Introduction to Hugging Face

A crucial open-source community member and a valuable AI IP meet in one company.

Although it might sound like something that erupted from an Aliens movie, Hugging Face is actually a central hub in the open-source AI ecosystem. The company started its journey to AI powerhouse as a chatbot startup marketing an AI product aimed at teenagers.1 The company subsequently made the proprietary natural language processing (NLP) model open source and pivoted as an organization to being an AI and machine learning (ML) platform.

Hugging Face’s niche in AI focuses on transformer-based models. Google introduced these models in 2017, and they represented a significant leap forward for NLP. Unlike previous NLP models that processed words sequentially, transformers enable NLP models to process words in relation to all other words in a sentence. This revolutionary innovation enables a deeper understanding of context and nuances in language, which in turn can lead to more accurate and sophisticated language models.

Hugging Face’s transformers library is unique in its extensive collection of pre-trained models. The library supports a multitude of transformer models like BERT, GPT-2, T5, and RoBERTa. These models cater to a wide range of languages and NLP tasks, including text classification, information extraction, and question answering.

In 2021, Hugging Face branched out into image-based models, introducing onto its platform models for image classification, segmentation, and generation. This move proved to be almost prescient with the 2022 release of Stable Diffusion®, a popular open-source, text-to-image AI model from Stability AI. Beyond Stable Diffusion, Hugging Face houses image-generation models such as VQ-VAE-2, OpenAI’s GLIDE, and models for image-to-image translation and image upscaling. As with its transformers library, Hugging Face has also become a major repository for image-based open-source models.

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