Machine learning models/Production/English Wikipedia draft 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	ORES Github, ORES training data, and ORES model binaries
Uses PII	No
In production?	Yes
Which projects?	English Wikipedia
This model uses article text to predict the likelihood that the article belongs to a set of topics.

Motivation

How can we predict what general topic a draft is in? Answering this question is useful for allocating editor resources and 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 a draft 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 to predict the topic of articles (not drafts) across about a dozen other projects. There is also a language agnostic article topic model, which is explicitly only meant to be used for pages in namespace 0. This model is explicitly designed to work in the draft namespace (namespace 118); please only use it there.

This model may be useful for high-level analyses of article creation dynamics, as well as in helping to coordinate the allocation of senior editor time (for example, if an editor for a wikiproject wants to keep track of all drafts in a given category to provide feedback).

Users and uses

Use this model for

• high-level analyses of Wikipedia dynamics such as pageview, article quality, or edit trends
• filtering to relevant drafts — e.g. filter articles only to those in the music category.
• helping to coordinate the allocation of senior editor time within a given topic

Don't use this model for

• definitively establishing what topic a draft pertains to
• automated editing of articles or topics without a human in the loop

Current uses

This model is a part of ORES, and generally accessible via API. It is used for high-level analysis of Wikipedia, platform research, and other on-wiki tasks.

Example API call: https://ores.wikimedia.org/v3/scores/enwiki/1152472770/drafttopic

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* 16147 13404 2743 1463 43295 Culture.Biography.Women 4041 2755 1286 808 56056 Culture.Food and drink 1222 726 496 93 59590 Culture.Internet culture 2732 1895 837 230 57943 Culture.Linguistics 1169 773 396 126 59610 Culture.Literature 5001 3302 1699 530 55374 Culture.Media.Books 1792 1121 671 260 58853 Culture.Media.Entertainment 1691 664 1027 189 59025 Culture.Media.Films 2259 1729 530 187 58459 Culture.Media.Media* 13536 10947 2589 1589 45780 Culture.Media.Music 2502 1849 653 267 58136 Culture.Media.Radio 1126 845 281 46 59733 Culture.Media.Software 1652 775 877 324 58929 Culture.Media.Television 2029 1332 697 202 58674 Culture.Media.Video games 1940 1597 343 66 58899 Culture.Performing arts 1257 705 552 126 59522 Culture.Philosophy and religion 2583 1258 1325 316 58006 Culture.Sports 5799 5094 705 310 54796 Culture.Visual arts.Architecture 2497 1700 797 278 58130 Culture.Visual arts.Comics and Anime 1399 1011 388 108 59398 Culture.Visual arts.Fashion 1110 651 459 72 59723 Culture.Visual arts.Visual arts* 5713 3909 1804 563 54629 Geography.Geographical 3399 2133 1266 271 57235 Geography.Regions.Africa.Africa* 6314 5249 1065 359 54232 Geography.Regions.Africa.Central Africa 1134 821 313 52 59719 Geography.Regions.Africa.Eastern Africa 1074 818 256 41 59790 Geography.Regions.Africa.Northern Africa 1259 895 364 89 59557 Geography.Regions.Africa.Southern Africa 1237 893 344 62 59606 Geography.Regions.Africa.Western Africa 1120 882 238 70 59715 Geography.Regions.Americas.Central America 1269 815 454 83 59553 Geography.Regions.Americas.North America 7174 4512 2662 1121 52610 Geography.Regions.Americas.South America 1531 1073 458 113 59261 Geography.Regions.Asia.Asia* 10799 8771 2028 857 49249 Geography.Regions.Asia.Central Asia 1097 785 312 59 59749 Geography.Regions.Asia.East Asia 2642 1816 826 275 57988 Geography.Regions.Asia.North Asia 1321 826 495 182 59402 Geography.Regions.Asia.South Asia 2354 1880 474 114 58437 Geography.Regions.Asia.Southeast Asia 1653 1173 480 114 59138 Geography.Regions.Asia.West Asia 2204 1702 502 142 58559 Geography.Regions.Europe.Eastern Europe 3005 2225 780 269 57631 Geography.Regions.Europe.Europe* 11882 8687 3195 1680 47343 Geography.Regions.Europe.Northern Europe 3947 2493 1454 578 56380 Geography.Regions.Europe.Southern Europe 2321 1571 750 299 58285 Geography.Regions.Europe.Western Europe 2971 1893 1078 462 57472 Geography.Regions.Oceania 2463 1876 587 150 58292 History and Society.Business and economics 3254 1286 1968 503 57148 History and Society.Education 2112 852 1260 226 58567 History and Society.History 3155 1063 2092 457 57293 History and Society.Military and warfare 3889 2540 1349 390 56626 History and Society.Politics and government 4429 2474 1955 433 56043 History and Society.Society 3807 1300 2507 336 56762 History and Society.Transportation 3425 2783 642 241 57239 STEM.Biology 2829 2216 613 138 57938 STEM.Chemistry 1222 733 489 147 59536 STEM.Computing 1966 1126 840 381 58558 STEM.Earth and environment 1552 1005 547 114 59239 STEM.Engineering 2242 1500 742 182 58481 STEM.Libraries & Information 1120 534 586 105 59680 STEM.Mathematics 1057 569 488 87 59761 STEM.Medicine & Health 1640 917 723 176 59089 STEM.Physics 1112 556 556 138 59655 STEM.STEM* 15719 13115 2604 1224 43962 STEM.Space 1343 1111 232 62 59500 STEM.Technology 3435 1816 1619 596 56874

