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12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 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Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul 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data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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id="validation-fe6786db-e57f-4644-8aec-633ae9389066" hidden="hidden"> <span class="FormControl-inlineValidation--visual"> <svg aria-hidden="true" height="12" viewBox="0 0 12 12" version="1.1" width="12" data-view-component="true" class="octicon octicon-alert-fill"> <path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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Ranking\u003c/h1\u003e\u003ca id=\"user-content-tensorflow-ranking\" class=\"anchor\" aria-label=\"Permalink: TensorFlow Ranking\" href=\"#tensorflow-ranking\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the\nTensorFlow platform. It contains the following components:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCommonly used loss functions including pointwise, pairwise, and listwise\nlosses.\u003c/li\u003e\n\u003cli\u003eCommonly used ranking metrics like\n\u003ca href=\"https://en.wikipedia.org/wiki/Mean_reciprocal_rank\" rel=\"nofollow\"\u003eMean Reciprocal Rank (MRR)\u003c/a\u003e\nand\n\u003ca href=\"https://en.wikipedia.org/wiki/Discounted_cumulative_gain\" rel=\"nofollow\"\u003eNormalized Discounted Cumulative Gain (NDCG)\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1811.04415\" rel=\"nofollow\"\u003eMulti-item (also known as groupwise) scoring functions\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ai.google/research/pubs/pub47258\" rel=\"nofollow\"\u003eLambdaLoss\u003c/a\u003e implementation for\ndirect ranking metric optimization.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cs.cornell.edu/people/tj/publications/joachims_etal_17a.pdf\" rel=\"nofollow\"\u003eUnbiased Learning-to-Rank\u003c/a\u003e\nfrom biased feedback data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eWe envision that this library will provide a convenient open platform for\nhosting and advancing state-of-the-art ranking models based on deep learning\ntechniques, and thus facilitate both academic research and industrial\napplications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTutorial Slides\u003c/h2\u003e\u003ca id=\"user-content-tutorial-slides\" class=\"anchor\" aria-label=\"Permalink: Tutorial Slides\" href=\"#tutorial-slides\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTF-Ranking was presented at premier conferences in Information Retrieval,\n\u003ca href=\"https://sigir.org/sigir2019/program/tutorials/\" rel=\"nofollow\"\u003eSIGIR 2019\u003c/a\u003e and\n\u003ca href=\"http://ictir2019.org/program/#tutorials\" rel=\"nofollow\"\u003eICTIR 2019\u003c/a\u003e! The slides are available\n\u003ca href=\"http://bendersky.github.io/res/TF-Ranking-ICTIR-2019.pdf\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDemos\u003c/h2\u003e\u003ca id=\"user-content-demos\" class=\"anchor\" aria-label=\"Permalink: Demos\" href=\"#demos\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe provide a demo, with no installation required, to get started on using\nTF-Ranking. This demo runs on a\n\u003ca href=\"https://research.google.com/colaboratory/faq.html\" rel=\"nofollow\"\u003ecolaboratory notebook\u003c/a\u003e, an\ninteractive Python environment. Using sparse features and embeddings in\nTF-Ranking\n\u003ca href=\"https://colab.research.google.com/github/tensorflow/ranking/blob/master/tensorflow_ranking/examples/handling_sparse_features.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ac8b00ff20f81a6c2ead1041470dbdea9f5654c2eab626ec092ce2f1ae5b50c6/68747470733a2f2f7777772e74656e736f72666c6f772e6f72672f696d616765732f636f6c61625f6c6f676f5f333270782e706e67\" alt=\"Run in Google Colab\" data-canonical-src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e.\nThis demo demonstrates how to:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUse sparse/embedding features\u003c/li\u003e\n\u003cli\u003eProcess data in TFRecord format\u003c/li\u003e\n\u003cli\u003eTensorboard integration in colab notebook, for Estimator API\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eAlso see \u003ca href=\"#running-scripts\"\u003eRunning Scripts\u003c/a\u003e for executable scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLinux Installation\u003c/h2\u003e\u003ca id=\"user-content-linux-installation\" class=\"anchor\" aria-label=\"Permalink: Linux Installation\" href=\"#linux-installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStable Builds\u003c/h3\u003e\u003ca id=\"user-content-stable-builds\" class=\"anchor\" aria-label=\"Permalink: Stable Builds\" href=\"#stable-builds\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install the latest version from\n\u003ca href=\"https://pypi.org/project/tensorflow-ranking/\" rel=\"nofollow\"\u003ePyPI\u003c/a\u003e, run the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Installing with the `--upgrade` flag ensures you'll get the latest version.\npip install --user --upgrade tensorflow_ranking\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing with the `--upgrade` flag ensures you'll get the latest version.