Machine learning models/Production/Basque 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?	Basque 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/euwiki/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* 14111 12792 1319 690 41643 Culture.Biography.Women 3558 2491 1067 324 52562 Culture.Food and drink 1464 1064 400 111 54869 Culture.Internet culture 2157 1885 272 144 54143 Culture.Linguistics 2171 1623 548 142 54131 Culture.Literature 4875 3743 1132 407 51162 Culture.Media.Books 1305 1083 222 73 55066 Culture.Media.Entertainment 2110 1216 894 252 54082 Culture.Media.Films 3102 2853 249 83 53259 Culture.Media.Media* 11523 10170 1353 956 43965 Culture.Media.Music 2715 2334 381 133 53596 Culture.Media.Radio 253 189 64 28 56163 Culture.Media.Software 2005 1867 138 178 54261 Culture.Media.Television 1733 1403 330 86 54625 Culture.Media.Video games 651 615 36 20 55773 Culture.Performing arts 1551 977 574 110 54783 Culture.Philosophy and religion 4242 2436 1806 370 51832 Culture.Sports 3639 2935 704 137 52668 Culture.Visual arts.Architecture 2502 1953 549 243 53699 Culture.Visual arts.Comics and Anime 1161 1008 153 39 55244 Culture.Visual arts.Fashion 618 428 190 40 55786 Culture.Visual arts.Visual arts* 4969 3733 1236 384 51091 Geography.Geographical 4627 3220 1407 643 51174 Geography.Regions.Africa.Africa* 3906 2632 1274 322 52216 Geography.Regions.Africa.Central Africa 808 503 305 85 55551 Geography.Regions.Africa.Eastern Africa 360 234 126 22 56062 Geography.Regions.Africa.Northern Africa 1527 1045 482 115 54802 Geography.Regions.Africa.Southern Africa 590 387 203 41 55813 Geography.Regions.Africa.Western Africa 77 49 28 24 56343 Geography.Regions.Americas.Central America 1406 814 592 138 54900 Geography.Regions.Americas.North America 6544 4963 1581 763 49137 Geography.Regions.Americas.South America 1784 1373 411 122 54538 Geography.Regions.Asia.Asia* 9866 7962 1904 829 45749 Geography.Regions.Asia.Central Asia 928 688 240 74 55442 Geography.Regions.Asia.East Asia 3315 2750 565 180 52949 Geography.Regions.Asia.North Asia 1410 1202 208 120 54914 Geography.Regions.Asia.South Asia 1800 1294 506 104 54540 Geography.Regions.Asia.Southeast Asia 1750 968 782 191 54503 Geography.Regions.Asia.West Asia 2383 1862 521 135 53926 Geography.Regions.Europe.Eastern Europe 3057 2564 493 162 53225 Geography.Regions.Europe.Europe* 16976 14458 2518 1901 37567 Geography.Regions.Europe.Northern Europe 4149 3271 878 310 51985 Geography.Regions.Europe.Southern Europe 5477 4245 1232 644 50323 Geography.Regions.Europe.Western Europe 5201 4151 1050 393 50850 Geography.Regions.Oceania 1895 1318 577 149 54400 History and Society.Business and economics 3125 2030 1095 230 53089 History and Society.Education 1777 921 856 116 54551 History and Society.History 6150 4002 2148 725 49569 History and Society.Military and warfare 4199 2821 1378 408 51837 History and Society.Politics and government 4713 2715 1998 504 51227 History and Society.Society 6946 3529 3417 657 48841 History and Society.Transportation 2533 2132 401 73 53838 STEM.Biology 6821 6232 589 159 49464 STEM.Chemistry 1541 1273 268 107 54796 STEM.Computing 2464 2198 266 142 53838 STEM.Earth and environment 1875 1366 509 103 54466 STEM.Engineering 2557 1862 695 201 53686 STEM.Libraries & Information 476 371 105 31 55937 STEM.Mathematics 1069 950 119 33 55342 STEM.Medicine & Health 1942 1393 549 132 54370 STEM.Physics 1574 1210 364 138 54732 STEM.STEM* 19988 18321 1667 805 35651 STEM.Space 1879 1736 143 28 54537 STEM.Technology 4389 3453 936 530 51525

Test data sample rates:

Test data sample rates
