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flex-justify-between flex-items-center width-full width-lg-auto"> <div class="flex-1"> <button aria-label="Toggle navigation" aria-expanded="false" type="button" data-view-component="true" class="js-details-target js-nav-padding-recalculate js-header-menu-toggle Button--link Button--medium Button d-lg-none color-fg-inherit p-1"> <span class="Button-content"> <span class="Button-label"><div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div></span> </span> </button> </div> <a class="mr-lg-3 color-fg-inherit flex-order-2 js-prevent-focus-on-mobile-nav" href="/" aria-label="Homepage" data-analytics-event="{&quot;category&quot;:&quot;Marketing nav&quot;,&quot;action&quot;:&quot;click to go to homepage&quot;,&quot;label&quot;:&quot;ref_page:Marketing;ref_cta:Logomark;ref_loc:Header&quot;}"> <svg height="32" aria-hidden="true" viewBox="0 0 24 24" version="1.1" width="32" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12.5.75C6.146.75 1 5.896 1 12.25c0 5.089 3.292 9.387 7.863 10.91.575.101.79-.244.79-.546 0-.273-.014-1.178-.014-2.142-2.889.532-3.636-.704-3.866-1.35-.13-.331-.69-1.352-1.18-1.625-.402-.216-.977-.748-.014-.762.906-.014 1.553.834 1.769 1.179 1.035 1.74 2.688 1.25 3.349.948.1-.747.402-1.25.733-1.538-2.559-.287-5.232-1.279-5.232-5.678 0-1.25.445-2.285 1.178-3.09-.115-.288-.517-1.467.115-3.048 0 0 .963-.302 3.163 1.179.92-.259 1.897-.388 2.875-.388.977 0 1.955.13 2.875.388 2.2-1.495 3.162-1.179 3.162-1.179.633 1.581.23 2.76.115 3.048.733.805 1.179 1.825 1.179 3.09 0 4.413-2.688 5.39-5.247 5.678.417.36.776 1.05.776 2.128 0 1.538-.014 2.774-.014 3.162 0 .302.216.662.79.547C20.709 21.637 24 17.324 24 12.25 24 5.896 18.854.75 12.5.75Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2Fgoogle-deepmind%2Fsynthid-text" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{&quot;event_type&quot;:&quot;authentication.click&quot;,&quot;payload&quot;:{&quot;location_in_page&quot;:&quot;site header menu&quot;,&quot;repository_id&quot;:null,&quot;auth_type&quot;:&quot;SIGN_UP&quot;,&quot;originating_url&quot;:&quot;https://github.com/google-deepmind/synthid-text&quot;,&quot;user_id&quot;:null}}" data-hydro-click-hmac="cd2fc4b9f7494f446912b8d3684cc74ced77a98a26fcad55e52632e08e9eb439" data-analytics-event="{&quot;category&quot;:&quot;Marketing nav&quot;,&quot;action&quot;:&quot;click to Sign in&quot;,&quot;label&quot;:&quot;ref_page:Marketing;ref_cta:Sign in;ref_loc:Header&quot;}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <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"> Product <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"> <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 pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;github_copilot&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;github_copilot_link_product_navbar&quot;}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 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0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;security&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;security_link_product_navbar&quot;}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;actions&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;actions_link_product_navbar&quot;}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 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1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;issues&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;issues_link_product_navbar&quot;}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;code_review&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;code_review_link_product_navbar&quot;}" href="https://github.com/features/code-review"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-review color-fg-subtle mr-3"> <path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;discussions&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;discussions_link_product_navbar&quot;}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </div> </a></li> <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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;code_search&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;code_search_link_product_navbar&quot;}" href="https://github.com/features/code-search"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;all_features&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;all_features_link_product_navbar&quot;}" href="https://github.com/features"> All features </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;documentation&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;documentation_link_product_navbar&quot;}" href="https://docs.github.com"> Documentation <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" 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 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<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> </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 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data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;startups&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;startups_link_solutions_navbar&quot;}" href="https://github.com/enterprise/startups"> Startups </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;nonprofits&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;nonprofits_link_solutions_navbar&quot;}" href="/solutions/industry/nonprofits"> Nonprofits </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-use-case-heading">By use case</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-use-case-heading"> <li> <a 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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 class="list-style-none f5" aria-labelledby="resources-topics-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;ai&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;ai_link_resources_navbar&quot;}" href="/resources/articles/ai"> AI </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;devops&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;devops_link_resources_navbar&quot;}" href="/resources/articles/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 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data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;collections&quot;,&quot;context&quot;:&quot;open_source&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;collections_link_open_source_navbar&quot;}" href="https://github.com/collections"> Collections </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"> Enterprise <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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;enterprise_platform&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;enterprise_platform_link_enterprise_navbar&quot;}" href="/enterprise"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-stack color-fg-subtle mr-3"> <path d="M11.063 1.456a1.749 1.749 0 0 1 1.874 0l8.383 5.316a1.751 1.751 0 0 1 0 2.956l-8.383 5.316a1.749 1.749 0 0 1-1.874 0L2.68 9.728a1.751 1.751 0 0 1 0-2.956Zm1.071 1.267a.25.25 0 0 0-.268 0L3.483 8.039a.25.25 0 0 0 0 .422l8.383 5.316a.25.25 0 0 0 .268 0l8.383-5.316a.25.25 0 0 0 0-.422Z"></path><path d="M1.867 12.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path><path d="M1.867 16.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path> </svg> <div> <div class="color-fg-default h4">Enterprise platform</div> AI-powered developer platform </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="enterprise-available-add-ons-heading">Available add-ons</span> <ul class="list-style-none f5" aria-labelledby="enterprise-available-add-ons-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;advanced_security&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;advanced_security_link_enterprise_navbar&quot;}" href="https://github.com/enterprise/advanced-security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Advanced Security</div> Enterprise-grade security features </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;github_copilot&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;github_copilot_link_enterprise_navbar&quot;}" href="/features/copilot#enterprise"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot 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2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Enterprise-grade AI features </div> </a></li> <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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;premium_support&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;premium_support_link_enterprise_navbar&quot;}" href="/premium-support"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Premium Support</div> Enterprise-grade 24/7 support </div> </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"> <a class="HeaderMenu-link no-underline px-0 px-lg-2 py-3 py-lg-2 d-block d-lg-inline-block" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;pricing&quot;,&quot;context&quot;:&quot;global&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;pricing_link_global_navbar&quot;}" href="https://github.com/pricing">Pricing</a> </li> </ul> </nav> <div class="d-flex flex-column flex-lg-row width-full flex-justify-end flex-lg-items-center 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dir=\"auto\"\u003eSynthID Text\u003c/h1\u003e\u003ca id=\"user-content-synthid-text\" class=\"anchor\" aria-label=\"Permalink: