{"id":10822,"date":"2024-11-18T19:44:06","date_gmt":"2024-11-18T19:44:06","guid":{"rendered":"https:\/\/www.aihello.com\/resources\/?p=10822"},"modified":"2026-09-23T02:27:40","modified_gmt":"2026-09-23T02:27:40","slug":"tokenization-and-its-application","status":"publish","type":"post","link":"https:\/\/www.aihello.com\/resources\/blog\/tokenization-and-its-application\/","title":{"rendered":"Tokenization and it\u2019s application"},"content":{"rendered":"<div class=\"wp-block-group ah-reading-guide ah-compact-toc\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\"><style>\n.ah-reading-guide{max-width:820px;margin:0 auto;color:#26364a;font-size:18px;line-height:1.8;counter-reset:ah-section}.ah-reading-guide *{box-sizing:border-box}.ah-reading-guide p{margin:0 0 22px;line-height:1.8}.ah-reading-guide strong{color:#142d48}.ah-reading-guide h2{position:relative;margin:56px 0 24px;padding-top:24px;border-top:1px solid #dce7ef;font-size:29px;line-height:1.35;letter-spacing:-.4px;color:#142d48}.ah-reading-guide h2.ah-numbered{padding-left:54px;counter-increment:ah-section}.ah-reading-guide h2.ah-numbered:before{content:counter(ah-section,decimal-leading-zero);position:absolute;left:0;top:26px;display:flex;align-items:center;justify-content:center;width:38px;height:38px;background:#eaf4fc;color:#216faf;border-radius:10px;font-size:15px;font-weight:700;letter-spacing:0}.ah-reading-guide h3{position:relative;margin:34px 0 16px;font-size:23px;line-height:1.4;color:#163f65}.ah-reading-guide h4{position:relative;margin:28px 0 14px;font-size:20px;line-height:1.45;color:#163f65}.ah-reading-guide .ah-lead{margin:0 0 28px;padding:24px 28px;border-left:4px solid #2479ba;border-radius:0 12px 12px 0;background:#eef6fc;font-size:20px;line-height:1.75}.ah-reading-guide .ah-callout,.ah-reading-guide .ah-formula{margin:28px 0;padding:24px 26px;border:1px solid #d7e5f1;border-left:4px solid #2479ba;border-radius:0 12px 12px 0;background:#f5f9fd}.ah-reading-guide .ah-formula{font-size:20px;font-weight:600;font-variant-numeric:tabular-nums;line-height:1.9;overflow-wrap:anywhere}.ah-reading-guide ul,.ah-reading-guide ol{margin:24px 0 30px;padding-left:28px}.ah-reading-guide li{margin:0 0 12px;padding-left:7px;line-height:1.75}.ah-reading-guide li::marker{color:#2479ba;font-weight:700}.ah-reading-guide ol.ah-steps{list-style:none;counter-reset:ah-step;padding:0;display:grid;gap:14px}.ah-reading-guide ol.ah-steps>li{position:relative;counter-increment:ah-step;margin:0;padding:22px 24px 22px 64px;border:1px solid #dbe7f1;border-radius:12px;background:#f6fafd}.ah-reading-guide ol.ah-steps>li:before{content:counter(ah-step);position:absolute;top:23px;left:20px;display:flex;align-items:center;justify-content:center;width:28px;height:28px;border-radius:50%;background:#2378b9;color:white;font-size:14px;font-weight:700}.ah-reading-guide ul.ah-cards{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:14px;padding:0;list-style:none}.ah-reading-guide ul.ah-cards>li{margin:0;padding:20px 22px;border:1px solid #dbe8f2;background:#f7fbfe;border-radius:12px;font-size:17px;line-height:1.7}.ah-reading-guide ul.ah-cards>li:last-child:nth-child(odd){grid-column:1\/-1}.ah-reading-guide .ah-cards>li>strong:first-child{display:block;margin-bottom:8px;color:#19598d}.ah-reading-guide .ah-step-panel{margin:22px 0;padding:24px 28px;border-left:4px solid #2479ba;border-radius:0 12px 12px 0;background:#f5f9fd}.ah-reading-guide .ah-step-panel h3{margin-top:0}.ah-reading-guide .ah-step-panel p:last-child{margin-bottom:0}.ah-reading-guide figure{max-width:100%;margin:30px 0}.ah-reading-guide figure.wp-block-image{padding:7px;border:1px solid #e0e8ef;border-radius:12px;background:white;box-shadow:0 5px 18px rgba(20,45,72,.06)}.ah-reading-guide img{max-width:100%;height:auto}.ah-reading-guide figure.wp-block-image img{display:block;border-radius:6px}.ah-reading-guide figcaption{font-size:14px;line-height:1.5;color:#587086;margin-top:10px}.ah-reading-guide figure.wp-block-table{overflow-x:auto;border:1px solid #d8e5ef;border-radius:10px}.ah-reading-guide table{width:100%;border-collapse:collapse;font-size:16px;line-height:1.6}.ah-reading-guide th,.ah-reading-guide td{padding:14px 16px;border:1px solid #d8e5ef;text-align:left;vertical-align:top}.ah-reading-guide th{background:#eaf4fc;color:#163f65;font-weight:700}.ah-reading-guide tbody tr:nth-child(even){background:#f6f9fc}.ah-reading-guide blockquote{margin:30px 0;padding:22px 26px;border-left:4px solid #2479ba;background:#f3f8fc;border-radius:0 10px 10px 0}.ah-reading-guide #ez-toc-container{width:100%;margin:32px 0 42px;padding:22px 24px;border:1px solid #cbdfee;border-top:4px solid #2479ba;border-radius:12px;background:#f7fbfe;box-shadow:none}.ah-reading-guide #ez-toc-container .ez-toc-title{margin:0;font-size:20px;font-weight:700;color:#163f65;line-height:1.5}.ah-reading-guide #ez-toc-container .ez-toc-title-container{margin-bottom:14px}.ah-reading-guide #ez-toc-container ul.ez-toc-list-level-1{display:block;column-count:2;column-gap:26px;padding:0;margin:0}.ah-reading-guide #ez-toc-container ul.ez-toc-list-level-1>li{break-inside:avoid;padding:11px 0;margin:0;border-bottom:1px solid #dce7ef;line-height:1.5;font-size:16px}.ah-reading-guide #ez-toc-container ul ul{margin:7px 0 0;padding-left:18px}.ah-reading-guide #ez-toc-container li li{padding:0;margin:5px 0;font-size:14px}.ah-reading-guide #ez-toc-container a{color:#19598d;text-decoration:none}.ah-reading-guide #ez-toc-container a:hover{text-decoration:underline}.ah-reading-guide #ez-toc-container a:focus-visible{outline:3px solid #2479ba;outline-offset:4px;border-radius:3px}.ah-reading-guide :is(h2,h3,h4)>.ez-toc-section{position:absolute;top:-100px;left:0}.ah-reading-guide :is(h2,h3,h4)>.ez-toc-section-end{position:absolute;top:0;left:0}.ah-reading-guide [id]{scroll-margin-top:100px}.ah-reading-guide .gspb_container{min-width:0}@media(max-width:700px){.ah-reading-guide{font-size:17px}.ah-reading-guide h2{font-size:25px;margin-top:44px}.ah-reading-guide h2.ah-numbered{padding-left:43px}.ah-reading-guide h2.ah-numbered:before{width:30px;height:30px;font-size:13px}.ah-reading-guide h3{font-size:21px}.ah-reading-guide ul.ah-cards{grid-template-columns:1fr}.ah-reading-guide .ah-lead,.ah-reading-guide .ah-callout,.ah-reading-guide .ah-formula,.ah-reading-guide .ah-step-panel{padding:20px}.ah-reading-guide .ah-lead,.ah-reading-guide .ah-formula{font-size:18px}.ah-reading-guide #ez-toc-container{padding:18px}.ah-reading-guide #ez-toc-container ul.ez-toc-list-level-1{column-count:1}}@media(prefers-reduced-motion:reduce){.ah-reading-guide [data-aos]{opacity:1!important;visibility:visible!important;transform:none!important;transition:none!important;animation:none!important}}\n\n.ah-reading-guide .ah-process-heading{padding-left:48px;min-height:36px}.ah-reading-guide .ah-process-heading:before{position:absolute;left:0;top:0;width:34px;height:34px;display:flex;align-items:center;justify-content:center;border-radius:9px;background:#eaf4fc;color:#216faf;font-size:15px;font-weight:700}.ah-reading-guide .ah-process-1:before{content:\"01\"}.ah-reading-guide .ah-process-2:before{content:\"02\"}.ah-reading-guide .ah-process-3:before{content:\"03\"}.ah-reading-guide .ah-process-4:before{content:\"04\"}.ah-reading-guide .ah-process-5:before{content:\"05\"}.ah-reading-guide .ah-process-6:before{content:\"06\"}.ah-reading-guide .ah-process-7:before{content:\"07\"}.ah-reading-guide .ah-process-8:before{content:\"08\"}.ah-reading-guide .ah-process-9:before{content:\"09\"}\n.ah-reading-guide .ah-definition{padding:20px 24px;margin:14px 0;border:1px solid #dbe7f1;border-radius:12px;background:#f8fbfe}.ah-reading-guide .ah-definition strong{color:#163f65}\n.ah-reading-guide .ah-long-form-list>li{margin-bottom:28px}.ah-reading-guide .ah-long-form-list>li>strong:first-child{display:block;font-size:22px;line-height:1.4;margin-bottom:16px}.ah-reading-guide .ah-long-form-list li img{display:block;margin:22px auto}.ah-reading-guide .ah-formula-comparison{font-variant-numeric:tabular-nums}\n.ah-reading-guide h5,.ah-reading-guide h6{font-size:18px;line-height:1.5;color:#163f65;margin:26px 0 14px;font-weight:700}\n.ah-reading-guide h2.ah-instruction-heading{font-size:21px;margin:24px 0 18px;padding-top:18px}.ah-reading-guide h2.ah-instruction-heading:before{top:18px}\n.ah-reading-guide ul.ah-stacked-cards{list-style:none;padding:0}.ah-reading-guide .ah-stacked-cards>li{margin:14px 0;padding:20px 24px;border:1px solid #dbe7f1;border-radius:12px;background:#f8fbfe}\n.ah-reading-guide .ah-minor-label{font-weight:700;color:#1e4e64;font-size:18px;margin:20px 0 8px;}\n.ah-reading-guide h3.ah-do,.ah-reading-guide h3.ah-dont{font-size:21px;padding:18px 22px;border-radius:0 10px 10px 0}.ah-reading-guide h3.ah-do{border-left:4px solid #20877e;background:#f0faf8}.ah-reading-guide h3.ah-dont{border-left:4px solid #ae6340;background:#fff7f0}\n.ah-reading-guide p.ah-fact-row{margin:6px 0;padding:12px 18px;background:#f4f8fc;border-left:3px solid #c7dcf0;border-radius:0 8px 8px 0;font-size:17px;line-height:1.65}.ah-reading-guide p.ah-cta a{display:inline-block;padding:12px 20px;border-radius:9px;background:#135faa;color:#fff;text-decoration:none;font-weight:700}.ah-reading-guide p.ah-cta a:hover{background:#104d89}.ah-reading-guide .ah-agency-summary td:first-child{width:38%;color:#184b75}\n.ah-reading-guide details.ah-original-index{padding:16px 20px;border:1px solid #dce7f0;border-radius:10px;margin:24px 0;background:#f8fafc}.ah-reading-guide details.ah-original-index summary{cursor:pointer;font-size:18px;font-weight:700;color:#244f74}.ah-reading-guide details.ah-original-index>ol{margin-top:18px}\n.ah-reading-guide ul.ah-cycle-cards{grid-template-columns:repeat(3,minmax(0,1fr));gap:22px}.ah-reading-guide ul.ah-cycle-cards>li{position:relative;text-align:center;display:flex;align-items:center;justify-content:center;min-height:110px;font-weight:700;color:#20567d}.ah-reading-guide ul.ah-cycle-cards>li:last-child:nth-child(odd){grid-column:auto}.ah-reading-guide ul.ah-cycle-cards>li:not(:last-child)::after{content:\"\u203a\";position:absolute;right:-19px;top:50%;transform:translateY(-50%);font-size:28px;color:#3785b4}@media(max-width:700px){.ah-reading-guide ul.ah-cycle-cards{grid-template-columns:1fr}.ah-reading-guide ul.ah-cycle-cards>li:not(:last-child)::after{right:50%;top:auto;bottom:-25px;transform:rotate(90deg)}}\n.ah-reading-guide .ah-process-10::before{content:\"10\"}\n.ah-reading-guide pre.wp-block-code{background:#122238;color:#eff6ff;border:1px solid #29415f;border-radius:12px;padding:22px 24px;margin:24px 0;overflow-x:auto;font-size:15px;line-height:1.7;white-space:pre;tab-size:4}.ah-reading-guide pre.wp-block-code code{background:transparent;color:inherit;padding:0;white-space:pre;font-family:Consolas,Monaco,monospace}.ah-reading-guide p code{background:#edf3fa;color:#204d78;border-radius:4px;padding:2px 5px}\n.ah-reading-guide ol.ah-steps.ah-start-2{counter-reset:ah-step 1}\n.ah-reading-guide ol.ah-steps.ah-start-3{counter-reset:ah-step 2}\n.ah-reading-guide ol.ah-steps.ah-start-4{counter-reset:ah-step 3}\n.ah-reading-guide ol.ah-steps.ah-start-5{counter-reset:ah-step 4}\n.ah-reading-guide ol.ah-steps.ah-start-6{counter-reset:ah-step 5}\n.ah-reading-guide ol.ah-steps.ah-start-7{counter-reset:ah-step 6}\n.ah-reading-guide ol.ah-steps.ah-start-8{counter-reset:ah-step 7}\n.ah-reading-guide ol.ah-steps.ah-start-9{counter-reset:ah-step 8}\n.ah-reading-guide ol.ah-steps.ah-start-10{counter-reset:ah-step 9}\n.ah-reading-guide ol.ah-steps.ah-start-11{counter-reset:ah-step 10}\n.ah-reading-guide ol.ah-steps.ah-start-12{counter-reset:ah-step 11}\n.ah-reading-guide #ez-toc-container nav{max-height:460px;overflow-y:auto;padding-right:8px}.ah-reading-guide