metadata
dataset_info:
features:
- name: token_count
dtype: int32
- name: text
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 10766525501
num_examples: 2000000
download_size: 5790667341
dataset_size: 10766525501
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
DraftWeb-v1 Dataset Card
Dataset Description
A large-scale dataset created by combining educational mathematics problems and web content to create a continued pretraining dataset for LLMs. The dataset contains 2,000,000 high-quality examples sorted by content relevance scores.
Data Source
- Math Content: Hugging Face
finemath-4plus
dataset (educational math problems) - Web Content: Hugging Face
fineweb-edu
dataset (web pages from Common Crawl)
Training Data
- 1,000,000 math examples with token counts
- 1,000,000 web examples with token counts
- Combined and shuffled for model training
Dataset Statistics
- Total Examples: 2,000,000
- Total Tokens: 2.7 billion
- Math Token Count: 1.61 billion
- Web Token Count: 1.11 billion
- Languages: English (primary)
- Content Types: Text-based math problems and web pages
Data Processing
- Removed non-essential metadata columns
- Standardized token count measurement
- Sorted by source dataset scores (descending)
- Selected top 1 million examples from each source
- Combined and shuffled for model training
Usage
- Fine-tuning foundation language models
- Instruction tuning for educational applications
- Web content understanding tasks
- Research in educational AI systems
Licensing
- Inherits licenses from source datasets:
finemath-4plus
: CC BY 4.0fineweb-edu
: CC BY-SA 4.0
- For commercial use, please verify specific license requirements
Evaluation
- Original datasets contained quality scores used for selection
- Recommended evaluation metrics:
- Text quality scoring
- Relevance prediction accuracy
- Downstream task performance (e.g., question answering)
Dataset Provenance
- Hosted on Hugging Face Hub:
qingy2024/DraftWeb-v1
- Creation date: July 14, 2025
- Version: 1.0
Dataset Expansion
- Potential future versions:
- Multilingual expansion
- Specialized domain versions
- Time-based snapshots