SynthID Text\" href=\"#synthid-text\"\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\"\u003eThis repository provides a reference implementation of the SynthID Text\nwatermarking and detection capabilities for the \u003ca href=\"https://doi.org/10.1038/s41586-024-08025-4\" rel=\"nofollow\"\u003eresearch paper\u003c/a\u003e\npublished in \u003cem\u003eNature\u003c/em\u003e. It is not intended for production use. The core library\nis \u003ca href=\"https://pypi.org/project/synthid-text/\" rel=\"nofollow\"\u003edistributed on PyPI\u003c/a\u003e for easy installation in the\n\u003ca href=\"https://colab.research.google.com/github/google-deepmind/synthid-text/blob/main/notebooks/synthid_text_huggingface_integration.ipynb\" rel=\"nofollow\"\u003ePython Notebook example\u003c/a\u003e, which demonstrates how to apply these\ntools with the \u003ca href=\"https://ai.google.dev/gemma/docs/model_card\" rel=\"nofollow\"\u003eGemma\u003c/a\u003e and \u003ca href=\"https://huggingface.co/openai-community/gpt2\" rel=\"nofollow\"\u003eGPT-2\u003c/a\u003e models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation and usage\u003c/h2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and usage\" href=\"#installation-and-usage\"\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 \u003ca href=\"https://colab.research.google.com/github/google-deepmind/synthid-text/blob/main/notebooks/synthid_text_huggingface_integration.ipynb\" rel=\"nofollow\"\u003eColab Notebook\u003c/a\u003e is self-contained reference implementation\nthat:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eExtends the \u003ca href=\"https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gemma/modeling_gemma.py#L996\"\u003e\u003ccode\u003eGemmaForCausalLM\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gpt2/modeling_gpt2.py#L1185\"\u003e\u003ccode\u003eGPT2LMHeadModel\u003c/code\u003e\u003c/a\u003e classes from\n\u003ca href=\"https://github.com/huggingface/transformers\"\u003eHugging Face Transformers\u003c/a\u003e with a \u003ca href=\"/google-deepmind/synthid-text/blob/main/src/synthid_text/synthid_mixin.py\"\u003emix-in\u003c/a\u003e to\nenable watermarking text content generated by models running in\n\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e; and\u003c/li\u003e\n\u003cli\u003eDetects the watermark. This can be done either with the simple \u003ca href=\"/google-deepmind/synthid-text/blob/main/src/synthid_text/detector_mean.py\"\u003eWeighted Mean\ndetector\u003c/a\u003e which requires no training, or with the\nmore powerful \u003ca href=\"/google-deepmind/synthid-text/blob/main/src/synthid_text/detector_bayesian.py\"\u003eBayesian detector\u003c/a\u003e that requires\n\u003ca href=\"/google-deepmind/synthid-text/blob/main/src/synthid_text/train_detector_bayesian.py\"\u003etraining\u003c/a\u003e. If using the \u003ca href=\"/google-deepmind/synthid-text/blob/main/src/synthid_text/detector_mean.py\"\u003eWeighted Mean\ndetector\u003c/a\u003e approach across texts of varying token lengths,\nwe recommend empirically/theoretically computing the thresholds at the desired\nfalse positives rate at specific token lengths, or using a weighted\nfrequentist approach as described in Appendix A.3.1.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eThe notebook is designed to be run end-to-end with either a Gemma or GPT-2\nmodel, and runs best on the following runtime hardware, some of which may\nrequire a \u003ca href=\"https://colab.research.google.com/signup\" rel=\"nofollow\"\u003eColab Subscription\u003c/a\u003e.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGemma v1.0 2B IT: Use a GPU with 16GB of memory, such as a T4.\u003c/li\u003e\n\u003cli\u003eGemma v1.0 7B IT: Use a GPU with 32GB of memory, such as an A100.\u003c/li\u003e\n\u003cli\u003eGPT-2: Any runtime will work, though a High-RAM CPU or any GPU will be\nfaster.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eNOTE: This implementation is for reference and research reproducibility purposes\nonly. Due to minor variations in Gemma and Mistral models across\nimplementations, we expect minor fluctuations in the detectability and\nperplexity results obtained from this repository versus those reported in the\npaper. The subclasses introduced herein are not designed to be used in\nproduction systems. Check out the official SynthID Text implementation in\n\u003ca href=\"/google-deepmind/synthid-text/blob/main/huggingface.co/blog/synthid-text\"\u003eHugging Face Transformers\u003c/a\u003e for a production-ready\nimplementation.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNOTE: The \u003ccode\u003esynthid_text.hashing_function.accumulate_hash()\u003c/code\u003e function, used while\ncomputing G values in this reference implementation, does not provide any\nguarantees of cryptographic security.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLocal notebook use\u003c/h3\u003e\u003ca id=\"user-content-local-notebook-use\" class=\"anchor\" aria-label=\"Permalink: Local notebook use\" href=\"#local-notebook-use\"\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 notebook can also be used locally if installed from source. Using a virtual\nenvironment is highly recommended for any local use.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Create and activate the virtual environment\npython3 -m venv ~/.venvs/synthid\nsource ~/.venvs/synthid/bin/activate\n\n# Download and install SynthID Text and Jupyter\ngit clone https://github.com/google-deepmind/synthid-text.git\ncd synthid-text\npip install '.[notebook-local]'\n\n# Start the Jupyter server\npython -m notebook\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create and activate the virtual environment\u003c/span\u003e\npython3 -m venv \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.venvs/synthid\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.venvs/synthid/bin/activate\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download and install SynthID Text and Jupyter\u003c/span\u003e\ngit clone https://github.com/google-deepmind/synthid-text.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e synthid-text\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e.[notebook-local]\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start the Jupyter server\u003c/span\u003e\npython -m notebook\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOnce your kernel is running navigate to .pynb file to execute.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRunning the tests\u003c/h3\u003e\u003ca id=\"user-content-running-the-tests\" class=\"anchor\" aria-label=\"Permalink: Running the tests\" href=\"#running-the-tests\"\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 source installation also includes a small test suite to verify that the\nlibrary is working as expected.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Create and activate the virtual environment\npython3 -m venv ~/.venvs/synthid\nsource ~/.venvs/synthid/bin/activate\n\n# Download and install SynthID Text with test dependencies from source\ngit clone https://github.com/google-deepmind/synthid-text.git\ncd synthid-text\npip install '.[test]'\n\n# Run the test suite\npytest .\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create and activate the virtual environment\u003c/span\u003e\npython3 -m venv \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.venvs/synthid\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.venvs/synthid/bin/activate\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download and install SynthID Text with test dependencies from source\u003c/span\u003e\ngit clone https://github.com/google-deepmind/synthid-text.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e synthid-text\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e.[test]\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run the test suite\u003c/span\u003e\npytest \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow it works\u003c/h2\u003e\u003ca id=\"user-content-how-it-works\" class=\"anchor\" aria-label=\"Permalink: How it works\" href=\"#how-it-works\"\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\"\u003eDefining a watermark configuration\u003c/h3\u003e\u003ca id=\"user-content-defining-a-watermark-configuration\" class=\"anchor\" aria-label=\"Permalink: Defining a watermark configuration\" href=\"#defining-a-watermark-configuration\"\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\"\u003eSynthID Text produces unique watermarks given a configuration, with the most\nimportant piece of these configurations being the \u003ccode\u003ekeys\u003c/code\u003e: a sequence of unique\nintegers where \u003ccode\u003elen(keys)\u003c/code\u003e corresponds to the number of layers in the\nwatermarking or detection models.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe structure of a configuration is described in the following \u003ccode\u003eTypedDict\u003c/code\u003e\nsubclass, though in practice, the \u003ca href=\"/google-deepmind/synthid-text/blob/main/src/synthid_text/synthid_mixin.py\"\u003emixin\u003c/a\u003e class in this library\nuses a static configuration.