{"id":134886,"date":"2026-09-07T13:31:20","date_gmt":"2026-09-07T08:01:20","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=134886"},"modified":"2026-09-07T13:31:22","modified_gmt":"2026-09-07T08:01:22","slug":"data-labeling-best-practices-for-supervised-learning","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/data-labeling-best-practices-for-supervised-learning\/","title":{"rendered":"Data Labeling Best Practices for Supervised Learning"},"content":{"rendered":"\n<p>Supervised learning depends on labeled examples to teach machine learning models how to recognize patterns and make predictions. If labels are inconsistent, inaccurate, or incomplete, even a well-designed model can produce unreliable results. <strong>Data Labeling<\/strong> is therefore a critical step in the machine learning lifecycle. This guide explains what data labeling involves, why label quality matters, common labeling approaches, and best practices for creating reliable datasets for supervised learning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>TL;DR Summary<\/strong><\/h3>\n\n\n\n<ul>\n<li>Data labeling assigns meaningful labels to raw data.<\/li>\n\n\n\n<li>High-quality labels are essential for supervised learning.<\/li>\n\n\n\n<li>Clear annotation guidelines improve consistency.<\/li>\n\n\n\n<li>Multiple annotators can reduce individual labeling errors.<\/li>\n\n\n\n<li>Regular quality checks help maintain dataset reliability.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Direct Answer&nbsp;<\/strong><\/h4>\n\n\n\n<figure class=\"wp-block-table has-medium-font-size\"><table><tbody><tr><td><strong>Data Labeling<\/strong> is the process of assigning meaningful labels or annotations to raw data so supervised learning models can learn from examples. Labels may identify objects in images, classify text, mark audio content, or represent numerical outcomes. Accurate and consistent labeling improves training data quality, helping machine learning models learn reliable patterns and produce better predictions.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Data Labeling Matters<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/types-of-supervised-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Supervised learning<\/a> models learn relationships between input data and labeled outcomes. Poor labels can teach the model incorrect patterns.<\/p>\n\n\n\n<p>High-quality labeling helps provide:<\/p>\n\n\n\n<ul>\n<li>Accurate training examples<\/li>\n\n\n\n<li>Consistent annotations<\/li>\n\n\n\n<li>Better model performance<\/li>\n\n\n\n<li>Reduced labeling errors<\/li>\n\n\n\n<li>Reliable evaluation datasets<\/li>\n\n\n\n<li>Stronger model generalization<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is Data Labeling?<\/strong><\/h2>\n\n\n\n<p><strong>Data Labeling<\/strong> involves adding information to raw datasets so machine learning algorithms can learn from them.<\/p>\n\n\n\n<p>Depending on the application, labels can include:<\/p>\n\n\n\n<ul>\n<li>Categories<\/li>\n\n\n\n<li>Object locations<\/li>\n\n\n\n<li>Sentiment<\/li>\n\n\n\n<li>Transcriptions<\/li>\n\n\n\n<li>Numerical values<\/li>\n\n\n\n<li>Semantic regions<\/li>\n<\/ul>\n\n\n\n<p>For example, an image dataset for detecting vehicles could contain images where cars, buses, and trucks are assigned appropriate labels.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Types of Data Labeling<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1200\" height=\"800\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Types.webp\" alt=\"Types of Data Labeling\" class=\"wp-image-134891\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Types.webp 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Types-300x200.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Types-768x512.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Types-150x100.webp 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>Different machine learning tasks require different annotation approaches.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Image Labeling<\/strong><\/h3>\n\n\n\n<p>Images can be labeled using categories, bounding boxes, polygons, or segmentation masks.<\/p>\n\n\n\n<p>Common applications include:<\/p>\n\n\n\n<ul>\n<li>Object detection<\/li>\n\n\n\n<li>Image classification<\/li>\n\n\n\n<li>Medical imaging<\/li>\n\n\n\n<li>Autonomous driving<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Text Labeling<\/strong><\/h3>\n\n\n\n<p>Text can be annotated for sentiment, topics, entities, intent, or other linguistic characteristics.<\/p>\n\n\n\n<p>Examples include:<\/p>\n\n\n\n<ul>\n<li>Positive or negative sentiment<\/li>\n\n\n\n<li>Named entities<\/li>\n\n\n\n<li>Customer intent<\/li>\n\n\n\n<li>Spam detection<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Audio Labeling<\/strong><\/h3>\n\n\n\n<p>Audio data can be labeled with transcriptions, speakers, sounds, or events.