Culture.Media.Entertainment": 0.0006821999958547265, "Culture.Media.Films": 0.030271891185779207, "Culture.Media.Media*": 0.008323998748143888, "Culture.Media.Music": 0.0002453251096654214, "Culture.Media.Radio": 0.0022820146632477724, "Culture.Media.Software": 0.004399225981860488, "Culture.Media.Television": 0.0006043194680847222, "Culture.Media.Video games": 0.00012009680849040888, "Culture.Performing arts": 0.0004276073083262229, "Culture.Philosophy and religion": 0.0006390607811047581, "Culture.Sports": 0.16329437697514143, "Culture.Visual arts.Architecture": 0.0001462376454522797, "Culture.Visual arts.Comics and Anime": 5.139459619313559e-05, "Culture.Visual arts.Fashion": 0.00010666140674559313, "Culture.Visual arts.Visual arts*": 0.004025427327690172, "Geography.Geographical": 0.9997938301645891, "Geography.Regions.Africa.Africa*": 0.9745448423349992, "Geography.Regions.Africa.Central Africa": 0.0027669019697055064, "Geography.Regions.Africa.Eastern Africa": 0.0012268282092543052, "Geography.Regions.Africa.Northern Africa": 0.0009715033768551983, "Geography.Regions.Africa.Southern Africa": 0.08328049473179015, "Geography.Regions.Africa.Western Africa": 0.010382244078188742, "Geography.Regions.Americas.Central America": 0.0008457881065380281, "Geography.Regions.Americas.North America": 0.0022683028023005257, "Geography.Regions.Americas.South America": 0.004731186807992782, "Geography.Regions.Asia.Asia*": 0.053922826964153146, "Geography.Regions.Asia.Central Asia": 0.0010540191978565911, "Geography.Regions.Asia.East Asia": 0.00015859255536353935, "Geography.Regions.Asia.North Asia": 0.001639170606177736, "Geography.Regions.Asia.South Asia": 0.00037981554072521193, "Geography.Regions.Asia.Southeast Asia": 0.1295969844969788, "Geography.Regions.Asia.West Asia": 0.0015758566417359654, "Geography.Regions.Europe.Eastern Europe": 0.006411703844818901, "Geography.Regions.Europe.Europe*": 0.04052637386454308, "Geography.Regions.Europe.Northern Europe": 0.009986087621959145, "Geography.Regions.Europe.Southern Europe": 0.009068248925919975, "Geography.Regions.Europe.Western Europe": 0.011069800202305278, "Geography.Regions.Oceania": 0.015890609972399248, "History and Society.Business and economics": 0.0993963928524104, "History and Society.Education": 0.06430872652420767, "History and Society.History": 0.03888750510751568, "History and Society.Military and warfare": 0.0011597465887236648, "History and Society.Politics and government": 0.3817495049698054, "History and Society.Society": 0.06829869812261144, "History and Society.Transportation": 0.0014330186028741118, "STEM.Biology": 0.004779911679802516, "STEM.Chemistry": 0.0005351128777525615, "STEM.Computing": 0.1260141460713239, "STEM.Earth and environment": 0.01341887313175579, "STEM.Engineering": 0.021532699737598353, "STEM.Libraries & Information": 0.000143195569809031, "STEM.Mathematics": 0.028134438670921486, "STEM.Medicine & Health": 0.016469415539396397, "STEM.Physics": 0.015386187697739985, "STEM.STEM*": 0.9754797739642432, "STEM.Space": 0.0026360704668903583, "STEM.Technology": 0.05185008787196212 } } } } } } }

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