Test data sample rates:

Test data sample rates
Label Sample Population Culture.Biography.Biography* 0.265 0.123 Culture.Biography.Women 0.066 0.015 Culture.Food and drink 0.02 0.002 Culture.Internet culture 0.045 0.003 Culture.Linguistics 0.019 0.007 Culture.Literature 0.082 0.015 Culture.Media.Books 0.029 0.004 Culture.Media.Entertainment 0.028 0.004 Culture.Media.Films 0.037 0.011 Culture.Media.Media* 0.222 0.058 Culture.Media.Music 0.041 0.024 Culture.Media.Radio 0.018 0.002 Culture.Media.Software 0.027 0.001 Culture.Media.Television 0.033 0.009 Culture.Media.Video games 0.032 0.003 Culture.Performing arts 0.021 0.003 Culture.Philosophy and religion 0.042 0.011 Culture.Sports 0.095 0.071 Culture.Visual arts.Architecture 0.041 0.011 Culture.Visual arts.Comics and Anime 0.023 0.002 Culture.Visual arts.Fashion 0.018 0.001 Culture.Visual arts.Visual arts* 0.094 0.018 Geography.Geographical 0.056 0.024 Geography.Regions.Africa.Africa* 0.104 0.008 Geography.Regions.Africa.Central Africa 0.019 0.001 Geography.Regions.Africa.Eastern Africa 0.018 0 Geography.Regions.Africa.Northern Africa 0.021 0.001 Geography.Regions.Africa.Southern Africa 0.02 0.001 Geography.Regions.Africa.Western Africa 0.018 0.001 Geography.Regions.Americas.Central America 0.021 0.003 Geography.Regions.Americas.North America 0.118 0.064 Geography.Regions.Americas.South America 0.025 0.006 Geography.Regions.Asia.Asia* 0.177 0.045 Geography.Regions.Asia.Central Asia 0.018 0.001 Geography.Regions.Asia.East Asia 0.043 0.011 Geography.Regions.Asia.North Asia 0.022 0.001 Geography.Regions.Asia.South Asia 0.039 0.015 Geography.Regions.Asia.Southeast Asia 0.027 0.006 Geography.Regions.Asia.West Asia 0.036 0.011 Geography.Regions.Europe.Eastern Europe 0.049 0.013 Geography.Regions.Europe.Europe* 0.195 0.076 Geography.Regions.Europe.Northern Europe 0.065 0.031 Geography.Regions.Europe.Southern Europe 0.038 0.013 Geography.Regions.Europe.Western Europe 0.049 0.019 Geography.Regions.Oceania 0.04 0.015 History and Society.Business and economics 0.053 0.01 History and Society.Education 0.035 0.007 History and Society.History 0.052 0.011 History and Society.Military and warfare 0.064 0.014 History and Society.Politics and government 0.073 0.028 History and Society.Society 0.063 0.013 History and Society.Transportation 0.056 0.015 STEM.Biology 0.046 0.034 STEM.Chemistry 0.02 0.002 STEM.Computing 0.032 0.003 STEM.Earth and environment 0.025 0.005 STEM.Engineering 0.037 0.005 STEM.Libraries & Information 0.018 0.001 STEM.Mathematics 0.017 0 STEM.Medicine & Health 0.027 0.006 STEM.Physics 0.018 0.001 STEM.STEM* 0.258 0.069 STEM.Space 0.022 0.006 STEM.Technology 0.056 0.005