Label Sample Population Culture.Biography.Biography* 0.25 0.123 Culture.Biography.Women 0.063 0.015 Culture.Food and drink 0.026 0.002 Culture.Internet culture 0.038 0.003 Culture.Linguistics 0.038 0.007 Culture.Literature 0.086 0.015 Culture.Media.Books 0.023 0.004 Culture.Media.Entertainment 0.037 0.004 Culture.Media.Films 0.055 0.011 Culture.Media.Media* 0.204 0.058 Culture.Media.Music 0.048 0.024 Culture.Media.Radio 0.004 0.002 Culture.Media.Software 0.036 0.001 Culture.Media.Television 0.031 0.009 Culture.Media.Video games 0.012 0.003 Culture.Performing arts 0.027 0.003 Culture.Philosophy and religion 0.075 0.011 Culture.Sports 0.064 0.071 Culture.Visual arts.Architecture 0.044 0.011 Culture.Visual arts.Comics and Anime 0.021 0.002 Culture.Visual arts.Fashion 0.011 0.001 Culture.Visual arts.Visual arts* 0.088 0.018 Geography.Geographical 0.082 0.024 Geography.Regions.Africa.Africa* 0.069 0.008 Geography.Regions.Africa.Central Africa 0.014 0.001 Geography.Regions.Africa.Eastern Africa 0.006 0 Geography.Regions.Africa.Northern Africa 0.027 0.001 Geography.Regions.Africa.Southern Africa 0.01 0.001 Geography.Regions.Africa.Western Africa 0.001 0.001 Geography.Regions.Americas.Central America 0.025 0.003 Geography.Regions.Americas.North America 0.116 0.064 Geography.Regions.Americas.South America 0.032 0.006 Geography.Regions.Asia.Asia* 0.175 0.045 Geography.Regions.Asia.Central Asia 0.016 0.001 Geography.Regions.Asia.East Asia 0.059 0.011 Geography.Regions.Asia.North Asia 0.025 0.001 Geography.Regions.Asia.South Asia 0.032 0.015 Geography.Regions.Asia.Southeast Asia 0.031 0.006 Geography.Regions.Asia.West Asia 0.042 0.011 Geography.Regions.Europe.Eastern Europe 0.054 0.013 Geography.Regions.Europe.Europe* 0.301 0.076 Geography.Regions.Europe.Northern Europe 0.074 0.031 Geography.Regions.Europe.Southern Europe 0.097 0.013 Geography.Regions.Europe.Western Europe 0.092 0.019 Geography.Regions.Oceania 0.034 0.015 History and Society.Business and economics 0.055 0.01 History and Society.Education 0.031 0.007 History and Society.History 0.109 0.011 History and Society.Military and warfare 0.074 0.014 History and Society.Politics and government 0.083 0.028 History and Society.Society 0.123 0.013 History and Society.Transportation 0.045 0.015 STEM.Biology 0.121 0.034 STEM.Chemistry 0.027 0.002 STEM.Computing 0.044 0.003 STEM.Earth and environment 0.033 0.005 STEM.Engineering 0.045 0.005 STEM.Libraries & Information 0.008 0.001 STEM.Mathematics 0.019 0 STEM.Medicine & Health 0.034 0.006 STEM.Physics 0.028 0.001 STEM.STEM* 0.354 0.069 STEM.Space 0.033 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* 0.126 0.874 0.907 0.886 0.896 0.974 0.983 0.955 Culture.Biography.Women 0.016 0.984 0.7 0.628 0.662 0.99 0.979 0.684 Culture.Food and drink 0.004 0.996 0.727 0.471 0.571 0.997 0.98 0.623 Culture.Internet culture 0.006 0.994 0.874 0.536 0.665 0.997 0.987 0.76 Culture.Linguistics 0.008 0.992 0.748 0.678 0.711 0.996 0.983 0.773 Culture.Literature 0.02 0.98 0.768 0.605 0.677 0.989 0.98 0.743 Culture.Media.Books 0.005 0.995 0.83 0.717 0.769 0.998 0.984 0.789 Culture.Media.Entertainment 0.007 0.993 0.576 0.309 0.402 0.994 0.973 0.342 Culture.Media.Films 0.011 0.989 0.92 0.863 0.89 0.998 0.988 0.929 Culture.Media.Media* 0.072 0.928 0.883 0.72 0.793 0.973 0.982 0.892 Culture.Media.Music 0.023 0.977 0.86 0.895 0.877 0.994 0.985 0.903 Culture.Media.Radio 0.002 0.998 0.747 0.764 0.755 0.999 0.948 0.58 Culture.Media.Software 0.005 0.995 0.931 0.275 0.424 0.997 0.989 0.569 Culture.Media.Television 0.009 0.991 0.81 0.821 0.815 0.997 0.985 0.844 Culture.Media.Video games 0.003 0.997 0.945 0.873 0.908 0.999 0.983 0.936 Culture.Performing arts 0.004 0.996 0.63 0.476 0.543 0.997 0.975 0.528 Culture.Philosophy and religion 0.013 0.987 0.574 0.466 0.514 0.988 0.959 0.52 