{"id":176407,"date":"2024-01-16T13:52:49","date_gmt":"2024-01-16T12:52:49","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=176407"},"modified":"2024-01-16T13:53:46","modified_gmt":"2024-01-16T12:53:46","slug":"empowering-natural-language-processing-with-hugging-face-transformers-api","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/empowering-natural-language-processing-with-hugging-face-transformers-api","title":{"rendered":"Empowering Natural Language Processing with Hugging Face Transformers API"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"176407\" class=\"elementor elementor-176407\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c578b91 e-flex e-con-boxed e-con\" data-id=\"c578b91\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d509d40 elementor-widget elementor-widget-heading\" data-id=\"d509d40\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">For years, AI has been attracting growing interest with the performance achieved through machine learning (<a href=\"https:\/\/datascientest.com\/en\/boosting-business-with-3-essential-machine-learning-algorithms\">Machine Learning<\/a>) and, more particularly, deep learning (<a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">Deep Learning<\/a>).<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6fff09f e-flex e-con-boxed e-con\" data-id=\"6fff09f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-627cb04 elementor-widget elementor-widget-text-editor\" data-id=\"627cb04\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>These technologies have played a decisive role in the evolution of<a href=\"https:\/\/datascientest.com\/en\/nlp-training-become-an-nlp-pro-and-master-the-art-of-natural-language-processing\"> natural language processing (NLP).<\/a> They have helped automate the most common <a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\">NLP tasks<\/a>, such as word and sentence classification, text generation, response extraction, and speech and vision recognition.<\/p><p>Today, there are <a href=\"https:\/\/datascientest.com\/en\/spacy-nlps-open-source-python-library\">several open source libraries<\/a> containing pre-trained models, <strong>enabling data<\/strong> scientists and enterprises to use existing models to save computational costs and time. Hugging Face is one of them.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3fc9c3c e-flex e-con-boxed e-con\" data-id=\"3fc9c3c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-da32f1a elementor-widget elementor-widget-heading\" data-id=\"da32f1a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">About Hugging Face Transformers<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-947db09 elementor-widget elementor-widget-text-editor\" data-id=\"947db09\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Hugging Face<\/strong> transformers is a platform for advancing AI by sharing model collections and datasets, enabling users to manage, share and <a href=\"https:\/\/datascientest.com\/en\/xai-or-explainable-artificial-intelligence-what-is-it-about\">develop their AI models.<\/a><\/p><p>It is an open-source Python-based deep learning library that provides a wide variety of pre-trained models for <a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\">different types of NLP<\/a> tasks. It provides an <a href=\"https:\/\/datascientest.com\/en\/api-the-path-to-seamless-integration\">API<\/a> for use in many pre-trained transformer architectures, such as BERT, GPT-2, DistilBERT, BART or <a href=\"https:\/\/datascientest.com\/en\/chatgpt-how-does-this-nlp-algorithm-work\">GPT-3,<\/a> enabling very good results to be obtained on a variety of NLP tasks such as text classification, named entity detection, text generation, etc.<\/p><p><strong>Hugging Face<\/strong>&#8216;s pre-trained transformers can be grouped into three categories:<\/p><ul><li><strong>Autoregressive transformers (GPT-type architecture):<\/strong> focused on predicting the next word in the sentence, they are suitable for generative tasks such as text generation.<\/li><li><strong>Auto-encoding transformers (BERT-type architecture)<\/strong>: more suitable for tasks requiring complete understanding of the input. Examples include sentence classification, named entity recognition and, more generally, word classification.<\/li><li><strong>Sequence-to-sequence transformers (BART\/T5 architecture):<\/strong> best suited to tasks involving the generation of new sentences based on a given input, such as text summarization, translation or question-and-answer generation.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ff49c0 elementor-widget elementor-widget-text-editor\" data-id=\"4ff49c0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In addition to the official pre-trained models, there are hundreds of sentence-transformer models on the <strong>Hugging Face Hub.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b0edfd6 e-flex e-con-boxed e-con\" data-id=\"b0edfd6\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1b7fc0b elementor-widget elementor-widget-heading\" data-id=\"1b7fc0b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What's the point of these Hugging Face Transformers? <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb11849 elementor-widget elementor-widget-text-editor\" data-id=\"bb11849\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The aim of <strong>Transformers<\/strong> is to facilitate the development of machine learning projects by <a href=\"https:\/\/datascientest.com\/en\/fastapi-everything-you-need-to-know-about-the-most-widely-used-python-web-framework-for-machine-learning\">providing APIs<\/a> and tools for downloading and training pre-trained models. Using these models can reduce computational costs, and save the time and resources needed to train a model from scratch.