<\/p>\n\n\n\n<p>Applications include:<\/p>\n\n\n\n<ul>\n<li><a href=\"https:\/\/www.guvi.in\/blog\/ai-speech-recognition\/\" target=\"_blank\" rel=\"noreferrer noopener\">Speech recognit<\/a><a href=\"https:\/\/www.guvi.in\/blog\/ai-speech-recognition\/\">ion<\/a><\/li>\n\n\n\n<li>Voice assistants<\/li>\n\n\n\n<li>Sound classification<\/li>\n\n\n\n<li>Call analysis<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Video Labeling<\/strong><\/h3>\n\n\n\n<p>Video annotation can identify objects, actions, events, or movements across frames.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Data Labeling Workflow<\/strong><\/h2>\n\n\n\n<p>A structured labeling process helps create consistent training datasets.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1: Define the Objective<\/strong><\/h3>\n\n\n\n<p>Clearly identify what the machine learning model needs to learn.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2: Create Labeling Guidelines<\/strong><\/h3>\n\n\n\n<p>Document how annotators should handle different data examples and edge cases.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 3: Select Representative Data<\/strong><\/h3>\n\n\n\n<p>Ensure the dataset contains examples that reflect the conditions the model will encounter.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 4: Label the Data<\/strong><\/h3>\n\n\n\n<p>Annotators assign labels according to the established guidelines.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 5: Review Labels<\/strong><\/h3>\n\n\n\n<p>Check annotations for mistakes, inconsistencies, and missing information.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 6: Measure Quality<\/strong><\/h3>\n\n\n\n<p>Use quality-control processes to identify disagreements and recurring labeling problems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Data Labeling Challenges<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Inconsistent Labels<\/strong><\/h3>\n\n\n\n<p>Different annotators may interpret the same example differently.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Ambiguous Data<\/strong><\/h3>\n\n\n\n<p>Some examples may not clearly belong to a single category.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Human Error<\/strong><\/h3>\n\n\n\n<p>Manual annotation can introduce mistakes, especially in large datasets.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Class Imbalance<\/strong><\/h3>\n\n\n\n<p>Some categories may have far fewer examples than others.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Changing Requirements<\/strong><\/h3>\n\n\n\n<p>Label definitions may need to evolve as the machine learning project develops.<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\"> \n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong> \n  <br \/><br \/> \nDo not start large-scale annotation before testing your labeling guidelines. Label a small sample first, review disagreements, clarify ambiguous cases, and then scale the process. This can prevent thousands of examples from being labeled incorrectly under unclear instructions.\n\n\n\n\n\n\n\n\n\n \n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Benefits of High-Quality Data Labeling<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Better Model Performance<\/strong><\/h3>\n\n\n\n<p>Accurate labels help models learn meaningful patterns from training data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Improved Generalization<\/strong><\/h3>\n\n\n\n<p>Representative and consistent labels can help models perform better on unseen examples.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>More Reliable Evaluation<\/strong><\/h3>\n\n\n\n<p>High-quality validation and test labels make model performance measurements more trustworthy.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Reduced Rework<\/strong><\/h3>\n\n\n\n<p>Clear guidelines and quality checks can prevent expensive relabeling later.<\/p>\n\n\n\n<p>Professionals interested in supervised learning, machine learning workflows, data preparation, and AI development can strengthen their expertise through <strong><a href=\"https:\/\/www.guvi.in\/courses\/bundles\/artificial-intelligence-machine-learning\/?utm_source=blog&amp;utm_medium=hyperlink+&amp;utm_campaign=data-labeling-best-practices-for-supervised-learning\" target=\"_blank\" rel=\"noreferrer noopener\">HCL GUVI&#8217;s Artificial Intelligence and Machine Learning Course<\/a><\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>When Should You Use Different Data Labeling Approaches?<\/strong><\/h2>\n\n\n\n<p>The right <strong>Data Labeling<\/strong> approach depends on the type of data, the ML task, and the level of detail the model needs to learn.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Image Classification<\/strong><\/h3>\n\n\n\n<p>Use simple category labels when the goal is to identify what an entire image contains.