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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" 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2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon 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0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center 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href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div 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method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="nxybOe8l5b6NYkcUIi21X9KGLkVQ+l8vsqjHzFDkUdFJiTsG0ldYqnv7KP65ckpZWMC2DSfgLZZQJ40pqAe2PA==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> 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dir=\"auto\"\u003eFast-Bert\u003c/h1\u003e\u003ca id=\"user-content-fast-bert\" class=\"anchor\" aria-label=\"Permalink: Fast-Bert\" href=\"#fast-bert\"\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://github.com/deepmipt/DeepPavlov/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c355f200ea90fddaa407b6eaab303663a669248ea3ca7b1fcf77dbe04ff5f48c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"License Apache 2.0\" data-canonical-src=\"https://img.shields.io/badge/license-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/fast-bert\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ecde12a97767241e17963221aa29306cd449f174826cd34c7502102dfab2377/68747470733a2f2f62616467652e667572792e696f2f70792f666173742d626572742e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/fast-bert.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8678d819e273bf32f9b3ad66409b523476e8e439a46e46934b7938c17295265a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e36253230253743253230332e372d677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8678d819e273bf32f9b3ad66409b523476e8e439a46e46934b7938c17295265a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e36253230253743253230332e372d677265656e2e737667\" alt=\"Python 3.6, 3.7\" data-canonical-src=\"https://img.shields.io/badge/python-3.6%20%7C%203.7-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNew - Learning Rate Finder for Text Classification Training (borrowed with thanks from \u003ca href=\"https://github.com/davidtvs/pytorch-lr-finder\"\u003ehttps://github.com/davidtvs/pytorch-lr-finder\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSupports LAMB optimizer for faster training.\u003c/strong\u003e\nPlease refer to \u003ca href=\"https://arxiv.org/abs/1904.00962\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1904.00962\u003c/a\u003e for the paper on LAMB optimizer.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSupports BERT and XLNet for both Multi-Class and Multi-Label text classification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFast-Bert is the deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe work on FastBert is built on solid foundations provided by the excellent \u003ca href=\"https://github.com/huggingface/pytorch-pretrained-BERT\"\u003eHugging Face BERT PyTorch library\u003c/a\u003e and is inspired by \u003ca href=\"https://github.com/fastai/fastai\"\u003efast.ai\u003c/a\u003e and strives to make the cutting edge deep learning technologies accessible for the vast community of machine learning practitioners.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWith FastBert, you will be able to:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTrain (more precisely fine-tune) BERT, RoBERTa and XLNet text classification models on your custom dataset.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTune model hyper-parameters such as epochs, learning rate, batch size, optimiser schedule and more.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSave and deploy trained model for inference (including on AWS Sagemaker).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eFast-Bert will support both multi-class and multi-label text classification for the following and in due course, it will support other NLU tasks such as Named Entity Recognition, Question Answering and Custom Corpus fine-tuning.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/google-research/bert\"\u003eBERT\u003c/a\u003e\u003c/strong\u003e (from Google) released with the paper \u003ca href=\"https://arxiv.org/abs/1810.04805\" rel=\"nofollow\"\u003eBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding\u003c/a\u003e by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/zihangdai/xlnet/\"\u003eXLNet\u003c/a\u003e\u003c/strong\u003e (from Google/CMU) released with the paper \u003ca href=\"https://arxiv.org/abs/1906.08237\" rel=\"nofollow\"\u003eXLNet: Generalized Autoregressive Pretraining for Language Understanding\u003c/a\u003e by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/1907.11692\" rel=\"nofollow\"\u003eRoBERTa\u003c/a\u003e\u003c/strong\u003e (from Facebook), a Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du et al.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDistilBERT (from HuggingFace)\u003c/strong\u003e, released together with the blogpost \u003ca href=\"https://medium.com/huggingface/distilbert-8cf3380435b5\" rel=\"nofollow\"\u003eSmaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT\u003c/a\u003e by Victor Sanh, Lysandre Debut and Thomas Wolf.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repo is tested on Python 3.6+.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWith pip\u003c/h3\u003e\u003ca id=\"user-content-with-pip\" class=\"anchor\" aria-label=\"Permalink: With pip\" href=\"#with-pip\"\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\"\u003ePyTorch-Transformers can be installed by pip as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install fast-bert\"\u003e\u003cpre\u003epip install fast-bert\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eClone the repository and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install [--editable] .\"\u003e\u003cpre\u003epip install [--editable] \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install git+https://github.com/kaushaltrivedi/fast-bert.git\"\u003e\u003cpre\u003epip install git+https://github.com/kaushaltrivedi/fast-bert.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou will also need to install NVIDIA Apex.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/NVIDIA/apex\ncd apex\npip install -v --no-cache-dir --global-option=\u0026quot;--cpp_ext\u0026quot; --global-option=\u0026quot;--cuda_ext\u0026quot; ./\"\u003e\u003cpre\u003egit clone https://github.com/NVIDIA/apex\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apex\npip install -v --no-cache-dir --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--cpp_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--cuda_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ./\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eText Classification\u003c/h2\u003e\u003ca id=\"user-content-text-classification\" class=\"anchor\" aria-label=\"Permalink: Text Classification\" href=\"#text-classification\"\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\"\u003e1. Create a DataBunch object\u003c/h3\u003e\u003ca id=\"user-content-1-create-a-databunch-object\" class=\"anchor\" aria-label=\"Permalink: 1. Create a DataBunch object\" href=\"#1-create-a-databunch-object\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe databunch object takes training, validation and test csv files and converts the data into internal representation for BERT, RoBERTa, DistilBERT or XLNet. The object also instantiates the correct data-loaders based on device profile and batch_size and max_sequence_length.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\nfrom fast_bert.data_cls import BertDataBunch\n\ndatabunch = BertDataBunch(DATA_PATH, LABEL_PATH,\n tokenizer='bert-base-uncased',\n train_file='train.csv',\n val_file='val.csv',\n label_file='labels.csv',\n text_col='text',\n label_col='label',\n batch_size_per_gpu=16,\n max_seq_length=512,\n multi_gpu=True,\n multi_label=False,\n model_type='bert')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efast_bert\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata_cls\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertDataBunch\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003edatabunch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eBertDataBunch\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003eDATA_PATH\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003eLABEL_PATH\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'bert-base-uncased'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etrain_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'train.csv'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eval_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'val.csv'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elabel_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'labels.csv'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etext_col\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'text'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elabel_col\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ebatch_size_per_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_seq_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e512\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emulti_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emulti_label\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emodel_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'bert'\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFile format for train.csv and val.csv\u003c/h4\u003e\u003ca id=\"user-content-file-format-for-traincsv-and-valcsv\" class=\"anchor\" aria-label=\"Permalink: File format for train.csv and val.csv\" href=\"#file-format-for-traincsv-and-valcsv\"\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\u003eindex\u003c/th\u003e\n\u003cth\u003etext\u003c/th\u003e\n\u003cth\u003elabel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003eLooking through the other comments, I'm amazed that there aren't any warnings to potential viewers of what they have to look forward to when renting this garbage. First off, I rented this thing with the understanding that it was a competently rendered Indiana Jones knock-off.\u003c/td\u003e\n\u003ctd\u003eneg\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eI've watched the first 17 episodes and this series is simply amazing! I haven't been this interested in an anime series since Neon Genesis Evangelion. This series is actually based off an h-game, which I'm not sure if it's been done before or not, I haven't played the game, but from what I've heard it follows it very well\u003c/td\u003e\n\u003ctd\u003epos\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003ehis movie is nothing short of a dark, gritty masterpiece. I may be bias, as the Apartheid era is an area I've always felt for.\u003c/td\u003e\n\u003ctd\u003epos\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eIn case the column names are different than the usual text and labels, you will have to provide those names in the databunch text_col and label_col parameters.