{"id":82587,"date":"2025-07-01T13:03:38","date_gmt":"2025-07-01T07:33:38","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=82587"},"modified":"2026-02-11T16:35:38","modified_gmt":"2026-02-11T11:05:38","slug":"what-are-nlp-transformers","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/what-are-nlp-transformers\/","title":{"rendered":"What are NLP Transformers: A Beginner&#8217;s Guide [2026]"},"content":{"rendered":"\n<p>Natural Language Processing (NLP), a vital subfield of Artificial Intelligence (AI), allows computers to understand, interpret, and generate human language. While traditional NLP methods relied on RNNs and LSTMs, the rise of transformer-based architectures revolutionized the field by enabling parallel processing, better context understanding, and superior performance.<\/p>\n\n\n\n<p>Mastering NLP has become an essential skill in today\u2019s tech world, but it\u2019s not easy to get started. That\u2019s what I\u2019m here for! At the forefront of this revolution is Hugging Face\u2019s Transformers library\u2014an open-source powerhouse that puts state-of-the-art NLP capabilities within your reach, even if you&#8217;re just starting.<\/p>\n\n\n\n<p>In this guide, you\u2019ll discover how NLP and transformers work, why Hugging Face\u2019s open-source Transformers library is a game-changer, and how to use pre-trained models like BERT and GPT\u20112 to build powerful NLP applications. Let\u2019s begin!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is Natural Language Processing (NLP)?<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/must-know-nlp-hacks-for-beginners\/\" target=\"_blank\" rel=\"noreferrer noopener\">Natural Language Processing (NLP)<\/a> is a subfield of <a href=\"https:\/\/www.guvi.in\/blog\/what-is-artificial-intelligence\/\" target=\"_blank\" rel=\"noreferrer noopener\">Artificial Intelligence<\/a> that enables machines to understand, interpret, and generate human language. Whether it&#8217;s voice assistants understanding your questions, <a href=\"https:\/\/www.guvi.in\/blog\/influence-of-chatbots-on-customer-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">chatbots<\/a> responding intelligently, or software translating languages, NLP bridges the gap between human communication and computer comprehension.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"630\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing-NLP_@2x-1200x630.png\" alt=\"\" class=\"wp-image-84090\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing-NLP_@2x-1200x630.png 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing-NLP_@2x-300x158.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing-NLP_@2x-768x403.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing-NLP_@2x-1536x806.png 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing-NLP_@2x-2048x1075.png 2048w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Natural-Language-Processing-NLP_@2x-150x79.png 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>It powers applications and <a href=\"https:\/\/www.guvi.in\/blog\/natural-language-processing-project-ideas\/\" target=\"_blank\" rel=\"noreferrer noopener\">NLP projects<\/a> like sentiment analysis, machine translation, speech recognition, and text summarization, making it one of the most impactful AI technologies in today\u2019s digital world.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Why Is It Essential to Learn NLP Today?<\/strong><\/h3>\n\n\n\n<p>With the explosion of unstructured data\u2014emails, social media, customer reviews\u2014NLP has become the backbone of modern AI applications. Here\u2019s why you should start learning it now:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"628\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Why-Is-It-Essential-to-Learn-NLP-Today_-1200x628.png\" alt=\"\" class=\"wp-image-84091\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Why-Is-It-Essential-to-Learn-NLP-Today_-1200x628.png 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Why-Is-It-Essential-to-Learn-NLP-Today_-300x157.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Why-Is-It-Essential-to-Learn-NLP-Today_-768x402.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Why-Is-It-Essential-to-Learn-NLP-Today_-1536x804.png 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Why-Is-It-Essential-to-Learn-NLP-Today_-2048x1072.png 2048w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Why-Is-It-Essential-to-Learn-NLP-Today_-150x79.png 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<ul>\n<li><strong>High Demand<\/strong>: NLP engineers are among the most sought-after AI professionals globally.<\/li>\n\n\n\n<li><strong>Versatile Use Cases<\/strong>: Powers chatbots, recommendation engines, virtual assistants, and more.<\/li>\n\n\n\n<li><strong>Rise of Conversational AI<\/strong>: NLP is core to <a href=\"https:\/\/www.guvi.in\/blog\/build-your-personal-voice-assistant\/\" target=\"_blank\" rel=\"noreferrer noopener\">voice-tech (Alexa, Siri)<\/a> and supports automation.<\/li>\n\n\n\n<li><strong>Data-Driven Insights<\/strong>: Transforms raw text into valuable business intelligence.<\/li>\n\n\n\n<li><strong>Global Impact<\/strong>: Used in healthcare (clinical data), finance (fraud detection), law (document analysis), and education (language tools).<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Are Transformers in NLP?<\/strong><\/h2>\n\n\n\n<p>Transformers are deep learning models introduced in the landmark 2017 paper, <em>\u201cAttention is All You Need.\u201d<\/em> Unlike sequential models like RNNs, transformers process all tokens in a sentence simultaneously using a mechanism called self-attention, enabling better contextual understanding and faster training.