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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="{"category":"Marketing nav","action":"click to go to homepage","label":"ref_page:Marketing;ref_cta:Logomark;ref_loc:Header"}"> <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 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2FPaddlePaddle%2FERNIE" 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="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/PaddlePaddle/ERNIE","user_id":null}}" data-hydro-click-hmac="550b6f7390a247878660e0d30e1d5142f37b68a3ca7e60189bc4cb094392b0c0" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > 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="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" 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 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 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> 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="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" 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 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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 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </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="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 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="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" 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="{"location":"navbar","action":"code_review","context":"product","tag":"link","label":"code_review_link_product_navbar"}" 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="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" 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 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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="{"location":"navbar","action":"all_features","context":"product","tag":"link","label":"all_features_link_product_navbar"}" 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="{"location":"navbar","action":"documentation","context":"product","tag":"link","label":"documentation_link_product_navbar"}" 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 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <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 .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 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button","repository_id":173544440,"auth_type":"LOG_IN","originating_url":"https://github.com/PaddlePaddle/ERNIE","user_id":null}}" data-hydro-click-hmac="d9aa7e6267b9efca62e79ab1487e6d822ddd4d7a13e069fab3cc40233ea33d5c" aria-label="You must be signed in to star a repository" data-view-component="true" class="tooltipped tooltipped-sw btn-sm btn"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star v-align-text-bottom d-inline-block mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 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1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <span class="text-bold">1.3k</span> forks </a> <a class="Link--secondary no-underline mr-3 d-inline-block" href="/PaddlePaddle/ERNIE/branches"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-git-branch mr-1"> <path d="M9.5 3.25a2.25 2.25 0 1 1 3 2.122V6A2.5 2.5 0 0 1 10 8.5H6a1 1 0 0 0-1 1v1.128a2.251 2.251 0 1 1-1.5 0V5.372a2.25 2.25 0 1 1 1.5 0v1.836A2.493 2.493 0 0 1 6 7h4a1 1 0 0 0 1-1v-.628A2.25 2.25 0 0 1 9.5 3.25Zm-6 0a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Zm8.25-.75a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5ZM4.25 12a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Z"></path> </svg> <span>Branches</span> </a> <a class="Link--secondary no-underline d-inline-block" href="/PaddlePaddle/ERNIE/tags"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag mr-1"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <span>Tags</span> </a> <a class="Link--secondary no-underline d-inline-block" href="/PaddlePaddle/ERNIE/activity"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pulse mr-1"> <path d="M6 2c.306 0 .582.187.696.471L10 10.731l1.304-3.26A.751.751 0 0 1 12 7h3.25a.75.75 0 0 1 0 1.5h-2.742l-1.812 4.528a.751.751 0 0 1-1.392 0L6 4.77 4.696 8.03A.75.75 0 0 1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span>Activity</span> </a> </div> 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6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg><span data-view-component="true" class="d-inline"> Star </span> </a></div> </div> <div class="flex-1"> <a href="/login?return_to=%2FPaddlePaddle%2FERNIE" rel="nofollow" id="files-overview-watch-button" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"notification subscription menu watch","repository_id":null,"auth_type":"LOG_IN","originating_url":"https://github.com/PaddlePaddle/ERNIE","user_id":null}}" data-hydro-click-hmac="5643503e01dea2054fbf52b66a6314ed9bdca0a46e8f45e7adb2e4e5160425fd" aria-label="You must be signed in to change notification settings" data-view-component="true" class="btn-sm btn btn-block"> <svg aria-hidden="true" height="16" 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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":173544440,"defaultBranch":"ernie-kit-open-v1.0","name":"ERNIE","ownerLogin":"PaddlePaddle","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2019-03-03T07:31:29.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/23534030?v=4","public":true,"private":false,"isOrgOwned":true},"currentUser":null,"refInfo":{"name":"ernie-kit-open-v1.0","listCacheKey":"v0:1725062987.0","canEdit":false,"refType":"branch","currentOid":"03d886406f507ec478e342212f70fda919f27b4f"},"tree":{"items":[{"name":".github","path":".github","contentType":"directory"},{"name":".metas","path":".metas","contentType":"directory"},{"name":"Research","path":"Research","contentType":"directory"},{"name":"applications","path":"applications","contentType":"directory"},{"name":"erniekit","path":"erniekit","contentType":"directory"},{"name":".gitignore","path":".gitignore","contentType":"file"},{"name":".pre-commit-config.yaml","path":".pre-commit-config.yaml","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"README_ENV.md","path":"README_ENV.md","contentType":"file"},{"name":"README_SOCRE.md","path":"README_SOCRE.md","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":10,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/PaddlePaddle/ERNIE/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/PaddlePaddle/ERNIE.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/.metas/ERNIE.png\"\u003e\u003cimg src=\"/PaddlePaddle/ERNIE/raw/ernie-kit-open-v1.0/.metas/ERNIE.png\" alt=\"ERNIE_milestone_20210519_zh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#\"\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\"\u003e文心大模型ERNIE是百度发布的产业级知识增强大模型，涵盖了NLP大模型和跨模态大模型。2019年3月，开源了国内首个开源预训练模型文心ERNIE 1.0，此后在语言与跨模态的理解和生成等领域取得一系列技术突破，并对外开源与开放了系列模型，助力大模型研究与产业化应用发展。提醒: ERNIE老版本代码已经迁移至repro分支，欢迎使用我们全新升级的基于动静结合的新版ERNIE套件进行开发。另外，也欢迎上\u003ca href=\"https://ai.baidu.com/easydl/pro\" rel=\"nofollow\"\u003eEasyDL\u003c/a\u003e、\u003ca href=\"https://ai.baidu.com/bml/app/overview\" rel=\"nofollow\"\u003eBML\u003c/a\u003e体验更丰富的功能。