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003elabels.csv\u003c/strong\u003e will contain a list of all unique labels. In this case the file will contain:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pos\nneg\"\u003e\u003cpre lang=\"csv\" class=\"notranslate\"\u003e\u003ccode\u003epos\nneg\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor multi-label classification, \u003cstrong\u003elabels.csv\u003c/strong\u003e will contain all possible labels:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"toxic\nsevere_toxic\nobscene\nthreat\ninsult\nidentity_hate\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003etoxic\nsevere_toxic\nobscene\nthreat\ninsult\nidentity_hate\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe file \u003cstrong\u003etrain.csv\u003c/strong\u003e will then contain one column for each label, with each column value being either 0 or 1. Don't forget to change \u003ccode\u003emulti_label=True\u003c/code\u003e for multi-label classification in \u003ccode\u003eBertDataBunch\u003c/code\u003e.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eid\u003c/th\u003e\n\u003cth\u003etext\u003c/th\u003e\n\u003cth\u003etoxic\u003c/th\u003e\n\u003cth\u003esevere_toxic\u003c/th\u003e\n\u003cth\u003eobscene\u003c/th\u003e\n\u003cth\u003ethreat\u003c/th\u003e\n\u003cth\u003einsult\u003c/th\u003e\n\u003cth\u003eidentity_hate\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003eWhy the edits made under my username Hardcore Metallica Fan were reverted?\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003eI will mess you up\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003elabel_col will be a list of label column names. In this case it will be:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"['toxic','severe_toxic','obscene','threat','insult','identity_hate']\"\u003e\u003cpre\u003e[\u003cspan class=\"pl-s\"\u003e'toxic'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'severe_toxic'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'obscene'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'threat'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'insult'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'identity_hate'\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTokenizer\u003c/h4\u003e\u003ca id=\"user-content-tokenizer\" class=\"anchor\" aria-label=\"Permalink: Tokenizer\" href=\"#tokenizer\"\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\"\u003eYou can either create a tokenizer object and pass it to DataBunch or you can pass the model name as tokenizer and DataBunch will automatically download and instantiate an appropriate tokenizer object.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example for using XLNet base cased model, set tokenizer parameter to 'xlnet-base-cased'. DataBunch will automatically download and instantiate XLNetTokenizer with the vocabulary for xlnet-base-cased model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Type\u003c/h4\u003e\u003ca id=\"user-content-model-type\" class=\"anchor\" aria-label=\"Permalink: Model Type\" href=\"#model-type\"\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\"\u003eFast-Bert supports XLNet, RoBERTa and BERT based classification models. Set model type parameter value to \u003cstrong\u003e'bert'\u003c/strong\u003e, \u003cstrong\u003eroberta\u003c/strong\u003e or \u003cstrong\u003e'xlnet'\u003c/strong\u003e in order to initiate an appropriate databunch object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. Create a Learner Object\u003c/h3\u003e\u003ca id=\"user-content-2-create-a-learner-object\" class=\"anchor\" aria-label=\"Permalink: 2. Create a Learner Object\" href=\"#2-create-a-learner-object\"\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\"\u003eBertLearner is the ‘learner’ object that holds everything together. It encapsulates the key logic for the lifecycle of the model such as training, validation and inference.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe learner object will take the databunch created earlier as as input alongwith some of the other parameters such as location for one of the pretrained models, FP16 training, multi_gpu and multi_label options.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe learner class contains the logic for training loop, validation loop, optimiser strategies and key metrics calculation. This help the developers focus on their custom use-cases without worrying about these repetitive activities.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAt the same time the learner object is flexible enough to be customised either via using flexible parameters or by creating a subclass of BertLearner and redefining relevant methods.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\nfrom fast_bert.learner_cls import BertLearner\nfrom fast_bert.metrics import accuracy\nimport logging\n\nlogger = logging.getLogger()\ndevice_cuda = torch.device(\u0026quot;cuda\u0026quot;)\nmetrics = [{'name': 'accuracy', 'function': accuracy}]\n\nlearner = BertLearner.from_pretrained_model(\n\t\t\t\t\t\tdatabunch,\n\t\t\t\t\t\tpretrained_path='bert-base-uncased',\n\t\t\t\t\t\tmetrics=metrics,\n\t\t\t\t\t\tdevice=device_cuda,\n\t\t\t\t\t\tlogger=logger,\n\t\t\t\t\t\toutput_dir=OUTPUT_DIR,\n\t\t\t\t\t\tfinetuned_wgts_path=None,\n\t\t\t\t\t\twarmup_steps=500,\n\t\t\t\t\t\tmulti_gpu=True,\n\t\t\t\t\t\tis_fp16=True,\n\t\t\t\t\t\tmulti_label=False,\n\t\t\t\t\t\tlogging_steps=50)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efast_bert\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elearner_cls\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertLearner\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efast_bert\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eaccuracy\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogging\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogging\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egetLogger\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003edevice_cuda\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edevice\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"cuda\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [{\u003cspan class=\"pl-s\"\u003e'name'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'accuracy'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'function'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eaccuracy\u003c/span\u003e}]\n\n\u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertLearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained_model\u003c/span\u003e(\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003edatabunch\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003epretrained_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'bert-base-uncased'\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edevice_cuda\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003eoutput_dir\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eOUTPUT_DIR\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003efinetuned_wgts_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003ewarmup_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e500\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003emulti_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003eis_fp16\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003emulti_label\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003elogging_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003edatabunch\u003c/td\u003e\n\u003ctd\u003eDatabunch object created earlier\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epretrained_path\u003c/td\u003e\n\u003ctd\u003eDirectory for the location of the pretrained model files or the name of one of the pretrained models i.e. bert-base-uncased, xlnet-large-cased, etc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emetrics\u003c/td\u003e\n\u003ctd\u003eList of metrics functions that you want the model to calculate on the validation set, e.g. accuracy, beta, etc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edevice\u003c/td\u003e\n\u003ctd\u003etorch.device of type \u003cem\u003ecuda\u003c/em\u003e or \u003cem\u003ecpu\u003c/em\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003elogger\u003c/td\u003e\n\u003ctd\u003elogger object\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput_dir\u003c/td\u003e\n\u003ctd\u003eDirectory for model to save trained artefacts, tokenizer vocabulary and tensorboard files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003efinetuned_wgts_path\u003c/td\u003e\n\u003ctd\u003eprovide the location for fine-tuned language model (experimental feature)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewarmup_steps\u003c/td\u003e\n\u003ctd\u003enumber of training warms steps for the scheduler\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emulti_gpu\u003c/td\u003e\n\u003ctd\u003emultiple GPUs available e.g. if running on AWS p3.8xlarge instance\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eis_fp16\u003c/td\u003e\n\u003ctd\u003eFP16 training\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emulti_label\u003c/td\u003e\n\u003ctd\u003emultilabel classification\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003elogging_steps\u003c/td\u003e\n\u003ctd\u003enumber of steps between each tensorboard metrics calculation. Set it to 0 to disable tensor flow logging. Keeping this value too low will lower the training speed as model will be evaluated each time the metrics are logged\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3. Find the optimal learning rate\u003c/h3\u003e\u003ca id=\"user-content-3-find-the-optimal-learning-rate\" class=\"anchor\" aria-label=\"Permalink: 3. Find the optimal learning rate\" href=\"#3-find-the-optimal-learning-rate\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe learning rate is one of the most important hyperparameters for model training. We have incorporated the learining rate finder that was proposed by Leslie Smith and then built into the fastai library.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"learner.lr_find(start_lr=1e-5,optimizer_type='lamb')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elr_find\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003estart_lr\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1e-5\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003eoptimizer_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'lamb'\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe code is heavily borrowed from David Silva's \u003ca href=\"https://github.com/davidtvs/pytorch-lr-finder\"\u003epytorch-lr-finder library\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/utterworks/fast-bert/blob/main/images/lr_finder.png\"\u003e\u003cimg src=\"/utterworks/fast-bert/raw/main/images/lr_finder.png\" alt=\"Learning rate range test\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e4. Train the model\u003c/h3\u003e\u003ca id=\"user-content-4-train-the-model\" class=\"anchor\" aria-label=\"Permalink: 4. Train the model\" href=\"#4-train-the-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"learner.fit(epochs=6,\n\t\t\tlr=6e-5,\n\t\t\tvalidate=True, \t# Evaluate the model after each epoch\n\t\t\tschedule_type=\u0026quot;warmup_cosine\u0026quot;,\n\t\t\toptimizer_type=\u0026quot;lamb\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eepochs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e,\n\t\t\t\u003cspan class=\"pl-s1\"\u003elr\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e6e-5\u003c/span\u003e,\n\t\t\t\u003cspan class=\"pl-s1\"\u003evalidate\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \t\u003cspan class=\"pl-c\"\u003e# Evaluate the model after each epoch\u003c/span\u003e\n\t\t\t\u003cspan class=\"pl-s1\"\u003eschedule_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"warmup_cosine\"\u003c/span\u003e,\n\t\t\t\u003cspan class=\"pl-s1\"\u003eoptimizer_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"lamb\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFast-Bert now supports LAMB optmizer. Due to the speed of training, we have set LAMB as the default optimizer. You can switch back to AdamW by setting optimizer_type to 'adamw'.