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Introduction - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/1?fw=pt | ## [](#introduction)Introduction
[](https://discuss.huggingface.co/t/chapter-1-questions)
## [](#welcome-to-the-course)Welcome to the 🤗 Course!
This course will teach you about natural language processing (NLP) using libraries from the [Hugging Face](https://huggingface.co/) ecosystem — [🤗 Transformers](https://github.com/huggingface/transformers), [🤗 Datasets](https://github.com/huggingface/datasets), [🤗 Tokenizers](https://github.com/huggingface/tokenizers), and [🤗 Accelerate](https://github.com/huggingface/accelerate) — as well as the [Hugging Face Hub](https://huggingface.co/models). It’s completely free and without ads.
## [](#what-to-expect)What to expect?
Here is a brief overview of the course:
 
- Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the [Hugging Face Hub](https://huggingface.co/models), fine-tune it on a dataset, and share your results on the Hub!
- Chapters 5 to 8 teach the basics of 🤗 Datasets and 🤗 Tokenizers before diving into classic NLP tasks. By the end of this part, you will be able to tackle the most common NLP problems by yourself.
- Chapters 9 to 12 go beyond NLP, and explore how Transformer models can be used to tackle tasks in speech processing and computer vision. Along the way, you’ll learn how to build and share demos of your models, and optimize them for production environments. By the end of this part, you will be ready to apply 🤗 Transformers to (almost) any machine learning problem!
This course:
- Requires a good knowledge of Python
- Is better taken after an introductory deep learning course, such as [fast.ai’s](https://www.fast.ai/) [Practical Deep Learning for Coders](https://course.fast.ai/) or one of the programs developed by [DeepLearning.AI](https://www.deeplearning.ai/)
- Does not expect prior [PyTorch](https://pytorch.org/) or [TensorFlow](https://www.tensorflow.org/) knowledge, though some familiarity with either of those will help
After you’ve completed this course, we recommend checking out DeepLearning.AI’s [Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing?utm_source=deeplearning-ai&utm_medium=institutions&utm_campaign=20211011-nlp-2-hugging_face-page-nlp-refresh), which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about!
## [](#who-are-we)Who are we?
About the authors:
[**Abubakar Abid**](https://huggingface.co/abidlabs) completed his PhD at Stanford in applied machine learning. During his PhD, he founded [Gradio](https://github.com/gradio-app/gradio), an open-source Python library that has been used to build over 600,000 machine learning demos. Gradio was acquired by Hugging Face, which is where Abubakar now serves as a machine learning team lead.
[**Matthew Carrigan**](https://huggingface.co/Rocketknight1) is a Machine Learning Engineer at Hugging Face. He lives in Dublin, Ireland and previously worked as an ML engineer at Parse.ly and before that as a post-doctoral researcher at Trinity College Dublin. He does not believe we’re going to get to AGI by scaling existing architectures, but has high hopes for robot immortality regardless.
[**Lysandre Debut**](https://huggingface.co/lysandre) is a Machine Learning Engineer at Hugging Face and has been working on the 🤗 Transformers library since the very early development stages. His aim is to make NLP accessible for everyone by developing tools with a very simple API.
[**Sylvain Gugger**](https://huggingface.co/sgugger) is a Research Engineer at Hugging Face and one of the core maintainers of the 🤗 Transformers library. Previously he was a Research Scientist at fast.ai, and he co-wrote _[Deep Learning for Coders with fastai and PyTorch](https://learning.oreilly.com/library/view/deep-learning-for/9781492045519/)_ with Jeremy Howard. The main focus of his research is on making deep learning more accessible, by designing and improving techniques that allow models to train fast on limited resources.
[**Dawood Khan**](https://huggingface.co/dawoodkhan82) is a Machine Learning Engineer at Hugging Face. He’s from NYC and graduated from New York University studying Computer Science. After working as an iOS Engineer for a few years, Dawood quit to start Gradio with his fellow co-founders. Gradio was eventually acquired by Hugging Face.
[**Merve Noyan**](https://huggingface.co/merve) is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone.
[**Lucile Saulnier**](https://huggingface.co/SaulLu) is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience.
[**Lewis Tunstall**](https://huggingface.co/lewtun) is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of the O’Reilly book [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/).
[**Leandro von Werra**](https://huggingface.co/lvwerra) is a machine learning engineer in the open-source team at Hugging Face and also a co-author of the O’Reilly book [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/). He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack..
## [](#faq)FAQ
Here are some answers to frequently asked questions:
- **Does taking this course lead to a certification?** Currently we do not have any certification for this course. However, we are working on a certification program for the Hugging Face ecosystem — stay tuned!