Culture.Sports 0.06 0.94 0.807 0.96 0.876 0.984 0.978 0.934 Culture.Visual arts.Architecture 0.013 0.987 0.781 0.649 0.709 0.993 0.984 0.763 Culture.Visual arts.Comics and Anime 0.003 0.997 0.868 0.73 0.793 0.999 0.988 0.837 Culture.Visual arts.Fashion 0.001 0.999 0.693 0.439 0.537 0.999 0.978 0.503 Culture.Visual arts.Visual arts* 0.021 0.979 0.751 0.652 0.698 0.988 0.977 0.739 Geography.Geographical 0.029 0.971 0.696 0.575 0.63 0.981 0.972 0.661 Geography.Regions.Africa.Africa* 0.011 0.989 0.674 0.464 0.549 0.991 0.974 0.584 Geography.Regions.Africa.Central Africa 0.002 0.998 0.623 0.205 0.308 0.998 0.981 0.233 Geography.Regions.Africa.Eastern Africa 0.001 0.999 0.65 0.43 0.517 0.999 0.964 0.324 Geography.Regions.Africa.Northern Africa 0.003 0.997 0.684 0.286 0.404 0.998 0.978 0.364 Geography.Regions.Africa.Southern Africa 0.002 0.998 0.656 0.512 0.575 0.999 0.968 0.423 Geography.Regions.Africa.Western Africa 0.001 0.999 0.636 0.505 0.563 0.999 0.832 0.313 Geography.Regions.Americas.Central America 0.004 0.996 0.579 0.433 0.496 0.996 0.971 0.467 Geography.Regions.Americas.North America 0.063 0.937 0.758 0.773 0.766 0.97 0.974 0.842 Geography.Regions.Americas.South America 0.007 0.993 0.77 0.686 0.726 0.996 0.983 0.806 Geography.Regions.Asia.Asia* 0.054 0.946 0.807 0.684 0.74 0.974 0.975 0.825 Geography.Regions.Asia.Central Asia 0.002 0.998 0.741 0.325 0.452 0.998 0.981 0.355 Geography.Regions.Asia.East Asia 0.013 0.987 0.83 0.739 0.781 0.995 0.983 0.81 Geography.Regions.Asia.North Asia 0.003 0.997 0.852 0.266 0.405 0.998 0.988 0.362 Geography.Regions.Asia.South Asia 0.013 0.987 0.719 0.853 0.78 0.994 0.976 0.801 Geography.Regions.Asia.Southeast Asia 0.007 0.993 0.553 0.489 0.519 0.994 0.975 0.538 Geography.Regions.Asia.West Asia 0.011 0.989 0.781 0.775 0.778 0.995 0.979 0.778 Geography.Regions.Europe.Eastern Europe 0.014 0.986 0.839 0.782 0.809 0.995 0.982 0.838 Geography.Regions.Europe.Europe* 0.109 0.891 0.852 0.593 0.699 0.944 0.968 0.804 Geography.Regions.Europe.Northern Europe 0.03 0.97 0.788 0.807 0.798 0.988 0.981 0.841 Geography.Regions.Europe.Southern Europe 0.023 0.977 0.775 0.448 0.567 0.985 0.98 0.639 Geography.Regions.Europe.Western Europe 0.023 0.977 0.798 0.67 0.729 0.989 0.982 0.806 Geography.Regions.Oceania 0.013 0.987 0.696 0.796 0.742 0.993 0.979 0.782 History and Society.Business and economics 0.011 0.989 0.65 0.605 0.627 0.992 0.972 0.64 History and Society.Education 0.006 0.994 0.518 0.644 0.574 0.994 0.97 0.544 History and Society.History 0.021 0.979 0.651 0.331 0.439 0.982 0.963 0.463 History and Society.Military and warfare 0.017 0.983 0.672 0.551 0.605 0.988 0.974 0.637 History and Society.Politics and government 0.026 0.974 0.576 0.632 0.603 0.979 0.956 0.636 History and Society.Society 0.02 0.98 0.508 0.328 0.399 0.981 0.938 0.381 History and Society.Transportation 0.014 0.986 0.842 0.905 0.872 0.996 0.985 0.918 STEM.Biology 0.034 0.966 0.914 0.908 0.911 0.994 0.986 0.954 STEM.Chemistry 0.003 0.997 0.826 0.398 0.537 0.998 0.987 0.58 STEM.Computing 0.005 0.995 0.892 0.478 0.622 0.997 0.99 0.702 STEM.Earth and environment 0.005 0.995 0.729 0.637 0.68 0.997 0.981 0.705 STEM.Engineering 0.008 0.992 0.728 0.507 0.598 0.995 0.978 0.637 STEM.Libraries & Information 0.001 0.999 0.779 0.467 0.584 0.999 0.951 0.605 STEM.Mathematics 0.001 0.999 0.889 0.383 0.536 0.999 0.99 0.538 STEM.Medicine & Health 0.007 0.993 0.717 0.656 0.685 0.996 0.978 0.719 STEM.Physics 0.003 0.997 0.769 0.206 0.325 0.997 0.985 0.341 STEM.STEM* 0.084 0.916 0.917 0.755 0.828 0.974 0.98 0.922 STEM.Space 0.006 0.994 0.924 0.916 0.92 0.999 0.992 0.947 STEM.Technology 0.014 0.986 0.787 0.285 0.418 0.989 0.98 0.474

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/euwiki/1234/articletopic

