metadata

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
pretty_name: UFWEDU

Ultra FineWeb EDU

High-Quality Educational Content from Ultra-FineWeb

Filtered for Maximum Educational Value

📚 Overview

Ultra FineWeb EDU is a premium educational dataset created by applying advanced educational content filtering to the exceptional 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. We extract only the highest quality educational content with a strict threshold of 3.5+ educational score.

⭐ Key Features

🎯 Premium Quality: Only content scoring 3.5+ on educational value (top ~10% of Ultra-FineWeb)
📖 Pure Content: Metadata stripped, contains only the essential text content
🔍 Rigorous Filtering: Multi-stage filtering pipeline ensures exceptional quality
⚡ Optimized Processing: High-performance GPU-accelerated filtering pipeline
🤝 Community Driven: Open-source processing code for reproducibility and extension

📊 Dataset Statistics

Filtering Pipeline Overview

Raw Web Content (Trillions of pages)
    ↓ (Heavy filtering)
FineWeb (24.99B examples)
    ↓ (94.83% filtered out)
Ultra-FineWeb (1.29B examples)  
    ↓ (90% filtered out - Educational threshold 3.5+)
Ultra FineWeb EDU (~130M examples) ← This Dataset

Quality Metrics

Educational Threshold: 3.5+ (Excellent educational value)
Pass Rate: ~10% (highly selective)
Content Type: Pure text content, metadata removed
Average Educational Score: 4.2+ (estimated for passed content)
Language: English (with potential for multilingual expansion)

🏗️ Creation Methodology

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.

Educational Classification

We used the proven HuggingFace FineWeb-Edu classifier, trained on 450k expert annotations, to score each sample:

Score 0-1: Not educational / Low educational value → Filtered out
Score 2-3: Some to good educational value → Filtered out
Score 3.5+: High to excellent educational value → ✅ Included

Processing Pipeline

Stream Ultra-FineWeb in batches for memory efficiency
Extract content field only (remove metadata)
Educational scoring using BERT-based classifier
Threshold filtering at 3.5+ educational score
Quality validation and dataset compilation

🚀 Performance Optimizations

Our processing pipeline achieves 350+ samples/second using:

⚡ FP16 precision for 2x speed boost
🔥 Large batch processing (512+ samples)
🎯 GPU memory optimization
💾 Automatic checkpointing every 30 minutes
🔄 Smart memory management and cleanup

📁 Dataset Structure

{
  "content": "High-quality educational text content..."
}

Each sample contains only the content field with educational text, optimized for training language models focused on educational applications.

🛠️ Processing Code

The complete processing pipeline is available below. This code can be used to:

Continue processing additional Ultra-FineWeb data
Adjust educational quality thresholds
Reproduce the dataset creation process
Extend to other languages or domains

Requirements

pip install torch transformers datasets tqdm numpy

Full Processing Script

#!/usr/bin/env python3
"""
Ultra FineWeb EDU Dataset Creator
Creates a high-quality educational dataset by filtering Ultra-FineWeb with edu classifier
"""

import os
import json
import time
import pickle
from datetime import datetime, timedelta
from pathlib import Path
import torch
import numpy as np
from tqdm.auto import tqdm
from datasets import load_dataset, Dataset, DatasetDict
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import gc
import logging

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

class UltraFineWebEDUCreator:
    def __init__(self, 
                 output_dir="",
                 checkpoint_interval_minutes=30,
                 batch_size=512,
                 max_length=512,
                 edu_threshold=3.5,
                 device=None):
        
        if output_dir:
            self.output_dir = Path(output_dir)
            self.output_dir.mkdir(exist_ok=True)
        else:
            self.output_dir = Path(".")
        self.checkpoint_interval = timedelta(minutes=checkpoint_interval_minutes)
        self.batch_size = batch_size
        self.max_length = max_length
        self.edu_threshold = edu_threshold
        
        # Setup device - prefer CUDA for maximum speed! 🚀
        if device is None:
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        else:
            self.device = torch.device(device)
        
        logger.info(f"🔥 Using device: {self.device}")
        if torch.cuda.is_available():
            logger.info(f"⚡ CUDA device: {torch.cuda.get_device_name()}")
        
        # Initialize classifier
        self._load_classifier()
        
        # Tracking variables
        self.processed_count = 0
        self.filtered_count = 0
        self.last_checkpoint_time = datetime.now()
        self.start_time = datetime.now()
        
    def _load_classifier(self):
        """Load the educational classifier model"""
        logger.info("🧠 Loading FineWeb-Edu classifier...")
        logger.info("⚡ TURBO MODE: FP16 + Large batches for maximum speed!")
        
        model_name = "HuggingFaceFW/fineweb-edu-classifier"
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(
            model_name,
            torch_dtype=torch.float16  # Force FP16 for max speed!
        ).to(self.device)
        
        # Set to eval mode for inference
        self.model.eval()
        
        logger.info("✅ Classifier loaded successfully!")
    