- **How much time should I spend on this course?** Each chapter in this course is designed to be completed in 1 week, with approximately 6-8 hours of work per week. However, you can take as much time as you need to complete the course.
- **Where can I ask a question if I have one?** If you have a question about any section of the course, just click on the ”_Ask a question_” banner at the top of the page to be automatically redirected to the right section of the [Hugging Face forums](https://discuss.huggingface.co/):
Note that a list of [project ideas](https://discuss.huggingface.co/c/course/course-event/25) is also available on the forums if you wish to practice more once you have completed the course.
- **Where can I get the code for the course?** For each section, click on the banner at the top of the page to run the code in either Google Colab or Amazon SageMaker Studio Lab:
The Jupyter notebooks containing all the code from the course are hosted on the [`huggingface/notebooks`](https://github.com/huggingface/notebooks) repo. If you wish to generate them locally, check out the instructions in the [`course`](https://github.com/huggingface/course#-jupyter-notebooks) repo on GitHub.
- **How can I contribute to the course?** There are many ways to contribute to the course! If you find a typo or a bug, please open an issue on the [`course`](https://github.com/huggingface/course) repo. If you would like to help translate the course into your native language, check out the instructions [here](https://github.com/huggingface/course#translating-the-course-into-your-language).
- **What were the choices made for each translation?** Each translation has a glossary and `TRANSLATING.txt` file that details the choices that were made for machine learning jargon etc. You can find an example for German [here](https://github.com/huggingface/course/blob/main/chapters/de/TRANSLATING.txt).
- **Can I reuse this course?** Of course! The course is released under the permissive [Apache 2 license](https://www.apache.org/licenses/LICENSE-2.0.html). This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. If you would like to cite the course, please use the following BibTeX:
```
@misc{huggingfacecourse,
author = {Hugging Face},
title = {The Hugging Face Course, 2022},
howpublished = "\url{https://huggingface.co/course}",
year = {2022},
note = "[Online; accessed <today>]"
}```
## [](#lets-go)Let's Go
Are you ready to roll? In this chapter, you will learn:
- How to use the `pipeline()` function to solve NLP tasks such as text generation and classification
- About the Transformer architecture
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Transformer models","isExpanded":true,"sections":[{"title":"Introduction","isExpanded":true,"id":"chapter1/1","url":"/learn/nlp-course/chapter1/1?fw=pt"},{"title":"Natural Language Processing","id":"chapter1/2","url":"/learn/nlp-course/chapter1/2?fw=pt"},{"title":"Transformers, what can they do?","id":"chapter1/3","url":"/learn/nlp-course/chapter1/3?fw=pt"},{"title":"How do Transformers work?","id":"chapter1/4","url":"/learn/nlp-course/chapter1/4?fw=pt"},{"title":"Encoder models","id":"chapter1/5","url":"/learn/nlp-course/chapter1/5?fw=pt"},{"title":"Decoder models","id":"chapter1/6","url":"/learn/nlp-course/chapter1/6?fw=pt"},{"title":"Sequence-to-sequence models","id":"chapter1/7","url":"/learn/nlp-course/chapter1/7?fw=pt"},{"title":"Bias and limitations","id":"chapter1/8","url":"/learn/nlp-course/chapter1/8?fw=pt"},{"title":"Summary","id":"chapter1/9","url":"/learn/nlp-course/chapter1/9?fw=pt"},{"title":"End-of-chapter quiz","quiz":1,"id":"chapter1/10","url":"/learn/nlp-course/chapter1/10?fw=pt"}]},{"title":"2. Using 🤗 Transformers","isExpanded":false,"sections":[{"title":"Introduction","id":"chapter2/1","url":"/learn/nlp-course/chapter2/1?fw=pt"},{"title":"Behind the pipeline","id":"chapter2/2","url":"/learn/nlp-course/chapter2/2?fw=pt"},{"title":"Models","id":"chapter2/3","url":"/learn/nlp-course/chapter2/3?fw=pt"},{"title":"Tokenizers","id":"chapter2/4","url":"/learn/nlp-course/chapter2/4?fw=pt"},{"title":"Handling multiple sequences","id":"chapter2/5","url":"/learn/nlp-course/chapter2/5?fw=pt"},{"title":"Putting it all together","id":"chapter2/6","url":"/learn/nlp-course/chapter2/6?fw=pt"},{"title":"Basic usage completed!","id":"chapter2/7","url":"/learn/nlp-course/chapter2/7?fw=pt"},{"title":"End-of-chapter