figure.ah-comparison-wide{max-height:650px;overflow:auto;border:1px solid #dae4ee;border-radius:12px}.ah-reading-guide .ah-comparison-wide table{min-width:760px;font-size:15px}.ah-reading-guide .ah-comparison-wide thead th{position:sticky;top:0;background:#183f65;color:#fff;z-index:1}.ah-reading-guide figure.ah-agency-summary tbody th{width:30%;background:#edf4fa;color:#244764;font-weight:700}\n.ah-reading-guide #ez-toc-container a.ez-toc-link::before{content:none!important;display:none!important}\n.ah-reading-guide .ah-comparison-wide thead th,.ah-reading-guide .ah-comparison-wide thead th *{color:#fff!important}\n.ah-reading-guide .ah-equation{display:block;margin:16px 0;padding:16px 20px;border-left:4px solid #2378b9;background:#edf4fa;border-radius:8px;font-size:20px;font-variant-numeric:tabular-nums;overflow-wrap:anywhere}\n.ah-reading-guide .ah-feature-comparison thead th:nth-child(1){width:18%}.ah-reading-guide .ah-feature-comparison thead th:nth-child(2){width:12%;white-space:nowrap}.ah-reading-guide .ah-feature-comparison thead th:nth-child(3){width:48%}.ah-reading-guide .ah-feature-comparison thead th:nth-child(4){width:22%}\n.ah-reading-guide.ah-compact-toc #ez-toc-container ul.ez-toc-list>li>ul>li>ul{display:none}.ah-reading-guide h4.ah-do{color:#276044!important}.ah-reading-guide h4.ah-dont{color:#855218!important}.ah-reading-guide p.ah-tool-bullet{margin:.45em 0!important;padding:.6em 1em;border-radius:8px;background:#f4f7fa;line-height:1.65}\n.ah-reading-guide .ah-glossary-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:20px;margin:30px 0}.ah-reading-guide .ah-glossary-entry{background:#f7f9fc;border:1px solid #dce5ef;border-radius:12px;padding:22px;height:100%;box-sizing:border-box}.ah-reading-guide .ah-glossary-entry h2{font-size:21px!important;margin:0 0 14px!important;line-height:1.4!important;border:0;padding:0}.ah-reading-guide .ah-glossary-entry p{font-size:16px!important;line-height:1.7!important;margin:0!important}@media(max-width:650px){.ah-reading-guide .ah-glossary-grid{grid-template-columns:1fr}}\n.ah-reading-guide .ah-glossary-grid{display:block}.ah-reading-guide .ah-glossary-grid>.wp-block-group__inner-container{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:20px;align-items:stretch}.ah-reading-guide .ah-glossary-entry{height:auto!important;margin:0!important;min-width:0}.ah-reading-guide .ah-glossary-grid>.wp-block-group__inner-container>.wp-block-greenshift-blocks-container{display:flex;min-width:0}.ah-reading-guide .ah-glossary-grid>.wp-block-group__inner-container>.wp-block-greenshift-blocks-container>.ah-glossary-entry{flex:1}@media(max-width:650px){.ah-reading-guide .ah-glossary-grid>.wp-block-group__inner-container{grid-template-columns:1fr}}\n.ah-reading-guide ol.ah-steps.ah-feature-grid{grid-template-columns:repeat(2,minmax(0,1fr))}@media(max-width:700px){.ah-reading-guide ol.ah-steps.ah-feature-grid{grid-template-columns:1fr}}\n.ah-reading-guide pre.wp-block-preformatted{background:#122238;color:#eff6ff;border:1px solid #29415f;border-radius:12px;padding:22px 24px;margin:24px 0;overflow-x:auto;font-size:15px;line-height:1.7;white-space:pre;tab-size:4;font-family:Consolas,Monaco,monospace}.ah-reading-guide pre.wp-block-preformatted code{background:transparent;color:inherit;padding:0;white-space:pre;font:inherit}.ah-reading-guide .ah-output-sample{max-height:360px;overflow:auto;padding:24px;border:1px solid #d7e5f1;border-radius:12px;background:#f5f9fd;font-size:16px;line-height:1.8}\n.ah-reading-guide pre.wp-block-preformatted,.ah-reading-guide pre.wp-block-preformatted code{white-space:pre!important;text-align:left!important}.ah-reading-guide pre br{display:initial!important}<\/style><noscript><style>.ah-reading-guide [data-aos]{opacity:1!important;visibility:visible!important;transform:none!important}<\/style><\/noscript><h2 id=\"what-is-tokenization\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_tokenization\"><\/span><strong>What is tokenization?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><figure class=\"wp-block-image\"><img decoding=\"async\" title=\"how to remove bad reviews on amazon | what does amazon marketplace mean | how to use amazon brand analytics\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/0*lVWdGYEDfCT08a5w.png\" alt=\"this is an image showing how tokenization works by dividing sentences into words\" \/><\/figure><div class=\"wp-block-group ah-definition\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\"><div id=\"8c72\"><p><a  data-ilj-link-preview=\"true\"  data-featured-image=\"https:\/\/www.aihello.com\/resources\/wp-content\/uploads\/2024\/08\/a-complete-overview-to-tokenization-300x169.png\"  data-excerpt=\"Explore tokenization, the essential text processing method for LLMs. Understand its importance and how character, word, and subword techniques enhance AI comprehension.\" href=\"https:\/\/www.aihello.com\/resources\/blog\/a-complete-overview-to-tokenization\/\">Tokenization<\/a> is the process of breaking down text into smaller units called <a  data-ilj-link-preview=\"true\"  data-featured-image=\"https:\/\/www.aihello.com\/resources\/wp-content\/uploads\/2024\/08\/a-complete-overview-to-tokenization-300x169.png\"  data-excerpt=\"Explore tokenization, the essential text processing method for LLMs. Understand its importance and how character, word, and subword techniques enhance AI comprehension.\" href=\"https:\/\/www.aihello.com\/resources\/blog\/a-complete-overview-to-tokenization\/\">tokens<\/a>, which can be words, subwords, or individual characters. These tokens represent the smallest meaningful elements of a text that a language model can process. For instance, the sentence \u201dHello, world!\u201d might be tokenized into [\u201cHello\u201d, \u201c,\u201d, \u201cworld\u201d, \u201c!\u201d]. This process simplifies the text, enabling the model to handle it as manageable data chunks.