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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+ # Ultra FineWeb EDU
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+
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+ <div align="center">
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+
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+ **High-Quality Educational Content from Ultra-FineWeb**
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+
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+ *Filtered for Maximum Educational Value*
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+
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+ [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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+ [![Dataset](https://img.shields.io/badge/πŸ€—%20Dataset-Ultra--FineWeb--EDU-yellow)](https://huggingface.co/datasets/)
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+ [![Quality](https://img.shields.io/badge/Quality-Premium%20Educational-green)]()
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+
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+ </div>
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+
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+ ## πŸ“š Overview
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+
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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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+
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+ ## ⭐ Key Features
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+
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+ - **🎯 Premium Quality**: Only content scoring 3.5+ on educational value (top ~10% of Ultra-FineWeb)
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+ - **πŸ“– Pure Content**: Metadata stripped, contains only the essential text content
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+ - **πŸ” Rigorous Filtering**: Multi-stage filtering pipeline ensures exceptional quality
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+ - **⚑ Optimized Processing**: High-performance GPU-accelerated filtering pipeline
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+ - **🀝 Community Driven**: Open-source processing code for reproducibility and extension
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+
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+ ## πŸ“Š Dataset Statistics
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+
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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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+ ↓ (Heavy filtering)
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+ FineWeb (24.99B examples)
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+ ↓ (94.83% filtered out)
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+ Ultra-FineWeb (1.29B examples)
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+ ↓ (90% filtered out - Educational threshold 3.5+)
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+ Ultra FineWeb EDU (~130M examples) ← This Dataset
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+ ```
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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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+
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+ ## πŸ—οΈ Creation Methodology
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+
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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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+
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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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+
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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†’ **βœ… Included**
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+
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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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+
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+ ## πŸš€ Performance Optimizations
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+
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+ Our processing pipeline achieves **350+ samples/second** using:
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+ - ⚑ FP16 precision for 2x speed boost
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+ - πŸ”₯ Large batch processing (512+ samples)
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+ - 🎯 GPU memory optimization
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+ - πŸ’Ύ Automatic checkpointing every 30 minutes
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+ - πŸ”„ Smart memory management and cleanup
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+
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+ ## πŸ“ Dataset Structure
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+
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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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+
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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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+
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+ ## πŸ› οΈ Processing Code
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+
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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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+
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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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+
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+ ### Full Processing Script
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+
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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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+
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+ ## πŸ“ˆ Quality Analysis
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+
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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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+
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+
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+
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+ ## 🎯 Use Cases
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+
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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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+
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+ ## 🀝 Community & Contributions
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+
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+ This dataset is the result of community-driven development. We encourage:
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+
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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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+
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+ ## πŸ“„ Citation
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+
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+ If you use Ultra FineWeb EDU in your research or applications, please cite:
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+
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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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+
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+ ## πŸ™ Acknowledgments
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+
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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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+
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+ ### Core Foundations
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+ - **πŸ† 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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+
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+ - **🧠 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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+
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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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+
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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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+
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+ ## πŸ“œ License
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+
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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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+
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+ ## πŸ”— Related Resources
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+
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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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+ ---
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+
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+ <div align="center">
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+
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+ **Created by ProCreations** | **Powered by Community Collaboration**
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+
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+ *Building better educational AI, one dataset at a time* πŸš€πŸ“š
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+
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+ </div>