\u003c/span\u003e\npip install --user --upgrade tensorflow_ranking\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo force a Python 3-specific install, replace \u003ccode\u003epip\u003c/code\u003e with \u003ccode\u003epip3\u003c/code\u003e in the above\ncommands. For additional installation help, guidance installing prerequisites,\nand (optionally) setting up virtual environments, see the\n\u003ca href=\"https://www.tensorflow.org/install\" rel=\"nofollow\"\u003eTensorFlow installation guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote: Since TensorFlow is now included as a dependency of the TensorFlow Ranking\npackage (in \u003ccode\u003esetup.py\u003c/code\u003e). If you wish to use different versions of TensorFlow\n(e.g., \u003ccode\u003etensorflow-gpu\u003c/code\u003e), you may need to uninstall the existing verison and\nthen install your desired version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip uninstall tensorflow\n$ pip install tensorflow-gpu\"\u003e\u003cpre\u003e$ pip uninstall tensorflow\n$ pip install tensorflow-gpu\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling from Source\u003c/h3\u003e\u003ca id=\"user-content-installing-from-source\" class=\"anchor\" aria-label=\"Permalink: Installing from Source\" href=\"#installing-from-source\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTo build TensorFlow Ranking locally, you will need to install:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://docs.bazel.build/versions/master/install.html\" rel=\"nofollow\"\u003eBazel\u003c/a\u003e, an open\nsource build tool.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ sudo apt-get update \u0026amp;\u0026amp; sudo apt-get install bazel\"\u003e\u003cpre\u003e$ sudo apt-get update \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sudo apt-get install bazel\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://pypi.org/project/pip/\" rel=\"nofollow\"\u003ePip\u003c/a\u003e, a Python package manager.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ sudo apt-get install python-pip\"\u003e\u003cpre\u003e$ sudo apt-get install python-pip\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://virtualenv.pypa.io/en/stable/installation/\" rel=\"nofollow\"\u003eVirtualEnv\u003c/a\u003e, a tool\nto create isolated Python environments.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install --user virtualenv\"\u003e\u003cpre\u003e$ pip install --user virtualenv\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eClone the TensorFlow Ranking repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ git clone https://github.com/tensorflow/ranking.git\"\u003e\u003cpre\u003e$ git clone https://github.com/tensorflow/ranking.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eBuild TensorFlow Ranking wheel file and store them in \u003ccode\u003e/tmp/ranking_pip\u003c/code\u003e\nfolder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ cd ranking # The folder which was cloned in Step 2.\n$ bazel build //tensorflow_ranking/tools/pip_package:build_pip_package\n$ bazel-bin/tensorflow_ranking/tools/pip_package/build_pip_package /tmp/ranking_pip\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ranking \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The folder which was cloned in Step 2.\u003c/span\u003e\n$ bazel build //tensorflow_ranking/tools/pip_package:build_pip_package\n$ bazel-bin/tensorflow_ranking/tools/pip_package/build_pip_package /tmp/ranking_pip\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInstall the wheel package using pip. Test in virtualenv, to avoid clash with\nany system dependencies.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ ~/.local/bin/virtualenv -p python3 /tmp/tfr\n$ source /tmp/tfr/bin/activate\n(tfr) $ pip install /tmp/ranking_pip/tensorflow_ranking*.whl\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.local/bin/virtualenv -p python3 /tmp/tfr\n$ \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e /tmp/tfr/bin/activate\n(tfr) $ pip install /tmp/ranking_pip/tensorflow_ranking\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.whl\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn some cases, you may want to install a specific version of tensorflow,\ne.g., \u003ccode\u003etensorflow-gpu\u003c/code\u003e or \u003ccode\u003etensorflow==2.0.0\u003c/code\u003e. To do so you can either\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(tfr) $ pip uninstall tensorflow\n(tfr) $ pip install tensorflow==2.0.0\"\u003e\u003cpre\u003e(tfr) $ pip uninstall tensorflow\n(tfr) $ pip install tensorflow==2.0.0\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(tfr) $ pip uninstall tensorflow\n(tfr) $ pip install tensorflow-gpu\"\u003e\u003cpre\u003e(tfr) $ pip uninstall tensorflow\n(tfr) $ pip install tensorflow-gpu\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRun all TensorFlow Ranking tests.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(tfr) $ bazel test //tensorflow_ranking/...\"\u003e\u003cpre\u003e(tfr) $ bazel \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e //tensorflow_ranking/...\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInvoke TensorFlow Ranking package in python (within virtualenv).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(tfr) $ python -c \u0026quot;import tensorflow_ranking\u0026quot;\"\u003e\u003cpre\u003e(tfr) $ python -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimport tensorflow_ranking\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRunning Scripts\u003c/h2\u003e\u003ca id=\"user-content-running-scripts\" class=\"anchor\" aria-label=\"Permalink: Running Scripts\" href=\"#running-scripts\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor ease of experimentation, we also provide\n\u003ca href=\"https://github.com/tensorflow/ranking/blob/master/tensorflow_ranking/examples/tf_ranking_tfrecord.py\"\u003ea TFRecord example\u003c/a\u003e\nand\n\u003ca href=\"https://github.com/tensorflow/ranking/blob/master/tensorflow_ranking/examples/tf_ranking_libsvm.py\"\u003ea LIBSVM example\u003c/a\u003e\nin the form of executable scripts. This is particularly useful for\nhyperparameter tuning, where the hyperparameters are supplied as flags to the\nscript.