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e5. Save trained model artifacts\u003c/h3\u003e\u003ca id=\"user-content-5-save-trained-model-artifacts\" class=\"anchor\" aria-label=\"Permalink: 5. Save trained model artifacts\" href=\"#5-save-trained-model-artifacts\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"learner.save_model()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esave_model\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eModel artefacts will be persisted in the output_dir/'model_out' path provided to the learner object. Following files will be persisted:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003epytorch_model.bin\u003c/td\u003e\n\u003ctd\u003etrained model weights\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003espiece.model\u003c/td\u003e\n\u003ctd\u003esentence tokenizer vocabulary (for xlnet models)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003evocab.txt\u003c/td\u003e\n\u003ctd\u003eworkpiece tokenizer vocabulary (for bert models)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especial_tokens_map.json\u003c/td\u003e\n\u003ctd\u003especial tokens mappings\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econfig.json\u003c/td\u003e\n\u003ctd\u003emodel config\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eadded_tokens.json\u003c/td\u003e\n\u003ctd\u003elist of new tokens\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eAs the model artefacts are all stored in the same folder, you will be able to instantiate the learner object to run inference by pointing pretrained_path to this location.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e6. Model Inference\u003c/h3\u003e\u003ca id=\"user-content-6-model-inference\" class=\"anchor\" aria-label=\"Permalink: 6. Model Inference\" href=\"#6-model-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you already have a Learner object with trained model instantiated, just call predict_batch method on the learner object with the list of text data:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"texts = ['I really love the Netflix original movies',\n\t\t 'this movie is not worth watching']\npredictions = learner.predict_batch(texts)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003etexts\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s\"\u003e'I really love the Netflix original movies'\u003c/span\u003e,\n\t\t \u003cspan class=\"pl-s\"\u003e'this movie is not worth watching'\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003epredictions\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epredict_batch\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etexts\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you have persistent trained model and just want to run inference logic on that trained model, use the second approach, i.e. the predictor object.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from fast_bert.prediction import BertClassificationPredictor\n\nMODEL_PATH = OUTPUT_DIR/'model_out'\n\npredictor = BertClassificationPredictor(\n\t\t\t\tmodel_path=MODEL_PATH,\n\t\t\t\tlabel_path=LABEL_PATH, # location for labels.csv file\n\t\t\t\tmulti_label=False,\n\t\t\t\tmodel_type='xlnet',\n\t\t\t\tdo_lower_case=False,\n\t\t\t\tdevice=None) # set custom torch.device, defaults to cuda if available\n\n# Single prediction\nsingle_prediction = predictor.predict(\u0026quot;just get me result for this text\u0026quot;)\n\n# Batch predictions\ntexts = [\n\t\u0026quot;this is the first text\u0026quot;,\n\t\u0026quot;this is the second text\u0026quot;\n\t]\n\nmultiple_predictions = predictor.predict_batch(texts)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efast_bert\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eprediction\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertClassificationPredictor\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eOUTPUT_DIR\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'model_out'\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003epredictor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eBertClassificationPredictor\u003c/span\u003e(\n\t\t\t\t\u003cspan class=\"pl-s1\"\u003emodel_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e,\n\t\t\t\t\u003cspan class=\"pl-s1\"\u003elabel_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eLABEL_PATH\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# location for labels.csv file\u003c/span\u003e\n\t\t\t\t\u003cspan class=\"pl-s1\"\u003emulti_label\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n\t\t\t\t\u003cspan class=\"pl-s1\"\u003emodel_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'xlnet'\u003c/span\u003e,\n\t\t\t\t\u003cspan class=\"pl-s1\"\u003edo_lower_case\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n\t\t\t\t\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# set custom torch.device, defaults to cuda if available\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Single prediction\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esingle_prediction\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epredictor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"just get me result for this text\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Batch predictions\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etexts\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\n\t\u003cspan class=\"pl-s\"\u003e\"this is the first text\"\u003c/span\u003e,\n\t\u003cspan class=\"pl-s\"\u003e\"this is the second text\"\u003c/span\u003e\n\t]\n\n\u003cspan class=\"pl-s1\"\u003emultiple_predictions\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epredictor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epredict_batch\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etexts\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLanguage Model Fine-tuning\u003c/h2\u003e\u003ca id=\"user-content-language-model-fine-tuning\" class=\"anchor\" aria-label=\"Permalink: Language Model Fine-tuning\" href=\"#language-model-fine-tuning\"\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\"\u003eA useful approach to use BERT based models on custom datasets is to first finetune the language model task for the custom dataset, an apporach followed by fast.ai's ULMFit. The idea is to start with a pre-trained model and further train the model on the raw text of the custom dataset. We will use the masked LM task to finetune the language model.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis section will describe the usage of FastBert to finetune the language model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1. Import the necessary libraries\u003c/h3\u003e\u003ca id=\"user-content-1-import-the-necessary-libraries\" class=\"anchor\" aria-label=\"Permalink: 1. Import the necessary libraries\" href=\"#1-import-the-necessary-libraries\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe necessary objects are stored in the files with '_lm' suffix.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Language model Databunch\nfrom fast_bert.data_lm import BertLMDataBunch\n# Language model learner\nfrom fast_bert.learner_lm import BertLMLearner\n\nfrom pathlib import Path\nfrom box import Box\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Language model Databunch\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efast_bert\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata_lm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertLMDataBunch\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Language model learner\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efast_bert\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elearner_lm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertLMLearner\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epathlib\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003ePath\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebox\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBox\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. Define parameters and setup datapaths\u003c/h3\u003e\u003ca id=\"user-content-2-define-parameters-and-setup-datapaths\" class=\"anchor\" aria-label=\"Permalink: 2. Define parameters and setup datapaths\" href=\"#2-define-parameters-and-setup-datapaths\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Box is a nice wrapper to create an object from a json dict\nargs = Box({\n \u0026quot;seed\u0026quot;: 42,\n \u0026quot;task_name\u0026quot;: 'imdb_reviews_lm',\n \u0026quot;model_name\u0026quot;: 'roberta-base',\n \u0026quot;model_type\u0026quot;: 'roberta',\n \u0026quot;train_batch_size\u0026quot;: 16,\n \u0026quot;learning_rate\u0026quot;: 4e-5,\n \u0026quot;num_train_epochs\u0026quot;: 20,\n \u0026quot;fp16\u0026quot;: True,\n \u0026quot;fp16_opt_level\u0026quot;: \u0026quot;O2\u0026quot;,\n \u0026quot;warmup_steps\u0026quot;: 1000,\n \u0026quot;logging_steps\u0026quot;: 0,\n \u0026quot;max_seq_length\u0026quot;: 512,\n \u0026quot;multi_gpu\u0026quot;: True if torch.cuda.device_count() \u0026gt; 1 else False\n})\n\nDATA_PATH = Path('../lm_data/')\nLOG_PATH = Path('../logs')\nMODEL_PATH = Path('../lm_model_{}/'.format(args.model_type))\n\nDATA_PATH.mkdir(exist_ok=True)\nMODEL_PATH.mkdir(exist_ok=True)\nLOG_PATH.mkdir(exist_ok=True)\n\n\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Box is a nice wrapper to create an object from a json dict\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eBox\u003c/span\u003e({\n \u003cspan class=\"pl-s\"\u003e\"seed\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e42\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"task_name\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'imdb_reviews_lm'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"model_name\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'roberta-base'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"model_type\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'roberta'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"train_batch_size\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"learning_rate\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4e-5\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"num_train_epochs\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"fp16\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"fp16_opt_level\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"O2\"\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"warmup_steps\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"logging_steps\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"max_seq_length\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e512\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"multi_gpu\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecuda\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edevice_count\u003c/span\u003e() \u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e\n})\n\n\u003cspan class=\"pl-c1\"\u003eDATA_PATH\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ePath\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'../lm_data/'\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eLOG_PATH\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ePath\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'../logs'\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ePath\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'../lm_model_{}/'\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodel_type\u003c/span\u003e))\n\n\u003cspan class=\"pl-c1\"\u003eDATA_PATH\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emkdir\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eexist_ok\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emkdir\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eexist_ok\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eLOG_PATH\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emkdir\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eexist_ok\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3. Create DataBunch object\u003c/h3\u003e\u003ca id=\"user-content-3-create-databunch-object\" class=\"anchor\" aria-label=\"Permalink: 3. Create DataBunch object\" href=\"#3-create-databunch-object\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe BertLMDataBunch class contains a static method 'from_raw_corpus' that will take the list of raw texts and create DataBunch for the language model learner.