--- title: A Complete Guide to BERT with Code | daily.dev description: The post provides a complete guide to BERT, including its history, architecture, pre-training objectives, and fine-tuning for sentiment analysis. It discusses... canonical: https://daily.dev/posts/a-complete-guide-to-bert-with-code-odukuaay8 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: A Complete Guide to BERT with Code | daily.dev og:description: The post provides a complete guide to BERT, including its history, architecture, pre-training objectives, and fine-tuning for sentiment analysis. It discusses... og:url: https://daily.dev/posts/a-complete-guide-to-bert-with-code-odukuaay8 og:image: https://api.daily.dev/og/posts/ODUkuAAy8.png og:image:alt: A Complete Guide to BERT with Code og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # A Complete Guide to BERT with Code **[Towards Data Science](https://daily.dev/sources/tds)** · 45 min read · 24 upvotes · 0 comments ## Summary The post provides a complete guide to BERT, including its history, architecture, pre-training objectives, and fine-tuning for sentiment analysis. It discusses the key features of BERT, such as its encoder-only architecture, pre-training approach, model fine-tuning, and use of bidirectional context. The post also covers the tokenization process, creating train and validation data loaders, instantiating a BERT model, and setting up an optimizer, loss function, and scheduler for fine-tuning. The fine-tuning loop is explained, highlighting the steps taken for each epoch and within each batch. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#nlp](https://daily.dev/tags/nlp), [#sentiment-analysis](https://daily.dev/tags/sentiment-analysis), [#bert](https://daily.dev/tags/bert) [View this post on daily.dev](https://daily.dev/posts/a-complete-guide-to-bert-with-code-odukuaay8) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"A Complete Guide to BERT with Code","url":"https://daily.dev/posts/a-complete-guide-to-bert-with-code-odukuaay8","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/a-complete-guide-to-bert-with-code-odukuaay8"},"datePublished":"2024-05-20T01:14:54.578Z","dateModified":"2024-05-20T01:15:11.382Z","description":"The post provides a complete guide to BERT, including its history, architecture, pre-training objectives, and fine-tuning for sentiment analysis. It discusses...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/1dcaf70850720f6c97dcb1a06fa0b587?_a=AQAEuiZ","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/1dcaf70850720f6c97dcb1a06fa0b587?_a=AQAEuiZ","isAccessibleForFree":true,"articleSection":"Towards Data Science","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/a-complete-guide-to-bert-with-code-odukuaay8","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":24},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,nlp,sentiment-analysis,bert","timeRequired":"PT45M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"A Complete Guide to BERT with Code"}]} ```