1. Introduction: Natural language generation (i.e. text generation) is one of the core tasks in natural language processing (NLP). In this blog, we introduce the current state-of-the-art decoding method, Contrastive Search, for neural    

It is well-known that a sentiment analysis model determines whether a text is positive, negative, or neutral. However, this process typically requires access to unencrypted text, which can pose privacy concerns. Homomorphic encryption is a type of encryption that allows for computation on encrypted data without needing to decrypt it first. This makes it well-suited for applications where user’s personal and potentially    

We’re very excited to announce that Hugging Face has collaborated with arXiv to make papers more    

Enterprises are full of documents containing knowledge that isn’t accessible by digital workflows. These documents can vary from letters, invoices, forms, reports, to receipts. With the improvements in text, vision, and multimodal AI, it’s now possible to unlock that information. This post shows you how your teams can use open-source models to build custom solutions for free! Document AI includes many data science tasks from image classification, image to text, document question answering, table question answering, and visual question answering. […]

If you’re interested in building ML solutions faster visit: hf.co/support today! 👋 Welcome back to our Director of ML Insights Series! If you missed earlier Editions you can find them here: 🚀 In this fourth installment, you’ll hear what the following top Machine Learning Directors say about Machine Learning’s impact on their respective industries: Javier Mansilla, Shaun Gittens, Samuel Franklin, and Evan Castle.    

Want to help build the future at — if we may say so ourselves — one of the coolest places in AI? Today we’re announcing our internship program for 2023. Together with your Hugging Face mentor(s), we’ll be working on cutting    

Vector Quantized Diffusion (VQ-Diffusion) is a conditional latent diffusion model developed by the University of Science and Technology of China and Microsoft. Unlike most commonly studied diffusion models, VQ-Diffusion’s noising and denoising processes operate on a quantized latent space, i.e., the latent space is composed of a discrete set of vectors. Discrete diffusion models are less explored than their continuous counterparts and