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from collections.abc import Sequence\nfrom typing import TypedDict\n\nimport torch\n\n\nclass WatermarkingConfig(TypedDict):\n ngram_len: int\n keys: Sequence[int]\n sampling_table_size: int\n sampling_table_seed: int\n context_history_size: int\n device: torch.device\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecollections\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eabc\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSequence\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etyping\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTypedDict\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\n\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eWatermarkingConfig\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eTypedDict\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003engram_len\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003eint\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003ekeys\u003c/span\u003e: \u003cspan class=\"pl-v\"\u003eSequence\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eint\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003esampling_table_size\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003eint\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003esampling_table_seed\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003eint\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003econtext_history_size\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003eint\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edevice\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eApplying a watermark\u003c/h3\u003e\u003ca id=\"user-content-applying-a-watermark\" class=\"anchor\" aria-label=\"Permalink: Applying a watermark\" href=\"#applying-a-watermark\"\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\"\u003eWatermarks are applied by a \u003ca href=\"/google-deepmind/synthid-text/blob/main/src/synthid_text/synthid_mixin.py\"\u003emix-in\u003c/a\u003e class that wraps the\n\u003ca href=\"https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gemma/modeling_gemma.py#L996\"\u003e\u003ccode\u003eGemmaForCausalLM\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gpt2/modeling_gpt2.py#L1185\"\u003e\u003ccode\u003eGPT2LMHeadModel\u003c/code\u003e\u003c/a\u003e classes from Transformers, which results\nin two subclasses with the same API that you are used to from Transformers.\nRemember that the mix-in provided by this library uses a static watermarking\nconfiguration, making it unsuitable for production use.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from synthid_text import synthid_mixin\nimport transformers\nimport torch\n\n\nDEVICE = (\n torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')\n)\nINPUTS = [\n \u0026quot;I enjoy walking with my cute dog\u0026quot;,\n \u0026quot;I am from New York\u0026quot;,\n \u0026quot;The test was not so very hard after all\u0026quot;,\n \u0026quot;I don't think they can score twice in so short a time\u0026quot;,\n]\nMODEL_NAME = 'google/gemma-2b-it'\nTEMPERATURE = 0.5\nTOP_K = 40\nTOP_P = 0.99\n\n# Initialize a standard tokenizer from Transformers.\ntokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_NAME)\n# Initialize a SynthID Text-enabled model.\nmodel = synthid_mixin.SynthIDGemmaForCausalLM.from_pretrained(\n MODEL_NAME,\n device_map='auto',\n torch_dtype=torch.bfloat16,\n)\n# Prepare your inputs in the usual way.\ninputs = tokenizer(\n INPUTS,\n return_tensors='pt',\n padding=True,\n).to(DEVICE)\n# Generate watermarked text.\noutputs = model.generate(\n **inputs,\n do_sample=True,\n max_length=1024,\n temperature=TEMPERATURE,\n top_k=TOP_K,\n top_p=TOP_P,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esynthid_text\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esynthid_mixin\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\n\n\n\u003cspan class=\"pl-c1\"\u003eDEVICE\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e (\n \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edevice\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'cuda:0'\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecuda\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eis_available\u003c/span\u003e() \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edevice\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'cpu'\u003c/span\u003e)\n)\n\u003cspan class=\"pl-c1\"\u003eINPUTS\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\n \u003cspan class=\"pl-s\"\u003e\"I enjoy walking with my cute dog\"\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"I am from New York\"\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"The test was not so very hard after all\"\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"I don't think they can score twice in so short a time\"\u003c/span\u003e,\n]\n\u003cspan class=\"pl-c1\"\u003eMODEL_NAME\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'google/gemma-2b-it'\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eTEMPERATURE\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eTOP_K\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e40\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eTOP_P\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.99\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Initialize a standard tokenizer from Transformers.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eAutoTokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003eMODEL_NAME\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Initialize a SynthID Text-enabled model.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esynthid_mixin\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSynthIDGemmaForCausalLM\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained\u003c/span\u003e(\n \u003cspan class=\"pl-c1\"\u003eMODEL_NAME\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003edevice_map\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'auto'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etorch_dtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebfloat16\u003c/span\u003e,\n)\n\u003cspan class=\"pl-c\"\u003e# Prepare your inputs in the usual way.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003etokenizer\u003c/span\u003e(\n \u003cspan class=\"pl-c1\"\u003eINPUTS\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ereturn_tensors\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'pt'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003epadding\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n).\u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003eDEVICE\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Generate watermarked text.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egenerate\u003c/span\u003e(\n \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003edo_sample\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1024\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etemperature\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTEMPERATURE\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etop_k\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTOP_K\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etop_p\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTOP_P\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDetecting a watermark\u003c/h3\u003e\u003ca id=\"user-content-detecting-a-watermark\" class=\"anchor\" aria-label=\"Permalink: Detecting a watermark\" href=\"#detecting-a-watermark\"\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\"\u003eWatermark detection can be done using a variety of scoring functions (see\npaper). This repository contains code for the Mean, Weighted Mean, and Bayesian\nscoring functions described in the paper. The colab contains examples for how\nto use these scoring functions.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe Bayesian detector must be trained on watermarked and unwatermarked data\nbefore it can be used. The Bayesian detector must be trained for each unique\nwatermarking key, and the training data used for this detector model should be\nindependent from, but representative of the expected character and quality of\nthe text content the system will generate in production.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import jax.numpy as jnp\nfrom synthid_text import train_detector_bayesian\n\n\ndef load_data():\n # Get your training and test data into the system.\n pass\n\n\ndef process_training_data(split):\n # Get the G values, masks, and labels for the provided split.\n pass\n\n\ntrain_split, test_split = load_data()\ntrain_g_values, train_masks, train_labels = process_training_data(train_split)\ntest_g_values, test_masks, test_labels = process_training_data(test_split)\n\ndetector, loss = train_detector_bayesian.optimize_model(\n jnp.squeeze(train_g_values),\n jnp.squeeze(train_masks),\n jnp.squeeze(train_labels),\n jnp.squeeze(test_g_values),\n jnp.squeeze(test_masks),\n jnp.squeeze(test_labels),\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejax\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enumpy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejnp\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esynthid_text\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrain_detector_bayesian\u003c/span\u003e\n\n\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eload_data\u003c/span\u003e():\n \u003cspan class=\"pl-c\"\u003e# Get your training and test data into the system.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\n\n\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eprocess_training_data\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esplit\u003c/span\u003e):\n \u003cspan class=\"pl-c\"\u003e# Get the G values, masks, and labels for the provided split.