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Object Detection<\/strong><\/h3>\n\n\n\n<p>Use bounding boxes when the model needs to identify objects and locate them within an image.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Image Segmentation<\/strong><\/h3>\n\n\n\n<p>Use pixel-level masks when precise object boundaries are important, such as in medical imaging or autonomous driving.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Text Classification<\/strong><\/h3>\n\n\n\n<p>Use category labels when training models to identify sentiment, intent, topics, spam, or other text characteristics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Speech Recognition<\/strong><\/h3>\n\n\n\n<p>Use transcriptions when the goal is to train a model to convert spoken audio into written text.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Concepts to Remember<\/strong><\/h2>\n\n\n\n<p>Understanding these principles helps create better labeled datasets.<\/p>\n\n\n\n<ul>\n<li>Define labels clearly before annotation begins.<\/li>\n\n\n\n<li>Use consistent labeling rules across the dataset.<\/li>\n\n\n\n<li>Include representative examples.<\/li>\n\n\n\n<li>Document ambiguous cases and edge cases.<\/li>\n\n\n\n<li>Review annotations for errors.<\/li>\n\n\n\n<li>Monitor disagreements between annotators.<\/li>\n\n\n\n<li>Keep training, validation, and test labels reliable.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A Practical Data Labeling Workflow<\/strong><\/h2>\n\n\n\n<p>A structured workflow can improve both annotation consistency and dataset quality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Define the ML Objective<\/strong><\/h3>\n\n\n\n<p>Determine exactly what the model needs to predict before creating labels.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Establish Label Definitions<\/strong><\/h3>\n\n\n\n<p>Create clear descriptions for every category, annotation type, or class.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Create Annotation Guidelines<\/strong><\/h3>\n\n\n\n<p>Explain how annotators should handle normal examples, difficult cases, and ambiguous data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Run a Pilot<\/strong><\/h3>\n\n\n\n<p>Label a small sample first and identify disagreements or unclear instructions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Scale Annotation<\/strong><\/h3>\n\n\n\n<p>Once the guidelines are stable, label the larger dataset.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6. Perform Quality Checks<\/strong><\/h3>\n\n\n\n<p>Review annotations and investigate systematic errors or disagreements.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7. Update Guidelines<\/strong><\/h3>\n\n\n\n<p>If recurring problems appear, refine the guidelines and apply the updated rules consistently.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Real-World Applications<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Healthcare<\/strong><\/h3>\n\n\n\n<ul>\n<li>Medical image annotation<\/li>\n\n\n\n<li>Disease classification<\/li>\n\n\n\n<li>Clinical text labeling<\/li>\n\n\n\n<li>Medical entity recognition<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Retail<\/strong><\/h3>\n\n\n\n<ul>\n<li>Product classification<\/li>\n\n\n\n<li>Customer sentiment labeling<\/li>\n\n\n\n<li>Product image annotation<\/li>\n\n\n\n<li>Recommendation data preparation<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Autonomous Vehicles<\/strong><\/h3>\n\n\n\n<ul>\n<li>Vehicle detection<\/li>\n\n\n\n<li>Pedestrian annotation<\/li>\n\n\n\n<li>Road segmentation<\/li>\n\n\n\n<li>Traffic sign labeling<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Customer Service<\/strong><\/h3>\n\n\n\n<ul>\n<li>Intent classification<\/li>\n\n\n\n<li>Sentiment analysis<\/li>\n\n\n\n<li>Conversation labeling<\/li>\n\n\n\n<li>Support ticket categorization<\/li>\n<\/ul>\n\n\n\n<p>The <strong>HCL GUVI&#8217;s Artificial Intelligence <\/strong><a href=\"https:\/\/www.guvi.in\/mlp\/genai-ebook\/?utm_source=blog&amp;utm_medium=hyperlink+&amp;utm_campaign=Data+Labeling+Best+Practices+for+Supervised+Learning\"><strong>eBook<\/strong> <\/a>introduces the fundamentals of artificial intelligence, machine learning, generative AI, and intelligent automation. It helps learners develop a broader understanding of AI concepts and the data-driven processes behind modern intelligent systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Best Practices<\/strong><\/h2>\n\n\n\n<ul>\n<li>Start with precise labeling guidelines.<\/li>\n\n\n\n<li>Train annotators before large-scale labeling.<\/li>\n\n\n\n<li>Use pilot datasets to identify ambiguity early.<\/li>\n\n\n\n<li>Measure inter-annotator agreement where appropriate.<\/li>\n\n\n\n<li>Regularly audit labeled samples.