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license: apache-2.0
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---
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license: apache-2.0
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task_categories:
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- text-generation
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language:
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- en
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pretty_name: UFWEDU
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---
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# Ultra FineWeb EDU
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<div align="center">
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**High-Quality Educational Content from Ultra-FineWeb**
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*Filtered for Maximum Educational Value*
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/datasets/)
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[]()
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</div>
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## π Overview
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Ultra FineWeb EDU is a premium educational dataset created by applying advanced educational content filtering to the exceptional [Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb) dataset. This work builds directly upon two foundational achievements: the rigorous data curation methodology of Ultra-FineWeb and the sophisticated educational classification capabilities of the [FineWeb-Edu classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier). We extract only the highest quality educational content with a strict threshold of **3.5+ educational score**.
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## β Key Features
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- **π― Premium Quality**: Only content scoring 3.5+ on educational value (top ~10% of Ultra-FineWeb)
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- **π Pure Content**: Metadata stripped, contains only the essential text content
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- **π Rigorous Filtering**: Multi-stage filtering pipeline ensures exceptional quality
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- **β‘ Optimized Processing**: High-performance GPU-accelerated filtering pipeline
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- **π€ Community Driven**: Open-source processing code for reproducibility and extension
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## π Dataset Statistics
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### Filtering Pipeline Overview
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```
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Raw Web Content (Trillions of pages)
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β (Heavy filtering)
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FineWeb (24.99B examples)
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β (94.83% filtered out)
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Ultra-FineWeb (1.29B examples)
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β (90% filtered out - Educational threshold 3.5+)
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Ultra FineWeb EDU (~130M examples) β This Dataset
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```
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### Quality Metrics
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- **Educational Threshold**: 3.5+ (Excellent educational value)
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- **Pass Rate**: ~10% (highly selective)
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- **Content Type**: Pure text content, metadata removed
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- **Average Educational Score**: 4.2+ (estimated for passed content)
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- **Language**: English (with potential for multilingual expansion)
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## ποΈ Creation Methodology
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**Building on Proven Excellence**: This dataset leverages the battle-tested methodologies from Ultra-FineWeb's efficient verification-based filtering and FineWeb-Edu's expert-validated educational classification.
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### Educational Classification
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We used the proven [HuggingFace FineWeb-Edu classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier), trained on 450k expert annotations, to score each sample:
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- **Score 0-1**: Not educational / Low educational value β **Filtered out**
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- **Score 2-3**: Some to good educational value β **Filtered out**
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- **Score 3.5+**: High to excellent educational value β **β
Included**
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### Processing Pipeline
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1. **Stream Ultra-FineWeb** in batches for memory efficiency
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2. **Extract content** field only (remove metadata)
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3. **Educational scoring** using BERT-based classifier
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4. **Threshold filtering** at 3.5+ educational score
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5. **Quality validation** and dataset compilation
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## π Performance Optimizations
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Our processing pipeline achieves **350+ samples/second** using:
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- β‘ FP16 precision for 2x speed boost
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- π₯ Large batch processing (512+ samples)
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- π― GPU memory optimization
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- πΎ Automatic checkpointing every 30 minutes
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- π Smart memory management and cleanup
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## π Dataset Structure
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```json
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{
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"content": "High-quality educational text content..."
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}
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```
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Each sample contains only the `content` field with educational text, optimized for training language models focused on educational applications.
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## π οΈ Processing Code
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The complete processing pipeline is available below. This code can be used to:
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- Continue processing additional Ultra-FineWeb data
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- Adjust educational quality thresholds
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- Reproduce the dataset creation process
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- Extend to other languages or domains
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### Requirements
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```bash
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pip install torch transformers datasets tqdm numpy
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```
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### Full Processing Script
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```python
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# TODO: Add the complete processing code here
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# (ProCreations will insert the Ultra FineWeb EDU creator script)
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```
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## π Quality Analysis
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### Educational Score Distribution (Sample Analysis)
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- **Score 3.5-4.0**: Solid educational content (60% of passed samples)
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- **Score 4.0-4.5**: High-quality educational material (30% of passed samples)
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- **Score 4.5-5.0**: Exceptional educational resources (10% of passed samples)
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## π― Use Cases
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- **Educational AI Training**: Train models specifically for educational applications
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- **Content Quality Research**: Study high-quality web content characteristics
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- **Educational Content Generation**: Fine-tune models for creating educational materials
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- **Knowledge Distillation**: Transfer educational knowledge to smaller models
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- **Curriculum Development**: Analyze educational content patterns and structures
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## π€ Community & Contributions
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This dataset is the result of community-driven development. We encourage:
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- **Extending the dataset**: Use our code to process additional data
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- **Quality improvements**: Suggest better filtering techniques
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- **Multilingual expansion**: Apply similar filtering to other languages
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- **Research applications**: Share interesting findings and use cases
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## π Citation
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If you use Ultra FineWeb EDU in your research or applications, please cite:
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```bibtex
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@dataset{procreations2025ultrafineweb_edu,
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title={Ultra FineWeb EDU: High-Quality Educational Content from Ultra-FineWeb},
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author={ProCreations},
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year={2025},
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url={https://huggingface.co/datasets/[dataset-url]},
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note={Filtered from Ultra-FineWeb using educational quality threshold 3.5+}
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}
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```
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## π Acknowledgments
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This dataset stands on the shoulders of giants and would not be possible without the groundbreaking work of several teams:
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### Core Foundations
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- **π Ultra-FineWeb Team ([openbmb](https://huggingface.co/openbmb))**: For creating the exceptional Ultra-FineWeb dataset through their innovative efficient verification-based filtering pipeline. Their work represents a quantum leap in data quality, reducing 25B samples to 1.3B through rigorous curation. This dataset directly builds upon their outstanding research and methodology. ([Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb), [Technical Report](https://arxiv.org/abs/2505.05427))
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- **π§ FineWeb-Edu Team ([HuggingFaceFW](https://huggingface.co/HuggingFaceFW))**: For developing the sophisticated educational content classifier that makes this work possible. Their BERT-based model, trained on 450k expert annotations, provides the critical educational quality assessment that enables precise filtering. ([FineWeb-Edu Classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier))
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### Additional Thanks
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- **FineWeb Team**: For the original high-quality web corpus that serves as the foundation for all subsequent work
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- **Llama3 Team**: For providing the annotations that trained the educational classifier
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- **Snowflake Arctic Team**: For the embedding model that powers the classifier
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- **Open Source Community**: For the tools, libraries, and collaborative spirit that enables this research
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### Special Recognition
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The methodologies, quality standards, and technical innovations developed by the Ultra-FineWeb and FineWeb-Edu teams form the core foundation of this dataset. This work is essentially an application and extension of their remarkable contributions to the field of high-quality dataset curation.
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## π License
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This dataset is released under the **Apache 2.0 License**, consistent with the source Ultra-FineWeb dataset. Please ensure compliance with the original dataset licenses when using this data.
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## π Related Resources
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- [Ultra-FineWeb Dataset](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
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- [FineWeb-Edu Classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier)
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- [Original FineWeb Dataset](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
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- [Processing Code Repository](https://github.com/[your-repo])
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---
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<div align="center">
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**Created by ProCreations** | **Powered by Community Collaboration**
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*Building better educational AI, one dataset at a time* ππ
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</div>
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