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTFRecord Example\u003c/h3\u003e\u003ca id=\"user-content-tfrecord-example\" class=\"anchor\" aria-label=\"Permalink: TFRecord Example\" href=\"#tfrecord-example\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSet up the data and directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"MODEL_DIR=/tmp/tf_record_model \u0026amp;\u0026amp; \\\nTRAIN=tensorflow_ranking/examples/data/train_elwc.tfrecord \u0026amp;\u0026amp; \\\nEVAL=tensorflow_ranking/examples/data/eval_elwc.tfrecord \u0026amp;\u0026amp; \\\nVOCAB=tensorflow_ranking/examples/data/vocab.txt\"\u003e\u003cpre\u003eMODEL_DIR=/tmp/tf_record_model \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nTRAIN=tensorflow_ranking/examples/data/train_elwc.tfrecord \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nEVAL=tensorflow_ranking/examples/data/eval_elwc.tfrecord \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nVOCAB=tensorflow_ranking/examples/data/vocab.txt\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eBuild and run.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"rm -rf $MODEL_DIR \u0026amp;\u0026amp; \\\nbazel build -c opt \\\ntensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \u0026amp;\u0026amp; \\\n./bazel-bin/tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \\\n--train_path=$TRAIN \\\n--eval_path=$EVAL \\\n--vocab_path=$VOCAB \\\n--model_dir=$MODEL_DIR \\\n--data_format=example_list_with_context\"\u003e\u003cpre\u003erm -rf \u003cspan class=\"pl-smi\"\u003e$MODEL_DIR\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nbazel build -c opt \\\ntensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n./bazel-bin/tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \\\n--train_path=\u003cspan class=\"pl-smi\"\u003e$TRAIN\u003c/span\u003e \\\n--eval_path=\u003cspan class=\"pl-smi\"\u003e$EVAL\u003c/span\u003e \\\n--vocab_path=\u003cspan class=\"pl-smi\"\u003e$VOCAB\u003c/span\u003e \\\n--model_dir=\u003cspan class=\"pl-smi\"\u003e$MODEL_DIR\u003c/span\u003e \\\n--data_format=example_list_with_context\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLIBSVM Example\u003c/h3\u003e\u003ca id=\"user-content-libsvm-example\" class=\"anchor\" aria-label=\"Permalink: LIBSVM Example\" href=\"#libsvm-example\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSet up the data and directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"OUTPUT_DIR=/tmp/libsvm \u0026amp;\u0026amp; \\\nTRAIN=tensorflow_ranking/examples/data/train.txt \u0026amp;\u0026amp; \\\nVALI=tensorflow_ranking/examples/data/vali.txt \u0026amp;\u0026amp; \\\nTEST=tensorflow_ranking/examples/data/test.txt\"\u003e\u003cpre\u003eOUTPUT_DIR=/tmp/libsvm \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nTRAIN=tensorflow_ranking/examples/data/train.txt \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nVALI=tensorflow_ranking/examples/data/vali.txt \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nTEST=tensorflow_ranking/examples/data/test.txt\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eBuild and run.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"rm -rf $OUTPUT_DIR \u0026amp;\u0026amp; \\\nbazel build -c opt \\\ntensorflow_ranking/examples/tf_ranking_libsvm_py_binary \u0026amp;\u0026amp; \\\n./bazel-bin/tensorflow_ranking/examples/tf_ranking_libsvm_py_binary \\\n--train_path=$TRAIN \\\n--vali_path=$VALI \\\n--test_path=$TEST \\\n--output_dir=$OUTPUT_DIR \\\n--num_features=136 \\\n--num_train_steps=100\"\u003e\u003cpre\u003erm -rf \u003cspan class=\"pl-smi\"\u003e$OUTPUT_DIR\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nbazel build -c opt \\\ntensorflow_ranking/examples/tf_ranking_libsvm_py_binary \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n./bazel-bin/tensorflow_ranking/examples/tf_ranking_libsvm_py_binary \\\n--train_path=\u003cspan class=\"pl-smi\"\u003e$TRAIN\u003c/span\u003e \\\n--vali_path=\u003cspan class=\"pl-smi\"\u003e$VALI\u003c/span\u003e \\\n--test_path=\u003cspan class=\"pl-smi\"\u003e$TEST\u003c/span\u003e \\\n--output_dir=\u003cspan class=\"pl-smi\"\u003e$OUTPUT_DIR\u003c/span\u003e \\\n--num_features=136 \\\n--num_train_steps=100\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTensorBoard\u003c/h3\u003e\u003ca id=\"user-content-tensorboard\" class=\"anchor\" aria-label=\"Permalink: TensorBoard\" href=\"#tensorboard\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe training results such as loss and metrics can be visualized using\n\u003ca href=\"https://github.com/tensorflow/tensorboard/blob/master/README.md\"\u003eTensorboard\u003c/a\u003e.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e(Optional) If you are working on remote server, set up port forwarding with\nthis command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ ssh \u0026lt;remote-server\u0026gt; -L 8888:127.0.0.1:8888\"\u003e\u003cpre\u003e$ ssh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eremote-server\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -L 8888:127.0.0.1:8888\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInstall Tensorboard and invoke it with the following commands.