Hugging Face is a leading open-source company focused on Natural Language Processing (NLP) and Artificial Intelligence (AI). It provides tools, libraries, and models that make it easy to build, train, and deploy machine learning models, especially in the field of NLP. Hugging Face’s Transformers library has become the go-to resource for working with state-of-the-art language models like BERT, GPT, T5, and more.
- Transformers Library: Access to thousands of pre-trained models for tasks like text classification, translation, summarization, and more.
- Datasets Library: Simplifies accessing and processing large-scale datasets.
- Tokenizers: Fast and efficient tokenization library built for handling large datasets.
- Inference API: Easily deploy models to production using Hugging Face’s hosted API.
- Hub: A platform to share, discover, and collaborate on machine learning models and datasets.
To get started with Hugging Face, install the transformers library:
pip install transformersYou may also want to install datasets for working with data:
pip install datasetsHere's a quick example of how to use a pre-trained model for text classification:
from transformers import pipeline
# Load sentiment-analysis pipeline
classifier = pipeline('sentiment-analysis')
# Classify text
result = classifier("Hugging Face is revolutionizing NLP!")
print(result)Output:
[{'label': 'POSITIVE', 'score': 0.9998}]
- Transformers – Pre-trained models for NLP tasks.
- Datasets – Ready-to-use datasets for ML projects.
- Tokenizers – Fast, efficient tokenization.
- Hugging Face Hub – Share and discover models and datasets.
- Hugging Face Forum – Connect with other developers and researchers.
- Documentation – Detailed guides and API references.
- GitHub – Explore Hugging Face’s open-source projects.
- Courses – Free tutorials and educational resources.
- Hugging Face Tutorial Colab Notebook
- Text Summarization Colab Notebook
- Fine-Tuning Course
Hugging Face libraries are open-source and released under the Apache 2.0 License.