Output:

Example output
{ "euwiki": { "models": { "articletopic": { "version": "1.4.0" } }, "scores": { "1234": { "articletopic": { "score": { "prediction": [ "STEM.STEM*" ], "probability": { "Culture.Biography.Biography*": 0.04474557328405812, "Culture.Biography.Women": 0.01497907119604387, "Culture.Food and drink": 0.004752578712389338, "Culture.Internet culture": 0.0023449403303050457, "Culture.Linguistics": 0.0022740280576232182, "Culture.Literature": 0.1182325750318887, "Culture.Media.Books": 0.019549951107536098, "Culture.Media.Entertainment": 0.01794452542211024, "Culture.Media.Films": 0.002490622258081927, "Culture.Media.Media*": 0.09069096640892323, "Culture.Media.Music": 0.001933147122339207, "Culture.Media.Radio": 0.015838931867138636, "Culture.Media.Software": 0.0024631575903988523, "Culture.Media.Television": 0.002503439185117112, "Culture.Media.Video games": 2.942126295513376e-05, "Culture.Performing arts": 0.004104411115017456, "Culture.Philosophy and religion": 0.08753806275640869, "Culture.Sports": 0.01539663101982379, "Culture.Visual arts.Architecture": 0.07475321770031441, "Culture.Visual arts.Comics and Anime": 0.0106637883730939, "Culture.Visual arts.Fashion": 0.008487458202515499, "Culture.Visual arts.Visual arts*": 0.07794234539600262, "Geography.Geographical": 0.07125542627710914, "Geography.Regions.Africa.Africa*": 0.05225989682090054, "Geography.Regions.Africa.Central Africa": 0.001331958383514617, "Geography.Regions.Africa.Eastern Africa": 0.0010447758438333376, "Geography.Regions.Africa.Northern Africa": 0.06864989790471651, "Geography.Regions.Africa.Southern Africa": 0.020527802857465454, "Geography.Regions.Africa.Western Africa": 0.0002347637092073111, "Geography.Regions.Americas.Central America": 0.0037047140715219433, "Geography.Regions.Americas.North America": 0.09146335046453571, "Geography.Regions.Americas.South America": 0.013138976351002755, "Geography.Regions.Asia.Asia*": 0.11804834121648503, "Geography.Regions.Asia.Central Asia": 0.0003669441969964404, "Geography.Regions.Asia.East Asia": 0.01393943345886899, "Geography.Regions.Asia.North Asia": 0.007152463634187921, "Geography.Regions.Asia.South Asia": 0.054313198796259836, "Geography.Regions.Asia.Southeast Asia": 0.04614969513772421, "Geography.Regions.Asia.West Asia": 0.01663646391547243, "Geography.Regions.Europe.Eastern Europe": 0.03664229764058916, "Geography.Regions.Europe.Europe*": 0.31386936084546685, "Geography.Regions.Europe.Northern Europe": 0.010818009917968689, "Geography.Regions.Europe.Southern Europe": 0.09413262097348489, "Geography.Regions.Europe.Western Europe": 0.02790924075522509, "Geography.Regions.Oceania": 0.004733876078865698, "History and Society.Business and economics": 0.047713624709890816, "History and Society.Education": 0.024040146797416194, "History and Society.History": 0.25198383619251563, "History and Society.Military and warfare": 0.03931141868901636, "History and Society.Politics and government": 0.04428292344027469, "History and Society.Society": 0.1284071527319192, "History and Society.Transportation": 0.020875200987459503, "STEM.Biology": 0.05144344976440123, "STEM.Chemistry": 0.002200494070686233, "STEM.Computing": 0.002307932122133117, "STEM.Earth and environment": 0.0028832527239357223, "STEM.Engineering": 0.06476645687968684, "STEM.Libraries & Information": 0.0006815428356950466, "STEM.Mathematics": 0.0025962242677785587, "STEM.Medicine & Health": 0.02667075270370134, "STEM.Physics": 0.009673325411912001, "STEM.STEM*": 0.8435791704707369, "STEM.Space": 0.0010474124653407004, "STEM.Technology": 0.04173308618717429 } } } } } } }

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