Discover the power of Hugging Face, the go-to AI platform for developers and data scientists. Explore pre-trained models, access datasets, and set up workflows to launch your AI-powered solutions.

Introduction to Hugging Face

Why build with Hugging Face?

Exploring models on the Hub!

Running Hugging Face models

Building a text generation pipeline

Inference providers

Hugging Face Datasets

Exploring datasets on the Hub!

Loading datasets

Manipulating datasets

Getting Started with Hugging Face

Learn to build AI pipelines with Hugging Face. Master text classification, summarize content, and extract insights from documents, equipping you to tackle real-world challenges across industries.

Text classification

Grammatical correctness

Question Natural Language Inference

Dynamic category assignment

Text summarization

Summarizing long text

Adjusting the summary length

Auto Models and Tokenizers

Options

Pipelines

Auto Classes

When to use AutoModels and AutoTokenizers?

Tokenizing text with AutoTokenizer

Using AutoClasses

Document Q&A

Text Classification

Summarization

Question Answering

What task fits this scenario?

Extracting text with PyPDF

Building a Q&A pipeline

Congratulations!

Building Pipelines with Hugging Face

english.arrow

imdb_train.arrow

imdb_test.arrow

common_language.arrow

Hugging Face is a vital platform for accessing machine learning and AI functionality. In this course, you'll explore the Hugging Face Hub to the most appropriate models and datasets for your tasks. You'll utilize Hugging Face's API to search its model repository by task, author, or popularity. You'll use pipelines, a fundamental Hugging Face component, to perform a whole host of AI tasks using a simple and intuitive syntax. You'll learn to classify text into categories, generate impactful and accurate summaries, and even have conversations with documents. Join the AI revolution and learn about Hugging Face today!

<h2>Start Building AI with Hugging Face</h2>In today's rapidly evolving landscape of machine learning (ML) and artificial intelligence (AI), Hugging Face stands out as a vital platform, allowing anyone to leverage the latest models, datasets, and functionality in their projects.<br><br><h2>Explore the Hugging Face Hub</h2>To begin, you'll navigate the Hugging Face Hub's vast model and dataset repository. You'll load, use, and save models from Hugging Face, and download and manipulate datasets for model training.<br><br><h2>Build Pipelines for Text Applications</h2>Utilize pre-trained models available on Hugging Face for text classification tasks, such as grammatical correctness and dynamic category assignment; summarize long passages of text using both extractive and abstractive summarization models, and even begin having conversations with documents!<br><br>By the end of the course, you'll be equipped with the knowledge and skills to tackle a wide range of text-based ML and AI tasks effectively using Hugging Face.

Introduction to Functions in Python

Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.

Working with Hugging Face | Your Guide to the Hub

Working with Hugging Face

Associate AI Engineer for Data Scientists

Associate AI Engineer for Developers

Developing AI Applications

De basis van Hugging Face

Lead Data Engineer

Pipelines: fast and simple

Auto Classes: flexible and powerful

AutoModels

AutoTokenizers

Different models, different tokenizers

Building a Pipeline with Auto Classes

Use Cases for AutoModels and AutoTokenizers

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_35336/subtitles/course_35336_83addc49cdc035a64303402eaf4e6331.vtt

Building Pipelines with Hugging Face - Auto Models and Tokenizers

Auto Models and Tokenizers

Create Your Free Account