BERT, short for Bidirectional Encoder Representations from Transformers, is a Machine Learning (ML) model for natural language processing. It was developed in 2018 by researchers at Google AI Language and serves as a swiss army knife solution to 11+ of the most common language tasks, such as sentiment analysis and named entity recognition. Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context. So, along came […]

This blog post assumes that the reader is familiar with text generation methods using the different variants of beam search, as explained in the blog post: “How to generate text: using different decoding methods for language generation with Transformers” Unlike ordinary beam search, constrained beam search allows us to exert control    

🤗 datasets is a library that makes it easy to access and share datasets. It also makes it easy to process data efficiently — including working with data which doesn’t fit into memory. When datasets was first launched, it was associated mostly with text data. However, recently, datasets has added increased    

This guide shows how you can fine-tune Segformer, a state-of-the-art semantic segmentation model. Our goal is to build a model for a pizza delivery robot, so it can see where to    

Hey friends! Welcome to Machine Learning Experts. I’m your host, Britney Muller and today’s guest is none other than Margaret Mitchell (Meg for short). Meg founded & co-led Google’s Ethical AI Group, is a pioneer in the field of Machine Learning, has published over 50 papers, and is a leading researcher in Ethical AI. You’ll hear Meg talk about the    

At Hugging Face, we are contributing to the ecosystem for Deep Reinforcement Learning researchers and enthusiasts. Recently, we have integrated Deep RL frameworks such as Stable-Baselines3. And today we are happy to announce that we integrated the    

Designing open-source libraries for modern machine learning “Don’t repeat yourself”, or DRY, is a well-known principle of software development. The principle originates from “The pragmatic programmer”, one of the most read books on code design. The principle’s simple message    

Santa Clara and San Francisco, CA, April 12th, 2022 Powered by deep learning, transformer models deliver state-of-the-art performance on a wide range of machine learning tasks, such as natural language processing, computer vision, speech, and more. However, training them at scale often requires a large amount of computing power, making the whole process unnecessarily long, complex, and costly. Today,