<\/p><\/div><\/div><\/div><p class=\"wp-block-paragraph\" id=\"26a9\">In workflows for LLM pre-training and inference, each token is mapped to a unique integer after tokenization. These integers serve as indices for a lookup table containing vector representations of the tokens. These vectors are then fed into the language model as inputs corresponding to their respective tokens.<\/p><h2 id=\"144e\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Need_of_tokenization\"><\/span><strong>Need of tokenization<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p class=\"wp-block-paragraph\" id=\"74e8\">Imagine you want to pre-train your own language model using 10 GB of text data. The challenge is that <a  data-ilj-link-preview=\"true\"  data-featured-image=\"https:\/\/www.aihello.com\/resources\/wp-content\/uploads\/2020\/04\/5770b06cdf39b-300x140.jpeg\"  data-excerpt=\"AiHello: Your AI e-commerce solution to automate Amazon &amp; eBay sales. Leverage machine learning for smart pricing, optimized keywords, and accurate sales forecasts.\" href=\"https:\/\/www.aihello.com\/resources\/blog\/welcome\/\">machine learning<\/a> models, including language models, can only work with numerical data, not raw text. This is where tokenization becomes essential.<\/p><div id=\"1533\"><p>Tokenization transforms text into smaller units called tokens, and each token is assigned a unique integer identifier. These integers are then converted into numerical vectors, which the language model processes. The model takes this vectorized input, performs computations, and produces output in the form of integers, which are later converted back into text.<\/p><\/div><p class=\"ah-callout wp-block-paragraph\" id=\"a1f7\">Without tokenization, it would be impossible for language models to handle raw textual data effectively. It serves as the crucial bridge between text and the numerical computations that drive these models.<\/p><h2 id=\"c738\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_Is_a_tokenizer\"><\/span><strong>What Is a tokenizer?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p class=\"wp-block-paragraph\" id=\"92ab\">A tokenizer is a <a  data-ilj-link-preview=\"true\"  data-featured-image=\"https:\/\/www.aihello.com\/resources\/wp-content\/uploads\/2022\/06\/amazon-brand-analytics-guide-300x200.jpg\"  data-excerpt=\"Master Amazon sales! Discover how Amazon Brand Analytics empowers sellers to stand out, optimize products,\" href=\"https:\/\/www.aihello.com\/resources\/blog\/amazon-brand-analytics-a-complete-guide-2026\/\">tool<\/a> that prepares text for processing by breaking it into smaller units called tokens, an essential step in natural language processing (NLP) tasks such as text classification, machine translation, and question answering.<\/p><p class=\"wp-block-paragraph\" id=\"580e\">Here\u2019s what tokenizers do:<\/p><div class=\"wp-block-greenshift-blocks-container gspb_container gspb_container-gsbp-f10822-1\" id=\"gspb_container-id-gsbp-f10822-1\" data-aos=\"fade\" data-aos-easing=\"ease\" data-aos-duration=\"500\" data-aos-once=\"true\"><ul class=\"ah-cards\"><li id=\"5def\">&#8211; Text <a  data-ilj-link-preview=\"true\"  data-featured-image=\"https:\/\/www.aihello.com\/resources\/wp-content\/uploads\/2023\/08\/how-to-reduce-acos-on-amazon-the-ultimate-guide-300x169.jpg\"  data-excerpt=\"Amazon PPC costs soaring? Learn expert strategies to reduce your ACoS. Discover prioritization, segmentation, dayparting, and ad analysis tips.\" href=\"https:\/\/www.aihello.com\/resources\/blog\/lower-acos-amazon\/\">segmentation<\/a>: Tokenizers decompose unstructured text into manageable units, which can be words, subwords, or characters.<\/li><li id=\"8539\">&#8211; Conversion to numerical form: They translate tokens into numerical data that machine learning models can understand and process.<\/li><li id=\"dcf1\">&#8211; Meaningful representation: Tokenizers aim to identify the most meaningful and compact representation of the text for the model, ensuring it captures essential information in an efficient format.<\/li><\/ul><\/div><h2 id=\"3117\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Types_of_Tokenizers\"><\/span><strong>Types of Tokenizers<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><figure class=\"wp-block-image\"><img decoding=\"async\" title=\"how to increase sales on amazon | can you make money selling books on amazon | how to make money from amazon\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:1280\/0*ZiAP6zCub6jWHWaf.jpg\" alt=\"this image depicts tokenizers and how they work for llms\" \/><\/figure><h3 id=\"e353\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Word-Level_Tokenizers\"><\/span>1. Word-Level Tokenizers<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"fdd4\">These tokenizers split text into individual words or phrases, typically using spaces or punctuation.<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"5739\">Example: \u201dI love NLP\u201d \u2192 [\u201cI\u201d, \u201clove\u201d, \u201cNLP\u201d]<\/p><div class=\"wp-block-greenshift-blocks-container gspb_container gspb_container-gsbp-f10822-2\" id=\"gspb_container-id-gsbp-f10822-2\" data-aos=\"fade\" data-aos-easing=\"ease\" data-aos-duration=\"500\" data-aos-once=\"true\"><ul class=\"ah-cards\"><li id=\"4037\">Pros: Simple and works well for languages with space-separated words like English.<\/li><li id=\"1b61\">Cons: Struggles with out-of-vocabulary (OOV) words and is inefficient for languages without clear word boundaries (e.g., Chinese).<\/li><\/ul><\/div><h3 id=\"f193\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Subword_Tokenizers\"><\/span>2. Subword Tokenizers<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"1568\">These break words into smaller meaningful units or subwords to balance vocabulary size and representation.