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(tfr) $ pip install tensorboard\n(tfr) $ tensorboard --logdir $OUTPUT_DIR\"\u003e\u003cpre\u003e(tfr) $ pip install tensorboard\n(tfr) $ tensorboard --logdir \u003cspan class=\"pl-smi\"\u003e$OUTPUT_DIR\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eJupyter Notebook\u003c/h3\u003e\u003ca id=\"user-content-jupyter-notebook\" class=\"anchor\" aria-label=\"Permalink: Jupyter Notebook\" href=\"#jupyter-notebook\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAn example jupyter notebook is available in\n\u003ccode\u003etensorflow_ranking/examples/handling_sparse_features.ipynb\u003c/code\u003e.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTo run this notebook, first follow the steps in installation to set up\n\u003ccode\u003evirtualenv\u003c/code\u003e environment with tensorflow_ranking package installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInstall jupyter within virtualenv.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(tfr) $ pip install jupyter\"\u003e\u003cpre\u003e(tfr) $ pip install jupyter\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eStart a jupyter notebook instance on remote server.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(tfr) $ jupyter notebook tensorflow_ranking/examples/handling_sparse_features.ipynb \\\n --NotebookApp.allow_origin='https://colab.research.google.com' \\\n --port=8888\"\u003e\u003cpre\u003e(tfr) $ jupyter notebook tensorflow_ranking/examples/handling_sparse_features.ipynb \\\n --NotebookApp.allow_origin=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003ehttps://colab.research.google.com\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n --port=8888\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e(Optional) If you are working on remote server, set up port forwarding with\nthis command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ ssh \u0026lt;remote-server\u0026gt; -L 8888:127.0.0.1:8888\"\u003e\u003cpre\u003e$ ssh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eremote-server\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -L 8888:127.0.0.1:8888\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRunning the notebook.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eStart jupyter notebook on your local machine at\n\u003ca href=\"http://localhost:8888/\" rel=\"nofollow\"\u003ehttp://localhost:8888/\u003c/a\u003e and browse to the\nipython notebook.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eAn alternative is to use colaboratory notebook via\n\u003ca href=\"http://colab.research.google.com\" rel=\"nofollow\"\u003ecolab.research.google.com\u003c/a\u003e and open\nthe notebook in the browser. Choose local runtime and link to port 8888.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael\nBendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan\nWolf. \u003cem\u003eTF-Ranking: Scalable TensorFlow Library for Learning-to-Rank.\u003c/em\u003e\n\u003ca href=\"https://ai.google/research/pubs/pub48160\" rel=\"nofollow\"\u003eKDD 2019.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eQingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael\nBendersky, Marc Najork. \u003cem\u003eLearning Groupwise Scoring Functions Using Deep\nNeural Networks.\u003c/em\u003e \u003ca href=\"https://ai.google/research/pubs/pub48348\" rel=\"nofollow\"\u003eICTIR 2019\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eXuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. \u003cem\u003eLearning\nto Rank with Selection Bias in Personal Search.\u003c/em\u003e\n\u003ca href=\"https://ai.google/research/pubs/pub45286\" rel=\"nofollow\"\u003eSIGIR 2016.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eXuanhui Wang, Cheng Li, Nadav Golbandi, Mike Bendersky, Marc Najork. \u003cem\u003eThe\nLambdaLoss Framework for Ranking Metric Optimization\u003c/em\u003e.\n\u003ca href=\"https://ai.google/research/pubs/pub47258\" rel=\"nofollow\"\u003eCIKM 2018.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use TensorFlow Ranking in your research and would like to cite it, we\nsuggest you use the following citation:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{TensorflowRankingKDD2019,\n author = {Rama Kumar Pasumarthi and Sebastian Bruch and Xuanhui Wang and Cheng Li and Michael Bendersky and Marc Najork and Jan Pfeifer and Nadav Golbandi and Rohan Anil and Stephan Wolf},\n title = {TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank},\n booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},\n year = {2019},\n pages = {2970--2978},\n location = {Anchorage, AK}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{TensorflowRankingKDD2019,\n author = {Rama Kumar Pasumarthi and Sebastian Bruch and Xuanhui Wang and Cheng Li and Michael Bendersky and Marc Najork and Jan Pfeifer and Nadav Golbandi and Rohan Anil and Stephan Wolf},\n title = {TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank},\n booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},\n year = {2019},\n pages = {2970--2978},\n location = {Anchorage, AK}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"TensorFlow Ranking","anchor":"tensorflow-ranking","htmlText":"TensorFlow Ranking"},{"level":2,"text":"Tutorial Slides","anchor":"tutorial-slides","htmlText":"Tutorial Slides"},{"level":2,"text":"Demos","anchor":"demos","htmlText":"Demos"},{"level":2,"text":"Linux Installation","anchor":"linux-installation","htmlText":"Linux Installation"},{"level":3,"text":"Stable Builds","anchor":"stable-builds","htmlText":"Stable Builds"},{"level":3,"text":"Installing from Source","anchor":"installing-from-source","htmlText":"Installing from Source"},{"level":2,"text":"Running Scripts","anchor":"running-scripts","htmlText":"Running Scripts"},{"level":3,"text":"TFRecord Example","anchor":"tfrecord-example","htmlText":"TFRecord 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QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">TensorFlow Ranking</h1><a id="user-content-tensorflow-ranking" class="anchor" aria-label="Permalink: TensorFlow Ranking" href="#tensorflow-ranking"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. It contains the following components:</p> <ul dir="auto"> <li>Commonly used loss functions including pointwise, pairwise, and listwise losses.