quiz","quiz":2,"id":"chapter2/8","url":"/learn/nlp-course/chapter2/8?fw=pt"}]},{"title":"3. Fine-tuning a pretrained model","isExpanded":false,"sections":[{"title":"Introduction","id":"chapter3/1","url":"/learn/nlp-course/chapter3/1?fw=pt"},{"title":"Processing the data","id":"chapter3/2","url":"/learn/nlp-course/chapter3/2?fw=pt"},{"title":"Fine-tuning a model with the Trainer API or Keras","local_fw":{"pt":"chapter3/3","tf":"chapter3/3_tf"},"id":"chapter3/3","url":"/learn/nlp-course/chapter3/3?fw=pt"},{"title":"A full training","id":"chapter3/4","url":"/learn/nlp-course/chapter3/4?fw=pt"},{"title":"Fine-tuning, Check!","id":"chapter3/5","url":"/learn/nlp-course/chapter3/5?fw=pt"},{"title":"End-of-chapter quiz","quiz":3,"id":"chapter3/6","url":"/learn/nlp-course/chapter3/6?fw=pt"}]},{"title":"4. Sharing models and tokenizers","isExpanded":false,"sections":[{"title":"The Hugging Face Hub","id":"chapter4/1","url":"/learn/nlp-course/chapter4/1?fw=pt"},{"title":"Using pretrained models","id":"chapter4/2","url":"/learn/nlp-course/chapter4/2?fw=pt"},{"title":"Sharing pretrained models","id":"chapter4/3","url":"/learn/nlp-course/chapter4/3?fw=pt"},{"title":"Building a model card","id":"chapter4/4","url":"/learn/nlp-course/chapter4/4?fw=pt"},{"title":"Part 1 completed!","id":"chapter4/5","url":"/learn/nlp-course/chapter4/5?fw=pt"},{"title":"End-of-chapter quiz","quiz":4,"id":"chapter4/6","url":"/learn/nlp-course/chapter4/6?fw=pt"}]},{"title":"5. The 🤗 Datasets library","isExpanded":false,"sections":[{"title":"Introduction","id":"chapter5/1","url":"/learn/nlp-course/chapter5/1?fw=pt"},{"title":"What if my dataset isn't on the Hub?","id":"chapter5/2","url":"/learn/nlp-course/chapter5/2?fw=pt"},{"title":"Time to slice and dice","id":"chapter5/3","url":"/learn/nlp-course/chapter5/3?fw=pt"},{"title":"Big data? 🤗 Datasets to the rescue!","id":"chapter5/4","url":"/learn/nlp-course/chapter5/4?fw=pt"},{"title":"Creating your own dataset","id":"chapter5/5","url":"/learn/nlp-course/chapter5/5?fw=pt"},{"title":"Semantic search with FAISS","id":"chapter5/6","url":"/learn/nlp-course/chapter5/6?fw=pt"},{"title":"🤗 Datasets, check!","id":"chapter5/7","url":"/learn/nlp-course/chapter5/7?fw=pt"},{"title":"End-of-chapter quiz","quiz":5,"id":"chapter5/8","url":"/learn/nlp-course/chapter5/8?fw=pt"}]},{"title":"6. The 🤗 Tokenizers library","isExpanded":false,"sections":[{"title":"Introduction","id":"chapter6/1","url":"/learn/nlp-course/chapter6/1?fw=pt"},{"title":"Training a new tokenizer from an old one","id":"chapter6/2","url":"/learn/nlp-course/chapter6/2?fw=pt"},{"title":"Fast tokenizers' special powers","id":"chapter6/3","url":"/learn/nlp-course/chapter6/3?fw=pt"},{"title":"Fast tokenizers in the QA pipeline","id":"chapter6/3b","url":"/learn/nlp-course/chapter6/3b?fw=pt"},{"title":"Normalization and pre-tokenization","id":"chapter6/4","url":"/learn/nlp-course/chapter6/4?fw=pt"},{"title":"Byte-Pair Encoding tokenization","id":"chapter6/5","url":"/learn/nlp-course/chapter6/5?fw=pt"},{"title":"WordPiece tokenization","id":"chapter6/6","url":"/learn/nlp-course/chapter6/6?fw=pt"},{"title":"Unigram tokenization","id":"chapter6/7","url":"/learn/nlp-course/chapter6/7?fw=pt"},{"title":"Building a tokenizer, block by block","id":"chapter6/8","url":"/learn/nlp-course/chapter6/8?fw=pt"},{"title":"Tokenizers, check!","id":"chapter6/9","url":"/learn/nlp-course/chapter6/9?fw=pt"},{"title":"End-of-chapter quiz","quiz":6,"id":"chapter6/10","url":"/learn/nlp-course/chapter6/10?fw=pt"}]},{"title":"7. Main NLP tasks","isExpanded":false,"sections":[{"title":"Introduction","id":"chapter7/1","url":"/learn/nlp-course/chapter7/1?fw=pt"},{"title":"Token classification","id":"chapter7/2","url":"/learn/nlp-course/chapter7/2?fw=pt"},{"title":"Fine-tuning a masked language model","id":"chapter7/3","url":"/learn/nlp-course/chapter7/3?fw=pt"},{"title":"Translation","id":"chapter7/4","url":"/learn/nlp-course/chapter7/4?fw=pt"},{"title":"Summarization","id":"chapter7/5","url":"/learn/nlp-course/chapter7/5?fw=pt"},{"title":"Training a causal language model from scratch","id":"chapter7/6","url":"/learn/nlp-course/chapter7/6?fw=pt"},{"title":"Question answering","id":"chapter7/7","url":"/learn/nlp-course/chapter7/7?fw=pt"},{"title":"Mastering NLP","id":"chapter7/8","url":"/learn/nlp-course/chapter7/8?fw=pt"},{"title":"End-of-chapter quiz","quiz":7,"id":"chapter7/9","url":"/learn/nlp-course/chapter7/9?fw=pt"}]},{"title":"8. How to ask for