New course with Hugging Face: Open Source Models with Hugging Face

DeepLearningAI
4 Mar 202403:06

TLDRThis video introduces an open-source collaboration with Hugging Face, highlighting the transformative impact of Hugging Face tools on AI development. The course teaches best practices for utilizing a vast array of pre-trained open-source models to rapidly create AI applications. By employing Hugging Face's Transformers library, participants will learn to combine models for tasks like image recognition and speech synthesis to aid visually impaired individuals. The course emphasizes the accessibility of open-source models and their significance to the AI community, aiming to inspire the creation of innovative AI-powered applications.

Takeaways

  • 🤖 Open-source AI models integrated with Hugging Face are introduced as a transformative tool for AI builders.
  • 🚀 The course teaches best practices for rapidly deploying a variety of pre-trained open-source models for AI applications.
  • 🌐 Models can be assembled to create new applications, such as image recognition, language models, and speech recognition.
  • 👁️‍🗨️ An example application is described where the course shows how to aid visually impaired individuals by narrating images.
  • 🏗️ The use of object detection models and text-to-speech models is highlighted for creating accessible applications.
  • 📚 Open-source models and their weights are freely available, promoting inclusivity and contribution to the AI community.
  • 🔍 The course covers tips for searching and selecting from thousands of open-source models on the Hugging Face Hub.
  • 🛠️ The Hugging Face Transformers library's pipeline object is used for simplifying input preprocessing and output postprocessing.
  • 📱 AI applications, like an image narration assistant, are demonstrated to be encapsulated within user-friendly interfaces.
  • 🌐 An AI-enabled image captioning service is deployed as an API using Hugging Face Spaces, allowing internet-based usage.
  • 🗣️ The course also explores combining automatic speech recognition and text-to-speech models to create a voice assistant.

Q & A

  • What is Hugging Face's role in the AI community?

    -Hugging Face serves as an open-source hub for machine learning and natural language processing, providing transformative tools for AI builders to quickly build AI applications using a variety of pre-trained models.

  • How do Hugging Face tools enhance AI development?

    -Hugging Face tools enable developers to rapidly deploy open-source models for various AI applications, streamlining the process of assembling different models into new applications and reducing development time significantly.

  • What kind of models can be found on Hugging Face?

    -Hugging Face offers a multitude of pre-trained open-source models for processing text, audio, and images, which can be used for tasks such as image recognition, language modeling, speech recognition, and more.

  • How can the Hugging Face Transformers library be utilized in this course?

    -The Hugging Face Transformers library is used to interact with various models, handling preprocessing and postprocessing of inputs and outputs, allowing for the creation of AI-enabled applications with minimal coding.

  • What is an example of an AI application built in the course?

    -An example application is an image narration assistant for visually impaired individuals, which combines object detection models to identify objects in an image and a text-to-speech model to narrate a summary of the image content aloud.

  • How do open-source models benefit the AI community?

    -Open-source models, with their weights openly available, allow anyone to download and use them, fostering innovation and collaboration within the AI community and making AI more accessible to a broader range of developers and researchers.

  • What are some of the tasks that can be performed using Hugging Face models?

    -Hugging Face models can perform various tasks such as summarizing text, translating between languages, engaging in conversations like a chatbot, and enabling automatic speech recognition and text-to-speech functionalities.

  • How can developers make their AI applications user-friendly?

    -Developers can wrap their AI applications in user-friendly interfaces using libraries like Gradio, and deploy them as APIs using Hugging Face Spaces, making them accessible over the internet for anyone to use.

  • What is the significance of the partnership between the course instructors and Hugging Face?

    -The partnership signifies the instructors' expertise in machine learning and their affiliation with Hugging Face, ensuring that the course content is informed by practical experience and the latest developments in the AI field.

  • What opportunities does this course aim to open up for learners?

    -The course aims to provide learners with the knowledge and skills to build AI-powered applications, explore the vast potential of open-source models, and contribute to the growing AI community.

Outlines

00:00

🤖 Introduction to AI Building with Hugging Face

This paragraph introduces a course designed to leverage open-source models provided by Hugging Face, aiming to transform AI building by making it quicker and more accessible. The course focuses on using the Hugging Face Transformers library and a variety of open-source models for processing text, audio, and images. A notable application taught in the course is an image narration assistant for visually impaired individuals, utilizing object detection and text-to-speech models. The paragraph highlights the open-source nature of the models used, promoting their accessibility and contribution to the AI community. Instructors Unice Bod, Mark Sun, and Maria Halis are introduced, each playing a role in guiding through the best practices of selecting and combining models from the Hugging Face Hub. The course promises to cover practical aspects like interacting with models through the pipeline object, creating user-friendly interfaces with the Gradio library, and deploying applications as APIs using Hugging Face Spaces. Ultimately, the course aims to empower learners to build AI-powered applications efficiently, opening up opportunities for innovative applications in AI.

