Basics of Hugging Face | Hugging Face Tutorial for Beginners [2024]

Hugging Face: A Comprehensive Overview and Practical Guide

Key Concepts:

• Hugging Face: A machine learning and data science platform hosting open-source models, datasets, and applications.
• Open Source vs. Closed Source Models: Open source models (like those on Hugging Face) allow access to implementation code, unlike closed source models (e.g., OpenAI's GPT models).
• Hugging Face Model Hub: A repository of over 900,000 open-source models.
• Hugging Face Datasets Hub: A repository of over 200,000 datasets.
• Hugging Face Spaces: A platform for hosting and showcasing machine learning applications.
• Inference API: A feature allowing users to test models directly in the browser before downloading them.
• Transformers Library: A Python library for working with pre-trained models, particularly for NLP tasks.
• Pipeline: A function within the Transformers library that simplifies the process of using pre-trained models for various tasks.

1. Introduction to Hugging Face

• Hugging Face is a platform for building, training, and deploying machine learning models.
• It hosts a vast collection of open-source models, datasets, and applications.
• The platform facilitates collaboration within the machine learning community.
• It supports various modalities, including text, image, video, audio, and 3D.
• Hugging Face offers both free and paid services, including inference APIs and GPU deployment options.
• Major companies like Microsoft, Meta, AWS, and Google utilize Hugging Face.
• Over 50,000 organizations are currently using Hugging Face.

2. Exploring the Hugging Face Model Hub

• The Model Hub can be accessed at huggingface.co/models.
• It contains over 900,000 open-source models.
• Users can search and filter models based on various parameters:
  • Modality: Multimodal, Computer Vision, NLP, Audio.
  • Task: Translation, Text Generation, Summarization, etc.
  • Libraries: Transformers, etc.
  • Language: Specific languages (over 4,000 supported).
• Each model page provides:
  • Model description and usage instructions.
  • Inference API for testing the model in the browser.
  • Code snippets for integration in Python, JavaScript, and cURL.
  • Information about the training dataset.
  • Links to Spaces using the model.
  • Evaluation results.
  • Implementation files and version history.
  • Community discussions and pull requests.
  • Options to train or deploy the model.
• Example: Facebook's Bart model for summarization.
  • The page provides a description, usage instructions using the Transformers library, and the option to test the model using the Inference API.
  • The Inference API allows users to input custom text and generate a summary directly in the browser.
  • Code snippets are provided for integrating the model into Python, JavaScript, or cURL applications.
  • The page also links to the dataset used to train the Bart model and Spaces that utilize it.

3. Leveraging the Hugging Face Datasets Hub

• The Datasets Hub can be accessed at huggingface.co/datasets.
• It hosts over 200,000 datasets.
• Users can search and filter datasets based on:
  • Modality:
  • Task: Visual Question Answering, Computer Vision, NLP, Audio.
  • Libraries:
  • Language: Over 8,000 languages supported.
  • License:
• Each dataset page provides:
  • Dataset description and statistics (downloads, size).
  • List of models trained on the dataset.
  • Links to Spaces using the dataset.
  • Files and version information.
  • Community discussions and pull requests.
• Example: A dataset for question answering.
  • The page provides information about the dataset's downloads, size, and the models trained on it.
  • Users can contribute to the dataset by creating pull requests or starting discussions.

4. Showcasing Applications with Hugging Face Spaces

• Hugging Face Spaces allows users to host and share machine learning applications.
• Users can try out applications shared by the community.
• Each Space provides:
  • A live application demo.
  • Access to the application's code and files.
  • Community discussions and pull requests.
• Example: An image generation application.
  • Users can input a text prompt and generate an image based on the prompt.
  • The application's code and files are accessible in the "Files" section.
  • Users can contribute to the application through discussions and pull requests.

5. Practical Implementation with the Transformers Library

• The Transformers library simplifies the use of pre-trained models for NLP tasks.
• Installation: pip install transformers
• Key function: pipeline (from transformers import pipeline)
• The pipeline function creates a pipeline object for a specific task.
• Example 1: Text Classification
  • classifier = pipeline("text-classification") (uses the default model)
  • classifier("I love the new features in this app, they are amazing") (returns sentiment and score)
  • Users can specify a model: classifier = pipeline(model="facebook/bart-large-mnli", task="text-classification")
• Example 2: Text Generation
  • generator = pipeline("text-generation")
  • generator("Once Upon a Time in a small village surrounded by mountains there lived a young girl who loved to explore one day she found a hidden path leading to") (generates continuation of the text)
  • Users can specify a model: generator = pipeline(model="distilgpt2", task="text-generation")
• Example 3: Summarization
  • summarizer = pipeline("summarization")
  • summarizer("Artificial intelligence has made significant strides in recent years...") (generates a summary of the text)
• The pipeline function simplifies the process of using pre-trained models for various NLP tasks.
• Users can specify the task and model to use.

6. Conclusion

Hugging Face is a powerful platform for machine learning practitioners, offering a vast collection of open-source models, datasets, and applications. The platform facilitates collaboration and provides tools like the Transformers library and Inference API to simplify the development and deployment of machine learning solutions. The pipeline function is a key component of the Transformers library, enabling users to easily leverage pre-trained models for various NLP tasks.