Test data performance:

Test data performance
Label Match rate Filter rate Recall Precision f1 Accuracy ROC AUC PR AUC Culture.Biography.Biography* 0.131 0.869 0.83 0.781 0.805 0.95 0.965 0.856 Culture.Biography.Women 0.024 0.976 0.682 0.415 0.516 0.981 0.967 0.469 Culture.Food and drink 0.003 0.997 0.594 0.485 0.534 0.997 0.971 0.45 Culture.Internet culture 0.006 0.994 0.694 0.381 0.492 0.995 0.975 0.595 Culture.Linguistics 0.007 0.993 0.661 0.698 0.679 0.995 0.957 0.625 Culture.Literature 0.02 0.98 0.66 0.523 0.584 0.985 0.965 0.593 Culture.Media.Books 0.007 0.993 0.626 0.365 0.461 0.994 0.974 0.388 Culture.Media.Entertainment 0.005 0.995 0.393 0.307 0.344 0.995 0.952 0.284 Culture.Media.Films 0.011 0.989 0.765 0.718 0.741 0.994 0.975 0.726 Culture.Media.Media* 0.079 0.921 0.809 0.6 0.689 0.957 0.968 0.784 Culture.Media.Music 0.022 0.978 0.739 0.798 0.767 0.989 0.978 0.793 Culture.Media.Radio 0.002 0.998 0.75 0.678 0.712 0.999 0.976 0.758 Culture.Media.Software 0.006 0.994 0.469 0.102 0.168 0.994 0.971 0.105 Culture.Media.Television 0.009 0.991 0.656 0.63 0.643 0.994 0.97 0.635 Culture.Media.Video games 0.003 0.997 0.823 0.658 0.731 0.998 0.986 0.78 Culture.Performing arts 0.004 0.996 0.561 0.435 0.49 0.997 0.965 0.425 Culture.Philosophy and religion 0.011 0.989 0.487 0.492 0.489 0.989 0.94 0.423 Culture.Sports 0.068 0.932 0.878 0.923 0.9 0.986 0.98 0.935 Culture.Visual arts.Architecture 0.012 0.988 0.681 0.604 0.64 0.992 0.972 0.668 Culture.Visual arts.Comics and Anime 0.003 0.997 0.723 0.467 0.567 0.998 0.979 0.554 Culture.Visual arts.Fashion 0.002 0.998 0.586 0.283 0.382 0.998 0.973 0.316 Culture.Visual arts.Visual arts* 0.023 0.977 0.684 0.556 0.613 0.984 0.961 0.656 Geography.Geographical 0.019 0.981 0.628 0.763 0.689 0.987 0.962 0.74 Geography.Regions.Africa.Africa* 0.013 0.987 0.831 0.498 0.623 0.992 0.977 0.654 Geography.Regions.Africa.Central Africa 0.001 0.999 0.724 0.345 0.467 0.999 0.977 0.578 Geography.Regions.Africa.Eastern Africa 0.001 0.999 0.762 0.336 0.466 0.999 0.98 0.378 Geography.Regions.Africa.Northern Africa 0.002 0.998 0.711 0.369 0.486 0.998 0.97 0.428 Geography.Regions.Africa.Southern Africa 0.002 0.998 0.722 0.45 0.554 0.999 0.974 0.526 Geography.Regions.Africa.Western Africa 0.002 0.998 0.787 0.315 0.45 0.999 0.98 0.371 Geography.Regions.Americas.Central America 0.004 0.996 0.642 0.604 0.623 0.997 0.97 0.566 Geography.Regions.Americas.North America 0.06 0.94 0.629 0.674 0.651 0.957 0.949 0.703 Geography.Regions.Americas.South