This paper presents BigBird, a large-scale language model that achieves state-of-the-art results on a range of natural language processing tasks.
The Pile is a large-scale dataset for machine learning of language, comprising over 885 GB of text from various sources, including books, articles, and websites.
Hugging Face provides a collection of large-scale language datasets, including the WikiText-103 and BookCorpus datasets, for use in machine learning models.
This course covers the fundamentals of natural language processing with large-scale language models, including transformer architectures and attention mechanisms.
This article discusses the challenges and opportunities of training large-scale language models on cloud infrastructure, including data storage, compute resources, and model optimization.
This lecture series explores the future of large-scale language models, including their potential applications, challenges, and societal implications.
The National Science Foundation provides funding for research on large-scale language datasets for machine learning, with a focus on improving the accuracy and efficiency of language models.
This article discusses the challenges of large-scale language modeling for low-resource languages and presents a new approach to improving language model performance in these languages.