<\/p><p class=\"wp-block-paragraph\" id=\"ed13\"><strong>&#8211; Byte Pair Encoding (BPE):<\/strong> Iteratively merges frequent character pairs into subwords.<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"54c8\">Example: \u201dunbelievable\u201d \u2192 [\u201cun\u201d, \u201cbeliev\u201d, \u201cable\u201d]<\/p><p class=\"wp-block-paragraph\" id=\"03a3\"><strong>&#8211; WordPiece:<\/strong> Optimized for probabilistic models (used in BERT).<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"31ad\">Example: \u201dplayground\u201d \u2192 [\u201cplay\u201d, \u201c##ground\u201d]<\/p><p class=\"wp-block-paragraph\" id=\"6708\"><strong>&#8211; SentencePiece:<\/strong> Does not require pre-tokenization and operates on raw text.<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"9845\">Example: \u201dHello\u201d \u2192 [\u201c_Hello\u201d]<\/p><ul class=\"ah-cards\"><li id=\"c96d\">Pros: Reduces vocabulary size and handles OOV words better by splitting them into meaningful subwords.<\/li><li id=\"7e46\">Cons: Requires pre-training on specific datasets to learn optimal splits.<\/li><\/ul><h3 id=\"2d4d\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Character-Level_Tokenizers\"><\/span>3. Character-Level Tokenizers<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"e8eb\">These treat each character as an individual token.<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"5cc1\">Example: \u201dhello\u201d \u2192 [\u201ch\u201d, \u201ce\u201d, \u201cl\u201d, \u201cl\u201d, \u201co\u201d]<\/p><ul class=\"ah-cards\"><li id=\"d48a\">Pros: No OOV issues and ideal for morphologically complex languages or noisy text.<\/li><li id=\"8a13\">Cons: Produces longer sequences, increasing computational <a  data-ilj-link-preview=\"true\"  data-featured-image=\"https:\/\/www.aihello.com\/resources\/wp-content\/uploads\/2022\/06\/selling-on-amazon-mexico-300x225.jpg\"  data-excerpt=\"Mexico&#039;s eCommerce boom presents huge opportunities for Amazon sellers. This guide explores the thriving Mexican online market, Amazon.mx&#039;s growth, and key considerations for successfully selling your products to millions of Mexican consumers. Learn how to navigate logistics, expand your reach, and capitalize on this expanding market.\" href=\"https:\/\/www.aihello.com\/resources\/blog\/how-to-launch-an-amazon-mexico-business\/\">costs<\/a> and reducing efficiency.<\/li><\/ul><h3 id=\"f60e\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Byte-Level_Tokenizers\"><\/span>4. Byte-Level Tokenizers<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"09dc\">These split text into bytes, treating each byte (numerical representation of a character) as a token.<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"1939\">Example: \u201dhello\u201d \u2192 [104, 101, 108, 108, 111] (ASCII values).<\/p><ul class=\"ah-cards\"><li id=\"d781\">Pros: Supports all languages without modification and handles special characters seamlessly.<\/li><li id=\"5df5\">Cons: Results in longer sequences compared to word or subword tokenizers.<\/li><\/ul><h3 id=\"83f7\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Token-Free_Models\"><\/span>5. Token-Free Models<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"1124\">These operate directly on text without predefined tokenization, often representing text at the character or byte level.<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"a920\">Example: Works directly with input like \u201dhello\u201d.<\/p><ul class=\"ah-cards\"><li id=\"ab85\">Pros: Avoids the complexities of vocabulary design and tokenization, making it suitable for multilingual or noisy datasets.<\/li><li id=\"8c78\">Cons: Requires specialized models to efficiently process raw input.<\/li><\/ul><h3 id=\"7499\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_Hybrid_Tokenizers\"><\/span>6. Hybrid Tokenizers<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"12fa\">These combine multiple approaches, such as word-level and subword-level tokenization, or byte-level with character-level tokenization.<\/p><p class=\"ah-formula wp-block-paragraph\" id=\"3d48\">Example: SentencePiece with BPE fallback to character-level for rare words.<\/p><ul class=\"ah-cards\"><li id=\"b54d\">Pros: Balances efficiency with representation quality, offering flexibility for diverse inputs.<\/li><li id=\"e89a\">Cons: More complex to implement and tune.<\/li><\/ul><div id=\"404b\"><p>Choosing the right tokenizer depends on the language, application, and quality of the input text. For modern large language models, subword tokenizers are often the standard due to their balance of efficiency and generalization capabilities. For noisy or unconventional text, character-level or byte-level tokenizers (e.g., SHIBA) are robust alternatives.<\/p><\/div><h2 id=\"92e9\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_Shiba\"><\/span><strong>What is Shiba?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><figure class=\"wp-block-image\"><img decoding=\"async\" title=\"how to make money selling books | how to know competitors keywords | how to start amazon fba uk\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:1184\/0*hk7KpQe0-Z7b-1UL.png\" alt=\"\" \/><\/figure><div class=\"wp-block-greenshift-blocks-container gspb_container gspb_container-gsbp-f10822-3\" id=\"gspb_container-id-gsbp-f10822-3\" data-aos=\"fade\" data-aos-easing=\"ease\" data-aos-duration=\"500\" data-aos-once=\"true\"><div class=\"wp-block-group ah-definition\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\"><div id=\"3b13\"><p>SHIBA is an approximate reimplementation of CANINE[1], built using raw PyTorch and pre-trained on the Japanese Wikipedia corpus with random span masking. For those unfamiliar with CANINE, it is a highly efficient character-level language model, roughly four times more efficient than BERT. Similarly, SHIBA operates at the character level, focusing on efficiency and effectiveness.