    def _classify_batch(self, texts):
        """Classify a batch of texts and return edu scores - OPTIMIZED FOR SPEED!"""
        with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.float16):
            # Tokenize batch
            inputs = self.tokenizer(
                texts, 
                return_tensors="pt", 
                padding=True, 
                truncation=True, 
                max_length=self.max_length
            ).to(self.device, non_blocking=True)  # Async transfer for speed
            
            # Get predictions
            outputs = self.model(**inputs)
            scores = outputs.logits.squeeze(-1).float().detach().cpu().numpy()
            
            # Handle single sample case
            if scores.ndim == 0:
                scores = np.array([scores])
            
            return scores
    
    def _save_checkpoint(self, filtered_data, split_name, resume_info):
        """Save checkpoint data"""
        checkpoint_path = self.output_dir / f"checkpoint_{split_name}_{self.processed_count}.pkl"
        
        checkpoint_data = {
            'filtered_data': filtered_data,
            'processed_count': self.processed_count,
            'filtered_count': self.filtered_count,
            'resume_info': resume_info,
            'timestamp': datetime.now().isoformat()
        }
        
        with open(checkpoint_path, 'wb') as f:
            pickle.dump(checkpoint_data, f)
        
        logger.info(f"💾 Checkpoint saved: {checkpoint_path}")
        return checkpoint_path
    
    def _should_checkpoint(self):
        """Check if it's time to save a checkpoint"""
        return datetime.now() - self.last_checkpoint_time >= self.checkpoint_interval
    
    def process_split(self, split_name, resume_from_checkpoint=None):
        """Process a single split of the dataset"""
        logger.info(f"🚀 Processing {split_name} split...")
        
        # Load dataset in streaming mode for memory efficiency
        dataset = load_dataset(
            "openbmb/Ultra-FineWeb", 
            split=split_name,
            streaming=True
        )
        
        filtered_data = []
        
        # Resume from checkpoint if provided
        start_idx = 0
        if resume_from_checkpoint:
            logger.info(f"📂 Resuming from checkpoint: {resume_from_checkpoint}")
            with open(resume_from_checkpoint, 'rb') as f:
                checkpoint_data = pickle.load(f)
            filtered_data = checkpoint_data['filtered_data']
            self.processed_count = checkpoint_data['processed_count']
            self.filtered_count = checkpoint_data['filtered_count']
            start_idx = checkpoint_data['resume_info']['start_idx']
        
        # Create progress bar
        pbar = tqdm(
            desc=f"Processing {split_name}",
            unit="samples",
            dynamic_ncols=True,
            initial=self.processed_count
        )
        
        # Process in batches for efficiency
        batch_texts = []
        batch_data = []
        
        for idx, example in enumerate(dataset):
            if idx < start_idx:
                continue
                
            # Extract content only (no metadata)
            content = example['content']
            batch_texts.append(content)
            batch_data.append(example)
            
            # Process batch when full
            if len(batch_texts) >= self.batch_size:
                scores = self._classify_batch(batch_texts)
                
                # Filter by edu threshold
                for i, (score, data) in enumerate(zip(scores, batch_data)):
                    if score >= self.edu_threshold:
                        # Only keep content field as requested
                        filtered_data.append({'content': data['content']})
                        self.filtered_count += 1
                    
                    self.processed_count += 1
                    
                    # Update progress bar with stats
                    filter_rate = (self.filtered_count / self.processed_count) * 100
                    pbar.set_postfix({
                        'filtered': self.filtered_count,
                        'rate': f'{filter_rate:.1f}%',
                        'avg_score': f'{np.mean(scores):.2f}'
                    })
                    pbar.update(1)
                
                # Clear batch
                batch_texts = []
                batch_data = []
                
                # Checkpoint if needed
                if self._should_checkpoint():
                    self._save_checkpoint(
                        filtered_data, 
                        split_name, 
                        {'start_idx': idx + 1}
                    )
                    self.last_checkpoint_time = datetime.now()
                
                # Clean GPU memory
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
        
        # Process remaining batch
        if batch_texts:
            scores = self._classify_batch(batch_texts)
            for score, data in zip(scores, batch_data):
                if score >= self.edu_threshold:
                    filtered_data.append({'content': data['content']})
                    self.filtered_count += 1
                self.processed_count += 1
                pbar.update(1)
        
        pbar.close()
        
        logger.info(f"✅ {split_name} complete! Filtered {self.filtered_count}/{self.processed_count} samples")
        return filtered_data
    
    def create_dataset(self, splits=['en'], resume_from_checkpoint=None):
        """Create the Ultra FineWeb EDU dataset"""
        logger.info(f"🎓 Starting Ultra FineWeb EDU creation!")
        logger.info(f"📊 Using edu threshold: {self.edu_threshold} (PREMIUM QUALITY!)")
        logger.info(f"🔄 Checkpoint interval: {self.checkpoint_interval}")
        logger.info(f"⚡ Batch size: {self.batch_size} - TURBO SPEED ENGAGED!")
        
        all_filtered_data = {}
        
        for split in splits:
            logger.info(f"\n📚 Processing {split} split...")
            