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"628\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-Are-Transformers-in-NLP_-1200x628.png\" alt=\"\" class=\"wp-image-84092\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-Are-Transformers-in-NLP_-1200x628.png 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-Are-Transformers-in-NLP_-300x157.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-Are-Transformers-in-NLP_-768x402.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-Are-Transformers-in-NLP_-1536x804.png 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-Are-Transformers-in-NLP_-2048x1072.png 2048w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-Are-Transformers-in-NLP_-150x79.png 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>Popular transformer-based models include:<\/p>\n\n\n\n<ul>\n<li><strong>BERT<\/strong> \u2013 Best for text classification and question answering<\/li>\n\n\n\n<li><strong>GPT-2\/GPT-3<\/strong> \u2013 Ideal for text generation (<a href=\"https:\/\/www.guvi.in\/blog\/everything-you-should-know-about-chatgpt\/\" target=\"_blank\" rel=\"noreferrer noopener\">ChatGPT<\/a>)<\/li>\n\n\n\n<li><strong>RoBERTa<\/strong> \u2013 Robustly optimized BERT approach<\/li>\n\n\n\n<li><strong>DistilBERT<\/strong> \u2013 Lightweight, faster BERT variant<\/li>\n\n\n\n<li><strong>XLNet<\/strong> \u2013 Combines autoregressive modeling with permutation-based training<\/li>\n<\/ul>\n\n\n\n<p>These transformers have unique usage in Natural Language Processing, for example, BERT is used for sentiment analysis, question-answering, text-summarization, etc, and GPT-2 is used for Natural Language Generation (NLG). Transformers uses these three most popular deep learning libraries: Jax, PyTorch, and TensorFlow.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Hugging Face\u2019s Transformers<\/strong><\/h3>\n\n\n\n<p>Hugging Face is a start-up AI community. The company provides open-source NLP technologies and thousands of pre-trained models that we can use for Natural Language Processing. The Transformer is the dominant architecture for natural language processing alternative to neural models.<\/p>\n\n\n\n<p>Transformers is an open-source library consisting of carefully engineered state-of-the-art Transformer architectures and a curated collection of pre-trained models available to use. Transformers provides APIs to quickly download and use those pre-trained models. We can use these pre-trained models to fine-tune them on our datasets as per our needs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Time to Implement: Sentiment Analysis using Transformer<\/strong><\/h2>\n\n\n\n<p>We have to install one of these libraries: Jax, PyTorch, and TensorFlow. After that, we have to install the Transformers library using the command pip install transformers<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>! pip install transformers<\/code><\/pre>\n\n\n\n<p>Then, transformers have to import pipeline, and also have to import AutoTokenizer and AutoModelForSequenceClassifier.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from transformers import pipeline\n\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification<\/code><\/pre>\n\n\n\n<p>We have to create the instance for both the AutoTokenizer and AutoModelForSequenceClassifier and pass the model\u2019s name that we have given in the variable as the argument. Here, the model we have used is distilbert, the light version of Bert, and trained on the same corpus as Bert.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>model_name=\"distilbert-base-uncased-finetuned-sst-2-english\"\n\nmodel=AutoModelForSequenceClassification.from_pretrained(model_name)\n\ntokenizer=AutoTokenizer.from_pretrained(model_name)<\/code><\/pre>\n\n\n\n<p>Then we have to give a model and a tokenizer in the pipeline and get the output.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>classifier = pipeline('sentiment-&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; analysis',model=model,tokenizer=tokenizer)\n\nresults = classifier(&#91;\"I am very happy to write this blog.\",\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\"I hate to eat junk foods.\"])\n\nfor result in results:\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;print(f\"label: {result&#91;'label']}, with score:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; {round(result&#91;'score'], 4)}\")<\/code><\/pre>\n\n\n\n<p>In the output, we get the labels and score i.e, the given sentence is positive or negative and the percentage of the positive or negative.<\/p>\n\n\n\n<p>OUTPUT: <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>label: POSITIVE, with score: 0.9998\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;label: NEGATIVE, with score: 0.9951<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Behind the Pipeline of Pre-Trained Models<\/strong><\/h2>\n\n\n\n<p>Let&#8217;s see what is happening in the pre-trained models of Transformers. Tokenizer is responsible for preprocessing the text. It converts the text data into a numerical array. Tokenizer finds the unique words in the text data, associates each token with a unique number, and encodes it using the mapping.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>tokens=tokenizer.tokenize(&#91;\"I am very happy to write this blog.\",\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\"I hate to eat junk foods.\"])\n\ntoken_id=tokenizer.convert_tokens_to_ids(tokens)\n\ninput_ids=tokenizer(&#91;\"I am very happy to write this blog.\",\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\"I hate to eat junk foods.\"])\n\nprint(f\"Tokens: {tokens}\")\n\nprint(f\"Token_id: {token_id}\")\n\nprint(f\"Input_ids: {input_ids}\")<\/code><\/pre>\n\n\n\n<p>OUTPUT:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Tokens: &#91;'i', 'am', 'very', 'happy', 'to', 'write', 'this', 'blog', '.', 'i', 'hate', 'to', 'eat', 'junk', 'foods', '.']