<\/p><\/div><\/div><\/div><\/div><p class=\"wp-block-paragraph\" id=\"aafe\">The name \u201cSHIBA\u201d is inspired by the Shiba Inu, a well-known Japanese dog breed, aligning with the naming convention of its predecessor.<\/p><h2 id=\"c8d8\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Performance_of_Shiba\"><\/span><strong>Performance of Shiba<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><h3 id=\"599f\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Task_1_Classification\"><\/span>Task 1: Classification<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"74b3\">The first evaluation task involved text classification on the *Livedoor News Corpus*, utilizing as much text from each article as could fit into the model in a single pass.<\/p><figure class=\"wp-block-image\"><img decoding=\"async\" title=\"what is brand analytics | what is a good acos percentage on amazon | how to advertise your amazon product\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:1236\/1*7kFGzR5lwPTSimMjZdW9cA.png\" alt=\"\" \/><\/figure><h3 id=\"63fc\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Task_2_Word_Segmentation\"><\/span>Task 2: Word Segmentation<span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"7ce4\">The second task focused on word segmentation using the *UD Japanese GSD corpus*.<\/p><figure class=\"wp-block-image\"><img decoding=\"async\" title=\"what is e tailing | are amazon sponsored ads worth it | how to start an amazon business uk\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:620\/1*kxI5WC2WCKZncc66Al425A.png\" alt=\"\" \/><\/figure><div class=\"wp-block-greenshift-blocks-container gspb_container gspb_container-gsbp-f10822-4\" id=\"gspb_container-id-gsbp-f10822-4\" data-aos=\"fade\" data-aos-easing=\"ease\" data-aos-duration=\"500\" data-aos-once=\"true\"><p class=\"ah-callout wp-block-paragraph\" id=\"edcc\">While SHIBA slightly lags behind MeCab, which excels in word segmentation due to its dictionary-based approach, SHIBA is expected to perform better on noisy or unconventional text where dictionary-based tools like MeCab struggle.<\/p><\/div><h2 id=\"07cc\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_to_encode_and_decode_text_using_shiba\"><\/span><strong>How to encode and decode text using shiba<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p class=\"wp-block-paragraph\" id=\"d7b9\">Here\u2019s a step-by-step guide to implement the provided code for processing PDF text using the SHIBA model. You can copy and paste this directly into your Medium article.<\/p><h3 id=\"21d5\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_1_Install_Required_Libraries\"><\/span><strong>Step 1: Install Required Libraries<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"d62e\">Start by installing the required libraries. You\u2019ll need `PyPDF2` for extracting text from PDFs and `shiba-model` for using the SHIBA model. Run the following command:<\/p><pre class=\"wp-block-preformatted\"><br><code>pip install PyPDF2 shiba-model<\/code><\/pre><h3 id=\"b24e\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_2_Import_Libraries_and_Initialize_the_SHIBA_Model\"><\/span><strong>Step 2: Import Libraries and Initialize the SHIBA Model<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"4fa0\">Import the necessary modules and initialize the SHIBA model and its tokenizer. SHIBA uses a pretrained character-level model, so we also disable dropout to ensure consistent outputs.<\/p><div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><pre class=\"wp-block-preformatted\"><br><code>import PyPDF2<br>from shiba import Shiba, CodepointTokenizer, get_pretrained_from_hub<\/code><\/pre><pre class=\"wp-block-preformatted\"><code># Load SHIBA model and tokenizer<br>shiba_model = Shiba()<br>shiba_model.load_state_dict(get_pretrained_from_hub())<br>shiba_model.eval() # Disable dropout<br>tokenizer = CodepointTokenizer()<\/code><\/pre><\/div><\/div><p class=\"ah-callout wp-block-paragraph\" id=\"7842\">&#8211; PyPDF2: Used to read and extract text from PDF files.<br>&#8211; Shiba: The main model for processing character-level text.<br>&#8211; CodepointTokenizer: Tokenizes text at the character level.<\/p><h3 id=\"8a52\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_3_Extract_Text_from_PDF\"><\/span><strong>Step 3: Extract Text from PDF<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"c6c3\">Define a function to extract text from a PDF file using PyPDF2. This function reads the PDF, iterates through its pages, and extracts text.<\/p><pre class=\"wp-block-preformatted\"><br><code>def extract_text_from_pdf(pdf_path):<br> \u201c\u201d\u201dExtract text from a given PDF file.\u201d\u201d\u201d<br> text = \u201c\u201d<br> with open(pdf_path, \u201crb\u201d) as file:<br> reader = PyPDF2.PdfReader(file)<br> for page in reader.pages:<br> text += page.extract_text()<br> return text<\/code><\/pre><h3 id=\"19e7\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_4_Split_Text_into_Chunks\"><\/span><strong>Step 4: Split Text into Chunks<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"2575\">Since SHIBA has a maximum input length, split the extracted text into smaller chunks. This function takes the full text and splits it into segments of a specified maximum length.<\/p><pre class=\"wp-block-preformatted\"><br><code>def split_text(text, max_length=1800):<br> \u201c\u201d\u201dSplit text into chunks within the max length allowed by SHIBA.\u201d\u201d\u201d<br> return [text[i:i + max_length] for i in range(0, len(text), max_length)]<\/code><\/pre><h3 id=\"43ec\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_5_Process_Text_with_SHIBA\"><\/span><strong>Step 5: Process Text with SHIBA<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"320e\">Process the text chunks using SHIBA. Each chunk is tokenized, encoded, and passed through the model to obtain outputs.