Research-EAI
Abstract
A large, 24-trillion-token Essential-Web v1.0 dataset annotated with a multi-category taxonomy outperforms or is competitive with existing datasets in various domains using simple filtering techniques.
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
Community
ESSENTIAL-WEB V1.0: 24T tokens of organized web data
Amazing work!! 🔥
Great work!
Fantastic!
Impressive!!!!!
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation (2025)
- Seed-Coder: Let the Code Model Curate Data for Itself (2025)
- Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data (2025)
- Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models (2025)
- Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs (2025)
- Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction (2025)
- Synthetic Document Question Answering in Hungarian (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 1
Datasets citing this paper 8
Browse 8 datasets citing this paperSpaces citing this paper 0
No Space linking this paper