\n\nToken_id: &#91;1045, 2572, 2200, 3407, 2000, 4339, 2023, 9927, 1012, 1045, 5223, 2000, 4521, 18015, 9440, 1012]\n\nInput_ids: {'input_ids': &#91;&#91;101, 1045, 2572, 2200, 3407, 2000, 4339, 2023, 9927, 1012, 102], &#91;101, 1045, 5223, 2000, 4521, 18015, 9440, 1012, 102]], 'attention_mask': &#91;&#91;1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], &#91;1, 1, 1, 1, 1, 1, 1, 1, 1]]}<\/code><\/pre>\n\n\n\n<p>In the above code, we get the input_ids and attention mask for each input_id. While using the Transformers library, we always have to use a tokenizer and model belonging to the same model checkpoint because models and tokenizers would have the same knowledge about the tokens and their encodings.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Use Cases of Transformers in NLP<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"628\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Use-Cases-of-Transformers-in-NLP-1200x628.png\" alt=\"\" class=\"wp-image-84093\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Use-Cases-of-Transformers-in-NLP-1200x628.png 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Use-Cases-of-Transformers-in-NLP-300x157.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Use-Cases-of-Transformers-in-NLP-768x402.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Use-Cases-of-Transformers-in-NLP-1536x804.png 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Use-Cases-of-Transformers-in-NLP-2048x1072.png 2048w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/Use-Cases-of-Transformers-in-NLP-150x79.png 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Application Area<\/strong><\/td><td><strong>How Transformers Help<\/strong><\/td><\/tr><tr><td><strong>Sentiment Analysis<\/strong><\/td><td>Understand customer emotions in text<\/td><\/tr><tr><td><strong>Text Summarization<\/strong><\/td><td>Compress lengthy content into short blurbs<\/td><\/tr><tr><td><strong>Named Entity Recognition (NER)<\/strong><\/td><td>Identify names, places, and dates<\/td><\/tr><tr><td><strong>Text Generation<\/strong><\/td><td>Auto-generate emails, captions, or articles<\/td><\/tr><tr><td><strong>Question Answering<\/strong><\/td><td>Build intelligent search engines and bots<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Pros &amp; Cons of Transformers<\/strong><\/h2>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul>\n<li>Easy access to SOTA NLP<\/li>\n\n\n\n<li>Lower compute cost due to pre-training<\/li>\n\n\n\n<li>High accuracy across varied tasks<\/li>\n\n\n\n<li>Large community and documentation<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul>\n<li>Not ideal for custom neural layer development<\/li>\n\n\n\n<li>May require a GPU for large-scale fine-tuning<\/li>\n<\/ul>\n\n\n\n<p><strong><em>Ready to level up your NLP skills? HCL GUVI\u2019s <\/em><\/strong><a href=\"https:\/\/www.guvi.in\/mlp\/artificial-intelligence-and-machine-learning-course?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=NLP+Using+Transformers%3A+A+Beginner%27s+Guide+%5B2025%5D\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><em>Artificial Intelligence &amp; Machine Learning Course<\/em><\/strong><\/a><strong><em>\u2014created in partnership with IIT-M and HCL GUVI\u2014offers hands-on projects, cutting-edge modules (including NLP with Transformers), and a verified certification to help you build real-world impact.<\/em><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Concluding Thoughts\u2026<\/strong><\/h2>\n\n\n\n<p>Transformers have redefined what\u2019s possible in Natural Language Processing. With Hugging Face\u2019s Transformers library, even beginners can tap into the power of BERT, GPT, and more with just a few lines of code.&nbsp;<\/p>\n\n\n\n<p>Whether you&#8217;re building a chatbot, a recommendation engine, or a content analysis tool, Transformers offer flexibility, performance, and scalability like never before. Ready to explore the future of language AI? Install the Transformers library today and start experimenting\u2014your NLP journey starts now. Good Luck!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Natural Language Processing (NLP), a vital subfield of Artificial Intelligence (AI), allows computers to understand, interpret, and generate human language. While traditional NLP methods relied on RNNs and LSTMs, the rise of transformer-based architectures revolutionized the field by enabling parallel processing, better context understanding, and superior performance. Mastering NLP has become an essential skill in [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":84088,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[739],"tags":[],"views":"2437","authorinfo":{"name":"Jaishree Tomar","url":"https:\/\/www.guvi.in\/blog\/author\/jaishree\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/NLP-Using-Transformers-300x116.png","jetpack_featured_media_url":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/NLP-Using-Transformers.png","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82587"}],"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\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=82587"}],"version-history":[{"count":7,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82587\/revisions"}],"predecessor-version":[{"id":100898,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82587\/revisions\/100898"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/84088"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=82587"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=82587"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=82587"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}