FinePDFs: Complete Guide to Hugging Face’s Revolutionary PDF Dataset
⏱️ Estimated Reading Time: 12 minutes
Introduction
FinePDFs represents a groundbreaking advancement in large-scale document processing, offering 4.7 million extracted PDF documents totaling 67.9GB of high-quality text data. This comprehensive tutorial will guide you through every aspect of working with this revolutionary dataset.
What Makes FinePDFs Special?
FinePDFs stands out as one of the most comprehensive PDF-derived datasets available, featuring:
- Massive Scale: 4.7M documents across 1,808 languages
- Advanced Extraction: Utilizes cutting-edge docling and RolmOCR technologies
- Domain Rich: Extensive coverage of scientific, legal, and technical content
- Open License: ODC-By license for commercial and research use
- Quality Focused: Minimal filtering to preserve authentic content diversity
Prerequisites and Setup
Environment Requirements
# Python 3.8+ recommended
python --version
# Required packages
pip install datasets transformers torch huggingface_hub
pip install pandas numpy matplotlib seaborn
pip install tqdm rich
Hardware Recommendations
- RAM: Minimum 16GB, recommended 32GB+
- Storage: 100GB+ free space for full dataset
- Network: Stable internet for initial download
- GPU: Optional but recommended for processing
Getting Started with FinePDFs
Basic Dataset Loading
from datasets import load_dataset
import pandas as pd
from collections import Counter
import matplotlib.pyplot as plt
# Load the dataset (starts with English subset)
print("Loading FinePDFs dataset...")
dataset = load_dataset("HuggingFaceFW/finepdfs", "eng_Latn")
print(f"Dataset size: {len(dataset['train'])} documents")
print(f"Available columns: {dataset['train'].column_names}")
# Inspect first few samples
for i, sample in enumerate(dataset['train'].take(3)):
print(f"\n--- Sample {i+1} ---")
print(f"Language: {sample['language']}")
print(f"Text length: {len(sample['text'])} characters")
print(f"Text preview: {sample['text'][:200]}...")
Exploring Available Languages
# Load language metadata
all_configs = ["eng_Latn", "arb_Arab", "spa_Latn", "fra_Latn", "deu_Latn",
"ita_Latn", "por_Latn", "rus_Cyrl", "jpn_Jpan", "kor_Hang"]
language_stats = {}
for config in all_configs[:5]: # Sample first 5 languages
try:
ds = load_dataset("HuggingFaceFW/finepdfs", config, split="train")
language_stats[config] = len(ds)
print(f"{config}: {len(ds):,} documents")
except Exception as e:
print(f"Could not load {config}: {e}")
# Visualize language distribution
plt.figure(figsize=(12, 6))
languages = list(language_stats.keys())
counts = list(language_stats.values())
plt.bar(languages, counts)
plt.title("Document Count by Language")
plt.xlabel("Language Code")
plt.ylabel("Number of Documents")
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig("finepdfs_language_distribution.png", dpi=300, bbox_inches='tight')
plt.show()
Advanced Dataset Operations
Text Quality Analysis
import re
from textstat import flesch_reading_ease, flesch_kincaid_grade
def analyze_text_quality(sample_texts, sample_size=1000):
"""Analyze text quality metrics for dataset samples"""
metrics = {
'avg_length': [],
'word_count': [],
'sentence_count': [],
'reading_ease': [],
'grade_level': [],
'special_chars_ratio': []
}
for text in sample_texts[:sample_size]:
# Basic metrics
text_length = len(text)
word_count = len(text.split())
sentence_count = len(re.split(r'[.!?]+', text))
# Reading metrics (handle errors)
try:
reading_ease = flesch_reading_ease(text)
grade_level = flesch_kincaid_grade(text)
except:
reading_ease = grade_level = 0
# Special character ratio
special_chars = len(re.findall(r'[^\w\s]', text))
special_ratio = special_chars / max(text_length, 1)
# Store metrics
metrics['avg_length'].append(text_length)
metrics['word_count'].append(word_count)
metrics['sentence_count'].append(sentence_count)
metrics['reading_ease'].append(reading_ease)
metrics['grade_level'].append(grade_level)
metrics['special_chars_ratio'].append(special_ratio)
return metrics
# Analyze dataset quality
print("Analyzing text quality...")
