Hugging Face is a company that provides a number of cloud-based services that revolve around Natural Language Processing (NLP) and Machine Learning (ML). Here are some key services they provide:
-
Hugging Face Transformers: This is a Python library for state-of-the-art Natural Language Processing, built on PyTorch and TensorFlow. It provides thousands of pretrained models which can be fine-tuned for tasks like text classification, question answering, and more.
-
Model Hub: Hugging Face provides a cloud-based model hosting service, where researchers and developers can share their trained models with the community. You can use any of the models in the hub in your own projects with just a few lines of code.
-
Datasets: Similar to the Model Hub, Hugging Face also provides a Datasets hub where users can share, discover, and download datasets for use in Machine Learning. The hub includes a library to easily load and preprocess these datasets in a distributed manner.
-
Inference API: Hugging Face provides a simple and powerful API to make use of their models for inference in production settings. This allows you to send text to their API and receive the model’s output in response, without needing to run the model on your own hardware.
-
AutoNLP: This is a tool for automating the training and deployment of Machine Learning models for Natural Language Processing. You provide your dataset, choose your task (e.g., text classification, sentiment analysis), and AutoNLP handles the rest.
-
Spaces: Hugging Face Spaces is a platform for hosting Machine Learning demo applications directly from a repository. It supports applications built with Gradio or Streamlit.
-
Accelerated Computing: Hugging Face provides a library named “Accelerate” for easy distributed and mixed-precision training using PyTorch. This allows developers to scale their models across multiple GPUs with minimal code changes.