help","isExpanded":false,"sections":[{"title":"Introduction","id":"chapter8/1","url":"/learn/nlp-course/chapter8/1?fw=pt"},{"title":"What to do when you get an error","id":"chapter8/2","url":"/learn/nlp-course/chapter8/2?fw=pt"},{"title":"Asking for help on the forums","id":"chapter8/3","url":"/learn/nlp-course/chapter8/3?fw=pt"},{"title":"Debugging the training pipeline","local_fw":{"pt":"chapter8/4","tf":"chapter8/4_tf"},"id":"chapter8/4","url":"/learn/nlp-course/chapter8/4?fw=pt"},{"title":"How to write a good issue","id":"chapter8/5","url":"/learn/nlp-course/chapter8/5?fw=pt"},{"title":"Part 2 completed!","id":"chapter8/6","url":"/learn/nlp-course/chapter8/6?fw=pt"},{"title":"End-of-chapter quiz","quiz":8,"id":"chapter8/7","url":"/learn/nlp-course/chapter8/7?fw=pt"}]},{"title":"9. Building and sharing demos","new":true,"isExpanded":false,"sections":[{"title":"Introduction to Gradio","id":"chapter9/1","url":"/learn/nlp-course/chapter9/1?fw=pt"},{"title":"Building your first demo","id":"chapter9/2","url":"/learn/nlp-course/chapter9/2?fw=pt"},{"title":"Understanding the Interface class","id":"chapter9/3","url":"/learn/nlp-course/chapter9/3?fw=pt"},{"title":"Sharing demos with others","id":"chapter9/4","url":"/learn/nlp-course/chapter9/4?fw=pt"},{"title":"Integrations with the Hugging Face Hub","id":"chapter9/5","url":"/learn/nlp-course/chapter9/5?fw=pt"},{"title":"Advanced Interface features","id":"chapter9/6","url":"/learn/nlp-course/chapter9/6?fw=pt"},{"title":"Introduction to Blocks","id":"chapter9/7","url":"/learn/nlp-course/chapter9/7?fw=pt"},{"title":"Gradio, check!","id":"chapter9/8","url":"/learn/nlp-course/chapter9/8?fw=pt"},{"title":"End-of-chapter quiz","quiz":9,"id":"chapter9/9","url":"/learn/nlp-course/chapter9/9?fw=pt"}]},{"title":"Course 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group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>0. Setup</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] after:rotate-90 after:transform"><span><span class="inline-block space-x-1 leading-5"><span>1. Transformer models</span> </span></span></div></div> <div class="flex flex-col"><a class="rounded-xl bg-gradient-to-br from-black to-gray-900 py-1 pr-2 pl-2 text-white first:mt-1 last:mb-4 dark:from-gray-800 dark:to-gray-900 ml-2" href="/learn/nlp-course/chapter1/1?fw=pt">Introduction </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/2?fw=pt">Natural Language Processing </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/3?fw=pt">Transformers, what can they do? </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/4?fw=pt">How do Transformers work? </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/5?fw=pt">Encoder models </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/6?fw=pt">Decoder models </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/7?fw=pt">Sequence-to-sequence models </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/8?fw=pt">Bias and limitations </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/9?fw=pt">Summary </a><a class="transform py-1 pr-2 pl-2 text-gray-500 first:mt-1 last:mb-4 hover:translate-x-px hover:text-black dark:hover:text-gray-300 ml-2" href="/learn/nlp-course/chapter1/10?fw=pt">End-of-chapter quiz </a> </div><div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>2. Using 🤗 Transformers</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>3. Fine-tuning a pretrained model</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>4. Sharing models and tokenizers</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>5. The 🤗 Datasets library</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>6. The 🤗 Tokenizers library</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>7. Main NLP tasks</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>8. How to ask for help</span> </span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>9. Building and sharing demos</span> <span class="ml-1 rounded bg-yellow-200 px-1 text-xs text-yellow-900 dark:bg-yellow-500">new</span></span></span></div></div> <div class="group flex cursor-pointer items-center pl-2 text-[0.8rem] font-semibold uppercase leading-9 hover:text-gray-700 dark:hover:text-gray-300 ml-0"><div class="flex after:absolute after:right-4 after:text-gray-500 group-hover:after:content-['▶'] false"><span><span class="inline-block space-x-1 leading-5"><span>Course Events</span> </span></span></div></div> </nav></div></div></div>