sample_texts = [item['text'] for item in dataset['train'].take(1000)]
quality_metrics = analyze_text_quality(sample_texts)
# Display results
for metric, values in quality_metrics.items():
avg_value = sum(values) / len(values)
print(f"{metric}: {avg_value:.2f}")
Domain-Specific Filtering
def filter_by_domain(dataset, domain_keywords, min_matches=2):
"""Filter dataset by domain-specific keywords"""
def contains_domain_keywords(example):
text_lower = example['text'].lower()
matches = sum(1 for keyword in domain_keywords if keyword in text_lower)
return matches >= min_matches
filtered_dataset = dataset.filter(contains_domain_keywords)
return filtered_dataset
# Example: Filter for scientific content
scientific_keywords = [
'research', 'study', 'analysis', 'experiment', 'hypothesis',
'methodology', 'results', 'conclusion', 'peer review',
'journal', 'publication', 'citation', 'abstract'
]
print("Filtering for scientific content...")
scientific_subset = filter_by_domain(dataset['train'], scientific_keywords)
print(f"Scientific documents: {len(scientific_subset)} / {len(dataset['train'])}")
# Example: Filter for legal content
legal_keywords = [
'court', 'judge', 'law', 'legal', 'statute', 'regulation',
'contract', 'agreement', 'plaintiff', 'defendant',
'jurisdiction', 'litigation', 'attorney'
]
legal_subset = filter_by_domain(dataset['train'], legal_keywords)
print(f"Legal documents: {len(legal_subset)} / {len(dataset['train'])}")
Practical Applications
1. Training a Language Model
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
import torch
def prepare_training_data(dataset, tokenizer, max_length=512):
"""Prepare dataset for language model training"""
def tokenize_function(examples):
# Tokenize texts
tokenized = tokenizer(
examples['text'],
truncation=True,
padding=True,
max_length=max_length,
return_tensors="pt"
)
# Set labels for causal LM
tokenized["labels"] = tokenized["input_ids"].clone()
return tokenized
tokenized_dataset = dataset.map(
tokenize_function,
batched=True,
remove_columns=dataset.column_names
)
return tokenized_dataset
# Initialize tokenizer and model
model_name = "distilgpt2" # Lightweight model for demonstration
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(model_name)
# Prepare training data (use a small subset for demo)
print("Preparing training data...")
train_subset = dataset['train'].select(range(1000)) # Small sample
tokenized_data = prepare_training_data(train_subset, tokenizer)
# Training configuration
training_args = TrainingArguments(
output_dir="./finepdfs-model",
num_train_epochs=1,
per_device_train_batch_size=4,
gradient_accumulation_steps=2,
warmup_steps=100,
logging_steps=50,
save_steps=500,
evaluation_strategy="no",
save_total_limit=2,
load_best_model_at_end=False,
)
# Initialize trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_data,
tokenizer=tokenizer,
)
print("Starting training... (This may take a while)")
# trainer.train() # Uncomment to actually train
print("Training configuration ready!")
2. Document Similarity Search
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
def create_document_embeddings(texts, model_name="all-MiniLM-L6-v2"):
"""Create embeddings for document similarity search"""
model = SentenceTransformer(model_name)
# Process in batches to manage memory
batch_size = 32
embeddings = []
for i in range(0, len(texts), batch_size):
batch_texts = texts[i:i+batch_size]
batch_embeddings = model.encode(batch_texts, show_progress_bar=True)
embeddings.extend(batch_embeddings)
return np.array(embeddings), model
def find_similar_documents(query, embeddings, texts, model, top_k=5):
"""Find most similar documents to a query"""
# Encode query
query_embedding = model.encode([query])
# Calculate similarities
similarities = cosine_similarity(query_embedding, embeddings)[0]
# Get top-k indices
top_indices = np.argsort(similarities)[-top_k:][::-1]
results = []
for idx in top_indices:
results.append({
'index': idx,
'similarity': similarities[idx],
'text': texts[idx][:200] + "..."