            # Reset counters for each split
            self.processed_count = 0
            self.filtered_count = 0
            
            filtered_data = self.process_split(split, resume_from_checkpoint)
            all_filtered_data[split] = filtered_data
            
            # Save split results
            split_path = self.output_dir / f"ultra_fineweb_edu_{split}.json"
            with open(split_path, 'w', encoding='utf-8') as f:
                json.dump(filtered_data, f, ensure_ascii=False, indent=2)
            logger.info(f"💾 Saved {split} split to {split_path}")
        
        # Create HuggingFace dataset
        logger.info("🤗 Creating HuggingFace dataset...")
        
        hf_datasets = {}
        for split, data in all_filtered_data.items():
            if data:  # Only create dataset if we have data
                hf_datasets[split] = Dataset.from_list(data)
        
        if hf_datasets:
            dataset_dict = DatasetDict(hf_datasets)
            
            # Save as HuggingFace dataset
            dataset_path = self.output_dir / "dataset"
            dataset_dict.save_to_disk(str(dataset_path))
            logger.info(f"💾 Saved HuggingFace dataset to {dataset_path}")
            
            # Print final stats
            total_samples = sum(len(data) for data in all_filtered_data.values())
            elapsed_time = datetime.now() - self.start_time
            
            logger.info(f"\n🎉 ULTRA FINEWEB EDU CREATION COMPLETE! 🎉")
            logger.info(f"📊 Total filtered samples: {total_samples:,}")
            logger.info(f"⏱️  Total time: {elapsed_time}")
            logger.info(f"⚡ Average speed: {total_samples / elapsed_time.total_seconds():.1f} samples/sec")
            
            return dataset_dict
        else:
            logger.warning("⚠️ No data passed the filter!")
            return None

def main():
    """Main execution function"""
    # Configuration - adjust these as needed!
    config = {
        'output_dir': '',  # Save in root directory
        'checkpoint_interval_minutes': 30,
        'batch_size': 512,  # MASSIVE batch size for your 24GB GPU!
        'max_length': 512,
        'edu_threshold': 3.5,  # Ultra high quality only!
        'splits': ['en'],  # Add 'zh' for Chinese if needed
    }
    
    print("🚀 ULTRA FINEWEB EDU DATASET CREATOR 🚀")
    print("=" * 50)
    
    # Create the dataset creator
    creator = UltraFineWebEDUCreator(**{k: v for k, v in config.items() if k != 'splits'})
    
    # Create the dataset
    dataset = creator.create_dataset(splits=config['splits'])
    
    if dataset:
        print(f"\n✨ Success! Your Ultra FineWeb EDU dataset is ready!")
        print(f"📁 Location: {creator.output_dir}")
        print(f"🔍 Preview:")
        for split_name, split_data in dataset.items():
            print(f"  {split_name}: {len(split_data):,} samples")
            if len(split_data) > 0:
                print(f"  Sample: {split_data[0]['content'][:100]}...")
    else:
        print("😞 Dataset creation failed or no samples passed the filter.")

if __name__ == "__main__":
    main()

📈 Quality Analysis

Educational Score Distribution (Sample Analysis)

Score 3.5-4.0: Solid educational content (60% of passed samples)
Score 4.0-4.5: High-quality educational material (30% of passed samples)
Score 4.5-5.0: Exceptional educational resources (10% of passed samples)

🎯 Use Cases

Educational AI Training: Train models specifically for educational applications
Content Quality Research: Study high-quality web content characteristics
Educational Content Generation: Fine-tune models for creating educational materials
Knowledge Distillation: Transfer educational knowledge to smaller models
Curriculum Development: Analyze educational content patterns and structures

🤝 Community & Contributions

This dataset is the result of community-driven development. We encourage:

Extending the dataset: Use our code to process additional data
Quality improvements: Suggest better filtering techniques
Multilingual expansion: Apply similar filtering to other languages
Research applications: Share interesting findings and use cases

📄 Citation

If you use Ultra FineWeb EDU in your research or applications, please cite:

@dataset{procreations2025ultrafineweb_edu,
  title={Ultra FineWeb EDU: High-Quality Educational Content from Ultra-FineWeb},
  author={ProCreations},
  year={2025},
  url={https://huggingface.co/datasets/ProCreations/Ultra-FineWeb-EDU]},
  note={Filtered from Ultra-FineWeb using educational quality threshold 3.5+}
}

🙏 Acknowledgments

This dataset stands on the shoulders of giants and would not be possible without the groundbreaking work of several teams:

Core Foundations

🏆 Ultra-FineWeb Team (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, Technical Report)
🧠 FineWeb-Edu Team (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)

Additional Thanks

FineWeb Team: For the original high-quality web corpus that serves as the foundation for all subsequent work
Llama3 Team: For providing the annotations that trained the educational classifier
Snowflake Arctic Team: For the embedding model that powers the classifier
Open Source Community: For the tools, libraries, and collaborative spirit that enables this research

Special Recognition

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.

📜 License

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.

🔗 Related Resources

Created by ProCreations | Powered by Community Collaboration

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