</li> <li>Commonly used ranking metrics like <a href="https://en.wikipedia.org/wiki/Mean_reciprocal_rank" rel="nofollow">Mean Reciprocal Rank (MRR)</a> and <a href="https://en.wikipedia.org/wiki/Discounted_cumulative_gain" rel="nofollow">Normalized Discounted Cumulative Gain (NDCG)</a>.</li> <li><a href="https://arxiv.org/abs/1811.04415" rel="nofollow">Multi-item (also known as groupwise) scoring functions</a>.</li> <li><a href="https://ai.google/research/pubs/pub47258" rel="nofollow">LambdaLoss</a> implementation for direct ranking metric optimization.</li> <li><a href="http://www.cs.cornell.edu/people/tj/publications/joachims_etal_17a.pdf" rel="nofollow">Unbiased Learning-to-Rank</a> from biased feedback data.</li> </ul> <p dir="auto">We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Tutorial Slides</h2><a id="user-content-tutorial-slides" class="anchor" aria-label="Permalink: Tutorial Slides" href="#tutorial-slides"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">TF-Ranking was presented at premier conferences in Information Retrieval, <a href="https://sigir.org/sigir2019/program/tutorials/" rel="nofollow">SIGIR 2019</a> and <a href="http://ictir2019.org/program/#tutorials" rel="nofollow">ICTIR 2019</a>! The slides are available <a href="http://bendersky.github.io/res/TF-Ranking-ICTIR-2019.pdf" rel="nofollow">here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Demos</h2><a id="user-content-demos" class="anchor" aria-label="Permalink: Demos" href="#demos"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We provide a demo, with no installation required, to get started on using TF-Ranking. This demo runs on a <a href="https://research.google.com/colaboratory/faq.html" rel="nofollow">colaboratory notebook</a>, an interactive Python environment. Using sparse features and embeddings in TF-Ranking <a href="https://colab.research.google.com/github/tensorflow/ranking/blob/master/tensorflow_ranking/examples/handling_sparse_features.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/ac8b00ff20f81a6c2ead1041470dbdea9f5654c2eab626ec092ce2f1ae5b50c6/68747470733a2f2f7777772e74656e736f72666c6f772e6f72672f696d616765732f636f6c61625f6c6f676f5f333270782e706e67" alt="Run in Google Colab" data-canonical-src="https://www.tensorflow.org/images/colab_logo_32px.png" style="max-width: 100%;"></a>. This demo demonstrates how to:</p> <ul dir="auto"> <li>Use sparse/embedding features</li> <li>Process data in TFRecord format</li> <li>Tensorboard integration in colab notebook, for Estimator API</li> </ul> <p dir="auto">Also see <a href="#running-scripts">Running Scripts</a> for executable scripts.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Linux Installation</h2><a id="user-content-linux-installation" class="anchor" aria-label="Permalink: Linux Installation" href="#linux-installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Stable Builds</h3><a id="user-content-stable-builds" class="anchor" aria-label="Permalink: Stable Builds" href="#stable-builds"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To install the latest version from <a href="https://pypi.org/project/tensorflow-ranking/" rel="nofollow">PyPI</a>, run the following:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Installing with the `--upgrade` flag ensures you'll get the latest version. pip install --user --upgrade tensorflow_ranking"><pre><span class="pl-c"><span class="pl-c">#</span> Installing with the `--upgrade` flag ensures you'll get the latest version.</span> pip install --user --upgrade tensorflow_ranking</pre></div> <p dir="auto">To force a Python 3-specific install, replace <code>pip</code> with <code>pip3</code> in the above commands. For additional installation help, guidance installing prerequisites, and (optionally) setting up virtual environments, see the <a href="https://www.tensorflow.org/install" rel="nofollow">TensorFlow installation guide</a>.</p> <p dir="auto">Note: Since TensorFlow is now included as a dependency of the TensorFlow Ranking package (in <code>setup.py</code>). If you wish to use different versions of TensorFlow (e.g., <code>tensorflow-gpu</code>), you may need to uninstall the existing verison and then install your desired version:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip uninstall tensorflow $ pip install tensorflow-gpu"><pre>$ pip uninstall tensorflow $ pip install tensorflow-gpu</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Installing from Source</h3><a id="user-content-installing-from-source" class="anchor" aria-label="Permalink: Installing from Source" href="#installing-from-source"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">To build TensorFlow Ranking locally, you will need to install:</p> <ul dir="auto"> <li> <p dir="auto"><a href="https://docs.bazel.build/versions/master/install.html" rel="nofollow">Bazel</a>, an open source build tool.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ sudo apt-get update && sudo apt-get install bazel"><pre>$ sudo apt-get update <span class="pl-k">&&</span> sudo apt-get install bazel</pre></div> </li> <li> <p dir="auto"><a href="https://pypi.org/project/pip/" rel="nofollow">Pip</a>, a Python package manager.