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<div class="prose-doc prose relative mx-auto max-w-4xl break-words"> <h1 class="relative group"><a id="introduction" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#introduction"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Introduction</span></h1> <div class="flex space-x-1 absolute z-10 right-0 top-0"><a href="https://discuss.huggingface.co/t/chapter-1-questions" target="_blank"><img alt="Ask a Question" class="!m-0" src="https://img.shields.io/badge/Ask%20a%20question-ffcb4c.svg?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgLTEgMTA0IDEwNiI+PGRlZnM+PHN0eWxlPi5jbHMtMXtmaWxsOiMyMzFmMjA7fS5jbHMtMntmaWxsOiNmZmY5YWU7fS5jbHMtM3tmaWxsOiMwMGFlZWY7fS5jbHMtNHtmaWxsOiMwMGE5NGY7fS5jbHMtNXtmaWxsOiNmMTVkMjI7fS5jbHMtNntmaWxsOiNlMzFiMjM7fTwvc3R5bGU+PC9kZWZzPjx0aXRsZT5EaXNjb3Vyc2VfbG9nbzwvdGl0bGU+PGcgaWQ9IkxheWVyXzIiPjxnIGlkPSJMYXllcl8zIj48cGF0aCBjbGFzcz0iY2xzLTEiIGQ9Ik01MS44NywwQzIzLjcxLDAsMCwyMi44MywwLDUxYzAsLjkxLDAsNTIuODEsMCw1Mi44MWw1MS44Ni0uMDVjMjguMTYsMCw1MS0yMy43MSw1MS01MS44N1M4MCwwLDUxLjg3LDBaIi8+PHBhdGggY2xhc3M9ImNscy0yIiBkPSJNNTIuMzcsMTkuNzRBMzEuNjIsMzEuNjIsMCwwLDAsMjQuNTgsNjYuNDFsLTUuNzIsMTguNEwzOS40LDgwLjE3YTMxLjYxLDMxLjYxLDAsMSwwLDEzLTYwLjQzWiIvPjxwYXRoIGNsYXNzPSJjbHMtMyIgZD0iTTc3LjQ1LDMyLjEyYTMxLjYsMzEuNiwwLDAsMS0zOC4wNSw0OEwxOC44Niw4NC44MmwyMC45MS0yLjQ3QTMxLjYsMzEuNiwwLDAsMCw3Ny40NSwzMi4xMloiLz48cGF0aCBjbGFzcz0iY2xzLTQiIGQ9Ik03MS42MywyNi4yOUEzMS42LDMxLjYsMCwwLDEsMzguOCw3OEwxOC44Niw4NC44MiwzOS40LDgwLjE3QTMxLjYsMzEuNiwwLDAsMCw3MS42MywyNi4yOVoiLz48cGF0aCBjbGFzcz0iY2xzLTUiIGQ9Ik0yNi40Nyw2Ny4xMWEzMS42MSwzMS42MSwwLDAsMSw1MS0zNUEzMS42MSwzMS42MSwwLDAsMCwyNC41OCw2Ni40MWwtNS43MiwxOC40WiIvPjxwYXRoIGNsYXNzPSJjbHMtNiIgZD0iTTI0LjU4LDY2LjQxQTMxLjYxLDMxLjYxLDAsMCwxLDcxLjYzLDI2LjI5YTMxLjYxLDMxLjYxLDAsMCwwLTQ5LDM5LjYzbC0zLjc2LDE4LjlaIi8+PC9nPjwvZz48L3N2Zz4="></a> </div> <h2 class="relative group"><a id="welcome-to-the-course" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#welcome-to-the-course"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Welcome to the 🤗 Course!</span></h2> <iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/00GKzGyWFEs" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe> <p>This course will teach you about natural language processing (NLP) using libraries from the <a href="https://huggingface.co/" rel="nofollow">Hugging Face</a> ecosystem — <a href="https://github.com/huggingface/transformers" rel="nofollow">🤗 Transformers</a>, <a href="https://github.com/huggingface/datasets" rel="nofollow">🤗 Datasets</a>, <a href="https://github.com/huggingface/tokenizers" rel="nofollow">🤗 Tokenizers</a>, and <a href="https://github.com/huggingface/accelerate" rel="nofollow">🤗 Accelerate</a> — as well as the <a href="https://huggingface.co/models" rel="nofollow">Hugging Face Hub</a>. It’s completely free and without ads.</p> <h2 class="relative group"><a id="what-to-expect" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#what-to-expect"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>What to expect?</span></h2> <p>Here is a brief overview of the course:</p> <div class="flex justify-center"><img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/summary.svg" alt="Brief overview of the chapters of the course."> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/summary-dark.svg" alt="Brief overview of the chapters of the course."></div> <ul><li>Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the <a href="https://huggingface.co/models" rel="nofollow">Hugging Face Hub</a>, fine-tune it on a dataset, and share your results on the Hub!</li> <li>Chapters 5 to 8 teach the basics of 🤗 Datasets and 🤗 Tokenizers before diving into classic NLP tasks. By the end of this part, you will be able to tackle the most common NLP problems by yourself.</li> <li>Chapters 9 to 12 go beyond NLP, and explore how Transformer models can be used to tackle tasks in speech processing and computer vision. Along the way, you’ll learn how to build and share demos of your models, and optimize them for production environments. By the end of this part, you will be ready to apply 🤗 Transformers to (almost) any machine learning problem!