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe method will at first preprocess the text list by removing html tags, extra spaces and more and then create files \u003ccode\u003elm_train.txt\u003c/code\u003e and \u003ccode\u003elm_val.txt\u003c/code\u003e. These files will be used for training and evaluating the language model finetuning task.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe next step will be to featurize the texts. The text will be tokenized, numericalized and split into blocks on 512 tokens (including special tokens).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"databunch_lm = BertLMDataBunch.from_raw_corpus(\n\t\t\t\t\tdata_dir=DATA_PATH,\n\t\t\t\t\ttext_list=texts,\n\t\t\t\t\ttokenizer=args.model_name,\n\t\t\t\t\tbatch_size_per_gpu=args.train_batch_size,\n\t\t\t\t\tmax_seq_length=args.max_seq_length,\n multi_gpu=args.multi_gpu,\n model_type=args.model_type,\n logger=logger)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003edatabunch_lm\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertLMDataBunch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_raw_corpus\u003c/span\u003e(\n\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003edata_dir\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eDATA_PATH\u003c/span\u003e,\n\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003etext_list\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etexts\u003c/span\u003e,\n\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodel_name\u003c/span\u003e,\n\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003ebatch_size_per_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etrain_batch_size\u003c/span\u003e,\n\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003emax_seq_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emax_seq_length\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emulti_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emulti_gpu\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emodel_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodel_type\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs this step can take some time based on the size of your custom dataset's text, the featurized data will be cached in pickled files in the data_dir/lm_cache folder.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe next time, instead of using from_raw_corpus method, you may want to directly instantiate the DataBunch object as shown below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"databunch_lm = BertLMDataBunch(\n\t\t\t\t\t\tdata_dir=DATA_PATH,\n\t\t\t\t\t\ttokenizer=args.model_name,\n batch_size_per_gpu=args.train_batch_size,\n max_seq_length=args.max_seq_length,\n multi_gpu=args.multi_gpu,\n model_type=args.model_type,\n logger=logger)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003edatabunch_lm\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eBertLMDataBunch\u003c/span\u003e(\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003edata_dir\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eDATA_PATH\u003c/span\u003e,\n\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodel_name\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ebatch_size_per_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etrain_batch_size\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_seq_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emax_seq_length\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emulti_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emulti_gpu\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emodel_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodel_type\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e4. Create the LM Learner object\u003c/h3\u003e\u003ca id=\"user-content-4-create-the-lm-learner-object\" class=\"anchor\" aria-label=\"Permalink: 4. Create the LM Learner object\" href=\"#4-create-the-lm-learner-object\"\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\"\u003eBertLearner is the ‘learner’ object that holds everything together. It encapsulates the key logic for the lifecycle of the model such as training, validation and inference.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe learner object will take the databunch created earlier as as input alongwith some of the other parameters such as location for one of the pretrained models, FP16 training, multi_gpu and multi_label options.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe learner class contains the logic for training loop, validation loop, and optimizer strategies. This help the developers focus on their custom use-cases without worrying about these repetitive activities.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAt the same time the learner object is flexible enough to be customized either via using flexible parameters or by creating a subclass of BertLearner and redefining relevant methods.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"learner = BertLMLearner.from_pretrained_model(\n\t\t\t\t\t\t\tdataBunch=databunch_lm,\n\t\t\t\t\t\t\tpretrained_path=args.model_name,\n\t\t\t\t\t\t\toutput_dir=MODEL_PATH,\n\t\t\t\t\t\t\tmetrics=[],\n\t\t\t\t\t\t\tdevice=device,\n\t\t\t\t\t\t\tlogger=logger,\n\t\t\t\t\t\t\tmulti_gpu=args.multi_gpu,\n\t\t\t\t\t\t\tlogging_steps=args.logging_steps,\n\t\t\t\t\t\t\tfp16_opt_level=args.fp16_opt_level)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBertLMLearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained_model\u003c/span\u003e(\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003edataBunch\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edatabunch_lm\u003c/span\u003e,\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003epretrained_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodel_name\u003c/span\u003e,\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003eoutput_dir\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e,\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[],\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e,\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elogger\u003c/span\u003e,\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003emulti_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emulti_gpu\u003c/span\u003e,\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003elogging_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elogging_steps\u003c/span\u003e,\n\t\t\t\t\t\t\t\u003cspan class=\"pl-s1\"\u003efp16_opt_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efp16_opt_level\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e5. Train the model\u003c/h3\u003e\u003ca id=\"user-content-5-train-the-model\" class=\"anchor\" aria-label=\"Permalink: 5. Train the model\" href=\"#5-train-the-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"learner.fit(epochs=6,\n\t\t\tlr=6e-5,\n\t\t\tvalidate=True, \t# Evaluate the model after each epoch\n\t\t\tschedule_type=\u0026quot;warmup_cosine\u0026quot;,\n\t\t\toptimizer_type=\u0026quot;lamb\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eepochs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e,\n\t\t\t\u003cspan class=\"pl-s1\"\u003elr\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e6e-5\u003c/span\u003e,\n\t\t\t\u003cspan class=\"pl-s1\"\u003evalidate\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \t\u003cspan class=\"pl-c\"\u003e# Evaluate the model after each epoch\u003c/span\u003e\n\t\t\t\u003cspan class=\"pl-s1\"\u003eschedule_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"warmup_cosine\"\u003c/span\u003e,\n\t\t\t\u003cspan class=\"pl-s1\"\u003eoptimizer_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"lamb\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFast-Bert now supports LAMB optmizer. Due to the speed of training, we have set LAMB as the default optimizer. You can switch back to AdamW by setting optimizer_type to 'adamw'.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e6. Save trained model artifacts\u003c/h3\u003e\u003ca id=\"user-content-6-save-trained-model-artifacts\" class=\"anchor\" aria-label=\"Permalink: 6. Save trained model artifacts\" href=\"#6-save-trained-model-artifacts\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"learner.save_model()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003elearner\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esave_model\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eModel artefacts will be persisted in the output_dir/'model_out' path provided to the learner object. Following files will be persisted:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003epytorch_model.bin\u003c/td\u003e\n\u003ctd\u003etrained model weights\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003espiece.model\u003c/td\u003e\n\u003ctd\u003esentence tokenizer vocabulary (for xlnet models)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003evocab.txt\u003c/td\u003e\n\u003ctd\u003eworkpiece tokenizer vocabulary (for bert models)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especial_tokens_map.json\u003c/td\u003e\n\u003ctd\u003especial tokens mappings\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econfig.json\u003c/td\u003e\n\u003ctd\u003emodel config\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eadded_tokens.json\u003c/td\u003e\n\u003ctd\u003elist of new tokens\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eThe pytorch_model.bin contains the finetuned weights and you can point the classification task learner object to this file throgh the \u003ccode\u003efinetuned_wgts_path\u003c/code\u003e parameter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAmazon Sagemaker Support\u003c/h2\u003e\u003ca id=\"user-content-amazon-sagemaker-support\" class=\"anchor\" aria-label=\"Permalink: Amazon Sagemaker Support\" href=\"#amazon-sagemaker-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe purpose of this library is to let you train and deploy production grade models. As transformer models require expensive GPUs to train, I have added support for training and deploying model on AWS SageMaker.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe repository contains the docker image and code for building BERT based classification models in Amazon SageMaker.