education-toolkit - GitLite

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #llm #go #android #machine-learning #linux #docker #awesome #vue #open-source

education-toolkit

(★ 388)

Educational materials for universities

파일 탐색기

최종 버전 다운로드 (.zip)

- book_cover.jpg
- datasets_card.png
- datasets_models_trained.png
- datasets_slices.png
- mode_card_tags.png
- model_card_content.png
- model_card_filters.png
- model_card_git.png
- model_card_inference_api.png
- model_card_updated_repo.png
- - 01_tour_hub_de_huggingface.md
  - 02_ml-demos-con-gradio.ipynb
  - 03_Primeros_pasos_con_Transformers.ipynb
  - README.md
- - 01_huggingface-hub-tour.md
  - 02_ml-demos-avec-gradio.ipynb
  - 03_débuter-avec-les-transformers.ipynb
  - README.md
- - README.md
- - 01_huggingface-hub-tour.md
  - 02_ml-demos-with-gradio.ipynb
  - 03_getting-started-with-transformers.ipynb
  - README.md
- - README.md
- - 01_huggingface-hub-tour.md
  - README.md
- - README.md
- - 01_huggingface-hub-tour.md
  - 02_ml-demos-with-gradio.ipynb
  - 03_getting-started-with-transformers.ipynb
  - README.md
- - 01_khám-phá-huggingface-hub.md
  - 02_demos-với-gradio.ipynb
  - 03_khởi-động-cùng-transformers.ipynb
  - README.md
.gitignore
01_huggingface-hub-tour.md
02_ml-demos-with-gradio.ipynb
03_getting-started-with-transformers.ipynb
LICENSE
README.md
requirements.txt
TRANSLATING.md

# 설치 가이드

1. 코드 내려받기

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

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd education-toolkit

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 🤗 Education Toolkit

<aside>

👋 **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.

</aside>

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)

<aside>
💡 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
</aside>

### **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)

<aside>
💡 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!
</aside>

### **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)

<aside>
💡 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!
</aside>

### **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.

<aside>
💡 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**

<img alt="book-cover" height=200 src="images/book_cover.jpg" id="book-cover"/>

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/)

<aside>
💡 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
</aside>

### **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)

<aside>
💡 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)

</aside>

### **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.

<aside>
✉️ If you have any questions, please contact violette@huggingface.co!

</aside>