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ sudo apt-get install python-pip"><pre>$ sudo apt-get install python-pip</pre></div> </li> <li> <p dir="auto"><a href="https://virtualenv.pypa.io/en/stable/installation/" rel="nofollow">VirtualEnv</a>, a tool to create isolated Python environments.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install --user virtualenv"><pre>$ pip install --user virtualenv</pre></div> </li> </ul> </li> <li> <p dir="auto">Clone the TensorFlow Ranking repository.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ git clone https://github.com/tensorflow/ranking.git"><pre>$ git clone https://github.com/tensorflow/ranking.git</pre></div> </li> <li> <p dir="auto">Build TensorFlow Ranking wheel file and store them in <code>/tmp/ranking_pip</code> folder.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ cd ranking # The folder which was cloned in Step 2. $ bazel build //tensorflow_ranking/tools/pip_package:build_pip_package $ bazel-bin/tensorflow_ranking/tools/pip_package/build_pip_package /tmp/ranking_pip"><pre>$ <span class="pl-c1">cd</span> ranking <span class="pl-c"><span class="pl-c">#</span> The folder which was cloned in Step 2.</span> $ bazel build //tensorflow_ranking/tools/pip_package:build_pip_package $ bazel-bin/tensorflow_ranking/tools/pip_package/build_pip_package /tmp/ranking_pip</pre></div> </li> <li> <p dir="auto">Install the wheel package using pip. Test in virtualenv, to avoid clash with any system dependencies.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ ~/.local/bin/virtualenv -p python3 /tmp/tfr $ source /tmp/tfr/bin/activate (tfr) $ pip install /tmp/ranking_pip/tensorflow_ranking*.whl"><pre>$ <span class="pl-k">~</span>/.local/bin/virtualenv -p python3 /tmp/tfr $ <span class="pl-c1">source</span> /tmp/tfr/bin/activate (tfr) $ pip install /tmp/ranking_pip/tensorflow_ranking<span class="pl-k">*</span>.whl</pre></div> <p dir="auto">In some cases, you may want to install a specific version of tensorflow, e.g., <code>tensorflow-gpu</code> or <code>tensorflow==2.0.0</code>. To do so you can either</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(tfr) $ pip uninstall tensorflow (tfr) $ pip install tensorflow==2.0.0"><pre>(tfr) $ pip uninstall tensorflow (tfr) $ pip install tensorflow==2.0.0</pre></div> <p dir="auto">or</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(tfr) $ pip uninstall tensorflow (tfr) $ pip install tensorflow-gpu"><pre>(tfr) $ pip uninstall tensorflow (tfr) $ pip install tensorflow-gpu</pre></div> </li> <li> <p dir="auto">Run all TensorFlow Ranking tests.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(tfr) $ bazel test //tensorflow_ranking/..."><pre>(tfr) $ bazel <span class="pl-c1">test</span> //tensorflow_ranking/...</pre></div> </li> <li> <p dir="auto">Invoke TensorFlow Ranking package in python (within virtualenv).</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(tfr) $ python -c "import tensorflow_ranking""><pre>(tfr) $ python -c <span class="pl-s"><span class="pl-pds">"</span>import tensorflow_ranking<span class="pl-pds">"</span></span></pre></div> </li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Running Scripts</h2><a id="user-content-running-scripts" class="anchor" aria-label="Permalink: Running Scripts" href="#running-scripts"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For ease of experimentation, we also provide <a href="https://github.com/tensorflow/ranking/blob/master/tensorflow_ranking/examples/tf_ranking_tfrecord.py">a TFRecord example</a> and <a href="https://github.com/tensorflow/ranking/blob/master/tensorflow_ranking/examples/tf_ranking_libsvm.py">a LIBSVM example</a> in the form of executable scripts. This is particularly useful for hyperparameter tuning, where the hyperparameters are supplied as flags to the script.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">TFRecord Example</h3><a id="user-content-tfrecord-example" class="anchor" aria-label="Permalink: TFRecord Example" href="#tfrecord-example"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Set up the data and directory.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="MODEL_DIR=/tmp/tf_record_model && \ TRAIN=tensorflow_ranking/examples/data/train_elwc.tfrecord && \ EVAL=tensorflow_ranking/examples/data/eval_elwc.tfrecord && \ VOCAB=tensorflow_ranking/examples/data/vocab.txt"><pre>MODEL_DIR=/tmp/tf_record_model <span class="pl-k">&&</span> \ TRAIN=tensorflow_ranking/examples/data/train_elwc.tfrecord <span class="pl-k">&&</span> \ EVAL=tensorflow_ranking/examples/data/eval_elwc.tfrecord <span class="pl-k">&&</span> \ VOCAB=tensorflow_ranking/examples/data/vocab.txt</pre></div> </li> <li> <p dir="auto">Build and run.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="rm -rf $MODEL_DIR && \ bazel build -c opt \ tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary && \ ./bazel-bin/tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \ --train_path=$TRAIN \ --eval_path=$EVAL \ --vocab_path=$VOCAB \ --model_dir=$MODEL_DIR \ --data_format=example_list_with_context"><pre>rm -rf <span class="pl-smi">$MODEL_DIR</span> <span class="pl-k">&&</span> \ bazel build -c opt \ tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary <span class="pl-k">&&</span> \ ./bazel-bin/tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \ --train_path=<span class="pl-smi">$TRAIN</span> \ --eval_path=<span class="pl-smi">$EVAL</span> \ --vocab_path=<span class="pl-smi">$VOCAB</span> \ --model_dir=<span class="pl-smi">$MODEL_DIR</span> \ --data_format=example_list_with_context</pre></div> </li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">LIBSVM Example</h3><a id="user-content-libsvm-example" class="anchor" aria-label="Permalink: LIBSVM Example" href="#libsvm-example"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Set up the data and directory.