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\n\n\n\u003cspan class=\"pl-s1\"\u003etrain_split\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etest_split\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eload_data\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003etrain_g_values\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrain_masks\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrain_labels\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eprocess_training_data\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_split\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003etest_g_values\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etest_masks\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etest_labels\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eprocess_training_data\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etest_split\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003edetector\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrain_detector_bayesian\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eoptimize_model\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003ejnp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esqueeze\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_g_values\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003ejnp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esqueeze\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_masks\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003ejnp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esqueeze\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_labels\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003ejnp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esqueeze\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etest_g_values\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003ejnp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esqueeze\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etest_masks\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003ejnp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esqueeze\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etest_labels\u003c/span\u003e),\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOnce the Bayesian detector is trained, use the \u003ccode\u003edetector.score()\u003c/code\u003e function to\ngenerate a per-example score indicating if the text was generated with the given\nwatermarking configuration. Score values will be between 0 and 1, with scores\ncloser to 1 indicating higher likelihood that the text was generated with the\ngiven watermark. You can adjust the acceptance threshold to your needs.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from synthid_text import logits_processing\n\n\nCONFIG = synthid_mixin.DEFAULT_WATERMARKING_CONFIG\n\nlogits_processor = logits_processing.SynthIDLogitsProcessor(\n **CONFIG, top_k=TOP_K, temperature=TEMPERATURE\n)\n\n# Get only the generated text from the models predictions.\noutputs = outputs[:, inputs_len:]\n\n# Compute the end-of-sequence mask, skipping first ngram_len - 1 tokens\n# \u0026lt;bool\u0026gt;[batch_size, output_len]\neos_token_mask = logits_processor.compute_eos_token_mask(\n input_ids=outputs,\n eos_token_id=tokenizer.eos_token_id,\n)[:, CONFIG['ngram_len'] - 1 :]\n# Compute the context repetition mask\n# \u0026lt;bool\u0026gt;[batch_size, output_len - (ngram_len - 1)]\ncontext_repetition_mask = logits_processor.compute_context_repetition_mask(\n input_ids=outputs\n)\n\n# Compute the mask that isolates the generated text.\ncombined_mask = context_repetition_mask * eos_token_mask\n# Compute the G values for the generated text.\ng_values = logits_processor.compute_g_values(input_ids=outputs)\n\n# Score the G values, given the combined mask, and output a per-example score\n# indicating whether the\ndetector.score(g_values.cpu().numpy(), combined_mask.cpu().numpy())\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esynthid_text\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogits_processing\u003c/span\u003e\n\n\n\u003cspan class=\"pl-c1\"\u003eCONFIG\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esynthid_mixin\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eDEFAULT_WATERMARKING_CONFIG\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003elogits_processor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogits_processing\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSynthIDLogitsProcessor\u003c/span\u003e(\n \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eCONFIG\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etop_k\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTOP_K\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etemperature\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTEMPERATURE\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e# Get only the generated text from the models predictions.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[:, \u003cspan class=\"pl-s1\"\u003einputs_len\u003c/span\u003e:]\n\n\u003cspan class=\"pl-c\"\u003e# Compute the end-of-sequence mask, skipping first ngram_len - 1 tokens\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# \u0026lt;bool\u0026gt;[batch_size, output_len]\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eeos_token_mask\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogits_processor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecompute_eos_token_mask\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003einput_ids\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eeos_token_id\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eeos_token_id\u003c/span\u003e,\n)[:, \u003cspan class=\"pl-c1\"\u003eCONFIG\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'ngram_len'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e :]\n\u003cspan class=\"pl-c\"\u003e# Compute the context repetition mask\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# \u0026lt;bool\u0026gt;[batch_size, output_len - (ngram_len - 1)]\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econtext_repetition_mask\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogits_processor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecompute_context_repetition_mask\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003einput_ids\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e# Compute the mask that isolates the generated text.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ecombined_mask\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003econtext_repetition_mask\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eeos_token_mask\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Compute the G values for the generated text.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eg_values\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogits_processor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecompute_g_values\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einput_ids\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Score the G values, given the combined mask, and output a per-example score\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# indicating whether the\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edetector\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003escore\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eg_values\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecpu\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003enumpy\u003c/span\u003e(), \u003cspan class=\"pl-s1\"\u003ecombined_mask\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecpu\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003enumpy\u003c/span\u003e())\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHuman Data\u003c/h2\u003e\u003ca id=\"user-content-human-data\" class=\"anchor\" aria-label=\"Permalink: Human Data\" href=\"#human-data\"\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 release the human evaluation data, where we compare watermarked text against unwatermarked text generated from the Gemma 7B model.\nThe data is located in \u003ccode\u003edata/human_eval.jsonl\u003c/code\u003e.\nTo get the prompts used for generating the responses, please use the following code.