How To Use Hugging Face Transformers In Your Code Eduwik

Leo Migdal

-Nov 18, 2025, 12:25 AM

how to use hugging face transformers in your code eduwik

The emergence of large language models has fundamentally reshaped natural language processing, unlocking a new generation of applications that can understand, generate, and translate human language with remarkable precision. At the center of this transformation is Hugging Face, a company that has made cutting-edge NLP accessible to all through its open-source Transformers library. This Python-based toolkit gives developers immediate access to powerful pre-trained models like BERT, GPT, RoBERTa, and T5, making it possible to build applications for text classification, summarization, translation, question answering, and conversational AI with... What sets Hugging Face Transformers apart is its blend of technical depth and ease of use. Developers no longer need to build neural networks from the ground up—instead, they can integrate high-performing, fine-tuned models that are trained on massive datasets, ready to plug into real-world products. Whether you’re crafting a sentiment analysis tool, an AI-powered writing assistant, or a sophisticated chatbot, Hugging Face offers the foundation to bring your vision to life.

This guide takes a deep dive into using Hugging Face Transformers in your codebase—from setup and core components to customization, fine-tuning, and deployment strategies. As we move through 2025 and beyond, mastering this library is an essential step for anyone building intelligent NLP systems. To begin using Hugging Face Transformers, you’ll need to set up your environment. The library supports Python 3.7+ and works seamlessly with PyTorch, TensorFlow, and JAX. Installation is straightforward via pip or conda, and for training optimization, tools like datasets and accelerate are recommended to speed up processing on GPUs and TPUs. Once installed, importing a pre-trained model and its tokenizer is just a few lines away.

Tokenizers are responsible for converting raw text into numeric token sequences, making them digestible for neural networks. Hugging Face abstracts much of this complexity, allowing you to jump straight into prototyping. and get access to the augmented documentation experience Transformers is designed to be fast and easy to use so that everyone can start learning or building with transformer models. The number of user-facing abstractions is limited to only three classes for instantiating a model, and two APIs for inference or training. This quickstart introduces you to Transformers’ key features and shows you how to:

To start, we recommend creating a Hugging Face account. An account lets you host and access version controlled models, datasets, and Spaces on the Hugging Face Hub, a collaborative platform for discovery and building. Create a User Access Token and log in to your account. and get access to the augmented documentation experience 🤗 transformers is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX. It provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

We are a bit biased, but we really like 🤗 transformers! There are over 630,000 transformers models in the Hub which you can find by filtering at the left of the models page. You can find models for many different tasks: You can try out the models directly in the browser if you want to test them out without downloading them thanks to the in-browser widgets! You might wonder, with the abundance of tutorials on Hugging Face already available, why create another? The answer lies in accessibility: most existing resources assume some technical background, including Python proficiency, which can prevent non-technical individuals from grasping ML fundamentals.

As someone who came from the business side of AI, I recognize that the learning curve presents a barrier and wanted to offer a more approachable path for like-minded learners. Therefore, this guide is tailored for a non-technical audience keen to better understand open-source machine learning without having to learn Python from scratch. We assume no prior knowledge and will explain concepts from the ground up to ensure clarity. If you're an engineer, you’ll find this guide a bit basic, but for beginners, it's an ideal starting point. Let’s get stuck in… but first some context. Hugging Face Transformers is an open-source Python library that provides access to thousands of pre-trained Transformers models for natural language processing (NLP), computer vision, audio tasks, and more.

It simplifies the process of implementing Transformer models by abstracting away the complexity of training or deploying models in lower level ML frameworks like PyTorch, TensorFlow and JAX. A library is just a collection of reusable pieces of code that can be integrated into projects to implement functionality more efficiently without the need to write your own code from scratch. Hugging Face is a leading open-source platform for building and deploying machine learning (ML) models, especially in natural language processing (NLP). It provides powerful tools like the Transformers library, a Model Hub with thousands of pre-trained models (e.g., GPT-2, BERT), and access to over 100,000 datasets for tasks in NLP, computer vision, and audio. We can quickly fine-tune models on custom data, tokenize text automatically, and even evaluate performance, all with minimal setup. The Hugging Face Hub lets us store, share, and reuse models, making collaboration and deployment seamless.

Now that we understand what Hugging Face offers, let’s walk through the steps to set up your environment. Hugging Face is free to use, and creating an account only requires an email address. In many ways, the platform is analogous to GitHub in its function as well as its approach - all the main features are free and open to the public without limits. Anyone can create and upload as many models as they want at no additional cost. The workflow shown in this tutorial saves the trained model to the Hub repo. The only additional (account) configuration necessary is the creation of a key that will provide access to a user profile from the notebook environment.

If you’re venturing into natural language processing (NLP) or machine learning, you’ve likely heard about Hugging Face and their revolutionary Transformers library. It has become the go-to toolkit for working with state-of-the-art language models like BERT, GPT, RoBERTa, and T5. Whether you’re performing sentiment analysis, question answering, or text generation, the Transformers library simplifies the integration and fine-tuning of these models. In this blog post, we’ll walk you through getting started with Hugging Face Transformers—from installation and basic usage to training your own models. Hugging Face Transformers is an open-source Python library that provides thousands of pre-trained models for tasks such as text classification, named entity recognition, summarization, translation, and question answering. It supports models from major architectures including BERT, GPT-2/3, T5, RoBERTa, and DistilBERT.

Beyond NLP, it now includes models for vision and audio tasks, thanks to the expanding support for multimodal learning. The beauty of this library lies in its simplicity. With just a few lines of code, you can load a transformer model, tokenize text, and generate predictions—all using a standardized and intuitive API. The first step in getting started with Hugging Face Transformers is to set up your development environment. Begin by installing the transformers library via pip. You’ll also need a backend deep learning framework like PyTorch or TensorFlow, although PyTorch is the more commonly used option.

For working with datasets, install the optional but highly recommended datasets package: and get access to the augmented documentation experience Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal model, for both inference and training. It centralizes the model definition so that this definition is agreed upon across the ecosystem. transformers is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, …), inference engines (vLLM, SGLang, TGI, …),... We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient.

There are over 1M+ Transformers model checkpoints on the Hugging Face Hub you can use. This repo contains the content that's used to create the Hugging Face course. The course teaches you about applying Transformers to various tasks in natural language processing and beyond. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It's completely free and open-source! As part of our mission to democratise machine learning, we'd love to have the course available in many more languages!

Please follow the steps below if you'd like to help translate the course into your language 🙏. To get started, navigate to the Issues page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the Translation template from the New issue button. Once an issue is created, post a comment to indicate which chapters you'd like to work on and we'll add your name to the list. Since it can be difficult to discuss translation details quickly over GitHub issues, we have created dedicated channels for each language on our Discord server. If you'd like to join, follow the instructions at this channel 👉: https://discord.gg/JfAtkvEtRb

How To Use Hugging Face Transformers In Your Code Eduwik

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