<\/p><p>They have rapidly become the dominant architecture for obtaining reliable results on a <a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\">variety of NLP tasks.<\/a><\/p><p>These models support common tasks in different modalities:<\/p><ul><li><strong>Text:<\/strong> For everything from classification, information extraction and question answering, to the generation, creation and translation of text in over 100 different languages.<\/li><li><strong>Speech:<\/strong> For tasks such as audio classification of objects and speech recognition.<\/li><li><strong>Vision:<\/strong> For object detection, image classification and segmentation.<\/li><li><strong>Tabular data:<\/strong> For regression and <a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i\">classification problems<\/a>.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1e59dc9 elementor-widget elementor-widget-text-editor\" data-id=\"1e59dc9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Transformers also support framework <a href=\"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework\">interoperability between PyTorch<\/a>, TensorFlow and JAX.<\/p><p>This makes it possible to use a different framework at each stage of a model&#8217;s life: form a model in three lines of code in one <a href=\"https:\/\/datascientest.com\/en\/scaled-agile-framework-safe-the-complete-guide\">framework, and load it for inference in another.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1ce015a elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"1ce015a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn how to use Hugging Face Transformers<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a46290e e-flex e-con-boxed e-con\" data-id=\"a46290e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cf8c04b elementor-widget elementor-widget-heading\" data-id=\"cf8c04b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Setting up the Transformers<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a1e0c67 elementor-widget elementor-widget-text-editor\" data-id=\"a1e0c67\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In this section, we&#8217;ll put Transformers into practice by learning about the pipeline tool, and then show how this tool facilitates the implementation of NLP and <a href=\"https:\/\/datascientest.com\/en\/opencv-everything-you-need-to-know-about-computer-visions-leading-tool\">computer vision tasks.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a81c1b8 elementor-widget elementor-widget-heading\" data-id=\"a81c1b8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Pipelines<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6f133f elementor-widget elementor-widget-text-editor\" data-id=\"f6f133f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Pipelines are a simple and effective way of extracting much of the complex code from the library providing an API for performing inference on a variety of tasks, including named entity recognition, sentiment analysis, question answering and so on.<\/p><p>They are used to encapsulate the overall process of each <strong>NLP<\/strong> task such as text cleaning, tokenization, embedding, etc.<\/p><p>It&#8217;s a simple function to use: a pipeline is instantiated via the pipeline() method. Simply specify the task to be launched.<\/p><p>The following is a non-exhaustive list of the pipelines available in the <strong>Hugging Face Transformers library:<\/strong><\/p><ul><li>feature-extraction (to obtain a vector representation of text)<\/li><li>fill-mask (to predict masked words)<\/li><li>ner (named entity recognition)<\/li><li>question-answering (to answer questions)<\/li><li>sentiment-analysis (to analyze feelings)<\/li><li>summarization (to summarize text)<\/li><li>text-generation (to produce new text)<\/li><li>translation (to translate a text)<\/li><li>zero-shot-classification (to classify new examples from unpublished classes)<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8547abb elementor-widget elementor-widget-heading\" data-id=\"8547abb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Text generation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52397e4 elementor-widget elementor-widget-text-editor\" data-id=\"52397e4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Nowadays, text generation has become a trend with the release and evolution of <a href=\"https:\/\/datascientest.com\/en\/autogpt-discover-the-new-tool-that-makes-chatgpt-autonomous\">ChatGPT<\/a>. In this context, the pipeline enables us to use advanced text generation models in a single line of code.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5090350 elementor-widget elementor-widget-image\" data-id=\"5090350\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image2-6.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8c0a5b1 elementor-widget elementor-widget-text-editor\" data-id=\"8c0a5b1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In the line above, we specify the use case we want (text generation), and it will automatically download the default model (in this case GPT-2). We can use other versions by specifying the &#8216;model&#8217; parameter.<\/p><p>Now let&#8217;s start generating the text by passing a phrase as a parameter.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e0908e7 elementor-widget elementor-widget-image\" data-id=\"e0908e7\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image4-5.