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import json\nimport tensorflow_datasets as tfds\n\nds = tfds.load('huggingface:eli5/LFQA_reddit', split='test_eli5')\nid_to_prompt = {}\nfor x in ds.as_numpy_iterator():\n id_to_prompt[x['q_id'].decode()] = x['title'].decode()\n\nfull_data = []\nwith open('./data/human_eval.jsonl') as f:\n for json_str in f:\n x = json.loads(json_str)\n x['question'] = id_to_prompt[x['q_id']]\n full_data.append(x)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etensorflow_datasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etfds\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eds\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etfds\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eload\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'huggingface:eli5/LFQA_reddit'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esplit\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'test_eli5'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eid_to_prompt\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {}\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eds\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eas_numpy_iterator\u003c/span\u003e():\n \u003cspan class=\"pl-s1\"\u003eid_to_prompt\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'q_id'\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003edecode\u003c/span\u003e()] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'title'\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003edecode\u003c/span\u003e()\n\n\u003cspan class=\"pl-s1\"\u003efull_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e []\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'./data/human_eval.jsonl'\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejson_str\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eloads\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ejson_str\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'question'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eid_to_prompt\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'q_id'\u003c/span\u003e]]\n \u003cspan class=\"pl-s1\"\u003efull_data\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCiting this work\u003c/h2\u003e\u003ca id=\"user-content-citing-this-work\" class=\"anchor\" aria-label=\"Permalink: Citing this work\" href=\"#citing-this-work\"\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=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@article{Dathathri2024,\n author={Dathathri, Sumanth and See, Abigail and Ghaisas, Sumedh and Huang, Po-Sen and McAdam, Rob and Welbl, Johannes and Bachani, Vandana and Kaskasoli, Alex and Stanforth, Robert and Matejovicova, Tatiana and Hayes, Jamie and Vyas, Nidhi and Merey, Majd Al and Brown-Cohen, Jonah and Bunel, Rudy and Balle, Borja and Cemgil, Taylan and Ahmed, Zahra and Stacpoole, Kitty and Shumailov, Ilia and Baetu, Ciprian and Gowal, Sven and Hassabis, Demis and Kohli, Pushmeet},\n title={Scalable watermarking for identifying large language model outputs},\n journal={Nature},\n year={2024},\n month={Oct},\n day={01},\n volume={634},\n number={8035},\n pages={818-823},\n issn={1476-4687},\n doi={10.1038/s41586-024-08025-4},\n url={https://doi.org/10.1038/s41586-024-08025-4}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@article\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eDathathri2024\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eDathathri, Sumanth and See, Abigail and Ghaisas, Sumedh and Huang, Po-Sen and McAdam, Rob and Welbl, Johannes and Bachani, Vandana and Kaskasoli, Alex and Stanforth, Robert and Matejovicova, Tatiana and Hayes, Jamie and Vyas, Nidhi and Merey, Majd Al and Brown-Cohen, Jonah and Bunel, Rudy and Balle, Borja and Cemgil, Taylan and Ahmed, Zahra and Stacpoole, Kitty and Shumailov, Ilia and Baetu, Ciprian and Gowal, Sven and Hassabis, Demis and Kohli, Pushmeet\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eScalable watermarking for identifying large language model outputs\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ejournal\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eNature\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2024\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003emonth\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eOct\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eday\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e01\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003evolume\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e634\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003enumber\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e8035\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epages\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e818-823\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eissn\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e1476-4687\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003edoi\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e10.1038/s41586-024-08025-4\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eurl\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ehttps://doi.org/10.1038/s41586-024-08025-4\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense and disclaimer\u003c/h2\u003e\u003ca id=\"user-content-license-and-disclaimer\" class=\"anchor\" aria-label=\"Permalink: License and disclaimer\" href=\"#license-and-disclaimer\"\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\"\u003eCopyright 2024 DeepMind Technologies Limited\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAll software is licensed under the Apache License, Version 2.0 (Apache 2.0);\nyou may not use this file except in compliance with the Apache 2.0 license.\nYou may obtain a copy of the Apache 2.0 license at:\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003ehttps://www.apache.org/licenses/LICENSE-2.0\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAll other materials are licensed under the Creative Commons Attribution 4.0\nInternational License (CC-BY). You may obtain a copy of the CC-BY license at:\n\u003ca href=\"https://creativecommons.org/licenses/by/4.0/legalcode\" rel=\"nofollow\"\u003ehttps://creativecommons.org/licenses/by/4.0/legalcode\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUnless required by applicable law or agreed to in writing, all software and\nmaterials distributed here under the Apache 2.0 or CC-BY licenses are\ndistributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\neither express or implied. See the licenses for the specific language governing\npermissions and limitations under those licenses.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis is not an official Google product.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"SynthID Text","anchor":"synthid-text","htmlText":"SynthID Text"},{"level":2,"text":"Installation and usage","anchor":"installation-and-usage","htmlText":"Installation and usage"},{"level":3,"text":"Local notebook use","anchor":"local-notebook-use","htmlText":"Local notebook use"},{"level":3,"text":"Running the tests","anchor":"running-the-tests","htmlText":"Running the tests"},{"level":2,"text":"How it works","anchor":"how-it-works","htmlText":"How it works"},{"level":3,"text":"Defining a watermark configuration","anchor":"defining-a-watermark-configuration","htmlText":"Defining a watermark configuration"},{"level":3,"text":"Applying a watermark","anchor":"applying-a-watermark","htmlText":"Applying a watermark"},{"level":3,"text":"Detecting a watermark","anchor":"detecting-a-watermark","htmlText":"Detecting a watermark"},{"level":2,"text":"Human Data","anchor":"human-data","htmlText":"Human Data"},{"level":2,"text":"Citing this work","anchor":"citing-this-work","htmlText":"Citing this work"},{"level":2,"text":"License and disclaimer","anchor":"license-and-disclaimer","htmlText":"License and 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LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text" 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1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 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">SynthID Text</h1><a id="user-content-synthid-text" class="anchor" aria-label="Permalink: SynthID Text" href="#synthid-text"><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">This repository provides a reference implementation of the SynthID Text watermarking and detection capabilities for the <a href="https://doi.org/10.1038/s41586-024-08025-4" rel="nofollow">research paper</a> published in <em>Nature</em>. It is not intended for production use. The core library is <a href="https://pypi.org/project/synthid-text/" rel="nofollow">distributed on PyPI</a> for easy installation in the <a href="https://colab.research.google.com/github/google-deepmind/synthid-text/blob/main/notebooks/synthid_text_huggingface_integration.ipynb" rel="nofollow">Python Notebook example</a>, which demonstrates how to apply these tools with the <a href="https://ai.google.dev/gemma/docs/model_card" rel="nofollow">Gemma</a> and <a href="https://huggingface.co/openai-community/gpt2" rel="nofollow">GPT-2</a> models.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation and usage</h2><a id="user-content-installation-and-usage" class="anchor" aria-label="Permalink: Installation and usage" href="#installation-and-usage"><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 <a href="https://colab.research.google.com/github/google-deepmind/synthid-text/blob/main/notebooks/synthid_text_huggingface_integration.ipynb" rel="nofollow">Colab Notebook</a> is self-contained reference implementation that:</p> <ol dir="auto"> <li>Extends the <a href="https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gemma/modeling_gemma.py#L996"><code>GemmaForCausalLM</code></a> and <a href="https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gpt2/modeling_gpt2.py#L1185"><code>GPT2LMHeadModel</code></a> classes from <a href="https://github.com/huggingface/transformers">Hugging Face Transformers</a> with a <a href="/google-deepmind/synthid-text/blob/main/src/synthid_text/synthid_mixin.py">mix-in</a> to enable watermarking text content generated by models running in <a href="https://pytorch.org/" rel="nofollow">PyTorch</a>; and</li> <li>Detects the watermark. This can be done either with the simple <a href="/google-deepmind/synthid-text/blob/main/src/synthid_text/detector_mean.py">Weighted Mean detector</a> which requires no training, or with the more powerful <a href="/google-deepmind/synthid-text/blob/main/src/synthid_text/detector_bayesian.py">Bayesian detector</a> that requires <a href="/google-deepmind/synthid-text/blob/main/src/synthid_text/train_detector_bayesian.py">training</a>. If using the <a href="/google-deepmind/synthid-text/blob/main/src/synthid_text/detector_mean.py">Weighted Mean detector</a> approach across texts of varying token lengths, we recommend empirically/theoretically computing the thresholds at the desired false positives rate at specific token lengths, or using a weighted frequentist approach as described in Appendix A.3.1.