<\/p><pre class=\"wp-block-preformatted\"><br><code>def process_pdf_with_shiba(pdf_path):<br> # Extract and clean text from PDF<br> text = extract_text_from_pdf(pdf_path)<br> # Split text into manageable chunks<br> text_chunks = split_text(text)<br> all_outputs = []<br> encoded_chunks = []<\/code><\/pre><pre class=\"wp-block-preformatted\"><code>for i, chunk in enumerate(text_chunks):<br> # Encode each chunk<br> encoded = tokenizer.encode_batch([chunk])<br> print(f\u201dChunk {i} encoding result:\u201d, encoded) # Debugging statement<\/code><\/pre><pre class=\"wp-block-preformatted\"><code># Access `input_ids` and `attention_mask` and structure inputs for the model<br> if \u2018input_ids\u2019 in encoded and \u2018attention_mask\u2019 in encoded:<br> input_ids = encoded[\u2018input_ids\u2019]<br> attention_mask = encoded[\u2018attention_mask\u2019]<br> inputs = {\u2018input_ids\u2019: input_ids, \u2018attention_mask\u2019: attention_mask}<br> outputs = shiba_model(**inputs)<br> all_outputs.append(outputs)<br> encoded_chunks.append(input_ids[0]) # Store input_ids for decoding<\/code><\/pre><pre class=\"wp-block-preformatted\"><code>return all_outputs, encoded_chunks<\/code><\/pre><h3 id=\"7c89\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_6_Decode_Tokens_Back_to_Text\"><\/span><strong>Step 6: Decode Tokens Back to Text<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"dd62\">After processing, decode the tokenized outputs back into readable text. This step helps verify that the input-output pipeline is functioning correctly.<\/p><pre class=\"wp-block-preformatted\"><br> <code>def decode_tokens(encoded_chunks):<br> decoded_text = \u201c\u201d<br> for tokens in encoded_chunks:<br> decoded_text += tokenizer.decode(tokens)<br> return decoded_text<\/code><\/pre><h3 id=\"f22a\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_7_Test_the_Implementation\"><\/span><strong>Step 7: Test the Implementation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"190e\">Provide the path to a sample PDF file and process it using the functions defined above. Print the decoded text to verify the result.<\/p><pre class=\"wp-block-preformatted\"><code><br># Example usage<br>pdf_path = \u201csample.pdf\u201d<br>outputs, encoded_chunks = process_pdf_with_shiba(pdf_path)<br>decoded_text = decode_tokens(encoded_chunks)<br>print(\u201cDecoded Text:\u201d, decoded_text)<\/code><\/pre><h3 id=\"1e9f\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Final_Thoughts\"><\/span><strong>Final Thoughts<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"wp-block-paragraph\" id=\"42a3\">This implementation demonstrates <a  data-ilj-link-preview=\"true\"  data-featured-image=\"https:\/\/www.aihello.com\/resources\/wp-content\/uploads\/2022\/08\/amazon-backend-search-terms-optimization-guide-basics-how-to-300x300.png\"  data-excerpt=\"Unlock Amazon success! Master backend keywords to boost product visibility, improve relevancy, and drive sales without keyword stuffing.\" href=\"https:\/\/www.aihello.com\/resources\/blog\/everything-about-optimizing-amazon-backend-keywords-guide-2022\/\">how to<\/a> integrate SHIBA into an NLP workflow, making it a valuable tool for processing text from PDFs or other sources. With its character-level tokenization and pretrained capabilities, SHIBA excels in handling noisy or unconventional text.<\/p><h2 id=\"15e3\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Results\"><\/span><strong>Results:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><h3 id=\"6059\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Input\"><\/span><strong>Input:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"ah-output-sample wp-block-paragraph\" id=\"95c0\"><strong><em>\u201c<\/em><\/strong><em>Sample PDF This is a simple PDF file. Fun fun fun. Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Phasellus facilisis odio sed mi. Curabitur suscipit. Nullam vel nisi. Etiam semper ipsum ut lectus. Proin aliquam, erat eget pharetra commodo, eros mi condimentum quam, sed commodo justo quam ut velit. Integer a erat. Cras laoreet ligula cursus enim. Aenean scelerisque velit et tellus. Vestibulum dictum aliquet sem. Nulla facilisi. Vestibulum accumsan ante vitae elit. Nulla erat dolor, blandit in, rutrum quis, semper pulvinar, enim. Nullam varius congue risus. Vivamus sollicitudin, metus ut interdum eleifend, nisi tellus pellentesque elit, tristique accumsan eros quam et risus. Suspendisse libero odio, mattis sit amet, aliquet eget, hendrerit vel, nulla. Sed vitae augue. Aliquam erat volutpat. Aliquam feugiat vulputate nisl. Suspendisse quis nulla pretium ante pretium mollis. Proin velit ligula, sagittis at, egestas a, pulvinar quis, nisl.\u201d<\/em><\/p><h3 id=\"7092\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Output\"><\/span><strong>Output:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><p class=\"ah-output-sample wp-block-paragraph\" id=\"c7f2\"><em>\u201cSample PDFThis is a simple PDF \ufb01le. Fun fun fun.Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Phasellus facilisis odio sed mi. Curabitur suscipit. Nullam vel nisi. Etiam semper ipsum ut lectus. Proin aliquam, erat eget pharetra commodo, eros mi condimentum quam, sed commodo justo quam ut velit. Integer a erat. Cras laoreet ligula cursus enim. Aenean scelerisque velit et tellus. Vestibulum dictum aliquet sem. Nulla facilisi. Vestibulum accumsan ante vitae elit. Nulla erat dolor, blandit in, rutrum quis, semper pulvinar, enim. Nullam varius congue risus. Vivamus sollicitudin, metus ut interdum eleifend, nisi tellus pellentesque elit, tristique accumsan eros quam et risus. Suspendisse libero odio, mattis sit amet, aliquet eget, hendrerit vel, nulla. Sed vitae augue. Aliquam erat volutpat. Aliquam feugiat vulputate nisl. Suspendisse quis nulla pretium ante pretium mollis. Proin velit ligula, sagittis at, egestas a, pulvinar quis, nisl.