\n\u003ca href=\"https://wenxin.baidu.com/\" rel=\"nofollow\"\u003e【了解更多】\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e开源Roadmap\u003c/h1\u003e\u003ca id=\"user-content-开源roadmap\" class=\"anchor\" aria-label=\"Permalink: 开源Roadmap\" href=\"#开源roadmap\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2022.8.18:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e图文跨模态预训练模型\u003ccode\u003eERNIE-ViL 2.0 (base)\u003c/code\u003e \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/ernie-kit-open-v1.0/Research/ERNIE-ViL2\"\u003e正式开源\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2022.5.20:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e最新开源ERNIE 3.0系列预训练模型:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e110M参数通用模型ERNIE 3.0 Base\u003c/li\u003e\n\u003cli\u003e280M参数重量级通用模型ERNIE 3.0 XBase\u003c/li\u003e\n\u003cli\u003e74M轻量级通用模型ERNIE 3.0 Medium\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e新增语音-语言跨模态模型ERNIE-SAT \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-sat\"\u003e正式开源\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e新增ERNIE-Gen（中文）预训练模型，支持多类主流生成任务：主要包括摘要、问题生成、对话、问答\u003c/li\u003e\n\u003cli\u003e动静结合的文心ERNIE开发套件：基于飞桨动态图功能，支持文心ERNIE模型动态图训练。您仅需要在模型训练开启前，修改一个参数配置，即可实现模型训练的动静切换。\u003c/li\u003e\n\u003cli\u003e将文本预处理、预训练模型、网络搭建、模型评估、上线部署等NLP开发流程规范封装。\u003c/li\u003e\n\u003cli\u003e支持NLP常用任务：文本分类、文本匹配、序列标注、信息抽取、文本生成、数据蒸馏等。\u003c/li\u003e\n\u003cli\u003e提供数据清洗、数据增强、分词、格式转换、大小写转换等数据预处理工具。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2021.12.3:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e多语言预训练模型\u003ccode\u003eERNIE-M\u003c/code\u003e \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-m\"\u003e正式开源\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2021.5.20:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eERNIE 最新开源四大预训练模型:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e多粒度语言知识模型\u003ccode\u003eERNIE-Gram\u003c/code\u003e \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/blob/develop/ernie-gram\"\u003e正式开源\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e超长文本双向建模预训练模型\u003ccode\u003eERNIE-Doc\u003c/code\u003e \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-doc\"\u003e正式开源\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e融合场景图知识的跨模态预训练模型教程\u003ccode\u003eERNIE-ViL\u003c/code\u003e \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-vil\"\u003e正式开源\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e语言与视觉一体的预训练模型\u003ccode\u003eERNIE-UNIMO\u003c/code\u003e \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-unimo\"\u003e正式开源\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2020.9.24:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eERNIE-ViL\u003c/code\u003e 技术发布! (\u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-vil\"\u003e点击进入\u003c/a\u003e)\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e面向视觉-语言知识增强的预训练框架，首次在视觉-语言预训练引入结构化的知识。\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e利用场景图中的知识，构建了物体、属性和关系预测任务，精细刻画模态间细粒度语义对齐。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e五项视觉-语言下游任务取得最好效果，\u003ca href=\"https://visualcommonsense.com/\" rel=\"nofollow\"\u003e视觉常识推理榜单\u003c/a\u003e取得第一。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2020.5.20:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eERNIE-GEN\u003c/code\u003e 模型正式开源! (\u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-gen\"\u003e点击进入\u003c/a\u003e)\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e最强文本生成预训练模型正式开源，相关工作已被 \u003ccode\u003eIJCAI-2020\u003c/code\u003e 收录。\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e首次把 ERNIE 预训练技术能力扩展至文本生成领域，在多个典型任务上取得最佳。\u003c/li\u003e\n\u003cli\u003e您现在即可下载论文报告的所有模型（包含 \u003ca href=\"https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-gen/README.zh.md#%E9%A2%84%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B\"\u003ebase/large/large-430G\u003c/a\u003e）。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e首次在预训练阶段加入span-by-span 生成任务，让模型每次能够生成一个语义完整的片段。\u003c/li\u003e\n\u003cli\u003e提出填充式生成机制和噪声感知机制来缓解曝光偏差问题。\u003c/li\u003e\n\u003cli\u003e精巧的 Mulit-Flow Attention 实现框架。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2020.4.30 发布\u003ca href=\"https://github.com/PaddlePaddle/PGL/tree/master/examples/erniesage\"\u003eERNIESage\u003c/a\u003e，一种新型图神经网络模型，采用ERNIE做为aggreagtor. 由\u003ca href=\"https://github.com/PaddlePaddle/PGL\"\u003ePGL\u003c/a\u003e实现。\u003c/li\u003e\n\u003cli\u003e2020.3.27 \u003ca href=\"https://www.jiqizhixin.com/articles/2020-03-27-8\" rel=\"nofollow\"\u003e在SemEval2020五项子任务上夺冠\u003c/a\u003e。\u003c/li\u003e\n\u003cli\u003e2019.12.26 \u003ca href=\"https://www.technologyreview.com/2019/12/26/131372/ai-baidu-ernie-google-bert-natural-language-glue/\" rel=\"nofollow\"\u003eGLUE榜第一名\u003c/a\u003e。\u003c/li\u003e\n\u003cli\u003e2019.11.6 发布\u003ca href=\"https://www.jiqizhixin.com/articles/2019-11-06-9\" rel=\"nofollow\"\u003eERNIE Tiny\u003c/a\u003e。\u003c/li\u003e\n\u003cli\u003e2019.7.7 发布\u003ca href=\"https://www.jiqizhixin.com/articles/2019-07-31-10\" rel=\"nofollow\"\u003eERNIE 2.0\u003c/a\u003e。\u003c/li\u003e\n\u003cli\u003e2019.3.16 发布\u003ca href=\"https://www.jiqizhixin.com/articles/2019-03-16-3\" rel=\"nofollow\"\u003eERNIE 1.0\u003c/a\u003e。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e环境安装\u003c/h1\u003e\u003ca id=\"user-content-环境安装\" class=\"anchor\" aria-label=\"Permalink: 环境安装\" href=\"#环境安装\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e安装环境依赖：\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/README_ENV.md\"\u003e环境安装\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e安装Ernie套件\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/PaddlePaddle/ERNIE.git\"\u003e\u003cpre lang=\"plain\" class=\"notranslate\"\u003e\u003ccode\u003egit clone https://github.com/PaddlePaddle/ERNIE.git\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e快速上手：使用文心ERNIE大模型进行训练\u003c/h1\u003e\u003ca id=\"user-content-快速上手使用文心ernie大模型进行训练\" class=\"anchor\" aria-label=\"Permalink: 快速上手：使用文心ERNIE大模型进行训练\" href=\"#快速上手使用文心ernie大模型进行训练\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e使用ERNIE3.0作为预训练模型，准备工作包括：\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e下载模型\u003c/li\u003e\n\u003cli\u003e准备数据\u003c/li\u003e\n\u003cli\u003e配置训练json文件\u003c/li\u003e\n\u003cli\u003e启动训练模型\u003c/li\u003e\n\u003cli\u003e配置预测json文件\u003c/li\u003e\n\u003cli\u003e启动预测\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e我们以文本分类任务为例，来快速上手ERNIE大模型的使用\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e下载模型\u003c/h2\u003e\u003ca id=\"user-content-下载模型\" class=\"anchor\" aria-label=\"Permalink: 下载模型\" href=\"#下载模型\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e使用ERNIE3.0预训练模型进行文本分类任务\u003c/li\u003e\n\u003cli\u003eERNNIE3.0预训练模型的下载与配置\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"# ernie_3.0 模型下载\n# 进入models_hub目录\ncd ./applications/models_hub\n# 运行下载脚本\nsh download_ernie_3.0_base_ch.sh\"\u003e\u003cpre lang=\"plain\" class=\"notranslate\"\u003e\u003ccode\u003e# ernie_3.0 模型下载\n# 进入models_hub目录\ncd ./applications/models_hub\n# 运行下载脚本\nsh download_ernie_3.0_base_ch.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e准备数据\u003c/h2\u003e\u003ca id=\"user-content-准备数据\" class=\"anchor\" aria-label=\"Permalink: 准备数据\" href=\"#准备数据\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e文心各个任务的data目录下自带一些示例数据，能够实现直接使用，方便快速熟悉文心的使用。\u003c/li\u003e\n\u003cli\u003e文本分类任务的数据\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#进入文本分类任务文件夹\ncd ./applications/tasks/text_classification/\n#查看文本分类任务自带数据集\nls ./data\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e进入文本分类任务文件夹\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./applications/tasks/text_classification/\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e查看文本分类任务自带数据集\u003c/span\u003e\nls ./data\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e注：示例数据仅作为格式演示使用，在真正训练模型时请替换为真实数据。