Mindmap

Keywords

💡Open-source models

Open-source models refer to AI models whose designs and trained weights are freely available for anyone to use, modify, and distribute. In the context of the video, these models serve as foundational building blocks that enable AI developers and researchers to quickly prototype and deploy a wide range of applications without starting from scratch. The video emphasizes the utility of open-source models available on Hugging Face, a platform known for hosting a diverse array of such models, facilitating rapid development and experimentation in AI.

💡Hugging Face

Hugging Face is a company and community known for its contributions to the AI field, particularly in natural language processing (NLP). The video highlights its partnership in providing access to a plethora of open-source models. Hugging Face's tools and libraries, like Transformers, are instrumental for AI builders in quickly developing applications by leveraging pre-trained models for text, audio, and image processing.

💡Transformers library

The Transformers library, developed by Hugging Face, is a collection of state-of-the-art machine learning models, primarily focused on NLP tasks but also extending to image and audio processing. In the video, the library is used as a key resource for accessing and deploying pre-trained models, showcasing its versatility and ease of use in building complex AI applications such as voice assistants or image narration tools for the visually impaired.

💡Object detection

Object detection is a computer vision technique that identifies and locates objects within an image or video. The video describes an application where a trained object detection model is used to recognize objects in an image, which is a crucial step in creating tools that can assist visually impaired individuals by describing their surroundings. This example illustrates the practical use of combining different AI models to enhance accessibility.

💡Text-to-speech

Text-to-speech (TTS) technology converts written text into spoken words. This technology is applied in the video's context to narrate the descriptions of images, processed by object detection models, aloud for visually impaired users. The integration of TTS models with other AI components exemplifies the creation of comprehensive solutions that cater to specific needs, such as accessibility enhancements.

💡API

An Application Programming Interface (API) is a set of rules and protocols for building and interacting with software applications. The video discusses deploying an AI-enabled image captioning service as an API using Hugging Face Spaces, allowing developers to integrate this service into their own applications over the internet. This showcases how AI functionalities can be encapsulated and made accessible for wider use through web services.

💡Gradio library

Gradio is a Python library used to create user-friendly interfaces for machine learning models. It is highlighted in the video as a tool to wrap AI applications, such as an image narration assistant, making them accessible to end-users through simple and intuitive GUIs. This emphasizes the importance of user experience in the deployment of AI technologies, ensuring they are usable by people with varying levels of technical expertise.

💡Automatic Speech Recognition (ASR)

Automatic Speech Recognition is the technology that enables computers to recognize and convert spoken language into text. The video mentions combining ASR with text-to-speech models to build components of a voice assistant, illustrating how diverse AI technologies can be integrated to create interactive and voice-controlled applications, enhancing user accessibility and experience.

💡Natural Language Processing (NLP)

Natural Language Processing involves the interaction between computers and humans using natural language. The video explains how open-source models can perform various NLP tasks, such as text summarization, translation, and conversational chatbots. This highlights the versatility and breadth of applications that can be built using Hugging Face's open-source models, facilitating a wide range of user interactions and services.

💡AI-powered applications

AI-powered applications refer to software that incorporates artificial intelligence to enhance functionality, automate tasks, or provide new services. The video's narrative is centered around teaching viewers to build such applications using Hugging Face's tools and open-source models, demonstrating the potential to rapidly prototype and deploy innovative solutions across various domains, from accessibility aids to conversational agents.

Highlights

Introduction of open-source models integrated with Hugging Face.

Partnership with Hugging Face to enhance AI development.

Hugging Face tools have transformed AI application building.

Quick deployment of a variety of trained open-source models.

Teaching best practices for AI application assembly.

Utilizing image recognition, language models, and speech recognition in creative applications.

Developing applications in tens of minutes using Hugging Face Transformers library.

Combining models to assist individuals with visual impairments.

Applying object detection models to identify and narrate images.

All course models are open source and freely available.

Hugging Face's significant contribution to the AI community.

Instructor introduction: Unice, Mark, and Maria from Hugging Face.

Learning to search and select from thousands of open-source models on Hugging Face Hub.

Interacting with models using the Hugging Face Transformers Library's pipeline object.

Creating user-friendly interfaces for AI applications using the Hugging Face Spaces library.

Building components for a voice assistant with automatic speech recognition and text-to-speech models.

Using open-source models for natural language tasks like summarization, translation, and chatting.

The course aims to unlock opportunities for building AI-powered applications.

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