Essential-Web v1.0: 24T tokens of organized web data
Authors: name: Essential AI, name: Andrew Hojel, name: Michael Pust, name: Tim Romanski, name: Yash Vanjani, name: Ritvik Kapila, name: Mohit Parmar, name: Adarsh Chaluvaraju, name: Alok Tripathy, name: Anil Thomas, name: Ashish Tanwer, name: Darsh J Shah, name: Ishaan Shah, name: Karl Stratos, name: Khoi Nguyen, name: Kurt Smith, name: Michael Callahan, name: Peter Rushton, name: Philip Monk, name: Platon Mazarakis, name: Saad Jamal, name: Saurabh Srivastava, name: Somanshu Singla, name: Ashish Vaswani

Data plays the most prominent role in how language models acquire skills and
knowledge. The lack of massive, well-organized pre-training datasets results in
costly and inaccessible data pipelines. We present Essential-Web v1.0, a
24-trillion-token dataset in which every document is annotated with a
twelve-category taxonomy covering topic, format, content complexity, and
quality. Taxonomy labels are produced by EAI-Distill-0.5b, a fine-tuned
0.5b-parameter model that achieves an annotator agreement within 3% of
Qwen2.5-32B-Instruct. With nothing more than SQL-style filters, we obtain
competitive web-curated datasets in math (-8.0% relative to SOTA), web code
(+14.3%), STEM (+24.5%) and medical (+8.6%). Essential-Web v1.0 is available on
HuggingFace: https://huggingface.co/datasets/EssentialAI/essential-web-v1.0