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlease refer to my blog \u003ca href=\"https://towardsdatascience.com/train-and-deploy-mighty-transformer-nlp-models-using-fastbert-and-aws-sagemaker-cc4303c51cf3\" rel=\"nofollow\"\u003eTrain and Deploy the Mighty BERT based NLP models using FastBert and Amazon SageMaker\u003c/a\u003e that provides detailed explanation on using SageMaker with FastBert.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease include a mention of \u003ca href=\"https://github.com/kaushaltrivedi/fast-bert\"\u003ethis library\u003c/a\u003e and HuggingFace \u003ca href=\"https://github.com/huggingface/pytorch-transformers\"\u003epytorch-transformers\u003c/a\u003e library and a link to the present repository if you use this work in a published or open-source project.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAlso include my blogs on this topic:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384\" rel=\"nofollow\"\u003eIntroducing FastBert — A simple Deep Learning library for BERT Models\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://medium.com/huggingface/multi-label-text-classification-using-bert-the-mighty-transformer-69714fa3fb3d\" rel=\"nofollow\"\u003eMulti-label Text Classification using BERT – The Mighty Transformer\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://towardsdatascience.com/train-and-deploy-mighty-transformer-nlp-models-using-fastbert-and-aws-sagemaker-cc4303c51cf3\" rel=\"nofollow\"\u003eTrain and Deploy the Mighty BERT based NLP models using FastBert and Amazon SageMaker\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Fast-Bert","anchor":"fast-bert","htmlText":"Fast-Bert"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"With pip","anchor":"with-pip","htmlText":"With pip"},{"level":3,"text":"From source","anchor":"from-source","htmlText":"From source"},{"level":2,"text":"Usage","anchor":"usage","htmlText":"Usage"},{"level":2,"text":"Text Classification","anchor":"text-classification","htmlText":"Text Classification"},{"level":3,"text":"1. Create a DataBunch object","anchor":"1-create-a-databunch-object","htmlText":"1. Create a DataBunch object"},{"level":4,"text":"File format for train.csv and val.csv","anchor":"file-format-for-traincsv-and-valcsv","htmlText":"File format for train.csv and val.csv"},{"level":4,"text":"Tokenizer","anchor":"tokenizer","htmlText":"Tokenizer"},{"level":4,"text":"Model Type","anchor":"model-type","htmlText":"Model Type"},{"level":3,"text":"2. Create a Learner Object","anchor":"2-create-a-learner-object","htmlText":"2. Create a Learner Object"},{"level":3,"text":"3. Find the optimal learning rate","anchor":"3-find-the-optimal-learning-rate","htmlText":"3. Find the optimal learning rate"},{"level":3,"text":"4. Train the model","anchor":"4-train-the-model","htmlText":"4. Train the model"},{"level":3,"text":"5. Save trained model artifacts","anchor":"5-save-trained-model-artifacts","htmlText":"5. Save trained model artifacts"},{"level":3,"text":"6. Model Inference","anchor":"6-model-inference","htmlText":"6. Model Inference"},{"level":2,"text":"Language Model Fine-tuning","anchor":"language-model-fine-tuning","htmlText":"Language Model Fine-tuning"},{"level":3,"text":"1. Import the necessary libraries","anchor":"1-import-the-necessary-libraries","htmlText":"1. Import the necessary libraries"},{"level":3,"text":"2. Define parameters and setup datapaths","anchor":"2-define-parameters-and-setup-datapaths","htmlText":"2. Define parameters and setup datapaths"},{"level":3,"text":"3. Create DataBunch object","anchor":"3-create-databunch-object","htmlText":"3. Create DataBunch object"},{"level":3,"text":"4. Create the LM Learner object","anchor":"4-create-the-lm-learner-object","htmlText":"4. Create the LM Learner object"},{"level":3,"text":"5. Train the model","anchor":"5-train-the-model","htmlText":"5. Train the model"},{"level":3,"text":"6. Save trained model artifacts","anchor":"6-save-trained-model-artifacts","htmlText":"6. Save trained model artifacts"},{"level":2,"text":"Amazon Sagemaker Support","anchor":"amazon-sagemaker-support","htmlText":"Amazon Sagemaker Support"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Futterworks%2Ffast-bert"}},{"displayName":"LICENSE","repoName":"fast-bert","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Futterworks%2Ffast-bert"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div 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0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Fast-Bert</h1><a id="user-content-fast-bert" class="anchor" aria-label="Permalink: Fast-Bert" href="#fast-bert"><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://github.com/deepmipt/DeepPavlov/blob/master/LICENSE"><img src="https://camo.githubusercontent.com/c355f200ea90fddaa407b6eaab303663a669248ea3ca7b1fcf77dbe04ff5f48c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d626c75652e737667" alt="License Apache 2.0" data-canonical-src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" style="max-width: 100%;"></a> <a href="https://badge.fury.io/py/fast-bert" rel="nofollow"><img src="https://camo.githubusercontent.com/4ecde12a97767241e17963221aa29306cd449f174826cd34c7502102dfab2377/68747470733a2f2f62616467652e667572792e696f2f70792f666173742d626572742e737667" alt="PyPI version" data-canonical-src="https://badge.fury.io/py/fast-bert.svg" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/8678d819e273bf32f9b3ad66409b523476e8e439a46e46934b7938c17295265a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e36253230253743253230332e372d677265656e2e737667"><img src="https://camo.githubusercontent.com/8678d819e273bf32f9b3ad66409b523476e8e439a46e46934b7938c17295265a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e36253230253743253230332e372d677265656e2e737667" alt="Python 3.6, 3.7" data-canonical-src="https://img.shields.io/badge/python-3.6%20%7C%203.7-green.svg" style="max-width: 100%;"></a></p> <p dir="auto"><strong>New - Learning Rate Finder for Text Classification Training (borrowed with thanks from <a href="https://github.com/davidtvs/pytorch-lr-finder">https://github.com/davidtvs/pytorch-lr-finder</a>)</strong></p> <p dir="auto"><strong>Supports LAMB optimizer for faster training.</strong> Please refer to <a href="https://arxiv.org/abs/1904.00962" rel="nofollow">https://arxiv.org/abs/1904.00962</a> for the paper on LAMB optimizer.</p> <p dir="auto"><strong>Supports BERT and XLNet for both Multi-Class and Multi-Label text classification.</strong></p> <p dir="auto">Fast-Bert is the deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification.</p> <p dir="auto">The work on FastBert is built on solid foundations provided by the excellent <a href="https://github.com/huggingface/pytorch-pretrained-BERT">Hugging Face BERT PyTorch library</a> and is inspired by <a href="https://github.com/fastai/fastai">fast.ai</a> and strives to make the cutting edge deep learning technologies accessible for the vast community of machine learning practitioners.</p> <p dir="auto">With FastBert, you will be able to:</p> <ol dir="auto"> <li> <p dir="auto">Train (more precisely fine-tune) BERT, RoBERTa and XLNet text classification models on your custom dataset.</p> </li> <li> <p dir="auto">Tune model hyper-parameters such as epochs, learning rate, batch size, optimiser schedule and more.</p> </li> <li> <p dir="auto">Save and deploy trained model for inference (including on AWS Sagemaker).</p> </li> </ol> <p dir="auto">Fast-Bert will support both multi-class and multi-label text classification for the following and in due course, it will support other NLU tasks such as Named Entity Recognition, Question Answering and Custom Corpus fine-tuning.</p> <ol dir="auto"> <li><strong><a href="https://github.com/google-research/bert">BERT</a></strong> (from Google) released with the paper <a href="https://arxiv.org/abs/1810.04805" rel="nofollow">BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding</a> by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.</li> </ol> <ol start="2" dir="auto"> <li> <p dir="auto"><strong><a href="https://github.com/zihangdai/xlnet/">XLNet</a></strong> (from Google/CMU) released with the paper <a href="https://arxiv.org/abs/1906.08237" rel="nofollow">XLNet: Generalized Autoregressive Pretraining for Language Understanding</a> by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.</p> </li> <li> <p dir="auto"><strong><a href="https://arxiv.org/abs/1907.11692" rel="nofollow">RoBERTa</a></strong> (from Facebook), a Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du et al.</p> </li> <li> <p dir="auto"><strong>DistilBERT (from HuggingFace)</strong>, released together with the blogpost <a href="https://medium.com/huggingface/distilbert-8cf3380435b5" rel="nofollow">Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT</a> by Victor Sanh, Lysandre Debut and Thomas Wolf.</p> </li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repo is tested on Python 3.6+.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">With pip</h3><a id="user-content-with-pip" class="anchor" aria-label="Permalink: With pip" href="#with-pip"><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">PyTorch-Transformers can be installed by pip as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install fast-bert"><pre>pip install fast-bert</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">From source</h3><a id="user-content-from-source" class="anchor" aria-label="Permalink: From source" href="#from-source"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Clone the repository and run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install [--editable] ."><pre>pip install [--editable] <span class="pl-c1">.</span></pre></div> <p dir="auto">or</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install git+https://github.com/kaushaltrivedi/fast-bert.git"><pre>pip install git+https://github.com/kaushaltrivedi/fast-bert.git</pre></div> <p dir="auto">You will also need to install NVIDIA Apex.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/NVIDIA/apex cd apex pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./"><pre>git clone https://github.com/NVIDIA/apex <span class="pl-c1">cd</span> apex pip install -v --no-cache-dir --global-option=<span class="pl-s"><span class="pl-pds">"</span>--cpp_ext<span class="pl-pds">"</span></span> --global-option=<span class="pl-s"><span class="pl-pds">"</span>--cuda_ext<span class="pl-pds">"</span></span> ./</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Usage</h2><a id="user-content-usage" class="anchor" aria-label="Permalink: Usage" href="#usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Text Classification</h2><a id="user-content-text-classification" class="anchor" aria-label="Permalink: Text Classification" href="#text-classification"><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">1. Create a DataBunch object</h3><a id="user-content-1-create-a-databunch-object" class="anchor" aria-label="Permalink: 1. Create a DataBunch object" href="#1-create-a-databunch-object"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The databunch object takes training, validation and test csv files and converts the data into internal representation for BERT, RoBERTa, DistilBERT or XLNet. The object also instantiates the correct data-loaders based on device profile and batch_size and max_sequence_length.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=" from fast_bert.data_cls import BertDataBunch databunch = BertDataBunch(DATA_PATH, LABEL_PATH, tokenizer='bert-base-uncased', train_file='train.csv', val_file='val.csv', label_file='labels.csv', text_col='text', label_col='label', batch_size_per_gpu=16, max_seq_length=512, multi_gpu=True, multi_label=False, model_type='bert')"><pre><span class="pl-k">from</span> <span class="pl-s1">fast_bert</span>.