![\"What](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing_@2x-1200x630.webp\" "\\"\\"")
<\/figure>\n\n\n\nNatural language processing (NLP)<\/a> is a branch of\u00a0 Artificial Intelligence (AI)<\/a> that gives computers the ability to understand text and spoken words in the same way human beings can.\u00a0<\/p>\n\n\n\n NLP resolves ambiguity in language and adds useful numeric structure to the data for a lot of applications, such as speech recognition or text analytics, etc. Transfer learning is one of the trending and hottest topics in NLP, and it is a novel way to train machine learning models.<\/p>\n\n\n\n Transfer Learning is a technique where a model trained on a large dataset (source task\/domain) is reused or adapted to perform a different but related task (target domain\/task). Instead of training from scratch, we build upon the knowledge the model has already learned, often improving performance with less data.<\/p>\n\n\n\n In the above picture, Transfer learning uses knowledge learned from a previous task\/domain for a new one. The formal definition for transfer learning states that, given a source domain, a corresponding source task, as well as a target domain and a target task, the objective of transfer learning now is to enable us to learn the target conditional probability distribution in target domain with the information gained from source domain<\/em> and where source domain \u2260target domain or source task \u2260 target task.<\/p>\n\n\n\n Especially in NLP, transfer learning forms the basis of most applications you see in production today.<\/p>\n\n\n\n A wide variety of transformer-based models are there for performing different NLP tasks, but the most important ones are:<\/p>\n\n\n\n Developed by Google, BERT<\/a> learns deep bidirectional representations by conditioning on both left and right context in all layers. It laid the foundation for modern NLP architectures.<\/p>\n\n\n\n BERT Base<\/strong>:<\/p>\n\n\n\n BERT Large<\/strong>:<\/p>\n\n\n\n Latest Update (2025): BERT is still widely used, often fine-tuned for task-specific applications or as a baseline for comparison.<\/p>\n\n\n\n Developed by Meta AI, RoBERTa improves BERT by:<\/p>\n\n\n\n Latest Update (2025): RoBERTa continues to be a preferred choice for text classification and QA, often outperforming vanilla BERT in benchmarks.<\/p>\n\n\n\n OpenAI\u2019s GPT-2, released in 2019, set the tone for generative NLP by showing how a transformer-based model could:<\/p>\n\n\n\n GPT-2 (1.5B parameters) was followed by:<\/p>\n\n\n\n Latest Update (2025): GPT-4o powers voice assistants, reasoning engines, and customer support agents. Though closed-source, open-source alternatives like Mistral, LLaMA, and Falcon offer similar capabilities with transfer learning approaches.<\/p>\n\n\n\n There are three main ways to adapt a pre-trained model:<\/p>\n\n\n\n Classifies text as positive, negative, or neutral. Used in social media monitoring, customer feedback, and product reviews.<\/p>\n\n\n\n BERT-based sentiment models now reach 95%+ accuracy with datasets like IMDB Reviews, Amazon Product Data, and Twitter Sentiment Corpus.<\/p>\n\n\n\n Identifies entities like:<\/p>\n\n\n\n RoBERTa and BERT fine-tuned with BIO tagging<\/strong> schemes dominate NER tasks in 2025.<\/p>\n\n\n\n If you want to learn more about how NLP helps in our day-to-day life and how learning can impact your surroundings, consider enrolling in HCL GUVI\u2019s IITM Pravartak Certified Artificial Intelligence and Machine Learning course<\/a> that teaches NLP, Cloud technologies, Deep learning<\/a>, and much more that you can learn directly from industry experts.<\/p>\n\n\n\n Transfer learning has completely transformed NLP\u2014from time-consuming model building to plug-and-play intelligence. Whether it\u2019s classifying a tweet, answering a customer query, or summarizing a legal document, the magic lies in leveraging powerful pre-trained models like BERT, RoBERTa, and GPT.<\/p>\n\n\n\n With the advancement of transformer architectures and fine-tuning techniques, NLP is only becoming more accurate, accessible, and impactful in 2025 and beyond.<\/p>\n\n\n\n If you’re looking to build an NLP application today, remember: you don\u2019t need to start from scratch; start with transfer learning.<\/p>\n","protected":false},"excerpt":{"rendered":" Have you ever wondered how chatbots like ChatGPT, voice assistants like Alexa, or search engines understand and respond so naturally? The answer lies in one of the hottest breakthroughs in machine learning\u2014Transfer Learning. It has revolutionized how we train models for Natural Language Processing (NLP) by dramatically reducing time, cost, and data requirements. In this […]<\/p>\n","protected":false},"author":22,"featured_media":83028,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933,739],"tags":[],"views":"1667","authorinfo":{"name":"Lukesh S","url":"https:\/\/www.guvi.in\/blog\/author\/lukesh\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/06\/Trending-and-Hottest-Topic-in-NLP-300x116.webp","jetpack_featured_media_url":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/06\/Trending-and-Hottest-Topic-in-NLP.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82338"}],"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\/22"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=82338"}],"version-history":[{"count":10,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82338\/revisions"}],"predecessor-version":[{"id":86880,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82338\/revisions\/86880"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/83028"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=82338"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=82338"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=82338"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}What is Transfer Learning?<\/strong><\/h2>\n\n\n\n
![\"What](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Transfer-Learning_@2x-1200x630.webp\" "\\"\\"")
<\/figure>\n\n\n\nWhy Use Transfer Learning?<\/strong><\/h3>\n\n\n\n
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Key Pretrained Transformer Models<\/strong><\/h2>\n\n\n\n
![\"Key](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Key-Pretrained-Transformer-Models@2x-1200x630.webp\" "\\"\\"")
<\/figure>\n\n\n\n1. BERT \u2013 Bidirectional Encoder Representations from Transformers<\/strong><\/h3>\n\n\n\n
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2. RoBERTa \u2013 Robustly Optimized BERT Approach<\/strong><\/h3>\n\n\n\n
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3. GPT-2 and Beyond<\/strong><\/h3>\n\n\n\n
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<\/li>\n<\/ul>\n\n\n\nFine-Tuning Methods in Transfer Learning<\/strong><\/h2>\n\n\n\n
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<\/li>\n\n\n\nReal-World NLP Tasks Using Transfer Learning<\/strong><\/h2>\n\n\n\n
1. Sentiment Analysis<\/strong><\/h3>\n\n\n\n
2. Named Entity Recognition (NER)<\/strong><\/h3>\n\n\n\n
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The Future of Transfer Learning in NLP<\/strong><\/h2>\n\n\n\n
![\"The](\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/The-Future-of-Transfer-Learning-in-NLP@2x-1200x630.webp\" "\\"\\"")
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<\/li>\n\n\n\nConclusion<\/strong><\/h2>\n\n\n\n