</li> </ol> <p dir="auto">The notebook is designed to be run end-to-end with either a Gemma or GPT-2 model, and runs best on the following runtime hardware, some of which may require a <a href="https://colab.research.google.com/signup" rel="nofollow">Colab Subscription</a>.</p> <ul dir="auto"> <li>Gemma v1.0 2B IT: Use a GPU with 16GB of memory, such as a T4.</li> <li>Gemma v1.0 7B IT: Use a GPU with 32GB of memory, such as an A100.</li> <li>GPT-2: Any runtime will work, though a High-RAM CPU or any GPU will be faster.</li> </ul> <p dir="auto">NOTE: This implementation is for reference and research reproducibility purposes only. Due to minor variations in Gemma and Mistral models across implementations, we expect minor fluctuations in the detectability and perplexity results obtained from this repository versus those reported in the paper. The subclasses introduced herein are not designed to be used in production systems. Check out the official SynthID Text implementation in <a href="/google-deepmind/synthid-text/blob/main/huggingface.co/blog/synthid-text">Hugging Face Transformers</a> for a production-ready implementation.</p> <p dir="auto">NOTE: The <code>synthid_text.hashing_function.accumulate_hash()</code> function, used while computing G values in this reference implementation, does not provide any guarantees of cryptographic security.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Local notebook use</h3><a id="user-content-local-notebook-use" class="anchor" aria-label="Permalink: Local notebook use" href="#local-notebook-use"><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 notebook can also be used locally if installed from source. Using a virtual environment is highly recommended for any local use.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Create and activate the virtual environment python3 -m venv ~/.venvs/synthid source ~/.venvs/synthid/bin/activate # Download and install SynthID Text and Jupyter git clone https://github.com/google-deepmind/synthid-text.git cd synthid-text pip install '.[notebook-local]' # Start the Jupyter server python -m notebook"><pre><span class="pl-c"><span class="pl-c">#</span> Create and activate the virtual environment</span> python3 -m venv <span class="pl-k">~</span>/.venvs/synthid <span class="pl-c1">source</span> <span class="pl-k">~</span>/.venvs/synthid/bin/activate <span class="pl-c"><span class="pl-c">#</span> Download and install SynthID Text and Jupyter</span> git clone https://github.com/google-deepmind/synthid-text.git <span class="pl-c1">cd</span> synthid-text pip install <span class="pl-s"><span class="pl-pds">'</span>.[notebook-local]<span class="pl-pds">'</span></span> <span class="pl-c"><span class="pl-c">#</span> Start the Jupyter server</span> python -m notebook</pre></div> <p dir="auto">Once your kernel is running navigate to .pynb file to execute.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Running the tests</h3><a id="user-content-running-the-tests" class="anchor" aria-label="Permalink: Running the tests" href="#running-the-tests"><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 source installation also includes a small test suite to verify that the library is working as expected.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Create and activate the virtual environment python3 -m venv ~/.venvs/synthid source ~/.venvs/synthid/bin/activate # Download and install SynthID Text with test dependencies from source git clone https://github.com/google-deepmind/synthid-text.git cd synthid-text pip install '.[test]' # Run the test suite pytest ."><pre><span class="pl-c"><span class="pl-c">#</span> Create and activate the virtual environment</span> python3 -m venv <span class="pl-k">~</span>/.venvs/synthid <span class="pl-c1">source</span> <span class="pl-k">~</span>/.venvs/synthid/bin/activate <span class="pl-c"><span class="pl-c">#</span> Download and install SynthID Text with test dependencies from source</span> git clone https://github.com/google-deepmind/synthid-text.git <span class="pl-c1">cd</span> synthid-text pip install <span class="pl-s"><span class="pl-pds">'</span>.[test]<span class="pl-pds">'</span></span> <span class="pl-c"><span class="pl-c">#</span> Run the test suite</span> pytest <span class="pl-c1">.</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How it works</h2><a id="user-content-how-it-works" class="anchor" aria-label="Permalink: How it works" href="#how-it-works"><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">Defining a watermark configuration</h3><a id="user-content-defining-a-watermark-configuration" class="anchor" aria-label="Permalink: Defining a watermark configuration" href="#defining-a-watermark-configuration"><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">SynthID Text produces unique watermarks given a configuration, with the most important piece of these configurations being the <code>keys</code>: a sequence of unique integers where <code>len(keys)</code> corresponds to the number of layers in the watermarking or detection models.</p> <p dir="auto">The structure of a configuration is described in the following <code>TypedDict</code> subclass, though in practice, the <a href="/google-deepmind/synthid-text/blob/main/src/synthid_text/synthid_mixin.py">mixin</a> class in this library uses a static configuration.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from collections.abc import Sequence from typing import TypedDict import torch class WatermarkingConfig(TypedDict): ngram_len: int keys: Sequence[int] sampling_table_size: int sampling_table_seed: int context_history_size: int device: torch.device"><pre><span class="pl-k">from</span> <span class="pl-s1">collections</span>.<span class="pl-s1">abc</span> <span class="pl-k">import</span> <span class="pl-v">Sequence</span> <span class="pl-k">from</span> <span class="pl-s1">typing</span> <span class="pl-k">import</span> <span class="pl-v">TypedDict</span> <span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-k">class</span> <span class="pl-v">WatermarkingConfig</span>(<span class="pl-v">TypedDict</span>): <span class="pl-s1">ngram_len</span>: <span class="pl-smi">int</span> <span class="pl-s1">keys</span>: <span class="pl-v">Sequence</span>[<span class="pl-smi">int</span>] <span class="pl-s1">sampling_table_size</span>: <span class="pl-smi">int</span> <span class="pl-s1">sampling_table_seed</span>: <span class="pl-smi">int</span> <span class="pl-s1">context_history_size</span>: <span class="pl-smi">int</span> <span class="pl-s1">device</span>: <span class="pl-s1">torch</span>.<span class="pl-c1">device</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Applying a watermark</h3><a id="user-content-applying-a-watermark" class="anchor" aria-label="Permalink: Applying a watermark" href="#applying-a-watermark"><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">Watermarks are applied by a <a href="/google-deepmind/synthid-text/blob/main/src/synthid_text/synthid_mixin.py">mix-in</a> class that wraps the <a href="https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gemma/modeling_gemma.py#L996"><code>GemmaForCausalLM</code></a> and <a href="https://github.com/huggingface/transformers/blob/e55b33ceb4b0ba3c8c11f20b6e8d6ca4b48246d4/src/transformers/models/gpt2/modeling_gpt2.py#L1185"><code>GPT2LMHeadModel</code></a> classes from Transformers, which results in two subclasses with the same API that you are used to from Transformers. Remember that the mix-in provided by this library uses a static watermarking configuration, making it unsuitable for production use.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from synthid_text import synthid_mixin import transformers import torch DEVICE = ( torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') ) INPUTS = [ &quot;I enjoy walking with my cute dog&quot;, &quot;I am from New York&quot;, &quot;The test was not so very hard after all&quot;, &quot;I don't think they can score twice in so short a time&quot;, ] MODEL_NAME = 'google/gemma-2b-it' TEMPERATURE = 0.5 TOP_K = 40 TOP_P = 0.99 # Initialize a standard tokenizer from Transformers. tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_NAME) # Initialize a SynthID Text-enabled model. model = synthid_mixin.SynthIDGemmaForCausalLM.from_pretrained( MODEL_NAME, device_map='auto', torch_dtype=torch.bfloat16, ) # Prepare your inputs in the usual way. inputs = tokenizer( INPUTS, return_tensors='pt', padding=True, ).to(DEVICE) # Generate watermarked text. outputs = model.generate( **inputs, do_sample=True, max_length=1024, temperature=TEMPERATURE, top_k=TOP_K, top_p=TOP_P, )"><pre><span class="pl-k">from</span> <span class="pl-s1">synthid_text</span> <span class="pl-k">import</span> <span class="pl-s1">synthid_mixin</span> <span class="pl-k">import</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-c1">DEVICE</span> <span class="pl-c1">=</span> ( <span class="pl-s1">torch</span>.