Welcome to the Hugging Face tutorial! This guide will help you explore the power of Transformers models using the Hugging Face library. Whether you're new to NLP or looking to deepen your understanding, this is your starting point.
📚 What is Hugging Face?
Hugging Face is a leading platform for machine learning and natural language processing (NLP). It offers a vast repository of pre-trained models, tools for model training, and seamless integration with frameworks like PyTorch and TensorFlow.
🔧 Key Features
- Pre-trained Models: Access over 100,000 models for tasks like text classification, translation, and summarization.
- Model Hub: Explore models on Hugging Face Model Hub 🌐
- Pipeline API: Simplify tasks with one-liner code snippets.
🧠 Quick Start Example
from transformers import pipeline
classifier = pipeline("text-classification")
result = classifier("I love using Hugging Face!")
print(result)
This code classifies text sentiment using a pre-trained model.
💡 Advanced Topics
- Fine-tuning Models: Adapt models to your specific tasks.
- Custom Pipelines: Build pipelines for complex workflows.
- Model Sharing: Upload and share your models on the Hugging Face Hub.
📌 Resources
For hands-on practice, try the Hugging Face Colab Notebooks to experiment with real-world examples! 📚✨