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https://github.com/huggingface/education-toolkit

Educational materials for universities
https://github.com/huggingface/education-toolkit
Last synced: about 1 month ago
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Educational materials for universities
Host: GitHub
URL: https://github.com/huggingface/education-toolkit
Owner: huggingface
License: apache-2.0
Created: 2022-02-22T08:39:52.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2023-09-29T03:58:40.000Z (about 1 year ago)
Last Synced: 2024-08-03T20:15:21.695Z (5 months ago)
Language: Jupyter Notebook
Homepage:
Size: 1.46 MB
Stars: 321
Watchers: 41
Forks: 63
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

        # 🤗 Education Toolkit

👋 **Welcome!**

We’ve assembled a toolkit that anyone can use to easily prepare workshops, events, homework or classes. The content is self-contained so that it can be easily incorporated in other material. This content is **free** and uses well-known Open Source technologies (`transformers`, `gradio`, etc).

Apart from tutorials, we also share other resources to go further into ML or that can assist in designing content.

Would you like to find the tutorials in other languages? You can find all the translations [here!](https://github.com/huggingface/education-toolkit#-translations)

## **Our Tutorials Catalog**

### 1️⃣ A Tour through the Hugging Face Hub

> In this tutorial, you get to:

>

> - Explore the over 30,000 models shared in the Hub.

> - Learn efficient ways to find the right model and datasets for your own task.

> - Learn how to contribute and work collaboratively in your ML workflows

>

> **_Duration: 20-40 minutes_**

>

> 👉 [click here to access the tutorial](https://github.com/huggingface/education-toolkit/blob/main/01_huggingface-hub-tour.md) or 👩‍🏫 [the lecture slides](https://docs.google.com/presentation/d/1zQqpFTcpNLV7haj2Inw2qKHq8DjfZEaiObW1ZkLvPWM/edit?usp=sharing).

### 2️⃣ Build and Host Machine Learning Demos with Gradio & Hugging Face

> In this tutorial, you get to:

>

> - Explore ML demos created by the community.

> - Build a quick demo for your machine learning model in Python using the `gradio` library

> - Host the demos for free with Hugging Face Spaces

> - Add your demo to the Hugging Face org for your class or conference

>

> **_Duration: 20-40 minutes_**

>

> 👉 [click here to access the tutorial](https://colab.research.google.com/github/huggingface/education-toolkit/blob/main/02_ml-demos-with-gradio.ipynb) or 👩‍🏫 [the lecture slides](https://docs.google.com/presentation/d/14EU_xjtINXtpidWLnUvfcEpmxN46ORS-PLpwfUf8C1I/edit?usp=sharing).

### 3️⃣ Getting Started with Transformers

> In this tutorial, you get to:

>

> - Transformer neural networks can be used to tackle a wide range of tasks in natural language processing and beyond.

> - Transfer learning allows one to adapt Transformers to specific tasks.

> - The `pipeline()` function from the `transformers` library can be used to run inference with models from the [Hugging Face Hub](https://huggingface.co/models).

>

> This tutorial is based on the first of our O'Reilly book *[Natural Language Processing with Transformers](https://transformersbook.com/)* - check it out if you want to dive deeper into the topic!

>

> **_Duration: 30-45 minutes_**

>

> 👉 [click here to access the tutorial](https://colab.research.google.com/github/huggingface/education-toolkit/blob/main/03_getting-started-with-transformers.ipynb)

> 👉 [click here to access hands-on Transformers exercices](https://github.com/NielsRogge/Transformers-Tutorials)

### 4️⃣ 🧨 diffusers for Democratizing Diffusion Models 

> In this tutorial, you get to know about:

>

> - Diffusion models

> - Various use cases of diffusion models

> - How to use the `diffusers` library to use pre-trained state-of-the-art diffusion models

>

>

> **_Duration: 30-45 minutes_**

>

> 👉 [click here to access the slides](https://docs.google.com/presentation/d/1uGjBkxs1qRov3p1-UMpqEXgbId95XDH6oLrKQcZiljk/edit?usp=sharing)

## **Our Teaching Guide: A Tour Through The 🤗 Hub & Gradio**

In this video, Nate and Lewis give you a guided tour of Transformers and transfer learning, along with an overview of Hugging Face's open science efforts and tools that enable people to work collaboratively in their Machine Learning projects.

[![A Tour Through The Hugging Face Hub & A Hands on Guide To Gradio](http://img.youtube.com/vi/k8sHYMeDitQ/0.jpg)](http://www.youtube.com/watch?v=k8sHYMeDitQ "A Tour Through The Hugging Face Hub & A Hands on Guide To Gradio")

## **Other resources to learn your way!**

### **The 🤗 NLP Course**

We provide a course (free and without ads) that teaches you about natural language processing (NLP) using libraries from the **[Hugging Face](https://huggingface.co/)** ecosystem.

👉 [click here to access the 🤗 Course](https://huggingface.co/course/chapter1/1)

💡 This course:

- Requires 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

### **The 🤗 Gradio Course**

We provide a course (free and without ads) that teaches you how to build interactive demos for your machine learning models using libraries from the **[Hugging Face](https://huggingface.co/)** ecosystem.

👉 [click here to access the 🤗 Course](https://huggingface.co/course/chapter9/1)

💡 This course:

- Its ultimate goal is to allow ML developers to easily present their work to a wide audience including non-technical teams or customers, researchers to more easily reproduce machine learning models and behavior, end users to more easily identify and debug failure points of models, and more!

### **The 🤗 RL Course**

We provide a course (free and without ads) that teaches you about Deep Reinforcement Learning using libraries from the **[Hugging Face](https://huggingface.co/)** ecosystem.