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="OUTPUT_DIR=/tmp/libsvm && \ TRAIN=tensorflow_ranking/examples/data/train.txt && \ VALI=tensorflow_ranking/examples/data/vali.txt && \ TEST=tensorflow_ranking/examples/data/test.txt"><pre>OUTPUT_DIR=/tmp/libsvm <span class="pl-k">&&</span> \ TRAIN=tensorflow_ranking/examples/data/train.txt <span class="pl-k">&&</span> \ VALI=tensorflow_ranking/examples/data/vali.txt <span class="pl-k">&&</span> \ TEST=tensorflow_ranking/examples/data/test.txt</pre></div> </li> <li> <p dir="auto">Build and run.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="rm -rf $OUTPUT_DIR && \ bazel build -c opt \ tensorflow_ranking/examples/tf_ranking_libsvm_py_binary && \ ./bazel-bin/tensorflow_ranking/examples/tf_ranking_libsvm_py_binary \ --train_path=$TRAIN \ --vali_path=$VALI \ --test_path=$TEST \ --output_dir=$OUTPUT_DIR \ --num_features=136 \ --num_train_steps=100"><pre>rm -rf <span class="pl-smi">$OUTPUT_DIR</span> <span class="pl-k">&&</span> \ bazel build -c opt \ tensorflow_ranking/examples/tf_ranking_libsvm_py_binary <span class="pl-k">&&</span> \ ./bazel-bin/tensorflow_ranking/examples/tf_ranking_libsvm_py_binary \ --train_path=<span class="pl-smi">$TRAIN</span> \ --vali_path=<span class="pl-smi">$VALI</span> \ --test_path=<span class="pl-smi">$TEST</span> \ --output_dir=<span class="pl-smi">$OUTPUT_DIR</span> \ --num_features=136 \ --num_train_steps=100</pre></div> </li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">TensorBoard</h3><a id="user-content-tensorboard" class="anchor" aria-label="Permalink: TensorBoard" href="#tensorboard"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The training results such as loss and metrics can be visualized using <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">Tensorboard</a>.</p> <ol dir="auto"> <li> <p dir="auto">(Optional) If you are working on remote server, set up port forwarding with this command.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ ssh <remote-server> -L 8888:127.0.0.1:8888"><pre>$ ssh <span class="pl-k"><</span>remote-server<span class="pl-k">></span> -L 8888:127.0.0.1:8888</pre></div> </li> <li> <p dir="auto">Install Tensorboard and invoke it with the following commands.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(tfr) $ pip install tensorboard (tfr) $ tensorboard --logdir $OUTPUT_DIR"><pre>(tfr) $ pip install tensorboard (tfr) $ tensorboard --logdir <span class="pl-smi">$OUTPUT_DIR</span></pre></div> </li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Jupyter Notebook</h3><a id="user-content-jupyter-notebook" class="anchor" aria-label="Permalink: Jupyter Notebook" href="#jupyter-notebook"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">An example jupyter notebook is available in <code>tensorflow_ranking/examples/handling_sparse_features.ipynb</code>.</p> <ol dir="auto"> <li> <p dir="auto">To run this notebook, first follow the steps in installation to set up <code>virtualenv</code> environment with tensorflow_ranking package installed.</p> </li> <li> <p dir="auto">Install jupyter within virtualenv.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(tfr) $ pip install jupyter"><pre>(tfr) $ pip install jupyter</pre></div> </li> <li> <p dir="auto">Start a jupyter notebook instance on remote server.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(tfr) $ jupyter notebook tensorflow_ranking/examples/handling_sparse_features.ipynb \ --NotebookApp.allow_origin='https://colab.research.google.com' \ --port=8888"><pre>(tfr) $ jupyter notebook tensorflow_ranking/examples/handling_sparse_features.ipynb \ --NotebookApp.allow_origin=<span class="pl-s"><span class="pl-pds">'</span>https://colab.research.google.com<span class="pl-pds">'</span></span> \ --port=8888</pre></div> </li> <li> <p dir="auto">(Optional) If you are working on remote server, set up port forwarding with this command.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ ssh <remote-server> -L 8888:127.0.0.1:8888"><pre>$ ssh <span class="pl-k"><</span>remote-server<span class="pl-k">></span> -L 8888:127.0.0.1:8888</pre></div> </li> <li> <p dir="auto">Running the notebook.</p> <ul dir="auto"> <li> <p dir="auto">Start jupyter notebook on your local machine at <a href="http://localhost:8888/" rel="nofollow">http://localhost:8888/</a> and browse to the ipython notebook.</p> </li> <li> <p dir="auto">An alternative is to use colaboratory notebook via <a href="http://colab.research.google.com" rel="nofollow">colab.research.google.com</a> and open the notebook in the browser. Choose local runtime and link to port 8888.</p> </li> </ul> </li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">References</h2><a id="user-content-references" class="anchor" aria-label="Permalink: References" href="#references"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto">Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan Wolf. <em>TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank.</em> <a href="https://ai.google/research/pubs/pub48160" rel="nofollow">KDD 2019.</a></p> </li> <li> <p dir="auto">Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork. <em>Learning Groupwise Scoring Functions Using Deep Neural Networks.