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e配置训练json文件\u003c/h2\u003e\u003ca id=\"user-content-配置训练json文件\" class=\"anchor\" aria-label=\"Permalink: 配置训练json文件\" href=\"#配置训练json文件\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e其预置json文件在./examples/目录下，使用ERNIE3.0预训练模型进行训练的配置文件为的./examples/cls_ernie_fc_ch.json，在该json文件中对数据、模型、训练方式等逻辑进行了配置。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#查看 ERNIE3.0预训练模型训练文本分类任务的配置文件\ncat ./examples/cls_ernie_fc_ch.json\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e查看 ERNIE3.0预训练模型训练文本分类任务的配置文件\u003c/span\u003e\ncat ./examples/cls_ernie_fc_ch.json\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e启动训练\u003c/h2\u003e\u003ca id=\"user-content-启动训练\" class=\"anchor\" aria-label=\"Permalink: 启动训练\" href=\"#启动训练\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e将数据集存放妥当，并配置好cls_ernie_fc_ch.json，我们就可以运行模型训练的命令。\u003c/li\u003e\n\u003cli\u003e其中，单卡指令为\u003ccode\u003epython run_trainer.py\u003c/code\u003e，如下所示，使用基于ernie的中文文本分类模型在训练集上进行本地模型训练。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ernie 中文文本分类模型\n# 基于json实现预置网络训练。其调用了配置文件./examples/cls_ernie_fc_ch.json\npython run_trainer.py --param_path ./examples/cls_ernie_fc_ch.json\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ernie 中文文本分类模型\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 基于json实现预置网络训练。其调用了配置文件./examples/cls_ernie_fc_ch.json\u003c/span\u003e\npython run_trainer.py --param_path ./examples/cls_ernie_fc_ch.json\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e多卡指令为:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"fleetrun --gpus=x,y run_trainer.py./examples/cls_ernie_fc_ch.json\"\u003e\u003cpre lang=\"plain\" class=\"notranslate\"\u003e\u003ccode\u003efleetrun --gpus=x,y run_trainer.py./examples/cls_ernie_fc_ch.json\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e训练运行的日志会自动保存在**./log/test.log**文件中。\u003c/li\u003e\n\u003cli\u003e训练中以及结束后产生的模型文件会默认保存在./output/\u003cstrong\u003e目录下，其中\u003c/strong\u003esave_inference_model/文件夹会保存用于预测的模型文件，\u003cstrong\u003esave_checkpoint/\u003c/strong\u003e 文件夹会保存用于热启动的模型文件。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e配置预测json文件\u003c/h2\u003e\u003ca id=\"user-content-配置预测json文件\" class=\"anchor\" aria-label=\"Permalink: 配置预测json文件\" href=\"#配置预测json文件\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e其预置json文件在./examples/目录下，使用ERNIE2.0预训练模型训练的模型进行预测的配置文件为的./examples/cls_ernie_fc_ch_infer.json\u003c/li\u003e\n\u003cli\u003e主要修改./examples/cls_ernie_fc_ch_infer.json文件的预测模型的输入路径、预测文件的输入路径、预测结果的输出路径，对应修改配置如下：\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"{\n\u0026quot;dataset_reader\u0026quot;:{\u0026quot;train_reader\u0026quot;:{\u0026quot;config\u0026quot;:{\u0026quot;data_path\u0026quot;:\u0026quot;./data/predict_data\u0026quot;}}},\n\u0026quot;inference\u0026quot;:{\u0026quot;inference_model_path\u0026quot;:\u0026quot;./output/cls_ernie_fc_ch/save_inference_model/inference_step_251\u0026quot;,\n \u0026quot;output_path\u0026quot;: \u0026quot;./output/predict_result.txt\u0026quot;}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e{\n\"dataset_reader\":{\"train_reader\":{\"config\":{\"data_path\":\"./data/predict_data\"}}},\n\"inference\":{\"inference_model_path\":\"./output/cls_ernie_fc_ch/save_inference_model/inference_step_251\",\n \"output_path\": \"./output/predict_result.txt\"}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e启动预测\u003c/h2\u003e\u003ca id=\"user-content-启动预测\" class=\"anchor\" aria-label=\"Permalink: 启动预测\" href=\"#启动预测\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e运行run_infer.py ，选择对应的参数配置文件即可。如下所示：\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python run_infer.py --param_path ./examples/cls_ernie_fc_ch_infer.json\"\u003e\u003cpre lang=\"plain\" class=\"notranslate\"\u003e\u003ccode\u003epython run_infer.py --param_path ./examples/cls_ernie_fc_ch_infer.json\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e预测过程中的日志自动保存在./output/predict_result.txt文件中。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e预训练模型介绍\u003c/h1\u003e\u003ca id=\"user-content-预训练模型介绍\" class=\"anchor\" aria-label=\"Permalink: 预训练模型介绍\" href=\"#预训练模型介绍\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e参考预训练模型原理介绍:\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/models_hub\"\u003e模型介绍\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e预训练模型下载：进入./applications/models_hub目录下,下载示例：\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"#进入预训练模型下载目录\ncd ./applications/models_hub\n#下载ERNIE3.0 base模型\nsh downlaod_ernie_3.0_base_ch.sh\"\u003e\u003cpre lang=\"plain\" class=\"notranslate\"\u003e\u003ccode\u003e#进入预训练模型下载目录\ncd ./applications/models_hub\n#下载ERNIE3.0 base模型\nsh downlaod_ernie_3.0_base_ch.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e更多开源模型，见\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/Research\"\u003eResearch\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e数据集下载\u003c/h1\u003e\u003ca id=\"user-content-数据集下载\" class=\"anchor\" aria-label=\"Permalink: 数据集下载\" href=\"#数据集下载\"\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\"\u003e\u003ca href=\"https://www.cluebenchmarks.com/\" rel=\"nofollow\"\u003eCLUE数据集\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://www.luge.ai/#/luge/dataDetail?id=5\" rel=\"nofollow\"\u003eDuIE2.0数据集\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://ernie-github.cdn.bcebos.com/data-msra_ner.tar.gz\" rel=\"nofollow\"\u003eMSRA_NER数据集\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e模型效果评估\u003c/h1\u003e\u003ca id=\"user-content-模型效果评估\" class=\"anchor\" aria-label=\"Permalink: 模型效果评估\" href=\"#模型效果评估\"\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\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e评估数据集\u003c/h2\u003e\u003ca id=\"user-content-评估数据集\" class=\"anchor\" aria-label=\"Permalink: 评估数据集\" href=\"#评估数据集\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e分类和匹配采用\u003ca href=\"https://www.cluebenchmarks.com/\" rel=\"nofollow\"\u003eCLUE数据集\u003c/a\u003e。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCLUE 评测结果:\u003c/h2\u003e\u003ca id=\"user-content-clue-评测结果\" class=\"anchor\" aria-label=\"Permalink: CLUE 评测结果:\" href=\"#clue-评测结果\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e配置\u003c/th\u003e\n\u003cth\u003e模型\u003c/th\u003e\n\u003cth\u003eCLUEWSC2020\u003c/th\u003e\n\u003cth\u003eIFLYTEK\u003c/th\u003e\n\u003cth\u003eTNEWS\u003c/th\u003e\n\u003cth\u003eAFQMC\u003c/th\u003e\n\u003cth\u003eCMNLI\u003c/th\u003e\n\u003cth\u003eCSL\u003c/th\u003e\n\u003cth\u003eOCNLI\u003c/th\u003e\n\u003cth\u003e平均值\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e24L1024H\u003c/td\u003e\n\u003ctd\u003eRoBERTa-wwm-ext-large\u003c/td\u003e\n\u003ctd\u003e90.79\u003c/td\u003e\n\u003ctd\u003e62.02\u003c/td\u003e\n\u003ctd\u003e59.33\u003c/td\u003e\n\u003ctd\u003e76.00\u003c/td\u003e\n\u003ctd\u003e83.88\u003c/td\u003e\n\u003ctd\u003e83.67\u003c/td\u003e\n\u003ctd\u003e78.81\u003c/td\u003e\n\u003ctd\u003e76.36\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e20L1024H\u003c/td\u003e\n\u003ctd\u003eERNIE 3.0-XBase\u003c/td\u003e\n\u003ctd\u003e91.12\u003c/td\u003e\n\u003ctd\u003e62.22\u003c/td\u003e\n\u003ctd\u003e60.34\u003c/td\u003e\n\u003ctd\u003e76.95\u003c/td\u003e\n\u003ctd\u003e84.98\u003c/td\u003e\n\u003ctd\u003e84.27\u003c/td\u003e\n\u003ctd\u003e82.07\u003c/td\u003e\n\u003ctd\u003e77.42\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12L768H\u003c/td\u003e\n\u003ctd\u003eRoBERTa-wwm-ext-base\u003c/td\u003e\n\u003ctd\u003e88.55\u003c/td\u003e\n\u003ctd\u003e61.22\u003c/td\u003e\n\u003ctd\u003e58.08\u003c/td\u003e\n\u003ctd\u003e74.75\u003c/td\u003e\n\u003ctd\u003e81.66\u003c/td\u003e\n\u003ctd\u003e81.63\u003c/td\u003e\n\u003ctd\u003e77.25\u003c/td\u003e\n\u003ctd\u003e74.73\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12L768H\u003c/td\u003e\n\u003ctd\u003eERNIE 