\u201d<\/em><\/p><p class=\"wp-block-paragraph\" id=\"e9a3\"><strong>You can checkout more results here:<\/strong>&nbsp;<a href=\"https:\/\/drive.google.com\/drive\/folders\/1MFw9jeTG6OS0XW1kzxI5umMiztLFnmEI?usp=sharing\" rel=\"noreferrer noopener\" target=\"_blank\">https:\/\/drive.google.com\/drive\/folders\/1MFw9jeTG6OS0XW1kzxI5umMiztLFnmEI?usp=sharing<\/a><\/p><h2 id=\"c4fa\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><div id=\"159b\"><p>Tokenization is a foundational step in the field of natural language processing (NLP) and large language models (LLMs). It bridges the gap between raw text and the numerical data that models require for computation. An effective tokenizer is essential for ensuring that the input representation is both compact and meaningful, enabling models to learn and infer efficiently.<\/p><\/div><div id=\"096b\"><p>SHIBA stands out as a highly efficient and versatile tokenizer and model, operating at the character level. Unlike traditional word-based or subword-based tokenizers, SHIBA\u2019s character-level approach eliminates the reliance on pre-defined vocabularies, making it more adaptable to languages with rich morphology, noisy or unconventional text, and low-resource settings. <\/p><p>Its character-level tokenization ensures no out-of-vocabulary tokens, which is a common limitation in many word-based models.<\/p><\/div><p class=\"wp-block-paragraph\" id=\"58a8\">In comparison to other tokenizers, SHIBA is approximately four times more efficient while maintaining high accuracy, as demonstrated in text classification and segmentation tasks. It is particularly robust in handling messy or informal text, where dictionary-based tools like MeCab may struggle.<\/p><div id=\"c970\"><p>The importance of tokenization in the LLM industry cannot be overstated. It directly impacts a model\u2019s ability to generalize across diverse datasets, adapt to new languages, and maintain computational efficiency. SHIBA exemplifies how innovative approaches to tokenization can enhance model performance, providing a competitive edge in the rapidly evolving NLP landscape.<\/p><\/div><p class=\"wp-block-paragraph\" id=\"3985\"><strong>Official repo of SHIBA<\/strong>&#8211;&nbsp;<a href=\"https:\/\/github.com\/octanove\/shiba\" rel=\"noreferrer noopener\" target=\"_blank\">https:\/\/github.com\/octanove\/shiba<\/a><\/p><p class=\"wp-block-paragraph\" id=\"7e83\"><strong>Github repo of the code-&nbsp;<\/strong><a href=\"https:\/\/github.com\/arnavgupta16\/shiba_tokenizer\" rel=\"noreferrer noopener\" target=\"_blank\">https:\/\/github.com\/arnavgupta16\/shiba_tokenizer<\/a><\/p><p class=\"wp-block-paragraph\"><strong>Linkedin-&nbsp;<\/strong><a href=\"https:\/\/www.linkedin.com\/in\/arnav-gupta-437a66256\/\" rel=\"noreferrer noopener\" target=\"_blank\">https:\/\/www.linkedin.com\/in\/arnav-gupta-437a66256\/<\/a><\/p><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Understanding tokenization: Learn why this NLP process is crucial for language models (LLMs). Discover how text is converted for AI and explore different tokenizer types.<\/p>\n","protected":false},"author":18,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":".gspb_container-id-gsbp-f10822-1,.gspb_container-id-gsbp-f10822-2,.gspb_container-id-gsbp-f10822-3,.gspb_container-id-gsbp-f10822-4{flex-direction:column;box-sizing:border-box}#gspb_container-id-gsbp-f10822-1.gspb_container>p:last-of-type,#gspb_container-id-gsbp-f10822-2.gspb_container>p:last-of-type,#gspb_container-id-gsbp-f10822-3.gspb_container>p:last-of-type,#gspb_container-id-gsbp-f10822-4.gspb_container>p:last-of-type{margin-bottom:0}#gspb_container-id-gsbp-f10822-1.gspb_container.aos-animate,#gspb_container-id-gsbp-f10822-1.gspb_container[data-gs-aos],#gspb_container-id-gsbp-f10822-2.gspb_container.aos-animate,#gspb_container-id-gsbp-f10822-2.gspb_container[data-gs-aos],#gspb_container-id-gsbp-f10822-3.gspb_container.aos-animate,#gspb_container-id-gsbp-f10822-3.gspb_container[data-gs-aos],#gspb_container-id-gsbp-f10822-4.gspb_container.aos-animate,#gspb_container-id-gsbp-f10822-4.gspb_container[data-gs-aos]{opacity:1;transform:translateZ(0)}#gspb_container-id-gsbp-f10822-1.gspb_container,#gspb_container-id-gsbp-f10822-2.gspb_container,#gspb_container-id-gsbp-f10822-3.gspb_container,#gspb_container-id-gsbp-f10822-4.gspb_container{position:relative;transition-duration:.5s;transition-timing-function:var(--gs-root-animation-easing, cubic-bezier(0.42, 0, 0.58, 1));opacity:var(--gs-root-animation-opacity, 0);transition-property:opacity,transform,filter;box-sizing:border-box}","footnotes":""},"categories":[1,86,85],"tags":[561,1222,1147,1148],"republish_tag":[],"class_list":["post-10822","post","type-post","status-publish","format-standard","hentry","category-advertising","category-product-marketing","category-tips","tag-ai-machine-learning","tag-nlp","tag-tokenization","tag-tokenization-techniques"],"_links":{"self":[{"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/posts\/10822","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/comments?post=10822"}],"version-history":[{"count":2,"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/posts\/10822\/revisions"}],"predecessor-version":[{"id":14251,"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/posts\/10822\/revisions\/14251"}],"wp:attachment":[{"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/media?parent=10822"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/categories?post=10822"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/tags?post=10822"},{"taxonomy":"republish_tag","embeddable":true,"href":"https:\/\/www.aihello.com\/resources\/wp-json\/wp\/v2\/republish_tag?post=10822"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}