America 0.006 0.994 0.701 0.7 0.701 0.996 0.97 0.711 Geography.Regions.Asia.Asia* 0.053 0.947 0.812 0.694 0.748 0.975 0.97 0.792 Geography.Regions.Asia.Central Asia 0.002 0.998 0.716 0.386 0.501 0.999 0.979 0.495 Geography.Regions.Asia.East Asia 0.013 0.987 0.687 0.627 0.656 0.992 0.972 0.654 Geography.Regions.Asia.North Asia 0.004 0.996 0.625 0.159 0.254 0.997 0.974 0.203 Geography.Regions.Asia.South Asia 0.014 0.986 0.799 0.863 0.83 0.995 0.977 0.828 Geography.Regions.Asia.Southeast Asia 0.006 0.994 0.71 0.69 0.7 0.996 0.971 0.685 Geography.Regions.Asia.West Asia 0.011 0.989 0.772 0.779 0.775 0.995 0.975 0.767 Geography.Regions.Europe.Eastern Europe 0.014 0.986 0.74 0.674 0.706 0.992 0.974 0.747 Geography.Regions.Europe.Europe* 0.087 0.913 0.731 0.638 0.681 0.948 0.953 0.738 Geography.Regions.Europe.Northern Europe 0.029 0.971 0.632 0.663 0.647 0.979 0.959 0.689 Geography.Regions.Europe.Southern Europe 0.014 0.986 0.677 0.637 0.656 0.991 0.968 0.639 Geography.Regions.Europe.Western Europe 0.02 0.98 0.637 0.61 0.623 0.985 0.963 0.624 Geography.Regions.Oceania 0.014 0.986 0.762 0.82 0.79 0.994 0.974 0.802 History and Society.Business and economics 0.013 0.987 0.395 0.316 0.351 0.985 0.941 0.289 History and Society.Education 0.007 0.993 0.403 0.437 0.42 0.992 0.941 0.365 History and Society.History 0.011 0.989 0.337 0.318 0.327 0.985 0.925 0.248 History and Society.Military and warfare 0.016 0.984 0.653 0.577 0.613 0.988 0.965 0.64 History and Society.Politics and government 0.023 0.977 0.559 0.679 0.613 0.98 0.944 0.644 History and Society.Society 0.01 0.99 0.341 0.426 0.379 0.986 0.898 0.318 History and Society.Transportation 0.016 0.984 0.813 0.748 0.779 0.993 0.981 0.803 STEM.Biology 0.029 0.971 0.783 0.92 0.846 0.99 0.973 0.887 STEM.Chemistry 0.003 0.997 0.6 0.275 0.378 0.997 0.975 0.276 STEM.Computing 0.008 0.992 0.573 0.193 0.289 0.992 0.974 0.222 STEM.Earth and environment 0.005 0.995 0.648 0.606 0.626 0.996 0.966 0.632 STEM.Engineering 0.007 0.993 0.669 0.532 0.592 0.995 0.97 0.582 STEM.Libraries & Information 0.002 0.998 0.477 0.144 0.222 0.998 0.959 0.242 STEM.Mathematics 0.002 0.998 0.538 0.134 0.214 0.998 0.968 0.214 STEM.Medicine & Health 0.007 0.993 0.559 0.548 0.554 0.994 0.967 0.534 STEM.Physics 0.003 0.997 0.5 0.155 0.237 0.997 0.975 0.228 STEM.STEM* 0.083 0.917 0.834 0.696 0.759 0.963 0.968 0.854 STEM.Space 0.006 0.994 0.827 0.828 0.828 0.998 0.983 0.847 STEM.Technology 0.013 0.987 0.529 0.208 0.299 0.987 0.958 0.27