3.0-Base\u003c/td\u003e\n\u003ctd\u003e88.18\u003c/td\u003e\n\u003ctd\u003e60.72\u003c/td\u003e\n\u003ctd\u003e58.73\u003c/td\u003e\n\u003ctd\u003e76.53\u003c/td\u003e\n\u003ctd\u003e83.65\u003c/td\u003e\n\u003ctd\u003e83.30\u003c/td\u003e\n\u003ctd\u003e80.31\u003c/td\u003e\n\u003ctd\u003e75.63\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e6L768H\u003c/td\u003e\n\u003ctd\u003eRBT6, Chinese\u003c/td\u003e\n\u003ctd\u003e75.00\u003c/td\u003e\n\u003ctd\u003e59.68\u003c/td\u003e\n\u003ctd\u003e56.62\u003c/td\u003e\n\u003ctd\u003e73.15\u003c/td\u003e\n\u003ctd\u003e79.26\u003c/td\u003e\n\u003ctd\u003e80.04\u003c/td\u003e\n\u003ctd\u003e73.15\u003c/td\u003e\n\u003ctd\u003e70.99\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e6L768H\u003c/td\u003e\n\u003ctd\u003eERNIE 3.0-Medium\u003c/td\u003e\n\u003ctd\u003e79.93\u003c/td\u003e\n\u003ctd\u003e60.14\u003c/td\u003e\n\u003ctd\u003e57.16\u003c/td\u003e\n\u003ctd\u003e74.56\u003c/td\u003e\n\u003ctd\u003e80.87\u003c/td\u003e\n\u003ctd\u003e81.23\u003c/td\u003e\n\u003ctd\u003e77.02\u003c/td\u003e\n\u003ctd\u003e72.99\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003cstrong\u003e具体评测方式\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-具体评测方式\" class=\"anchor\" aria-label=\"Permalink: 具体评测方式\" href=\"#具体评测方式\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e以上所有任务均基于 Grid Search 方式进行超参寻优。分类任务训练每间隔 100 steps 评估验证集效果，取验证集最优效果作为表格中的汇报指标。\u003c/li\u003e\n\u003cli\u003e分类任务 Grid Search 超参范围: batch_size: 16, 32, 64; learning rates: 1e-5, 2e-5, 3e-5, 5e-5；因为 CLUEWSC2020 数据集较小，所以模型在该数据集上的效果对 batch_size 较敏感，所以对 CLUEWSC2020 评测时额外增加了 batch_size = 8 的超参搜索；因为CLUEWSC2020 和 IFLYTEK 数据集对 dropout 概率值较为敏感，所以对 CLUEWSC2020 和 IFLYTEK 数据集评测时增加dropout_prob = 0.0 的超参搜索。\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e下游任务的固定超参配置\u003c/h2\u003e\u003ca id=\"user-content-下游任务的固定超参配置\" class=\"anchor\" aria-label=\"Permalink: 下游任务的固定超参配置\" href=\"#下游任务的固定超参配置\"\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\"\u003e\u003cstrong\u003e分类和匹配任务:\u003c/strong\u003e\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTASK\u003c/th\u003e\n\u003cth\u003eAFQMC\u003c/th\u003e\n\u003cth\u003eTNEWS\u003c/th\u003e\n\u003cth\u003eIFLYTEK\u003c/th\u003e\n\u003cth\u003eCMNLI\u003c/th\u003e\n\u003cth\u003eOCNLI\u003c/th\u003e\n\u003cth\u003eCLUEWSC2020\u003c/th\u003e\n\u003cth\u003eCSL\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eepoch\u003c/td\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003e5\u003c/td\u003e\n\u003ctd\u003e50\u003c/td\u003e\n\u003ctd\u003e5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emax_seq_length\u003c/td\u003e\n\u003ctd\u003e128\u003c/td\u003e\n\u003ctd\u003e128\u003c/td\u003e\n\u003ctd\u003e128\u003c/td\u003e\n\u003ctd\u003e128\u003c/td\u003e\n\u003ctd\u003e128\u003c/td\u003e\n\u003ctd\u003e128\u003c/td\u003e\n\u003ctd\u003e256\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewarmup_proportion\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE模型Grid Search 最优超参\u003c/h2\u003e\u003ca id=\"user-content-ernie模型grid-search-最优超参\" class=\"anchor\" aria-label=\"Permalink: ERNIE模型Grid Search 最优超参\" href=\"#ernie模型grid-search-最优超参\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003eAFQMC\u003c/th\u003e\n\u003cth\u003eTNEWS\u003c/th\u003e\n\u003cth\u003eIFLYTEK\u003c/th\u003e\n\u003cth\u003eCMNLI\u003c/th\u003e\n\u003cth\u003eOCNLI\u003c/th\u003e\n\u003cth\u003eCLUEWSC2020\u003c/th\u003e\n\u003cth\u003eCSL\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eERNIE 3.0-Medium\u003c/td\u003e\n\u003ctd\u003ebsz_32_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_3e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_5e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_1e-05/bsz_64_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_64_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_8_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_32_lr_1e-05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eERNIE 3.0-Base\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_64_lr_3e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_5e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_8_lr_2e-05(drop_out _0.1)\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_3e-05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eERNIE 3.0-XBase\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_1e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_3e-05\u003c/td\u003e\n\u003ctd\u003ebsz_16_lr_1e-05\u003c/td\u003e\n\u003ctd\u003ebsz_32_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_8_lr_2e-05\u003c/td\u003e\n\u003ctd\u003ebsz_64_lr_1e-05\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e应用场景\u003c/h1\u003e\u003ca id=\"user-content-应用场景\" class=\"anchor\" aria-label=\"Permalink: 应用场景\" href=\"#应用场景\"\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\"\u003e文本分类（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/text_classification\"\u003e文本分类\u003c/a\u003e）\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e文本匹配（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/text_matching\"\u003e文本匹配\u003c/a\u003e）\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e序列标注（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/sequence_labeling\"\u003e序列标注\u003c/a\u003e）\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e信息抽取（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/information_extraction_many_to_many\"\u003e信息抽取\u003c/a\u003e）\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e文本生成（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/text_generation\"\u003e文本生成\u003c/a\u003e）\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e图文匹配（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/Research/ERNIE-ViL2\"\u003e图文匹配\u003c/a\u003e）\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e数据蒸馏（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/data_distillation\"\u003e数据蒸馏\u003c/a\u003e）\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e工具使用（\u003ca href=\"/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tools\"\u003e工具使用\u003c/a\u003e）\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e文献引用\u003c/h1\u003e\u003ca id=\"user-content-文献引用\" class=\"anchor\" aria-label=\"Permalink: 文献引用\" href=\"#文献引用\"\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\"\u003eERNIE 1.0\u003c/h3\u003e\u003ca id=\"user-content-ernie-10\" class=\"anchor\" aria-label=\"Permalink: ERNIE 1.0\" href=\"#ernie-10\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{sun2019ernie,\n title={Ernie: Enhanced representation through knowledge integration},\n author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua},\n journal={arXiv preprint arXiv:1904.09223},\n year={2019}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{sun2019ernie,\n title={Ernie: Enhanced representation through knowledge integration},\n author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua},\n journal={arXiv preprint arXiv:1904.09223},\n year={2019}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE 2.0\u003c/h3\u003e\u003ca id=\"user-content-ernie-20\" class=\"anchor\" aria-label=\"Permalink: ERNIE 2.0\" href=\"#ernie-20\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{sun2020ernie,\n title={Ernie 2.0: A continual pre-training framework for language understanding},\n author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},\n booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n volume={34},\n number={05},\n pages={8968--8975},\n year={2020}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{sun2020ernie,\n title={Ernie 2.0: A continual pre-training framework for language understanding},\n author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},\n booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n volume={34},\n number={05},\n pages={8968--8975},\n year={2020}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE-GEN\u003c/h3\u003e\u003ca id=\"user-content-ernie-gen\" class=\"anchor\" aria-label=\"Permalink: ERNIE-GEN\" href=\"#ernie-gen\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{xiao2020ernie,\n title={Ernie-gen: An enhanced multi-flow pre-training and fine-tuning framework for natural language generation},\n author={Xiao, Dongling and Zhang, Han and Li, Yukun and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2001.11314},\n year={2020}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{xiao2020ernie,\n title={Ernie-gen: An enhanced multi-flow pre-training and fine-tuning framework for natural language generation},\n author={Xiao, Dongling and Zhang, Han and Li, Yukun and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2001.11314},\n year={2020}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE-ViL\u003c/h3\u003e\u003ca id=\"user-content-ernie-vil\" class=\"anchor\" aria-label=\"Permalink: ERNIE-ViL\" href=\"#ernie-vil\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{yu2020ernie,\n title={Ernie-vil: Knowledge enhanced vision-language representations through scene graph},\n author={Yu, Fei and Tang, Jiji and Yin, Weichong and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2006.16934},\n year={2020}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{yu2020ernie,\n title={Ernie-vil: Knowledge enhanced vision-language representations through scene graph},\n author={Yu, Fei and Tang, Jiji and Yin, Weichong and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2006.16934},\n year={2020}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE-Gram\u003c/h3\u003e\u003ca id=\"user-content-ernie-gram\" class=\"anchor\" aria-label=\"Permalink: ERNIE-Gram\" href=\"#ernie-gram\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{xiao2020ernie,\n title={ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding},\n author={Xiao, Dongling and Li, Yu-Kun and Zhang, Han and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2010.12148},\n year={2020}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{xiao2020ernie,\n title={ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding},\n author={Xiao, Dongling and Li, Yu-Kun and Zhang, Han and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2010.12148},\n year={2020}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE-Doc\u003c/h3\u003e\u003ca id=\"user-content-ernie-doc\" class=\"anchor\" aria-label=\"Permalink: ERNIE-Doc\" href=\"#ernie-doc\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{ding2020ernie,\n title={ERNIE-Doc: A retrospective long-document modeling transformer},\n author={Ding, Siyu and Shang, Junyuan and Wang, Shuohuan and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2012.15688},\n year={2020}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{ding2020ernie,\n title={ERNIE-Doc: A retrospective long-document modeling transformer},\n author={Ding, Siyu and Shang, Junyuan and Wang, Shuohuan and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2012.15688},\n year={2020}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE-UNIMO\u003c/h3\u003e\u003ca id=\"user-content-ernie-unimo\" class=\"anchor\" aria-label=\"Permalink: ERNIE-UNIMO\" href=\"#ernie-unimo\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{li2020unimo,\n title={Unimo: Towards unified-modal understanding and generation via cross-modal contrastive learning},\n author={Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2012.15409},\n year={2020}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{li2020unimo,\n title={Unimo: Towards unified-modal understanding and generation via cross-modal contrastive learning},\n author={Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2012.15409},\n year={2020}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eERNIE-M\u003c/h3\u003e\u003ca id=\"user-content-ernie-m\" class=\"anchor\" aria-label=\"Permalink: ERNIE-M\" href=\"#ernie-m\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{ouyang2020ernie,\n title={Ernie-m: Enhanced multilingual representation by aligning cross-lingual semantics with monolingual corpora},\n author={Ouyang, Xuan and Wang, Shuohuan and Pang, Chao and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2012.15674},\n year={2020}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{ouyang2020ernie,\n title={Ernie-m: Enhanced multilingual representation by aligning cross-lingual semantics with monolingual corpora},\n author={Ouyang, Xuan and Wang, Shuohuan and Pang, Chao and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},\n journal={arXiv preprint arXiv:2012.15674},\n year={2020}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"","anchor":"","htmlText":""},{"level":1,"text":"开源Roadmap","anchor":"开源roadmap","htmlText":"开源Roadmap"},{"level":1,"text":"环境安装","anchor":"环境安装","htmlText":"环境安装"},{"level":1,"text":"快速上手：使用文心ERNIE大模型进行训练","anchor":"快速上手使用文心ernie大模型进行训练","htmlText":"快速上手：使用文心ERNIE大模型进行训练"},{"level":2,"text":"下载模型","anchor":"下载模型","htmlText":"下载模型"},{"level":2,"text":"准备数据","anchor":"准备数据","htmlText":"准备数据"},{"level":2,"text":"配置训练json文件","anchor":"配置训练json文件","htmlText":"配置训练json文件"},{"level":2,"text":"启动训练","anchor":"启动训练","htmlText":"启动训练"},{"level":2,"text":"配置预测json文件","anchor":"配置预测json文件","htmlText":"配置预测json文件"},{"level":2,"text":"启动预测","anchor":"启动预测","htmlText":"启动预测"},{"level":1,"text":"预训练模型介绍","anchor":"预训练模型介绍","htmlText":"预训练模型介绍"},{"level":1,"text":"数据集下载","anchor":"数据集下载","htmlText":"数据集下载"},{"level":1,"text":"模型效果评估","anchor":"模型效果评估","htmlText":"模型效果评估"},{"level":2,"text":"评估数据集","anchor":"评估数据集","htmlText":"评估数据集"},{"level":2,"text":"CLUE 评测结果:","anchor":"clue-评测结果","htmlText":"CLUE 评测结果:"},{"level":2,"text":"具体评测方式","anchor":"具体评测方式","htmlText":"具体评测方式"},{"level":2,"text":"下游任务的固定超参配置","anchor":"下游任务的固定超参配置","htmlText":"下游任务的固定超参配置"},{"level":2,"text":"ERNIE模型Grid Search 最优超参","anchor":"ernie模型grid-search-最优超参","htmlText":"ERNIE模型Grid Search 最优超参"},{"level":1,"text":"应用场景","anchor":"应用场景","htmlText":"应用场景"},{"level":1,"text":"文献引用","anchor":"文献引用","htmlText":"文献引用"},{"level":3,"text":"ERNIE 1.0","anchor":"ernie-10","htmlText":"ERNIE 1.0"},{"level":3,"text":"ERNIE 2.0","anchor":"ernie-20","htmlText":"ERNIE 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href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/.metas/ERNIE.png"><img src="/PaddlePaddle/ERNIE/raw/ernie-kit-open-v1.0/.metas/ERNIE.png" alt="ERNIE_milestone_20210519_zh" style="max-width: 100%;"></a></h1><a id="" class="anchor" aria-label="Permalink: " href="#"><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">文心大模型ERNIE是百度发布的产业级知识增强大模型，涵盖了NLP大模型和跨模态大模型。2019年3月，开源了国内首个开源预训练模型文心ERNIE 1.0，此后在语言与跨模态的理解和生成等领域取得一系列技术突破，并对外开源与开放了系列模型，助力大模型研究与产业化应用发展。提醒: ERNIE老版本代码已经迁移至repro分支，欢迎使用我们全新升级的基于动静结合的新版ERNIE套件进行开发。另外，也欢迎上<a href="https://ai.baidu.com/easydl/pro" rel="nofollow">EasyDL</a>、<a href="https://ai.baidu.com/bml/app/overview" rel="nofollow">BML</a>体验更丰富的功能。 <a href="https://wenxin.baidu.com/" rel="nofollow">【了解更多】</a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">开源Roadmap</h1><a id="user-content-开源roadmap" class="anchor" aria-label="Permalink: 开源Roadmap" href="#开源roadmap"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>2022.8.18: <ul dir="auto"> <li>图文跨模态预训练模型<code>ERNIE-ViL 2.0 (base)</code> <a href="https://github.com/PaddlePaddle/ERNIE/tree/ernie-kit-open-v1.0/Research/ERNIE-ViL2">正式开源</a></li> </ul> </li> <li>2022.5.20: <ul dir="auto"> <li>最新开源ERNIE 3.0系列预训练模型: <ul dir="auto"> <li>110M参数通用模型ERNIE 3.0 Base</li> <li>280M参数重量级通用模型ERNIE 3.0 XBase</li> <li>74M轻量级通用模型ERNIE 3.0 Medium</li> </ul> </li> <li>新增语音-语言跨模态模型ERNIE-SAT <a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-sat">正式开源</a></li> <li>新增ERNIE-Gen（中文）预训练模型，支持多类主流生成任务：主要包括摘要、问题生成、对话、问答</li> <li>动静结合的文心ERNIE开发套件：基于飞桨动态图功能，支持文心ERNIE模型动态图训练。您仅需要在模型训练开启前，修改一个参数配置，即可实现模型训练的动静切换。</li> <li>将文本预处理、预训练模型、网络搭建、模型评估、上线部署等NLP开发流程规范封装。</li> <li>支持NLP常用任务：文本分类、文本匹配、序列标注、信息抽取、文本生成、数据蒸馏等。</li> <li>提供数据清洗、数据增强、分词、格式转换、大小写转换等数据预处理工具。</li> </ul> </li> <li>2021.12.3: <ul dir="auto"> <li>多语言预训练模型<code>ERNIE-M</code> <a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-m">正式开源</a></li> </ul> </li> <li>2021.5.20: <ul dir="auto"> <li>ERNIE 最新开源四大预训练模型: <ul dir="auto"> <li>多粒度语言知识模型<code>ERNIE-Gram</code> <a href="https://github.com/PaddlePaddle/ERNIE/blob/develop/ernie-gram">正式开源</a></li> <li>超长文本双向建模预训练模型<code>ERNIE-Doc</code> <a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-doc">正式开源</a></li> <li>融合场景图知识的跨模态预训练模型教程<code>ERNIE-ViL</code> <a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-vil">正式开源</a></li> <li>语言与视觉一体的预训练模型<code>ERNIE-UNIMO</code> <a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-unimo">正式开源</a></li> </ul> </li> </ul> </li> <li>2020.9.24: <ul dir="auto"> <li><code>ERNIE-ViL</code> 技术发布! (<a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-vil">点击进入</a>) <ul dir="auto"> <li>面向视觉-语言知识增强的预训练框架，首次在视觉-语言预训练引入结构化的知识。 <ul dir="auto"> <li>利用场景图中的知识，构建了物体、属性和关系预测任务，精细刻画模态间细粒度语义对齐。</li> </ul> </li> <li>五项视觉-语言下游任务取得最好效果，<a href="https://visualcommonsense.com/" rel="nofollow">视觉常识推理榜单</a>取得第一。</li> </ul> </li> </ul> </li> <li>2020.5.20: <ul dir="auto"> <li><code>ERNIE-GEN</code> 模型正式开源! (<a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-gen">点击进入</a>) <ul dir="auto"> <li>最强文本生成预训练模型正式开源，相关工作已被 <code>IJCAI-2020</code> 收录。 <ul dir="auto"> <li>首次把 ERNIE 预训练技术能力扩展至文本生成领域，在多个典型任务上取得最佳。</li> <li>您现在即可下载论文报告的所有模型（包含 <a href="https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-gen/README.zh.md#%E9%A2%84%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B">base/large/large-430G</a>）。</li> </ul> </li> <li>首次在预训练阶段加入span-by-span 生成任务，让模型每次能够生成一个语义完整的片段。</li> <li>提出填充式生成机制和噪声感知机制来缓解曝光偏差问题。</li> <li>精巧的 Mulit-Flow Attention 实现框架。</li> </ul> </li> </ul> </li> <li>2020.4.30 发布<a href="https://github.com/PaddlePaddle/PGL/tree/master/examples/erniesage">ERNIESage</a>，一种新型图神经网络模型，采用ERNIE做为aggreagtor. 由<a href="https://github.com/PaddlePaddle/PGL">PGL</a>实现。</li> <li>2020.3.27 <a href="https://www.jiqizhixin.com/articles/2020-03-27-8" rel="nofollow">在SemEval2020五项子任务上夺冠</a>。</li> <li>2019.12.26 <a href="https://www.technologyreview.com/2019/12/26/131372/ai-baidu-ernie-google-bert-natural-language-glue/" rel="nofollow">GLUE榜第一名</a>。</li> <li>2019.11.6 发布<a href="https://www.jiqizhixin.com/articles/2019-11-06-9" rel="nofollow">ERNIE Tiny</a>。</li> <li>2019.7.7 发布<a href="https://www.jiqizhixin.com/articles/2019-07-31-10" rel="nofollow">ERNIE 2.0</a>。</li> <li>2019.3.16 发布<a href="https://www.jiqizhixin.com/articles/2019-03-16-3" rel="nofollow">ERNIE 1.0</a>。</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">环境安装</h1><a id="user-content-环境安装" class="anchor" aria-label="Permalink: 环境安装" href="#环境安装"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>安装环境依赖：<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/README_ENV.md">环境安装</a></li> <li>安装Ernie套件</li> </ol> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="git clone https://github.com/PaddlePaddle/ERNIE.git"><pre lang="plain" class="notranslate"><code>git clone https://github.com/PaddlePaddle/ERNIE.git </code></pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">快速上手：使用文心ERNIE大模型进行训练</h1><a id="user-content-快速上手使用文心ernie大模型进行训练" class="anchor" aria-label="Permalink: 快速上手：使用文心ERNIE大模型进行训练" href="#快速上手使用文心ernie大模型进行训练"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>使用ERNIE3.0作为预训练模型，准备工作包括： <ul dir="auto"> <li>下载模型</li> <li>准备数据</li> <li>配置训练json文件</li> <li>启动训练模型</li> <li>配置预测json文件</li> <li>启动预测</li> </ul> </li> <li>我们以文本分类任务为例，来快速上手ERNIE大模型的使用</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">下载模型</h2><a id="user-content-下载模型" class="anchor" aria-label="Permalink: 下载模型" href="#下载模型"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>使用ERNIE3.0预训练模型进行文本分类任务</li> <li>ERNNIE3.0预训练模型的下载与配置</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="# ernie_3.0 模型下载 # 进入models_hub目录 cd ./applications/models_hub # 运行下载脚本 sh download_ernie_3.0_base_ch.sh"><pre lang="plain" class="notranslate"><code># ernie_3.0 模型下载 # 进入models_hub目录 cd ./applications/models_hub # 运行下载脚本 sh download_ernie_3.0_base_ch.sh </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">准备数据</h2><a id="user-content-准备数据" class="anchor" aria-label="Permalink: 准备数据" href="#准备数据"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>文心各个任务的data目录下自带一些示例数据，能够实现直接使用，方便快速熟悉文心的使用。</li> <li>文本分类任务的数据</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#进入文本分类任务文件夹 cd ./applications/tasks/text_classification/ #查看文本分类任务自带数据集 ls ./data"><pre><span class="pl-c"><span class="pl-c">#</span>进入文本分类任务文件夹</span> <span class="pl-c1">cd</span> ./applications/tasks/text_classification/ <span class="pl-c"><span class="pl-c">#</span>查看文本分类任务自带数据集</span> ls ./data</pre></div> <ul dir="auto"> <li>注：示例数据仅作为格式演示使用，在真正训练模型时请替换为真实数据。</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">配置训练json文件</h2><a id="user-content-配置训练json文件" class="anchor" aria-label="Permalink: 配置训练json文件" href="#配置训练json文件"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>其预置json文件在./examples/目录下，使用ERNIE3.0预训练模型进行训练的配置文件为的./examples/cls_ernie_fc_ch.json，在该json文件中对数据、模型、训练方式等逻辑进行了配置。</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#查看 ERNIE3.0预训练模型训练文本分类任务的配置文件 cat ./examples/cls_ernie_fc_ch.json"><pre><span class="pl-c"><span class="pl-c">#</span>查看 ERNIE3.0预训练模型训练文本分类任务的配置文件</span> cat ./examples/cls_ernie_fc_ch.json</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">启动训练</h2><a id="user-content-启动训练" class="anchor" aria-label="Permalink: 启动训练" href="#启动训练"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>将数据集存放妥当，并配置好cls_ernie_fc_ch.json，我们就可以运行模型训练的命令。</li> <li>其中，单卡指令为<code>python run_trainer.py</code>，如下所示，使用基于ernie的中文文本分类模型在训练集上进行本地模型训练。</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ernie 中文文本分类模型 # 基于json实现预置网络训练。其调用了配置文件./examples/cls_ernie_fc_ch.json python run_trainer.py --param_path ./examples/cls_ernie_fc_ch.json"><pre><span class="pl-c"><span class="pl-c">#</span> ernie 中文文本分类模型</span> <span class="pl-c"><span class="pl-c">#</span> 基于json实现预置网络训练。其调用了配置文件./examples/cls_ernie_fc_ch.json</span> python run_trainer.py --param_path ./examples/cls_ernie_fc_ch.json</pre></div> <ul dir="auto"> <li>多卡指令为:</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="fleetrun --gpus=x,y run_trainer.py./examples/cls_ernie_fc_ch.json"><pre lang="plain" class="notranslate"><code>fleetrun --gpus=x,y run_trainer.py./examples/cls_ernie_fc_ch.json </code></pre></div> <ul dir="auto"> <li>训练运行的日志会自动保存在**./log/test.log**文件中。</li> <li>训练中以及结束后产生的模型文件会默认保存在./output/<strong>目录下，其中</strong>save_inference_model/文件夹会保存用于预测的模型文件，<strong>save_checkpoint/</strong> 文件夹会保存用于热启动的模型文件。