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-890ee9c elementor-widget elementor-widget-text-editor\" data-id=\"890ee9c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In this example, we asked the model &#8220;Why <a href=\"https:\/\/datascientest.com\/en\/xai-or-explainable-artificial-intelligence-what-is-it-about\">artificial intelligence<\/a>? &#8220;.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff35dd2 elementor-widget elementor-widget-image\" data-id=\"ff35dd2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image3-4.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-945041c elementor-widget elementor-widget-text-editor\" data-id=\"945041c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The model has generated a response in the &#8216;generated_text&#8217; key, and we can generate another response by executing the code one more time. This example shows how Transformers can help us implement a chatbot in a very short time.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-39dc3ea elementor-widget elementor-widget-heading\" data-id=\"39dc3ea\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Image classification<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-461d9dd elementor-widget elementor-widget-text-editor\" data-id=\"461d9dd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Image classification is a <strong>Machine Learning<\/strong> problem. With the Transformers API, it&#8217;s easy to acquire the model for this problem.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52f8ac5 elementor-widget elementor-widget-image\" data-id=\"52f8ac5\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image6-4.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1966901 elementor-widget elementor-widget-text-editor\" data-id=\"1966901\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In one line, we acquired the Image Classifier template. We could easily pass an image file from any source to identify the image with this template.<\/p><p>For example, we&#8217;ll pass the image link below and let the template classify it.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1ebbf2a elementor-widget elementor-widget-image\" data-id=\"1ebbf2a\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image5-3.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e20589c elementor-widget elementor-widget-text-editor\" data-id=\"e20589c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The Pipeline helps us do this in a single line of code. The model proposes five classifications, each associated with a probability. The first has the highest score, so we can take &#8220;sports car&#8221; as our main prediction, and it&#8217;s indeed right.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f4c1761 elementor-widget elementor-widget-image\" data-id=\"f4c1761\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image1-4.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-77dea0c elementor-widget elementor-widget-text-editor\" data-id=\"77dea0c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>This example proves that we can implement a sophisticated model in a short space of time and without a great deal of AI knowledge.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-99d4743 elementor-widget elementor-widget-heading\" data-id=\"99d4743\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Model specification in a pipeline<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e4b2f4 elementor-widget elementor-widget-text-editor\" data-id=\"4e4b2f4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In the previous examples we have used the default model for the task in question, but it is also possible to choose a particular Hub model and use it in a pipeline for a specific task.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d5d8435 e-flex e-con-boxed e-con\" data-id=\"d5d8435\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-58863fe elementor-widget elementor-widget-heading\" data-id=\"58863fe\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae0e71c elementor-widget elementor-widget-text-editor\" data-id=\"ae0e71c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Today, Hugging Face Transformers is the most widely adopted software library by beginners and professionals alike for machine learning models to handle <strong>NLP tasks.<\/strong> It provides the user with a pre-trained model that was previously inaccessible due to technical requirements.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d485344 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d485344\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/deep-learning-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Starting a Deep Learning course<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4da3ea2 e-flex e-con-boxed e-con\" data-id=\"4da3ea2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a758000 elementor-widget elementor-widget-html\" data-id=\"a758000\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"About Hugging Face Transformers\",\n    \"acceptedAnswer\": {\n      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They have helped automate the most common NLP tasks, such as word and sentence classification, text [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":176412,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Empowering Natural Language Processing with Hugging Face Transformers API<\/title>\n<meta name=\"description\" content=\"Hugging Face is a company and an open-source platform that is known for its work in natural language processing (NLP) and most of...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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