</em> <a href="https://ai.google/research/pubs/pub48348" rel="nofollow">ICTIR 2019</a></p> </li> <li> <p dir="auto">Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. <em>Learning to Rank with Selection Bias in Personal Search.</em> <a href="https://ai.google/research/pubs/pub45286" rel="nofollow">SIGIR 2016.</a></p> </li> <li> <p dir="auto">Xuanhui Wang, Cheng Li, Nadav Golbandi, Mike Bendersky, Marc Najork. <em>The LambdaLoss Framework for Ranking Metric Optimization</em>. <a href="https://ai.google/research/pubs/pub47258" rel="nofollow">CIKM 2018.</a></p> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Citation</h3><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you use TensorFlow Ranking in your research and would like to cite it, we suggest you use the following citation:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{TensorflowRankingKDD2019, author = {Rama Kumar Pasumarthi and Sebastian Bruch and Xuanhui Wang and Cheng Li and Michael Bendersky and Marc Najork and Jan Pfeifer and Nadav Golbandi and Rohan Anil and Stephan Wolf}, title = {TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank}, booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, year = {2019}, pages = {2970--2978}, location = {Anchorage, AK} }"><pre class="notranslate"><code>@inproceedings{TensorflowRankingKDD2019, author = {Rama Kumar Pasumarthi and Sebastian Bruch and Xuanhui Wang and Cheng Li and Michael Bendersky and Marc Najork and Jan Pfeifer and Nadav Golbandi and Rohan Anil and Stephan Wolf}, title = {TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank}, booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, year = {2019}, pages = {2970--2978}, location = {Anchorage, AK} } </code></pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="WyG39nTlLLayjhOX/53S0lTk1v/L/azQnhrX+aJbKl5KmNbaQhHem3fYfpCEuuyY8tDMipb7cdAGf0o22PY7qA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Learning to Rank in TensorFlow </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a href="/topics/information-retrieval" title="Topic: information-retrieval" data-view-component="true" class="topic-tag topic-tag-link"> information-retrieval </a> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/ranking" title="Topic: ranking" data-view-component="true" class="topic-tag topic-tag-link"> ranking </a> <a href="/topics/learning-to-rank" title="Topic: learning-to-rank" data-view-component="true" class="topic-tag topic-tag-link"> learning-to-rank </a> <a href="/topics/recommender-systems" title="Topic: recommender-systems" data-view-component="true" class="topic-tag topic-tag-link"> recommender-systems </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository 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0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>94</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/tensorflow/ranking/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>478</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Ftensorflow%2Franking&report=tensorflow+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/tensorflow/ranking/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="18" data-view-component="true" class="Counter">18</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/tensorflow/ranking/releases/tag/v0.5.2"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">TensorFlow Ranking v0.5.2</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2023-02-01T02:48:12Z" class="no-wrap">Feb 1, 2023</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/tensorflow/ranking/releases" data-view-component="true" class="Link">+ 17 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/tensorflow/packages?repo_name=ranking" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/tensorflow/ranking/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/tensorflow/ranking/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="35" data-view-component="true" class="Counter ml-1">35</span></a></h2> <include-fragment src="/tensorflow/ranking/contributors_list?count=35&current_repository=ranking&items_to_show=14" aria-busy="true" aria-label="Loading contributors"> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> </ul> </include-fragment> <div data-view-component="true" class="mt-3"> <a text="small" href="/tensorflow/ranking/graphs/contributors" data-view-component="true" class="Link--inTextBlock Link">+ 21 contributors</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#3572A5 !important;;width: 91.3%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA5B0B !important;;width: 6.6%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#76d275 !important;;width: 2.0%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 0.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/ranking/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>91.3%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/ranking/search?l=jupyter-notebook" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#DA5B0B;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Jupyter Notebook</span> <span>6.6%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/ranking/search?l=starlark" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#76d275;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Starlark</span> <span>2.0%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/ranking/search?l=shell" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Shell</span> <span>0.1%</span> </a> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" class="footer-octicon mr-2" href="https://github.com"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" 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