<\/li>\n\n\n\n<li>Track and resolve recurring annotation errors.<\/li>\n\n\n\n<li>Keep label definitions consistent throughout the project.<\/li>\n\n\n\n<li>Recheck labels when the ML objective or class definitions change.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion&nbsp;<\/strong><\/h2>\n\n\n\n<p><strong>Data Labeling<\/strong> is one of the most important foundations of supervised learning. Accurate labels help models learn meaningful relationships, while inconsistent annotations can introduce errors that affect training and evaluation. By defining clear guidelines, using representative data, reviewing annotations, and continuously monitoring quality, teams can build reliable datasets that support better machine learning performance and stronger real-world generalization.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1787375865475\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>1. What is Data Labeling?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p><strong>Data Labeling<\/strong> is the process of assigning meaningful annotations or labels to raw data so supervised machine learning models can learn relationships between inputs and expected outputs.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787375875841\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>2. Why is Data Labeling important?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>High-quality labels help machine learning models learn accurate patterns, improve generalization, and produce more reliable predictions on unseen data.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787375893461\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>3. What types of data can be labeled?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Images, text, audio, and video can all be labeled. The specific annotation method depends on the machine learning task.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787375908699\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>4. What are labeling guidelines?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Labeling guidelines are documented instructions that explain how annotators should assign labels and handle ambiguous or unusual examples.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787375918309\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>5. How can labeling errors be reduced?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Errors can be reduced through clear guidelines, annotator training, pilot labeling, regular quality reviews, and systematic resolution of disagreements.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787376093462\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>6. What is a pilot labeling phase?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A pilot phase involves labeling a small sample before starting large-scale annotation. It helps identify unclear categories, inconsistent interpretations, and problems in the guidelines.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787376108507\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>7. How does data labeling affect supervised learning?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p><strong>Data Labeling<\/strong> directly influences what a supervised learning model learns. Accurate, consistent, and representative labels provide stronger training signals and help models generalize more reliably to new data.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Supervised learning depends on labeled examples to teach machine learning models how to recognize patterns and make predictions. If labels are inconsistent, inaccurate, or incomplete, even a well-designed model can produce unreliable results. Data Labeling is therefore a critical step in the machine learning lifecycle. This guide explains what data labeling involves, why label quality [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":137262,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933],"tags":[],"views":"21","authorinfo":{"name":"HCL GUVI","url":"https:\/\/www.guvi.in\/blog\/author\/guvipr\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/09\/Data-Labeling-Best-Practices-for-Supervised-Learning-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134886"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=134886"}],"version-history":[{"count":4,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134886\/revisions"}],"predecessor-version":[{"id":137608,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134886\/revisions\/137608"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/137262"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=134886"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=134886"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=134886"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}