<span class="pl-c1">device</span>(<span class="pl-s">'cuda:0'</span>) <span class="pl-k">if</span> <span class="pl-s1">torch</span>.<span class="pl-c1">cuda</span>.<span class="pl-c1">is_available</span>() <span class="pl-k">else</span> <span class="pl-s1">torch</span>.<span class="pl-c1">device</span>(<span class="pl-s">'cpu'</span>) ) <span class="pl-c1">INPUTS</span> <span class="pl-c1">=</span> [ <span class="pl-s">"I enjoy walking with my cute dog"</span>, <span class="pl-s">"I am from New York"</span>, <span class="pl-s">"The test was not so very hard after all"</span>, <span class="pl-s">"I don't think they can score twice in so short a time"</span>, ] <span class="pl-c1">MODEL_NAME</span> <span class="pl-c1">=</span> <span class="pl-s">'google/gemma-2b-it'</span> <span class="pl-c1">TEMPERATURE</span> <span class="pl-c1">=</span> <span class="pl-c1">0.5</span> <span class="pl-c1">TOP_K</span> <span class="pl-c1">=</span> <span class="pl-c1">40</span> <span class="pl-c1">TOP_P</span> <span class="pl-c1">=</span> <span class="pl-c1">0.99</span> <span class="pl-c"># Initialize a standard tokenizer from Transformers.</span> <span class="pl-s1">tokenizer</span> <span class="pl-c1">=</span> <span class="pl-s1">transformers</span>.<span class="pl-c1">AutoTokenizer</span>.<span class="pl-c1">from_pretrained</span>(<span class="pl-c1">MODEL_NAME</span>) <span class="pl-c"># Initialize a SynthID Text-enabled model.</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-s1">synthid_mixin</span>.<span class="pl-c1">SynthIDGemmaForCausalLM</span>.<span class="pl-c1">from_pretrained</span>( <span class="pl-c1">MODEL_NAME</span>, <span class="pl-s1">device_map</span><span class="pl-c1">=</span><span class="pl-s">'auto'</span>, <span class="pl-s1">torch_dtype</span><span class="pl-c1">=</span><span class="pl-s1">torch</span>.<span class="pl-c1">bfloat16</span>, ) <span class="pl-c"># Prepare your inputs in the usual way.</span> <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-en">tokenizer</span>( <span class="pl-c1">INPUTS</span>, <span class="pl-s1">return_tensors</span><span class="pl-c1">=</span><span class="pl-s">'pt'</span>, <span class="pl-s1">padding</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, ).<span class="pl-c1">to</span>(<span class="pl-c1">DEVICE</span>) <span class="pl-c"># Generate watermarked text.</span> <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">model</span>.<span class="pl-c1">generate</span>( <span class="pl-c1">**</span><span class="pl-s1">inputs</span>, <span class="pl-s1">do_sample</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">max_length</span><span class="pl-c1">=</span><span class="pl-c1">1024</span>, <span class="pl-s1">temperature</span><span class="pl-c1">=</span><span class="pl-c1">TEMPERATURE</span>, <span class="pl-s1">top_k</span><span class="pl-c1">=</span><span class="pl-c1">TOP_K</span>, <span class="pl-s1">top_p</span><span class="pl-c1">=</span><span class="pl-c1">TOP_P</span>, )</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Detecting a watermark</h3><a id="user-content-detecting-a-watermark" class="anchor" aria-label="Permalink: Detecting a watermark" href="#detecting-a-watermark"><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">Watermark detection can be done using a variety of scoring functions (see paper). This repository contains code for the Mean, Weighted Mean, and Bayesian scoring functions described in the paper. The colab contains examples for how to use these scoring functions.</p> <p dir="auto">The Bayesian detector must be trained on watermarked and unwatermarked data before it can be used. The Bayesian detector must be trained for each unique watermarking key, and the training data used for this detector model should be independent from, but representative of the expected character and quality of the text content the system will generate in production.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import jax.numpy as jnp from synthid_text import train_detector_bayesian def load_data(): # Get your training and test data into the system. pass def process_training_data(split): # Get the G values, masks, and labels for the provided split. pass train_split, test_split = load_data() train_g_values, train_masks, train_labels = process_training_data(train_split) test_g_values, test_masks, test_labels = process_training_data(test_split) detector, loss = train_detector_bayesian.optimize_model( jnp.squeeze(train_g_values), jnp.squeeze(train_masks), jnp.squeeze(train_labels), jnp.squeeze(test_g_values), jnp.squeeze(test_masks), jnp.squeeze(test_labels), )"><pre><span class="pl-k">import</span> <span class="pl-s1">jax</span>.<span class="pl-s1">numpy</span> <span class="pl-k">as</span> <span class="pl-s1">jnp</span> <span class="pl-k">from</span> <span class="pl-s1">synthid_text</span> <span class="pl-k">import</span> <span class="pl-s1">train_detector_bayesian</span> <span class="pl-k">def</span> <span class="pl-en">load_data</span>(): <span class="pl-c"># Get your training and test data into the system.</span> <span class="pl-k">pass</span> <span class="pl-k">def</span> <span class="pl-en">process_training_data</span>(<span class="pl-s1">split</span>): <span class="pl-c"># Get the G values, masks, and labels for the provided split.</span> <span class="pl-k">pass</span> <span class="pl-s1">train_split</span>, <span class="pl-s1">test_split</span> <span class="pl-c1">=</span> <span class="pl-en">load_data</span>() <span class="pl-s1">train_g_values</span>, <span class="pl-s1">train_masks</span>, <span class="pl-s1">train_labels</span> <span class="pl-c1">=</span> <span class="pl-en">process_training_data</span>(<span class="pl-s1">train_split</span>) <span class="pl-s1">test_g_values</span>, <span class="pl-s1">test_masks</span>, <span class="pl-s1">test_labels</span> <span class="pl-c1">=</span> <span class="pl-en">process_training_data</span>(<span class="pl-s1">test_split</span>) <span class="pl-s1">detector</span>, <span class="pl-s1">loss</span> <span class="pl-c1">=</span> <span class="pl-s1">train_detector_bayesian</span>.<span class="pl-c1">optimize_model</span>( <span class="pl-s1">jnp</span>.<span class="pl-c1">squeeze</span>(<span class="pl-s1">train_g_values</span>), <span class="pl-s1">jnp</span>.<span class="pl-c1">squeeze</span>(<span class="pl-s1">train_masks</span>), <span class="pl-s1">jnp</span>.<span class="pl-c1">squeeze</span>(<span class="pl-s1">train_labels</span>), <span class="pl-s1">jnp</span>.<span class="pl-c1">squeeze</span>(<span class="pl-s1">test_g_values</span>), <span class="pl-s1">jnp</span>.<span class="pl-c1">squeeze</span>(<span class="pl-s1">test_masks</span>), <span class="pl-s1">jnp</span>.<span class="pl-c1">squeeze</span>(<span class="pl-s1">test_labels</span>), )</pre></div> <p dir="auto">Once the Bayesian detector is trained, use the <code>detector.score()</code> function to generate a per-example score indicating if the text was generated with the given watermarking configuration. Score values will be between 0 and 1, with scores closer to 1 indicating higher likelihood that the text was generated with the given watermark. You can adjust the acceptance threshold to your needs.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from synthid_text import logits_processing CONFIG = synthid_mixin.DEFAULT_WATERMARKING_CONFIG logits_processor = logits_processing.SynthIDLogitsProcessor( **CONFIG, top_k=TOP_K, temperature=TEMPERATURE ) # Get only the generated text from the models predictions. outputs = outputs[:, inputs_len:] # Compute the end-of-sequence mask, skipping first ngram_len - 1 tokens # &lt;bool&gt;[batch_size, output_len] eos_token_mask = logits_processor.compute_eos_token_mask( input_ids=outputs, eos_token_id=tokenizer.eos_token_id, )[:, CONFIG['ngram_len'] - 1 :] # Compute the context repetition mask # &lt;bool&gt;[batch_size, output_len - (ngram_len - 1)] context_repetition_mask = logits_processor.compute_context_repetition_mask( input_ids=outputs ) # Compute the mask that isolates the generated text. combined_mask = context_repetition_mask * eos_token_mask # Compute the G values for the generated text. g_values = logits_processor.compute_g_values(input_ids=outputs) # Score the G values, given the combined mask, and output a per-example score # indicating whether the detector.score(g_values.cpu().numpy(), combined_mask.cpu().numpy())"><pre><span class="pl-k">from</span> <span class="pl-s1">synthid_text</span> <span class="pl-k">import</span> <span class="pl-s1">logits_processing</span> <span class="pl-c1">CONFIG</span> <span class="pl-c1">=</span> <span class="pl-s1">synthid_mixin</span>.<span class="pl-c1">DEFAULT_WATERMARKING_CONFIG</span> <span class="pl-s1">logits_processor</span> <span class="pl-c1">=</span> <span class="pl-s1">logits_processing</span>.<span class="pl-c1">SynthIDLogitsProcessor</span>( <span class="pl-c1">**</span><span class="pl-c1">CONFIG</span>, <span class="pl-s1">top_k</span><span class="pl-c1">=</span><span class="pl-c1">TOP_K</span>, <span class="pl-s1">temperature</span><span class="pl-c1">=</span><span class="pl-c1">TEMPERATURE</span> ) <span class="pl-c"># Get only the generated text from the models predictions.