</li></ul> <p>This course:</p> <ul><li>Requires a good knowledge of Python</li> <li>Is better taken after an introductory deep learning course, such as <a href="https://www.fast.ai/" rel="nofollow">fast.ai’s</a> <a href="https://course.fast.ai/" rel="nofollow">Practical Deep Learning for Coders</a> or one of the programs developed by <a href="https://www.deeplearning.ai/" rel="nofollow">DeepLearning.AI</a></li> <li>Does not expect prior <a href="https://pytorch.org/" rel="nofollow">PyTorch</a> or <a href="https://www.tensorflow.org/" rel="nofollow">TensorFlow</a> knowledge, though some familiarity with either of those will help</li></ul> <p>After you’ve completed this course, we recommend checking out DeepLearning.AI’s <a href="https://www.coursera.org/specializations/natural-language-processing?utm_source=deeplearning-ai&utm_medium=institutions&utm_campaign=20211011-nlp-2-hugging_face-page-nlp-refresh" rel="nofollow">Natural Language Processing Specialization</a>, which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about!</p> <h2 class="relative group"><a id="who-are-we" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#who-are-we"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Who are we?</span></h2> <p>About the authors:</p> <p><a href="https://huggingface.co/abidlabs" rel="nofollow"><strong>Abubakar Abid</strong></a> completed his PhD at Stanford in applied machine learning. During his PhD, he founded <a href="https://github.com/gradio-app/gradio" rel="nofollow">Gradio</a>, an open-source Python library that has been used to build over 600,000 machine learning demos. Gradio was acquired by Hugging Face, which is where Abubakar now serves as a machine learning team lead.</p> <p><a href="https://huggingface.co/Rocketknight1" rel="nofollow"><strong>Matthew Carrigan</strong></a> is a Machine Learning Engineer at Hugging Face. He lives in Dublin, Ireland and previously worked as an ML engineer at Parse.ly and before that as a post-doctoral researcher at Trinity College Dublin. He does not believe we’re going to get to AGI by scaling existing architectures, but has high hopes for robot immortality regardless.</p> <p><a href="https://huggingface.co/lysandre" rel="nofollow"><strong>Lysandre Debut</strong></a> is a Machine Learning Engineer at Hugging Face and has been working on the 🤗 Transformers library since the very early development stages. His aim is to make NLP accessible for everyone by developing tools with a very simple API.</p> <p><a href="https://huggingface.co/sgugger" rel="nofollow"><strong>Sylvain Gugger</strong></a> is a Research Engineer at Hugging Face and one of the core maintainers of the 🤗 Transformers library. Previously he was a Research Scientist at fast.ai, and he co-wrote <em><a href="https://learning.oreilly.com/library/view/deep-learning-for/9781492045519/" rel="nofollow">Deep Learning for Coders with fastai and PyTorch</a></em> with Jeremy Howard. The main focus of his research is on making deep learning more accessible, by designing and improving techniques that allow models to train fast on limited resources.</p> <p><a href="https://huggingface.co/dawoodkhan82" rel="nofollow"><strong>Dawood Khan</strong></a> is a Machine Learning Engineer at Hugging Face. He’s from NYC and graduated from New York University studying Computer Science. After working as an iOS Engineer for a few years, Dawood quit to start Gradio with his fellow co-founders. Gradio was eventually acquired by Hugging Face.</p> <p><a href="https://huggingface.co/merve" rel="nofollow"><strong>Merve Noyan</strong></a> is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone.</p> <p><a href="https://huggingface.co/SaulLu" rel="nofollow"><strong>Lucile Saulnier</strong></a> is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience.</p> <p><a href="https://huggingface.co/lewtun" rel="nofollow"><strong>Lewis Tunstall</strong></a> is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of the O’Reilly book <a href="https://www.oreilly.com/library/view/natural-language-processing/9781098136789/" rel="nofollow">Natural Language Processing with Transformers</a>.</p> <p><a href="https://huggingface.co/lvwerra" rel="nofollow"><strong>Leandro von Werra</strong></a> is a machine learning engineer in the open-source team at Hugging Face and also a co-author of the O’Reilly book <a href="https://www.oreilly.com/library/view/natural-language-processing/9781098136789/" rel="nofollow">Natural Language Processing with Transformers</a>. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack..</p> <h2 class="relative group"><a id="faq" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#faq"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>FAQ</span></h2> <p>Here are some answers to frequently asked questions:</p> <ul><li><p><strong>Does taking this course lead to a certification?</strong>