Implementation

Model architecture

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

Output schema

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

Example input and output

GET https://ores.wikimedia.org/v3/scores/enwiki/1152472770/drafttopic

Example output
{ "enwiki": { "models": { "drafttopic": { "version": "1.3.0" } }, "scores": { "1152472770": { "drafttopic": { "score": { "prediction": [ "STEM.STEM*" ], "probability": { "Culture.Biography.Biography*": 0.06452236665740299, "Culture.Biography.Women": 0.006796060518028034, "Culture.Food and drink": 0.0034858262488036413, "Culture.Internet culture": 0.01316980405300498, "Culture.Linguistics": 0.035812004854272474, "Culture.Literature": 0.10115882192713645, "Culture.Media.Books": 0.03692282820672749, "Culture.Media.Entertainment": 0.010711530036756643, "Culture.Media.Films": 0.0018885814226450477, "Culture.Media.Media*": 0.060425961704107765, "Culture.Media.Music": 0.0005378419722095707, "Culture.Media.Radio": 0.00018413020403263772, "Culture.Media.Software": 0.01752211431542162, "Culture.Media.Television": 0.00014328795512380692, "Culture.Media.Video games": 0.0009752589178008641, "Culture.Performing arts": 0.0013685116679310173, "Culture.Philosophy and religion": 0.00846981320538092, "Culture.Sports": 0.003389583061610164, "Culture.Visual arts.Architecture": 0.0010183172994406827, "Culture.Visual arts.Comics and Anime": 0.0006059058820116823, "Culture.Visual arts.Fashion": 0.0006165224577984327, "Culture.Visual arts.Visual arts*": 0.0026821398497912726, "Geography.Geographical": 0.002990758243166088, "Geography.Regions.Africa.Africa*": 0.024533447773077168, "Geography.Regions.Africa.Central Africa": 0.0005372129002643086, "Geography.Regions.Africa.Eastern Africa": 0.00010226239258595537, "Geography.Regions.Africa.Northern Africa": 0.0019899126298877174, "Geography.Regions.Africa.Southern Africa": 0.0005447025091373446, "Geography.Regions.Africa.Western Africa": 0.0004794410419489356, "Geography.Regions.Americas.Central America": 0.00012002646395075807, "Geography.Regions.Americas.North America": 0.014892613371859462, "Geography.Regions.Americas.South America": 8.654232339890368e-05, "Geography.Regions.Asia.Asia*": 0.06598737942971031, "Geography.Regions.Asia.Central Asia": 0.0010281638982847462, "Geography.Regions.Asia.East Asia": 0.057227777418167265, "Geography.Regions.Asia.North Asia": 0.003630396633774084, "Geography.Regions.Asia.South Asia": 0.003770692176326443, "Geography.Regions.Asia.Southeast Asia": 0.0014236857900671192, "Geography.Regions.Asia.West Asia": 0.0025550618998285823, "Geography.Regions.Europe.Eastern Europe": 0.011534806088388073, "Geography.Regions.Europe.Europe*": 0.03968287435557793, "Geography.Regions.Europe.Northern Europe": 0.007221978589819678, "Geography.Regions.Europe.Southern Europe": 0.003195096135472935, "Geography.Regions.Europe.Western Europe": 0.033237338362704344, "Geography.Regions.Oceania": 0.013285832354418657, "History and Society.Business and economics": 0.0049991531676727725, "History and Society.Education": 0.0016651028696874242, "History and Society.History": 0.018926782363533286, "History and Society.Military and warfare": 0.13824543680295232, "History and Society.Politics and government": 0.07383956357812226, "History and Society.Society": 0.03989083348214337, "History and Society.Transportation": 0.002495466492861629, "STEM.Biology": 0.008636873191791305, "STEM.Chemistry": 0.00770361972201387, "STEM.Computing": 0.15437336403388235, "STEM.Earth and environment": 0.0015758212652492313, "STEM.Engineering": 0.0030028383273395993, "STEM.Libraries & Information": 0.24354593114533452, "STEM.Mathematics": 0.028947136516221175, "STEM.Medicine & Health": 0.004470949173618504, "STEM.Physics": 0.007280836697859179, "STEM.STEM*": 0.9164947800360729, "STEM.Space": 0.0008405497649641965, "STEM.Technology": 0.07558409273303593 } } } } } } }

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 separated from test data during training using the drafttopic git repository (which trains both drafttopic and articletopic models).

Test data

Test data was automatically separated from training data during training using the drafttopic git repository (which trains both drafttopic and articletopic models).

Licenses

• Code: MIT License
• Model: MIT License

Citation

Cite this model as:

@misc{2020_enwiki_articletopic,
   title={English Wikipedia draft topic},
   author={Hal Triedman},
   year={2023},
   url={https://meta.wikimedia.org/Machine_learning_models/Production/English_Wikipedia_draft_topic}
}