</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">配置预测json文件</h2><a id="user-content-配置预测json文件" class="anchor" aria-label="Permalink: 配置预测json文件" href="#配置预测json文件"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>其预置json文件在./examples/目录下，使用ERNIE2.0预训练模型训练的模型进行预测的配置文件为的./examples/cls_ernie_fc_ch_infer.json</li> <li>主要修改./examples/cls_ernie_fc_ch_infer.json文件的预测模型的输入路径、预测文件的输入路径、预测结果的输出路径，对应修改配置如下：</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="{ "dataset_reader":{"train_reader":{"config":{"data_path":"./data/predict_data"}}}, "inference":{"inference_model_path":"./output/cls_ernie_fc_ch/save_inference_model/inference_step_251", "output_path": "./output/predict_result.txt"} }"><pre class="notranslate"><code>{ "dataset_reader":{"train_reader":{"config":{"data_path":"./data/predict_data"}}}, "inference":{"inference_model_path":"./output/cls_ernie_fc_ch/save_inference_model/inference_step_251", "output_path": "./output/predict_result.txt"} } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">启动预测</h2><a id="user-content-启动预测" class="anchor" aria-label="Permalink: 启动预测" href="#启动预测"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>运行run_infer.py ，选择对应的参数配置文件即可。如下所示：</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python run_infer.py --param_path ./examples/cls_ernie_fc_ch_infer.json"><pre lang="plain" class="notranslate"><code>python run_infer.py --param_path ./examples/cls_ernie_fc_ch_infer.json </code></pre></div> <ul dir="auto"> <li>预测过程中的日志自动保存在./output/predict_result.txt文件中。</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">预训练模型介绍</h1><a id="user-content-预训练模型介绍" class="anchor" aria-label="Permalink: 预训练模型介绍" href="#预训练模型介绍"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>参考预训练模型原理介绍:<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/models_hub">模型介绍</a></li> <li>预训练模型下载：进入./applications/models_hub目录下,下载示例：</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="#进入预训练模型下载目录 cd ./applications/models_hub #下载ERNIE3.0 base模型 sh downlaod_ernie_3.0_base_ch.sh"><pre lang="plain" class="notranslate"><code>#进入预训练模型下载目录 cd ./applications/models_hub #下载ERNIE3.0 base模型 sh downlaod_ernie_3.0_base_ch.sh </code></pre></div> <ul dir="auto"> <li>更多开源模型，见<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/Research">Research</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">数据集下载</h1><a id="user-content-数据集下载" class="anchor" aria-label="Permalink: 数据集下载" href="#数据集下载"><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"><a href="https://www.cluebenchmarks.com/" rel="nofollow">CLUE数据集</a></p> <p dir="auto"><a href="https://www.luge.ai/#/luge/dataDetail?id=5" rel="nofollow">DuIE2.0数据集</a></p> <p dir="auto"><a href="https://ernie-github.cdn.bcebos.com/data-msra_ner.tar.gz" rel="nofollow">MSRA_NER数据集</a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">模型效果评估</h1><a id="user-content-模型效果评估" class="anchor" aria-label="Permalink: 模型效果评估" href="#模型效果评估"><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"><h2 tabindex="-1" class="heading-element" dir="auto">评估数据集</h2><a id="user-content-评估数据集" class="anchor" aria-label="Permalink: 评估数据集" href="#评估数据集"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>分类和匹配采用<a href="https://www.cluebenchmarks.com/" rel="nofollow">CLUE数据集</a>。</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">CLUE 评测结果:</h2><a id="user-content-clue-评测结果" class="anchor" aria-label="Permalink: CLUE 评测结果:" href="#clue-评测结果"><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> <markdown-accessiblity-table><table> <thead> <tr> <th>配置</th> <th>模型</th> <th>CLUEWSC2020</th> <th>IFLYTEK</th> <th>TNEWS</th> <th>AFQMC</th> <th>CMNLI</th> <th>CSL</th> <th>OCNLI</th> <th>平均值</th> </tr> </thead> <tbody> <tr> <td>24L1024H</td> <td>RoBERTa-wwm-ext-large</td> <td>90.79</td> <td>62.02</td> <td>59.33</td> <td>76.00</td> <td>83.88</td> <td>83.67</td> <td>78.81</td> <td>76.36</td> </tr> <tr> <td>20L1024H</td> <td>ERNIE 3.0-XBase</td> <td>91.12</td> <td>62.22</td> <td>60.34</td> <td>76.95</td> <td>84.98</td> <td>84.27</td> <td>82.07</td> <td>77.42</td> </tr> <tr> <td>12L768H</td> <td>RoBERTa-wwm-ext-base</td> <td>88.55</td> <td>61.22</td> <td>58.08</td> <td>74.75</td> <td>81.66</td> <td>81.63</td> <td>77.25</td> <td>74.73</td> </tr> <tr> <td>12L768H</td> <td>ERNIE 3.0-Base</td> <td>88.18</td> <td>60.72</td> <td>58.73</td> <td>76.53</td> <td>83.65</td> <td>83.30</td> <td>80.31</td> <td>75.63</td> </tr> <tr> <td>6L768H</td> <td>RBT6, Chinese</td> <td>75.00</td> <td>59.68</td> <td>56.62</td> <td>73.15</td> <td>79.26</td> <td>80.04</td> <td>73.15</td> <td>70.99</td> </tr> <tr> <td>6L768H</td> <td>ERNIE 3.0-Medium</td> <td>79.93</td> <td>60.14</td> <td>57.16</td> <td>74.56</td> <td>80.87</td> <td>81.23</td> <td>77.02</td> <td>72.99</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto"><strong>具体评测方式</strong></h2><a id="user-content-具体评测方式" class="anchor" aria-label="Permalink: 具体评测方式" href="#具体评测方式"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>以上所有任务均基于 Grid Search 方式进行超参寻优。分类任务训练每间隔 100 steps 评估验证集效果，取验证集最优效果作为表格中的汇报指标。</li> <li>分类任务 Grid Search 超参范围: batch_size: 16, 32, 64; learning rates: 1e-5, 2e-5, 3e-5, 5e-5；因为 CLUEWSC2020 数据集较小，所以模型在该数据集上的效果对 batch_size 较敏感，所以对 CLUEWSC2020 评测时额外增加了 batch_size = 8 的超参搜索；因为CLUEWSC2020 和 IFLYTEK 数据集对 dropout 概率值较为敏感，所以对 CLUEWSC2020 和 IFLYTEK 数据集评测时增加dropout_prob = 0.0 的超参搜索。</li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">下游任务的固定超参配置</h2><a id="user-content-下游任务的固定超参配置" class="anchor" aria-label="Permalink: 下游任务的固定超参配置" href="#下游任务的固定超参配置"><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"><strong>分类和匹配任务:</strong></p> <markdown-accessiblity-table><table> <thead> <tr> <th>TASK</th> <th>AFQMC</th> <th>TNEWS</th> <th>IFLYTEK</th> <th>CMNLI</th> <th>OCNLI</th> <th>CLUEWSC2020</th> <th>CSL</th> </tr> </thead> <tbody> <tr> <td>epoch</td> <td>3</td> <td>3</td> <td>3</td> <td>2</td> <td>5</td> <td>50</td> <td>5</td> </tr> <tr> <td>max_seq_length</td> <td>128</td> <td>128</td> <td>128</td> <td>128</td> <td>128</td> <td>128</td> <td>256</td> </tr> <tr> <td>warmup_proportion</td> <td>0.1</td> <td>0.1</td> <td>0.1</td> <td>0.1</td> <td>0.1</td> <td>0.1</td> <td>0.1</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">ERNIE模型Grid Search 最优超参</h2><a id="user-content-ernie模型grid-search-最优超参" class="anchor" aria-label="Permalink: ERNIE模型Grid Search 最优超参" href="#ernie模型grid-search-最优超参"><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> <markdown-accessiblity-table><table> <thead> <tr> <th>Model</th> <th>AFQMC</th> <th>TNEWS</th> <th>IFLYTEK</th> <th>CMNLI</th> <th>OCNLI</th> <th>CLUEWSC2020</th> <th>CSL</th> </tr> </thead> <tbody> <tr> <td>ERNIE 3.0-Medium</td> <td>bsz_32_lr_2e-05</td> <td>bsz_16_lr_3e-05</td> <td>bsz_16_lr_5e-05</td> <td>bsz_16_lr_1e-05/bsz_64_lr_2e-05</td> <td>bsz_64_lr_2e-05</td> <td>bsz_8_lr_2e-05</td> <td>bsz_32_lr_1e-05</td> </tr> <tr> <td>ERNIE 3.0-Base</td> <td>bsz_16_lr_2e-05</td> <td>bsz_64_lr_3e-05</td> <td>bsz_16_lr_5e-05</td> <td>bsz_16_lr_2e-05</td> <td>bsz_16_lr_2e-05</td> <td>bsz_8_lr_2e-05(drop_out _0.1)</td> <td>bsz_16_lr_3e-05</td> </tr> <tr> <td>ERNIE 3.0-XBase</td> <td>bsz_16_lr_1e-05</td> <td>bsz_16_lr_2e-05</td> <td>bsz_16_lr_3e-05</td> <td>bsz_16_lr_1e-05</td> <td>bsz_32_lr_2e-05</td> <td>bsz_8_lr_2e-05</td> <td>bsz_64_lr_1e-05</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">应用场景</h1><a id="user-content-应用场景" class="anchor" aria-label="Permalink: 应用场景" href="#应用场景"><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">文本分类（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/text_classification">文本分类</a>）</p> <p