})
return results
# Example usage
print("Creating document embeddings...")
sample_texts = [item['text'] for item in dataset['train'].take(100)]
embeddings, model = create_document_embeddings(sample_texts)
# Search for similar documents
query = "machine learning algorithms and artificial intelligence"
similar_docs = find_similar_documents(query, embeddings, sample_texts, model)
print(f"\nTop similar documents for: '{query}'")
for i, doc in enumerate(similar_docs):
print(f"\n{i+1}. Similarity: {doc['similarity']:.3f}")
print(f"Text: {doc['text']}")
3. Multi-language Analysis
def analyze_multilingual_content(language_configs, sample_size=500):
"""Analyze content across multiple languages"""
results = {}
for lang_config in language_configs:
try:
print(f"Loading {lang_config}...")
ds = load_dataset("HuggingFaceFW/finepdfs", lang_config, split="train")
# Sample documents
sample = ds.take(min(sample_size, len(ds)))
texts = [item['text'] for item in sample]
# Calculate statistics
avg_length = sum(len(text) for text in texts) / len(texts)
avg_words = sum(len(text.split()) for text in texts) / len(texts)
results[lang_config] = {
'total_docs': len(ds),
'avg_length': avg_length,
'avg_words': avg_words,
'sample_text': texts[0][:100] + "..." if texts else ""
}
except Exception as e:
print(f"Error loading {lang_config}: {e}")
results[lang_config] = {'error': str(e)}
return results
# Analyze multiple languages
multilingual_configs = ["eng_Latn", "spa_Latn", "fra_Latn", "deu_Latn"]
multilingual_analysis = analyze_multilingual_content(multilingual_configs)
# Display results
for lang, stats in multilingual_analysis.items():
print(f"\n--- {lang} ---")
if 'error' in stats:
print(f"Error: {stats['error']}")
else:
print(f"Total documents: {stats['total_docs']:,}")
print(f"Average length: {stats['avg_length']:.0f} characters")
print(f"Average words: {stats['avg_words']:.0f}")
print(f"Sample: {stats['sample_text']}")
Performance Optimization
Memory Management
import gc
from datasets import Dataset
def process_large_dataset_in_chunks(dataset, chunk_size=1000, process_func=None):
"""Process large datasets in memory-efficient chunks"""
total_size = len(dataset)
results = []
for i in range(0, total_size, chunk_size):
print(f"Processing chunk {i//chunk_size + 1}/{(total_size-1)//chunk_size + 1}")
# Get chunk
chunk = dataset.select(range(i, min(i + chunk_size, total_size)))
# Process chunk
if process_func:
chunk_result = process_func(chunk)
results.append(chunk_result)
# Force garbage collection
gc.collect()
return results
def chunk_text_analysis(chunk):
"""Example processing function for chunks"""
texts = [item['text'] for item in chunk]
return {
'chunk_size': len(texts),
'avg_length': sum(len(text) for text in texts) / len(texts),
'total_chars': sum(len(text) for text in texts)
}
# Example: Process dataset in chunks
print("Processing dataset in memory-efficient chunks...")
