The Pile is a large-scale dataset for language modeling, consisting of 885 GB of text from various sources, including books, articles, and websites.
BigLanguage is an open-source dataset for training large language models, featuring a diverse range of texts from the internet, books, and user-generated content.
This article reviews various language model training datasets, including their strengths, weaknesses, and applications, providing insights for researchers and developers.
Hugging Face Datasets offers a wide range of pre-trained language models and datasets, including popular ones like BERT, RoBERTa, and XLNet, for various natural language processing tasks.
This survey provides an overview of large language models, their training datasets, and methods, highlighting recent advances and challenges in the field of natural language processing.
Google's language model training dataset is a massive collection of text from various sources, including books, articles, and websites, used to train its language models for improved performance.
This article discusses the importance of diverse training datasets for language models, highlighting the need for representative and inclusive data to mitigate biases and improve model performance.
This guide provides best practices for creating language model training datasets, covering topics such as data collection, preprocessing, and evaluation, to help developers and researchers create high-quality datasets.