👉 [click here to access the 🤗 Course](https://github.com/huggingface/deep-rl-class)

💡 This course:

- Study Deep Reinforcement Learning in theory and practice 

- Learn to use famous Deep RL libraries

- Train agents in unique environments

- Publish your trained agents in one line of code to the Hugging Face Hub, and more!

### **The Sentence Transformers educational material**

We provide several tutorials on one of the most powerful libraries for industrial and academic applications. [Sentence Transformers](https://huggingface.co/sentence-transformers) allows you to create state-of-the-art embeddings from images and text for free.

💡 We recommend following the tutorials in this order:

- Introduction to working with embeddings using the Inference API and the 🤗 Datasets library ([link](https://t.co/gcqqilyJYn)).

- Interactive tutorial on Semantic Search ([link](https://t.co/lboHZKmygR)).

- Share and load Sentence Transformers models from the Hub ([link](https://www.sbert.net/docs/hugging_face.html)).

- Guide to start with your Sentence Transformers project ([link](https://t.co/BDTP6XoATu)).

- Sentence Transformers models and links in the Hub ([link](https://huggingface.co/sentence-transformers)).

### **The 🤗 Book**



Released February 2022

From experts at Hugging Face, learn all about Transformers and their applications to a wide range of NLP tasks.

👉 [click here to visit the book’s website](https://transformersbook.com/)

💡 This book:

- Is written for data scientists and machine learning engineers who may have heard about the recent breakthroughs involving transformers, but are lacking an in-depth guide to help them adapt these models to their own use cases.

- Assumes you have some practical experience with training models on GPUs.

- Does not expect prior **[PyTorch](https://pytorch.org/)** or **[TensorFlow](https://www.tensorflow.org/)** knowledge, though some familiarity with either of those will help

### **The 🤗 Classroom**

Classrooms provide teachers & students with dedicated collaborative workspaces to take advantage of Hugging Face resources in a more powerful manner than the average user.

👉 [click here to create your 🤗 Classroom for free](https://huggingface.co/classrooms)

💡 From classrooms, you can:

- **Empower your students with state-of-the-art resources:** build machine learning applications with Hugging Face and collaborate with your students easily on all their datasets, models and ML demos hosted within your classroom workspace.

- **Give your students unlimited access to modern machine learning tools:**  upload datasets, models and demos for free. Train, fine-tune, experiment and deploy, then share models and demos with the classroom or community, all hosted for free.

- **Benefit from free advanced computational resources** such as access to Accelerated Inference API. [click here to enhance your Classroom](https://docs.google.com/forms/d/e/1FAIpQLSfQ22dZHmsh-vHpjboLwcyMJvEC5kpKX8k9N_ihM_lyGgcXHA/viewform)

### **The 🤗 Education Events & News**

- **10/28**[EVENT]: ML Demo.cratization tour in Ireland at 6pm (CEST time). [link coming]()

- **11/16**[EVENT]: ML Demo.cratization tour in Chile at noon (CLST time). [link coming]()

- **11/30**[EVENT]: ML Demo.cratization tour in Colorado at 10.30 am (MST time). [link coming]()

- **12/06**[EVENT]: ML Demo.cratization tour in Georgia at 5.00 pm (GMT+4 time). [link coming]()

## 🌎 Translations

|     Language     |                                              Source                                             | Contributors                                                                                |

|:----------------:|:-----------------------------------------------------------------------------------------------:|---------------------------------------------------------------------------------------------|

|      Italian     |   [ `tutorials/IT` ](https://github.com/huggingface/education-toolkit/tree/main/tutorials/IT)   | @[MorenoLaQuatra](https://github.com/MorenoLaQuatra)                                        |

|      Spanish     |  [ `tutorials/ES` ]( https://github.com/huggingface/education-toolkit/tree/main/tutorials/ES )  | @[Fabioburgos](https://github.com/Fabioburgos)                                              |

|      Turkish     |  [ `tutorials/TR` ]( https://github.com/huggingface/education-toolkit/tree/main/tutorials/TR )  | @[emrecgty](https://github.com/emrecgty/) @[farukozderim](https://github.com/FarukOzderim/) |

|   French (WIP)   |  [  `tutorials/FR`  ](https://github.com/huggingface/education-toolkit/tree/main/tutorials/FR)  | @[g0bel1n](https://github.com/g0bel1n) @[lbourdois](https://github.com/lbourdois)                                                      |

| Hebrew (WIP)     | [   `tutorials/HE`   ](https://github.com/huggingface/education-toolkit/tree/main/tutorials/IW) | @[omer-dor](https://github.com/omer-dor)                                                    |

|  Japanese (WIP)  |  [ `tutorials/JA` ]( https://github.com/huggingface/education-toolkit/tree/main/tutorials/JA )  | @[Wataru-Nakata](https://github.com/Wataru-Nakata)                                          |

| Korean (WIP)     | [  `tutorials/KO`  ]( https://github.com/huggingface/education-toolkit/tree/main/tutorials/KO ) | @[oikosohn](https://github.com/oikosohn) @[eunseojo](https://github.com/oikosohn)                                                  |

| Portuguese (WIP) |  [ `tutorials/PT` ]( https://github.com/huggingface/education-toolkit/tree/main/tutorials/PT )  | @[johnnv1](https://github.com/johnnv1/)                                                     |

| Vietnamese |  [ `tutorials/VI` ]( https://github.com/huggingface/education-toolkit/tree/main/tutorials/VI )  | @[honghanhh](https://github.com/honghanhh/)                                                     |

If you would like to translate the tutorials to your language, see our [TRANSLATING](https://github.com/huggingface/education-toolkit/blob/main/TRANSLATING.md) guide.

✉️ If you have any questions, please contact [email protected]!