dir="auto">文本匹配（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/text_matching">文本匹配</a>）</p> <p dir="auto">序列标注（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/sequence_labeling">序列标注</a>）</p> <p dir="auto">信息抽取（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/information_extraction_many_to_many">信息抽取</a>）</p> <p dir="auto">文本生成（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/text_generation">文本生成</a>）</p> <p dir="auto">图文匹配（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/Research/ERNIE-ViL2">图文匹配</a>）</p> <p dir="auto">数据蒸馏（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tasks/data_distillation">数据蒸馏</a>）</p> <p dir="auto">工具使用（<a href="/PaddlePaddle/ERNIE/blob/ernie-kit-open-v1.0/applications/tools">工具使用</a>）</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">文献引用</h1><a id="user-content-文献引用" class="anchor" aria-label="Permalink: 文献引用" href="#文献引用"><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">ERNIE 1.0</h3><a id="user-content-ernie-10" class="anchor" aria-label="Permalink: ERNIE 1.0" href="#ernie-10"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{sun2019ernie, title={Ernie: Enhanced representation through knowledge integration}, author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua}, journal={arXiv preprint arXiv:1904.09223}, year={2019} }"><pre class="notranslate"><code>@article{sun2019ernie, title={Ernie: Enhanced representation through knowledge integration}, author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua}, journal={arXiv preprint arXiv:1904.09223}, year={2019} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ERNIE 2.0</h3><a id="user-content-ernie-20" class="anchor" aria-label="Permalink: ERNIE 2.0" href="#ernie-20"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{sun2020ernie, title={Ernie 2.0: A continual pre-training framework for language understanding}, author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={34}, number={05}, pages={8968--8975}, year={2020} }"><pre class="notranslate"><code>@inproceedings{sun2020ernie, title={Ernie 2.0: A continual pre-training framework for language understanding}, author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={34}, number={05}, pages={8968--8975}, year={2020} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ERNIE-GEN</h3><a id="user-content-ernie-gen" class="anchor" aria-label="Permalink: ERNIE-GEN" href="#ernie-gen"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{xiao2020ernie, title={Ernie-gen: An enhanced multi-flow pre-training and fine-tuning framework for natural language generation}, author={Xiao, Dongling and Zhang, Han and Li, Yukun and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2001.11314}, year={2020} }"><pre class="notranslate"><code>@article{xiao2020ernie, title={Ernie-gen: An enhanced multi-flow pre-training and fine-tuning framework for natural language generation}, author={Xiao, Dongling and Zhang, Han and Li, Yukun and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2001.11314}, year={2020} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ERNIE-ViL</h3><a id="user-content-ernie-vil" class="anchor" aria-label="Permalink: ERNIE-ViL" href="#ernie-vil"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{yu2020ernie, title={Ernie-vil: Knowledge enhanced vision-language representations through scene graph}, author={Yu, Fei and Tang, Jiji and Yin, Weichong and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2006.16934}, year={2020} }"><pre class="notranslate"><code>@article{yu2020ernie, title={Ernie-vil: Knowledge enhanced vision-language representations through scene graph}, author={Yu, Fei and Tang, Jiji and Yin, Weichong and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2006.16934}, year={2020} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ERNIE-Gram</h3><a id="user-content-ernie-gram" class="anchor" aria-label="Permalink: ERNIE-Gram" href="#ernie-gram"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{xiao2020ernie, title={ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding}, author={Xiao, Dongling and Li, Yu-Kun and Zhang, Han and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2010.12148}, year={2020} }"><pre class="notranslate"><code>@article{xiao2020ernie, title={ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding}, author={Xiao, Dongling and Li, Yu-Kun and Zhang, Han and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2010.12148}, year={2020} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ERNIE-Doc</h3><a id="user-content-ernie-doc" class="anchor" aria-label="Permalink: ERNIE-Doc" href="#ernie-doc"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{ding2020ernie, title={ERNIE-Doc: A retrospective long-document modeling transformer}, author={Ding, Siyu and Shang, Junyuan and Wang, Shuohuan and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2012.15688}, year={2020} }"><pre class="notranslate"><code>@article{ding2020ernie, title={ERNIE-Doc: A retrospective long-document modeling transformer}, author={Ding, Siyu and Shang, Junyuan and Wang, Shuohuan and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2012.15688}, year={2020} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ERNIE-UNIMO</h3><a id="user-content-ernie-unimo" class="anchor" aria-label="Permalink: ERNIE-UNIMO" href="#ernie-unimo"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{li2020unimo, title={Unimo: Towards unified-modal understanding and generation via cross-modal contrastive learning}, author={Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2012.15409}, year={2020} }"><pre class="notranslate"><code>@article{li2020unimo, title={Unimo: Towards unified-modal understanding and generation via cross-modal contrastive learning}, author={Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2012.15409}, year={2020} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ERNIE-M</h3><a id="user-content-ernie-m" class="anchor" aria-label="Permalink: ERNIE-M" href="#ernie-m"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{ouyang2020ernie, title={Ernie-m: Enhanced multilingual representation by aligning cross-lingual semantics with monolingual corpora}, author={Ouyang, Xuan and Wang, Shuohuan and Pang, Chao and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2012.15674}, year={2020} }"><pre class="notranslate"><code>@article{ouyang2020ernie, title={Ernie-m: Enhanced multilingual representation by aligning cross-lingual semantics with monolingual corpora}, author={Ouyang, Xuan and Wang, Shuohuan and Pang, Chao and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2012.15674}, year={2020} } </code></pre></div> </article></div></div></div></div></div>   <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="D+4YDcWwfUPM1toYFAQPW7viKqqJTJhYpjQ+BrZge7EbDMaFYEGJ3903e1nrcZyiB1Q8uBS8cHRWLUz79cHOlA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/nlp" title="Topic: nlp" data-view-component="true" class="topic-tag topic-tag-link"> nlp </a> <a href="/topics/natural-language-processing" title="Topic: 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color-fg-muted"><relative-time datetime="2020-06-30T03:16:31Z" class="no-wrap">Jun 30, 2020</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/PaddlePaddle/ERNIE/releases" data-view-component="true" class="Link">+ 5 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/PaddlePaddle/packages?repo_name=ERNIE" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/PaddlePaddle/ERNIE/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div 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