Currently we do not have any certification for this course. However, we are working on a certification program for the Hugging Face ecosystem — stay tuned!</p></li> <li><p><strong>How much time should I spend on this course?</strong>
Each chapter in this course is designed to be completed in 1 week, with approximately 6-8 hours of work per week. However, you can take as much time as you need to complete the course.</p></li> <li><p><strong>Where can I ask a question if I have one?</strong>
If you have a question about any section of the course, just click on the ”<em>Ask a question</em>” banner at the top of the page to be automatically redirected to the right section of the <a href="https://discuss.huggingface.co/" rel="nofollow">Hugging Face forums</a>:</p></li></ul> <img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/forum-button.png" alt="Link to the Hugging Face forums" width="75%"> <p>Note that a list of <a href="https://discuss.huggingface.co/c/course/course-event/25" rel="nofollow">project ideas</a> is also available on the forums if you wish to practice more once you have completed the course.</p> <ul><li><strong>Where can I get the code for the course?</strong>
For each section, click on the banner at the top of the page to run the code in either Google Colab or Amazon SageMaker Studio Lab:</li></ul> <img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/notebook-buttons.png" alt="Link to the Hugging Face course notebooks" width="75%"> <p>The Jupyter notebooks containing all the code from the course are hosted on the <a href="https://github.com/huggingface/notebooks" rel="nofollow"><code>huggingface/notebooks</code></a> repo. If you wish to generate them locally, check out the instructions in the <a href="https://github.com/huggingface/course#-jupyter-notebooks" rel="nofollow"><code>course</code></a> repo on GitHub.</p> <ul><li><p><strong>How can I contribute to the course?</strong>
There are many ways to contribute to the course! If you find a typo or a bug, please open an issue on the <a href="https://github.com/huggingface/course" rel="nofollow"><code>course</code></a> repo. If you would like to help translate the course into your native language, check out the instructions <a href="https://github.com/huggingface/course#translating-the-course-into-your-language" rel="nofollow">here</a>.</p></li> <li><p><strong>What were the choices made for each translation?</strong>
Each translation has a glossary and <code>TRANSLATING.txt</code> file that details the choices that were made for machine learning jargon etc. You can find an example for German <a href="https://github.com/huggingface/course/blob/main/chapters/de/TRANSLATING.txt" rel="nofollow">here</a>.</p></li></ul> <ul><li><strong>Can I reuse this course?</strong>
Of course! The course is released under the permissive <a href="https://www.apache.org/licenses/LICENSE-2.0.html" rel="nofollow">Apache 2 license</a>. This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. If you would like to cite the course, please use the following BibTeX:</li></ul> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre>@misc{huggingfacecourse,
<span class="hljs-attr">author</span> = {Hugging Face},
<span class="hljs-attr">title</span> = {The Hugging Face Course, <span class="hljs-number">2022</span>},
<span class="hljs-attr">howpublished</span> = <span class="hljs-string">"\url{https://huggingface.co/course}"</span>,
<span class="hljs-attr">year</span> = {<span class="hljs-number">2022</span>},
<span class="hljs-attr">note</span> = <span class="hljs-string">"[Online; accessed <today>]"</span>
}</pre></div> <h2 class="relative group"><a id="lets-go" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#lets-go"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Let's Go</span></h2>
Are you ready to roll? In this chapter, you will learn:
<ul><li>How to use the <code>pipeline()</code> function to solve NLP tasks such as text generation and classification</li> <li>About the Transformer architecture</li> <li>How to distinguish between encoder, decoder, and encoder-decoder architectures and use cases</li></ul> <div id="svelte-announcer" aria-live="assertive" aria-atomic="true" style="position: absolute; left: 0px; top: 0px; clip: rect(0px, 0px, 0px, 0px); clip-path: inset(50%); overflow: hidden; white-space: nowrap; width: 1px; height: 1px;"></div></div>