<span class="pl-s1">data_cls</span> <span class="pl-k">import</span> <span class="pl-v">BertDataBunch</span> <span class="pl-s1">databunch</span> <span class="pl-c1">=</span> <span class="pl-en">BertDataBunch</span>(<span class="pl-c1">DATA_PATH</span>, <span class="pl-c1">LABEL_PATH</span>, <span class="pl-s1">tokenizer</span><span class="pl-c1">=</span><span class="pl-s">'bert-base-uncased'</span>, <span class="pl-s1">train_file</span><span class="pl-c1">=</span><span class="pl-s">'train.csv'</span>, <span class="pl-s1">val_file</span><span class="pl-c1">=</span><span class="pl-s">'val.csv'</span>, <span class="pl-s1">label_file</span><span class="pl-c1">=</span><span class="pl-s">'labels.csv'</span>, <span class="pl-s1">text_col</span><span class="pl-c1">=</span><span class="pl-s">'text'</span>, <span class="pl-s1">label_col</span><span class="pl-c1">=</span><span class="pl-s">'label'</span>, <span class="pl-s1">batch_size_per_gpu</span><span class="pl-c1">=</span><span class="pl-c1">16</span>, <span class="pl-s1">max_seq_length</span><span class="pl-c1">=</span><span class="pl-c1">512</span>, <span class="pl-s1">multi_gpu</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">multi_label</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">model_type</span><span class="pl-c1">=</span><span class="pl-s">'bert'</span>)</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">File format for train.csv and val.csv</h4><a id="user-content-file-format-for-traincsv-and-valcsv" class="anchor" aria-label="Permalink: File format for train.csv and val.csv" href="#file-format-for-traincsv-and-valcsv"><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>index</th> <th>text</th> <th>label</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>Looking through the other comments, I'm amazed that there aren't any warnings to potential viewers of what they have to look forward to when renting this garbage. First off, I rented this thing with the understanding that it was a competently rendered Indiana Jones knock-off.</td> <td>neg</td> </tr> <tr> <td>1</td> <td>I've watched the first 17 episodes and this series is simply amazing! I haven't been this interested in an anime series since Neon Genesis Evangelion. This series is actually based off an h-game, which I'm not sure if it's been done before or not, I haven't played the game, but from what I've heard it follows it very well</td> <td>pos</td> </tr> <tr> <td>2</td> <td>his movie is nothing short of a dark, gritty masterpiece. I may be bias, as the Apartheid era is an area I've always felt for.</td> <td>pos</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">In case the column names are different than the usual text and labels, you will have to provide those names in the databunch text_col and label_col parameters.</p> <p dir="auto"><strong>labels.csv</strong> will contain a list of all unique labels. In this case the file will contain:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pos neg"><pre lang="csv" class="notranslate"><code>pos neg </code></pre></div> <p dir="auto">For multi-label classification, <strong>labels.csv</strong> will contain all possible labels:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="toxic severe_toxic obscene threat insult identity_hate"><pre class="notranslate"><code>toxic severe_toxic obscene threat insult identity_hate </code></pre></div> <p dir="auto">The file <strong>train.csv</strong> will then contain one column for each label, with each column value being either 0 or 1. Don't forget to change <code>multi_label=True</code> for multi-label classification in <code>BertDataBunch</code>.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>id</th> <th>text</th> <th>toxic</th> <th>severe_toxic</th> <th>obscene</th> <th>threat</th> <th>insult</th> <th>identity_hate</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>Why the edits made under my username Hardcore Metallica Fan were reverted?</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <td>0</td> <td>I will mess you up</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">label_col will be a list of label column names. In this case it will be:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="['toxic','severe_toxic','obscene','threat','insult','identity_hate']"><pre>[<span class="pl-s">'toxic'</span>,<span class="pl-s">'severe_toxic'</span>,<span class="pl-s">'obscene'</span>,<span class="pl-s">'threat'</span>,<span class="pl-s">'insult'</span>,<span class="pl-s">'identity_hate'</span>]</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Tokenizer</h4><a id="user-content-tokenizer" class="anchor" aria-label="Permalink: Tokenizer" href="#tokenizer"><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">You can either create a tokenizer object and pass it to DataBunch or you can pass the model name as tokenizer and DataBunch will automatically download and instantiate an appropriate tokenizer object.</p> <p dir="auto">For example for using XLNet base cased model, set tokenizer parameter to 'xlnet-base-cased'. DataBunch will automatically download and instantiate XLNetTokenizer with the vocabulary for xlnet-base-cased model.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Model Type</h4><a id="user-content-model-type" class="anchor" aria-label="Permalink: Model Type" href="#model-type"><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">Fast-Bert supports XLNet, RoBERTa and BERT based classification models. Set model type parameter value to <strong>'bert'</strong>, <strong>roberta</strong> or <strong>'xlnet'</strong> in order to initiate an appropriate databunch object.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">2. Create a Learner Object</h3><a id="user-content-2-create-a-learner-object" class="anchor" aria-label="Permalink: 2. Create a Learner Object" href="#2-create-a-learner-object"><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">BertLearner is the ‘learner’ object that holds everything together. It encapsulates the key logic for the lifecycle of the model such as training, validation and inference.</p> <p dir="auto">The learner object will take the databunch created earlier as as input alongwith some of the other parameters such as location for one of the pretrained models, FP16 training, multi_gpu and multi_label options.</p> <p dir="auto">The learner class contains the logic for training loop, validation loop, optimiser strategies and key metrics calculation. This help the developers focus on their custom use-cases without worrying about these repetitive activities.</p> <p dir="auto">At the same time the learner object is flexible enough to be customised either via using flexible parameters or by creating a subclass of BertLearner and redefining relevant methods.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=" from fast_bert.learner_cls import BertLearner from fast_bert.metrics import accuracy import logging logger = logging.getLogger() device_cuda = torch.device("cuda") metrics = [{'name': 'accuracy', 'function': accuracy}] learner = BertLearner.from_pretrained_model( databunch, pretrained_path='bert-base-uncased', metrics=metrics, device=device_cuda, logger=logger, output_dir=OUTPUT_DIR, finetuned_wgts_path=None, warmup_steps=500, multi_gpu=True, is_fp16=True, multi_label=False, logging_steps=50)"><pre><span class="pl-k">from</span> <span class="pl-s1">fast_bert</span>.<span class="pl-s1">learner_cls</span> <span class="pl-k">import</span> <span class="pl-v">BertLearner</span> <span class="pl-k">from</span> <span class="pl-s1">fast_bert</span>.<span class="pl-s1">metrics</span> <span class="pl-k">import</span> <span class="pl-s1">accuracy</span> <span class="pl-k">import</span> <span class="pl-s1">logging</span> <span class="pl-s1">logger</span> <span class="pl-c1">=</span> <span class="pl-s1">logging</span>.<span class="pl-c1">getLogger</span>() <span class="pl-s1">device_cuda</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-c1">device</span>(<span class="pl-s">"cuda"</span>) <span class="pl-s1">metrics</span> <span class="pl-c1">=</span> [{<span class="pl-s">'name'</span>: <span class="pl-s">'accuracy'</span>, <span class="pl-s">'function'</span>: <span class="pl-s1">accuracy</span>}] <span class="pl-s1">learner</span> <span class="pl-c1">=</span> <span class="pl-v">BertLearner</span>.<span class="pl-c1">from_pretrained_model</span>( <span class="pl-s1">databunch</span>, <span class="pl-s1">pretrained_path</span><span class="pl-c1">=</span><span class="pl-s">'bert-base-uncased'</span>, <span class="pl-s1">metrics</span><span class="pl-c1">=</span><span class="pl-s1">metrics</span>, <span class="pl-s1">device</span><span class="pl-c1">=</span><span class="pl-s1">device_cuda</span>, <span class="pl-s1">logger</span><span class="pl-c1">=</span><span class="pl-s1">logger</span>, <span class="pl-s1">output_dir</span><span class="pl-c1">=</span><span class="pl-c1">OUTPUT_DIR</span>, <span class="pl-s1">finetuned_wgts_path</span><span class="pl-c1">=</span><span class="pl-c1">None</span>, <span class="pl-s1">warmup_steps</span><span class="pl-c1">=</span><span class="pl-c1">500</span>, <span class="pl-s1">multi_gpu</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">is_fp16</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">multi_label</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">logging_steps</span><span class="pl-c1">=</span><span class="pl-c1">50</span>)</pre></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>databunch</td> <td>Databunch object created earlier</td> </tr> <tr> <td>pretrained_path</td> <td>Directory for the location of the pretrained model files or the name of one of the pretrained models i.e. bert-base-uncased, xlnet-large-cased, etc</td> </tr> <tr> <td>metrics</td> <td>List of metrics functions that you want the model to calculate on the validation set, e.g. accuracy, beta, etc</td> </tr> <tr> <td>device</td> <td>torch.device of type <em>cuda</em> or <em>cpu</em></td> </tr> <tr> <td>logger</td> <td>logger object</td> </tr> <tr> <td>output_dir</td> <td>Directory for model to save trained artefacts, tokenizer vocabulary and tensorboard files</td> </tr> <tr> <td>finetuned_wgts_path</td> <td>provide the location for fine-tuned language model (experimental feature)</td> </tr> <tr> <td>warmup_steps</td> <td>number of training warms steps for the scheduler</td> </tr> <tr> <td>multi_gpu</td> <td>multiple GPUs available e.g. if running on AWS p3.8xlarge instance</td> </tr> <tr> <td>is_fp16</td> <td>FP16 training</td> </tr> <tr> <td>multi_label</td> <td>multilabel classification</td> </tr> <tr> <td>logging_steps</td> <td>number of steps between each tensorboard metrics calculation. Set it to 0 to disable tensor flow logging. Keeping this value too low will lower the training speed as model will be evaluated each time the metrics are logged</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">3. Find the optimal learning rate</h3><a id="user-content-3-find-the-optimal-learning-rate" class="anchor" aria-label="Permalink: 3. Find the optimal learning rate" href="#3-find-the-optimal-learning-rate"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The learning rate is one of the most important hyperparameters for model training. We have incorporated the learining rate finder that was proposed by Leslie Smith and then built into the fastai library.