</span> <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">outputs</span>[:, <span class="pl-s1">inputs_len</span>:] <span class="pl-c"># Compute the end-of-sequence mask, skipping first ngram_len - 1 tokens</span> <span class="pl-c"># &lt;bool&gt;[batch_size, output_len]</span> <span class="pl-s1">eos_token_mask</span> <span class="pl-c1">=</span> <span class="pl-s1">logits_processor</span>.<span class="pl-c1">compute_eos_token_mask</span>( <span class="pl-s1">input_ids</span><span class="pl-c1">=</span><span class="pl-s1">outputs</span>, <span class="pl-s1">eos_token_id</span><span class="pl-c1">=</span><span class="pl-s1">tokenizer</span>.<span class="pl-c1">eos_token_id</span>, )[:, <span class="pl-c1">CONFIG</span>[<span class="pl-s">'ngram_len'</span>] <span class="pl-c1">-</span> <span class="pl-c1">1</span> :] <span class="pl-c"># Compute the context repetition mask</span> <span class="pl-c"># &lt;bool&gt;[batch_size, output_len - (ngram_len - 1)]</span> <span class="pl-s1">context_repetition_mask</span> <span class="pl-c1">=</span> <span class="pl-s1">logits_processor</span>.<span class="pl-c1">compute_context_repetition_mask</span>( <span class="pl-s1">input_ids</span><span class="pl-c1">=</span><span class="pl-s1">outputs</span> ) <span class="pl-c"># Compute the mask that isolates the generated text.</span> <span class="pl-s1">combined_mask</span> <span class="pl-c1">=</span> <span class="pl-s1">context_repetition_mask</span> <span class="pl-c1">*</span> <span class="pl-s1">eos_token_mask</span> <span class="pl-c"># Compute the G values for the generated text.</span> <span class="pl-s1">g_values</span> <span class="pl-c1">=</span> <span class="pl-s1">logits_processor</span>.<span class="pl-c1">compute_g_values</span>(<span class="pl-s1">input_ids</span><span class="pl-c1">=</span><span class="pl-s1">outputs</span>) <span class="pl-c"># Score the G values, given the combined mask, and output a per-example score</span> <span class="pl-c"># indicating whether the</span> <span class="pl-s1">detector</span>.<span class="pl-c1">score</span>(<span class="pl-s1">g_values</span>.<span class="pl-c1">cpu</span>().<span class="pl-c1">numpy</span>(), <span class="pl-s1">combined_mask</span>.<span class="pl-c1">cpu</span>().<span class="pl-c1">numpy</span>())</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Human Data</h2><a id="user-content-human-data" class="anchor" aria-label="Permalink: Human Data" href="#human-data"><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 release the human evaluation data, where we compare watermarked text against unwatermarked text generated from the Gemma 7B model. The data is located in <code>data/human_eval.jsonl</code>. To get the prompts used for generating the responses, please use the following code.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import json import tensorflow_datasets as tfds ds = tfds.load('huggingface:eli5/LFQA_reddit', split='test_eli5') id_to_prompt = {} for x in ds.as_numpy_iterator(): id_to_prompt[x['q_id'].decode()] = x['title'].decode() full_data = [] with open('./data/human_eval.jsonl') as f: for json_str in f: x = json.loads(json_str) x['question'] = id_to_prompt[x['q_id']] full_data.append(x)"><pre><span class="pl-k">import</span> <span class="pl-s1">json</span> <span class="pl-k">import</span> <span class="pl-s1">tensorflow_datasets</span> <span class="pl-k">as</span> <span class="pl-s1">tfds</span> <span class="pl-s1">ds</span> <span class="pl-c1">=</span> <span class="pl-s1">tfds</span>.<span class="pl-c1">load</span>(<span class="pl-s">'huggingface:eli5/LFQA_reddit'</span>, <span class="pl-s1">split</span><span class="pl-c1">=</span><span class="pl-s">'test_eli5'</span>) <span class="pl-s1">id_to_prompt</span> <span class="pl-c1">=</span> {} <span class="pl-k">for</span> <span class="pl-s1">x</span> <span class="pl-c1">in</span> <span class="pl-s1">ds</span>.<span class="pl-c1">as_numpy_iterator</span>(): <span class="pl-s1">id_to_prompt</span>[<span class="pl-s1">x</span>[<span class="pl-s">'q_id'</span>].<span class="pl-c1">decode</span>()] <span class="pl-c1">=</span> <span class="pl-s1">x</span>[<span class="pl-s">'title'</span>].<span class="pl-c1">decode</span>() <span class="pl-s1">full_data</span> <span class="pl-c1">=</span> [] <span class="pl-k">with</span> <span class="pl-en">open</span>(<span class="pl-s">'./data/human_eval.jsonl'</span>) <span class="pl-k">as</span> <span class="pl-s1">f</span>: <span class="pl-k">for</span> <span class="pl-s1">json_str</span> <span class="pl-c1">in</span> <span class="pl-s1">f</span>: <span class="pl-s1">x</span> <span class="pl-c1">=</span> <span class="pl-s1">json</span>.<span class="pl-c1">loads</span>(<span class="pl-s1">json_str</span>) <span class="pl-s1">x</span>[<span class="pl-s">'question'</span>] <span class="pl-c1">=</span> <span class="pl-s1">id_to_prompt</span>[<span class="pl-s1">x</span>[<span class="pl-s">'q_id'</span>]] <span class="pl-s1">full_data</span>.<span class="pl-c1">append</span>(<span class="pl-s1">x</span>)</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citing this work</h2><a id="user-content-citing-this-work" class="anchor" aria-label="Permalink: Citing this work" href="#citing-this-work"><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="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@article{Dathathri2024, author={Dathathri, Sumanth and See, Abigail and Ghaisas, Sumedh and Huang, Po-Sen and McAdam, Rob and Welbl, Johannes and Bachani, Vandana and Kaskasoli, Alex and Stanforth, Robert and Matejovicova, Tatiana and Hayes, Jamie and Vyas, Nidhi and Merey, Majd Al and Brown-Cohen, Jonah and Bunel, Rudy and Balle, Borja and Cemgil, Taylan and Ahmed, Zahra and Stacpoole, Kitty and Shumailov, Ilia and Baetu, Ciprian and Gowal, Sven and Hassabis, Demis and Kohli, Pushmeet}, title={Scalable watermarking for identifying large language model outputs}, journal={Nature}, year={2024}, month={Oct}, day={01}, volume={634}, number={8035}, pages={818-823}, issn={1476-4687}, doi={10.1038/s41586-024-08025-4}, url={https://doi.org/10.1038/s41586-024-08025-4} }"><pre><span class="pl-k">@article</span>{<span class="pl-en">Dathathri2024</span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Dathathri, Sumanth and See, Abigail and Ghaisas, Sumedh and Huang, Po-Sen and McAdam, Rob and Welbl, Johannes and Bachani, Vandana and Kaskasoli, Alex and Stanforth, Robert and Matejovicova, Tatiana and Hayes, Jamie and Vyas, Nidhi and Merey, Majd Al and Brown-Cohen, Jonah and Bunel, Rudy and Balle, Borja and Cemgil, Taylan and Ahmed, Zahra and Stacpoole, Kitty and Shumailov, Ilia and Baetu, Ciprian and Gowal, Sven and Hassabis, Demis and Kohli, Pushmeet<span class="pl-pds">}</span></span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>Scalable watermarking for identifying large language model outputs<span class="pl-pds">}</span></span>, <span class="pl-s">journal</span>=<span class="pl-s"><span class="pl-pds">{</span>Nature<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2024<span class="pl-pds">}</span></span>, <span class="pl-s">month</span>=<span class="pl-s"><span class="pl-pds">{</span>Oct<span class="pl-pds">}</span></span>, <span class="pl-s">day</span>=<span class="pl-s"><span class="pl-pds">{</span>01<span class="pl-pds">}</span></span>, <span class="pl-s">volume</span>=<span class="pl-s"><span class="pl-pds">{</span>634<span class="pl-pds">}</span></span>, <span class="pl-s">number</span>=<span class="pl-s"><span class="pl-pds">{</span>8035<span class="pl-pds">}</span></span>, <span class="pl-s">pages</span>=<span class="pl-s"><span class="pl-pds">{</span>818-823<span class="pl-pds">}</span></span>, <span class="pl-s">issn</span>=<span class="pl-s"><span class="pl-pds">{</span>1476-4687<span class="pl-pds">}</span></span>, <span class="pl-s">doi</span>=<span class="pl-s"><span class="pl-pds">{</span>10.1038/s41586-024-08025-4<span class="pl-pds">}</span></span>, <span class="pl-s">url</span>=<span class="pl-s"><span class="pl-pds">{</span>https://doi.org/10.1038/s41586-024-08025-4<span class="pl-pds">}</span></span> }</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">License and disclaimer</h2><a id="user-content-license-and-disclaimer" class="anchor" aria-label="Permalink: License and disclaimer" href="#license-and-disclaimer"><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">Copyright 2024 DeepMind Technologies Limited</p> <p dir="auto">All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: <a href="https://www.apache.org/licenses/LICENSE-2.0" rel="nofollow">https://www.apache.org/licenses/LICENSE-2.0</a></p> <p dir="auto">All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" rel="nofollow">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p dir="auto">Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.</p> <p dir="auto">This is not an official Google product.</p> </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="e9u7jvf9MQAGzL8jEeM2jnEcGp2l3Oc5vAXa7JWxMsfnDSz4PyILoYnv0Mlhc8mg7CdNCx8NYXQc9LTFVBuhug==" /> </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> <div class="f4 my-3 color-fg-muted text-italic"> No description, website, or topics provided. </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{&quot;category&quot;:&quot;Repository 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class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 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>18</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/google-deepmind/synthid-text/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" 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