<div class="mx-auto mt-16 flex max-w-4xl items-center pb-8 font-sans font-medium leading-6 xl:mt-32"><a href="/learn/nlp-course/chapter0/1?fw=pt" class="mr-8 flex transform items-center text-gray-600 transition-all hover:-translate-x-px hover:text-gray-900 dark:hover:text-gray-300"><span class="mr-2 translate-y-px">←</span>Introduction</a>
<a href="/learn/nlp-course/chapter1/2?fw=pt" class="ml-auto flex transform items-center text-right text-gray-600 transition-all hover:translate-x-px hover:text-gray-900 dark:hover:text-gray-300">Natural Language Processing<span class="ml-2 translate-y-px">→</span></a></div></div></div>
<div class="sticky top-0 self-start"><div class="SVELTE_HYDRATER contents" data-props="{"chapter":{"title":"Introduction","isExpanded":false,"id":"introduction","url":"#introduction","sections":[{"title":"Welcome to the 🤗 Course!","id":"welcome-to-the-course","url":"#welcome-to-the-course"},{"title":"What to expect?","id":"what-to-expect","url":"#what-to-expect"},{"title":"Who are we?","id":"who-are-we","url":"#who-are-we"},{"title":"FAQ","id":"faq","url":"#faq"},{"title":"Let's Go","id":"lets-go","url":"#lets-go"}]}}" data-target="SubSideMenu"><nav class="hidden h-screen w-[270px] flex-none flex-col space-y-3 overflow-y-auto break-words border-l pt-24 pl-6 pr-10 pb-16 text-sm lg:flex 2xl:w-[305px]"><a href="#introduction" class=" text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-introduction"><wbr>Introduction</a> <a href="#welcome-to-the-course" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-welcome-to-the-course"><wbr>Welcome to the 🤗 <wbr>Course!</a> <a href="#what-to-expect" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-what-to-expect"><wbr>What to expect?</a> <a href="#who-are-we" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-who-are-we"><wbr>Who are we?</a> <a href="#faq" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-faq">FAQ</a> <a href="#lets-go" class="pl-4 text-gray-400 transform hover:translate-x-px hover:text-gray-700 dark:hover:text-gray-300" id="nav-lets-go"><wbr>Let's <wbr>Go</a> </nav></div></div></div>
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Transformers, what can they do? - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/3?fw=pt | "## [](#transformers-what-can-they-do)Transformers, what can they do?\n\n[ | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:03.574Z |
Natural Language Processing - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/2?fw=pt | "NLP Course documentation\n\nNatural Language Processing\n\n3\\. Fine-tuning a pretrained model\n\n4(...TRUNCATED) | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:03.635Z |
Encoder models - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/5?fw=pt | "## [](#encoder-models)Encoder models\n\n[ | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:04.874Z |
How do Transformers work? - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/4?fw=pt | "## [](#how-do-transformers-work)How do Transformers work?\n\n[ | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:04.982Z |
Decoder models - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt | "## [](#decoder-models)Decoder models\n\n[ | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:05.751Z |
Sequence-to-sequence models[sequence-to-sequence-models] - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/7?fw=pt | "## [](#sequencetosequence-modelssequencetosequencemodels)Sequence-to-sequence models\\[sequence-to-(...TRUNCATED) | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:05.887Z |
Bias and limitations - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/8?fw=pt | "3\\. Fine-tuning a pretrained model\n\n4\\. Sharing models and tokenizers\n\n5\\. The 🤗 Datasets(...TRUNCATED) | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:06.452Z |
Summary - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/9?fw=pt | "## [](#summary)Summary\n\n[ | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:06.604Z |
End-of-chapter quiz - Hugging Face NLP Course | https://huggingface.co/learn/nlp-course/chapter1/10?fw=pt | "3\\. Fine-tuning a pretrained model\n\n4\\. Sharing models and tokenizers\n\n5\\. The 🤗 Datasets(...TRUNCATED) | "<!DOCTYPE html><html class=\"\"><head>\n\t\t<meta charset=\"utf-8\">\n\t\t<meta name=\"viewport\" c(...TRUNCATED) | 2023-06-27T20:00:06.996Z |
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