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="learner.lr_find(start_lr=1e-5,optimizer_type='lamb')"><pre><span class="pl-s1">learner</span>.<span class="pl-c1">lr_find</span>(<span class="pl-s1">start_lr</span><span class="pl-c1">=</span><span class="pl-c1">1e-5</span>,<span class="pl-s1">optimizer_type</span><span class="pl-c1">=</span><span class="pl-s">'lamb'</span>)</pre></div> <p dir="auto">The code is heavily borrowed from David Silva's <a href="https://github.com/davidtvs/pytorch-lr-finder">pytorch-lr-finder library</a>.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/utterworks/fast-bert/blob/main/images/lr_finder.png"><img src="/utterworks/fast-bert/raw/main/images/lr_finder.png" alt="Learning rate range test" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">4. Train the model</h3><a id="user-content-4-train-the-model" class="anchor" aria-label="Permalink: 4. Train the model" href="#4-train-the-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="learner.fit(epochs=6, lr=6e-5, validate=True, # Evaluate the model after each epoch schedule_type="warmup_cosine", optimizer_type="lamb")"><pre><span class="pl-s1">learner</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">epochs</span><span class="pl-c1">=</span><span class="pl-c1">6</span>, <span class="pl-s1">lr</span><span class="pl-c1">=</span><span class="pl-c1">6e-5</span>, <span class="pl-s1">validate</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-c"># Evaluate the model after each epoch</span> <span class="pl-s1">schedule_type</span><span class="pl-c1">=</span><span class="pl-s">"warmup_cosine"</span>, <span class="pl-s1">optimizer_type</span><span class="pl-c1">=</span><span class="pl-s">"lamb"</span>)</pre></div> <p dir="auto">Fast-Bert now supports LAMB optmizer. Due to the speed of training, we have set LAMB as the default optimizer. You can switch back to AdamW by setting optimizer_type to 'adamw'.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">5. Save trained model artifacts</h3><a id="user-content-5-save-trained-model-artifacts" class="anchor" aria-label="Permalink: 5. Save trained model artifacts" href="#5-save-trained-model-artifacts"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="learner.save_model()"><pre><span class="pl-s1">learner</span>.<span class="pl-c1">save_model</span>()</pre></div> <p dir="auto">Model artefacts will be persisted in the output_dir/'model_out' path provided to the learner object. Following files will be persisted:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>File name</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>pytorch_model.bin</td> <td>trained model weights</td> </tr> <tr> <td>spiece.model</td> <td>sentence tokenizer vocabulary (for xlnet models)</td> </tr> <tr> <td>vocab.txt</td> <td>workpiece tokenizer vocabulary (for bert models)</td> </tr> <tr> <td>special_tokens_map.json</td> <td>special tokens mappings</td> </tr> <tr> <td>config.json</td> <td>model config</td> </tr> <tr> <td>added_tokens.json</td> <td>list of new tokens</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">As the model artefacts are all stored in the same folder, you will be able to instantiate the learner object to run inference by pointing pretrained_path to this location.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">6. Model Inference</h3><a id="user-content-6-model-inference" class="anchor" aria-label="Permalink: 6. Model Inference" href="#6-model-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you already have a Learner object with trained model instantiated, just call predict_batch method on the learner object with the list of text data:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="texts = ['I really love the Netflix original movies', 'this movie is not worth watching'] predictions = learner.predict_batch(texts)"><pre><span class="pl-s1">texts</span> <span class="pl-c1">=</span> [<span class="pl-s">'I really love the Netflix original movies'</span>, <span class="pl-s">'this movie is not worth watching'</span>] <span class="pl-s1">predictions</span> <span class="pl-c1">=</span> <span class="pl-s1">learner</span>.<span class="pl-c1">predict_batch</span>(<span class="pl-s1">texts</span>)</pre></div> <p dir="auto">If you have persistent trained model and just want to run inference logic on that trained model, use the second approach, i.e. the predictor object.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from fast_bert.prediction import BertClassificationPredictor MODEL_PATH = OUTPUT_DIR/'model_out' predictor = BertClassificationPredictor( model_path=MODEL_PATH, label_path=LABEL_PATH, # location for labels.csv file multi_label=False, model_type='xlnet', do_lower_case=False, device=None) # set custom torch.device, defaults to cuda if available # Single prediction single_prediction = predictor.predict("just get me result for this text") # Batch predictions texts = [ "this is the first text", "this is the second text" ] multiple_predictions = predictor.predict_batch(texts)"><pre><span class="pl-k">from</span> <span class="pl-s1">fast_bert</span>.<span class="pl-s1">prediction</span> <span class="pl-k">import</span> <span class="pl-v">BertClassificationPredictor</span> <span class="pl-c1">MODEL_PATH</span> <span class="pl-c1">=</span> <span class="pl-c1">OUTPUT_DIR</span><span class="pl-c1">/</span><span class="pl-s">'model_out'</span> <span class="pl-s1">predictor</span> <span class="pl-c1">=</span> <span class="pl-en">BertClassificationPredictor</span>( <span class="pl-s1">model_path</span><span class="pl-c1">=</span><span class="pl-c1">MODEL_PATH</span>, <span class="pl-s1">label_path</span><span class="pl-c1">=</span><span class="pl-c1">LABEL_PATH</span>, <span class="pl-c"># location for labels.csv file</span> <span class="pl-s1">multi_label</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">model_type</span><span class="pl-c1">=</span><span class="pl-s">'xlnet'</span>, <span class="pl-s1">do_lower_case</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">device</span><span class="pl-c1">=</span><span class="pl-c1">None</span>) <span class="pl-c"># set custom torch.device, defaults to cuda if available</span> <span class="pl-c"># Single prediction</span> <span class="pl-s1">single_prediction</span> <span class="pl-c1">=</span> <span class="pl-s1">predictor</span>.<span class="pl-c1">predict</span>(<span class="pl-s">"just get me result for this text"</span>) <span class="pl-c"># Batch predictions</span> <span class="pl-s1">texts</span> <span class="pl-c1">=</span> [ <span class="pl-s">"this is the first text"</span>, <span class="pl-s">"this is the second text"</span> ] <span class="pl-s1">multiple_predictions</span> <span class="pl-c1">=</span> <span class="pl-s1">predictor</span>.<span class="pl-c1">predict_batch</span>(<span class="pl-s1">texts</span>)</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Language Model Fine-tuning</h2><a id="user-content-language-model-fine-tuning" class="anchor" aria-label="Permalink: Language Model Fine-tuning" href="#language-model-fine-tuning"><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 useful approach to use BERT based models on custom datasets is to first finetune the language model task for the custom dataset, an apporach followed by fast.ai's ULMFit. The idea is to start with a pre-trained model and further train the model on the raw text of the custom dataset. We will use the masked LM task to finetune the language model.</p> <p dir="auto">This section will describe the usage of FastBert to finetune the language model.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">1. Import the necessary libraries</h3><a id="user-content-1-import-the-necessary-libraries" class="anchor" aria-label="Permalink: 1. Import the necessary libraries" href="#1-import-the-necessary-libraries"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The necessary objects are stored in the files with '_lm' suffix.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Language model Databunch from fast_bert.data_lm import BertLMDataBunch # Language model learner from fast_bert.learner_lm import BertLMLearner from pathlib import Path from box import Box"><pre><span class="pl-c"># Language model Databunch</span> <span class="pl-k">from</span> <span class="pl-s1">fast_bert</span>.<span class="pl-s1">data_lm</span> <span class="pl-k">import</span> <span class="pl-v">BertLMDataBunch</span> <span class="pl-c"># Language model learner</span> <span class="pl-k">from</span> <span class="pl-s1">fast_bert</span>.<span class="pl-s1">learner_lm</span> <span class="pl-k">import</span> <span class="pl-v">BertLMLearner</span> <span class="pl-k">from</span> <span class="pl-s1">pathlib</span> <span class="pl-k">import</span> <span class="pl-v">Path</span> <span class="pl-k">from</span> <span class="pl-s1">box</span> <span class="pl-k">import</span> <span class="pl-v">Box</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">2. Define parameters and setup datapaths</h3><a id="user-content-2-define-parameters-and-setup-datapaths" class="anchor" aria-label="Permalink: 2. Define parameters and setup datapaths" href="#2-define-parameters-and-setup-datapaths"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Box is a nice wrapper to create an object from a json dict args = Box({ "seed": 42, "task_name": 'imdb_reviews_lm', "model_name": 'roberta-base', "model_type": 'roberta', "train_batch_size": 16, "learning_rate": 4e-5, "num_train_epochs": 20, "fp16": True, "fp16_opt_level": "O2", "warmup_steps": 1000, "logging_steps": 0, "max_seq_length": 512, "multi_gpu": True if torch.cuda.device_count() > 1 else False }) DATA_PATH = Path('../lm_data/') LOG_PATH = Path('../logs') MODEL_PATH = Path('../lm_model_{}/'.format(args.model_type)) DATA_PATH.mkdir(exist_ok=True) MODEL_PATH.mkdir(exist_ok=True) LOG_PATH.mkdir(exist_ok=True) "><pre><span class="pl-c"># Box is a nice wrapper to create an object from a json dict</span> <span class="pl-s1">args</span> <span class="pl-c1">=</span> <span class="pl-en">Box</span>({ <span class="pl-s">"seed"</span>: <span class="pl-c1">42</span>, <span class="pl-s">"task_name"</span>: <span class="pl-s">'imdb_reviews_lm'</span>, <span class="pl-s">"model_name"</span>: <span class="pl-s">'roberta-base'</span>, <span class="pl-s">"model_type"</span>: <span class="pl-s">'roberta'</span>, <span class="pl-s">"train_batch_size"</span>: <span class="pl-c1">16</span>, <span class="pl-s">"learning_rate"</span>: <span class="pl-c1">4e-5</span>, <span class="pl-s">"num_train_epochs"</span>: <span class="pl-c1">20</span>, <span class="pl-s">"fp16"</span>: <span class="pl-c1">True</span>, <span class="pl-s">"fp16_opt_level"</span>: <span class="pl-s">"O2"</span>, <span class="pl-s">"warmup_steps"</span>: <span class="pl-c1">1000</span>, <span class="pl-s">"logging_steps"</span>: <span class="pl-c1">0</span>, <span class="pl-s">"max_seq_length"</span>: <span class="pl-c1">512</span>, <span class="pl-s">"multi_gpu"</span>: <span class="pl-c1">True</span> <span class="pl-k">if</span> <span class="pl-s1">torch</span>.<span class="pl-c1">cuda</span>.<span class="pl-c1">device_count</span>() <span class="pl-c1">></span> <span class="pl-c1">1</span> <span class="pl-k">else</span> <span class="pl-c1">False</span> }) <span class="pl-c1">DATA_PATH</span> <span class="pl-c1">=</span> <span class="pl-en">Path</span>(<span class="pl-s">'../lm_data/'</span>) <span class="pl-c1">LOG_PATH</span> <span class="pl-c1">=</span> <span class="pl-en">Path</span>(<span class="pl-s">'../logs'</span>) <span class="pl-c1">MODEL_PATH</span> <span class="pl-c1">=</span> <span class="pl-en">Path</span>(<span class="pl-s">'../lm_model_{}/'</span>.<span class="pl-c1">format</span>(<span class="pl-s1">args</span>.<span class="pl-c1">model_type</span>)) <span class="pl-c1">DATA_PATH</span>.<span class="pl-c1">mkdir</span>(<span class="pl-s1">exist_ok</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-c1">MODEL_PATH</span>.<span class="pl-c1">mkdir</span>(<span class="pl-s1">exist_ok</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-c1">LOG_PATH</span>.<span class="pl-c1">mkdir</span>(<span class="pl-s1">exist_ok</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) </pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">3. Create DataBunch object</h3><a id="user-content-3-create-databunch-object" class="anchor" aria-label="Permalink: 3. Create DataBunch object" href="#3-create-databunch-object"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The BertLMDataBunch class contains a static method 'from_raw_corpus' that will take the list of raw texts and create DataBunch for the language model learner.</p> <p dir="auto">The method will at first preprocess the text list by removing html tags, extra spaces and more and then create files <code>lm_train.txt</code> and <code>lm_val.txt</code>. These files will be used for training and evaluating the language model finetuning task.</p> <p dir="auto">The next step will be to featurize the texts. The text will be tokenized, numericalized and split into blocks on 512 tokens (including special tokens).</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="databunch_lm = BertLMDataBunch.from_raw_corpus( data_dir=DATA_PATH, text_list=texts, tokenizer=args.model_name, batch_size_per_gpu=args.train_batch_size, max_seq_length=args.max_seq_length, multi_gpu=args.multi_gpu, model_type=args.model_type, logger=logger)"><pre><span class="pl-s1">databunch_lm</span> <span class="pl-c1">=</span> <span class="pl-v">BertLMDataBunch</span>.<span class="pl-c1">from_raw_corpus</span>( <span class="pl-s1">data_dir</span><span class="pl-c1">=</span><span class="pl-c1">DATA_PATH</span>, <span class="pl-s1">text_list</span><span class="pl-c1">=</span><span class="pl-s1">texts</span>, <span class="pl-s1">tokenizer</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">model_name</span>, <span class="pl-s1">batch_size_per_gpu</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">train_batch_size</span>, <span class="pl-s1">max_seq_length</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">max_seq_length</span>, <span class="pl-s1">multi_gpu</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">multi_gpu</span>, <span class="pl-s1">model_type</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">model_type</span>, <span class="pl-s1">logger</span><span class="pl-c1">=</span><span class="pl-s1">logger</span>)</pre></div> <p dir="auto">As this step can take some time based on the size of your custom dataset's text, the featurized data will be cached in pickled files in the data_dir/lm_cache folder.</p> <p dir="auto">The next time, instead of using from_raw_corpus method, you may want to directly instantiate the DataBunch object as shown below:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="databunch_lm = BertLMDataBunch( data_dir=DATA_PATH, tokenizer=args.model_name, batch_size_per_gpu=args.train_batch_size, max_seq_length=args.max_seq_length, multi_gpu=args.multi_gpu, model_type=args.model_type, logger=logger)"><pre><span class="pl-s1">databunch_lm</span> <span class="pl-c1">=</span> <span class="pl-en">BertLMDataBunch</span>( <span class="pl-s1">data_dir</span><span class="pl-c1">=</span><span class="pl-c1">DATA_PATH</span>, <span class="pl-s1">tokenizer</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">model_name</span>, <span class="pl-s1">batch_size_per_gpu</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">train_batch_size</span>, <span class="pl-s1">max_seq_length</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">max_seq_length</span>, <span class="pl-s1">multi_gpu</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">multi_gpu</span>, <span class="pl-s1">model_type</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">model_type</span>, <span class="pl-s1">logger</span><span class="pl-c1">=</span><span class="pl-s1">logger</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">4. Create the LM Learner object</h3><a id="user-content-4-create-the-lm-learner-object" class="anchor" aria-label="Permalink: 4. Create the LM Learner object" href="#4-create-the-lm-learner-object"><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">BertLearner is the ‘learner’ object that holds everything together. It encapsulates the key logic for the lifecycle of the model such as training, validation and inference.</p> <p dir="auto">The learner object will take the databunch created earlier as as input alongwith some of the other parameters such as location for one of the pretrained models, FP16 training, multi_gpu and multi_label options.</p> <p dir="auto">The learner class contains the logic for training loop, validation loop, and optimizer strategies. This help the developers focus on their custom use-cases without worrying about these repetitive activities.</p> <p dir="auto">At the same time the learner object is flexible enough to be customized either via using flexible parameters or by creating a subclass of BertLearner and redefining relevant methods.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="learner = BertLMLearner.from_pretrained_model( dataBunch=databunch_lm, pretrained_path=args.model_name, output_dir=MODEL_PATH, metrics=[], device=device, logger=logger, multi_gpu=args.multi_gpu, logging_steps=args.logging_steps, fp16_opt_level=args.fp16_opt_level)"><pre><span class="pl-s1">learner</span> <span class="pl-c1">=</span> <span class="pl-v">BertLMLearner</span>.<span class="pl-c1">from_pretrained_model</span>( <span class="pl-s1">dataBunch</span><span class="pl-c1">=</span><span class="pl-s1">databunch_lm</span>, <span class="pl-s1">pretrained_path</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">model_name</span>, <span class="pl-s1">output_dir</span><span class="pl-c1">=</span><span class="pl-c1">MODEL_PATH</span>, <span class="pl-s1">metrics</span><span class="pl-c1">=</span>[], <span class="pl-s1">device</span><span class="pl-c1">=</span><span class="pl-s1">device</span>, <span class="pl-s1">logger</span><span class="pl-c1">=</span><span class="pl-s1">logger</span>, <span class="pl-s1">multi_gpu</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">multi_gpu</span>, <span class="pl-s1">logging_steps</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">logging_steps</span>, <span class="pl-s1">fp16_opt_level</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-c1">fp16_opt_level</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">5. Train the model</h3><a id="user-content-5-train-the-model" class="anchor" aria-label="Permalink: 5. Train the model" href="#5-train-the-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="learner.fit(epochs=6, lr=6e-5, validate=True, # Evaluate the model after each epoch schedule_type="warmup_cosine", optimizer_type="lamb")"><pre><span class="pl-s1">learner</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">epochs</span><span class="pl-c1">=</span><span class="pl-c1">6</span>, <span class="pl-s1">lr</span><span class="pl-c1">=</span><span class="pl-c1">6e-5</span>, <span class="pl-s1">validate</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-c"># Evaluate the model after each epoch</span> <span class="pl-s1">schedule_type</span><span class="pl-c1">=</span><span class="pl-s">"warmup_cosine"</span>, <span class="pl-s1">optimizer_type</span><span class="pl-c1">=</span><span class="pl-s">"lamb"</span>)</pre></div> <p dir="auto">Fast-Bert now supports LAMB optmizer. Due to the speed of training, we have set LAMB as the default optimizer. You can switch back to AdamW by setting optimizer_type to 'adamw'.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">6. Save trained model artifacts</h3><a id="user-content-6-save-trained-model-artifacts" class="anchor" aria-label="Permalink: 6. Save trained model artifacts" href="#6-save-trained-model-artifacts"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="learner.save_model()"><pre><span class="pl-s1">learner</span>.<span class="pl-c1">save_model</span>()</pre></div> <p dir="auto">Model artefacts will be persisted in the output_dir/'model_out' path provided to the learner object. Following files will be persisted:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>File name</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>pytorch_model.bin</td> <td>trained model weights</td> </tr> <tr> <td>spiece.model</td> <td>sentence tokenizer vocabulary (for xlnet models)</td> </tr> <tr> <td>vocab.txt</td> <td>workpiece tokenizer vocabulary (for bert models)</td> </tr> <tr> <td>special_tokens_map.json</td> <td>special tokens mappings</td> </tr> <tr> <td>config.json</td> <td>model config</td> </tr> <tr> <td>added_tokens.json</td> <td>list of new tokens</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">The pytorch_model.bin contains the finetuned weights and you can point the classification task learner object to this file throgh the <code>finetuned_wgts_path</code> parameter.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Amazon Sagemaker Support</h2><a id="user-content-amazon-sagemaker-support" class="anchor" aria-label="Permalink: Amazon Sagemaker Support" href="#amazon-sagemaker-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The purpose of this library is to let you train and deploy production grade models. As transformer models require expensive GPUs to train, I have added support for training and deploying model on AWS SageMaker.</p> <p dir="auto">The repository contains the docker image and code for building BERT based classification models in Amazon SageMaker.</p> <p dir="auto">Please refer to my blog <a href="https://towardsdatascience.com/train-and-deploy-mighty-transformer-nlp-models-using-fastbert-and-aws-sagemaker-cc4303c51cf3" rel="nofollow">Train and Deploy the Mighty BERT based NLP models using FastBert and Amazon SageMaker</a> that provides detailed explanation on using SageMaker with FastBert.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please include a mention of <a href="https://github.com/kaushaltrivedi/fast-bert">this library</a> and HuggingFace <a href="https://github.com/huggingface/pytorch-transformers">pytorch-transformers</a> library and a link to the present repository if you use this work in a published or open-source project.</p> <p dir="auto">Also include my blogs on this topic:</p> <ul dir="auto"> <li> <p dir="auto"><a href="https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384" rel="nofollow">Introducing FastBert — A simple Deep Learning library for BERT Models</a></p> </li> <li> <p dir="auto"><a href="https://medium.com/huggingface/multi-label-text-classification-using-bert-the-mighty-transformer-69714fa3fb3d" rel="nofollow">Multi-label Text Classification using BERT – The Mighty Transformer</a></p> </li> <li> <p dir="auto"><a href="https://towardsdatascience.com/train-and-deploy-mighty-transformer-nlp-models-using-fastbert-and-aws-sagemaker-cc4303c51cf3" rel="nofollow">Train and Deploy the Mighty BERT based NLP models using FastBert and Amazon SageMaker</a></p> </li> </ul> </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" 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