chunk_results = process_large_dataset_in_chunks(
dataset['train'].select(range(5000)), # Use subset for demo
chunk_size=1000,
process_func=chunk_text_analysis
)
for i, result in enumerate(chunk_results):
print(f"Chunk {i+1}: {result}")
Streaming for Large Datasets
from datasets import load_dataset
def stream_process_dataset(dataset_name, config, process_func, max_items=None):
"""Stream process dataset without loading into memory"""
# Load as streaming dataset
dataset = load_dataset(dataset_name, config, streaming=True, split="train")
processed_count = 0
results = []
for item in dataset:
if max_items and processed_count >= max_items:
break
result = process_func(item)
results.append(result)
processed_count += 1
if processed_count % 100 == 0:
print(f"Processed {processed_count} items...")
return results
def extract_keywords(item):
"""Example processing function"""
text = item['text'].lower()
# Simple keyword extraction (replace with more sophisticated methods)
keywords = []
for word in ['ai', 'machine learning', 'neural network', 'algorithm']:
if word in text:
keywords.append(word)
return {'keywords': keywords, 'text_length': len(text)}
# Example: Stream processing
print("Stream processing dataset...")
stream_results = stream_process_dataset(
"HuggingFaceFW/finepdfs",
"eng_Latn",
extract_keywords,
max_items=1000
)
print(f"Processed {len(stream_results)} items via streaming")
Advanced Use Cases
1. Building Domain-Specific Corpora
def build_domain_corpus(dataset, domain_rules, output_path=None):
"""Build specialized corpus based on domain rules"""
def evaluate_domain_match(text, rules):
score = 0
text_lower = text.lower()
# Required keywords (must have at least one)
required = rules.get('required', [])
if required and any(keyword in text_lower for keyword in required):
score += 2
elif required: # Required keywords exist but none found
return 0
# Bonus keywords
bonus = rules.get('bonus', [])
for keyword in bonus:
if keyword in text_lower:
score += 1
# Penalty keywords
penalty = rules.get('penalty', [])
for keyword in penalty:
if keyword in text_lower:
score -= 2
return max(0, score)
# Define domain rules
scientific_rules = {
'required': ['research', 'study', 'analysis', 'experiment'],
'bonus': ['peer review', 'methodology', 'hypothesis', 'statistical'],
'penalty': ['advertisement', 'promotional', 'buy now']
}
# Score and filter documents
scored_docs = []
for item in dataset:
score = evaluate_domain_match(item['text'], scientific_rules)
if score >= 3: # Minimum score threshold
scored_docs.append({
'text': item['text'],
'language': item.get('language', 'unknown'),
'domain_score': score
})
print(f"Built domain corpus with {len(scored_docs)} documents")
if output_path:
# Save corpus
import json
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(scored_docs, f, ensure_ascii=False, indent=2)
print(f"Corpus saved to {output_path}")
return scored_docs
# Build scientific corpus
scientific_corpus = build_domain_corpus(
dataset['train'].take(2000), # Use subset for demo
{}, # Rules defined in function
"scientific_corpus.json"
)
2. Quality Assessment Pipeline
def assess_document_quality(text):
"""Comprehensive quality assessment for documents"""
# Basic metrics
char_count = len(text)
word_count = len(text.split())
sentence_count = len(re.split(r'[.!?]+', text))
# Quality indicators
quality_score = 0
issues = []
# Length checks
if word_count < 50:
issues.append("Too short")
quality_score -= 2
elif word_count > 100:
quality_score += 1
# Coherence checks
avg_word_length = sum(len(word) for word in text.split()) / max(word_count, 1)
if avg_word_length > 6:
issues.append("Overly complex vocabulary")
quality_score -= 1
# Content diversity
unique_words = len(set(text.lower().split()))
diversity_ratio = unique_words / max(word_count, 1)
if diversity_ratio > 0.7:
quality_score += 2
elif diversity_ratio < 0.3:
issues.append("Low vocabulary diversity")
quality_score -= 1
# Special character noise
special_char_ratio = len(re.findall(r'[^\w\s]', text)) / max(char_count, 1)
if special_char_ratio > 0.3:
issues.append("High special character noise")
quality_score -= 2
return {
'quality_score': quality_score,
'issues': issues,
'metrics': {
'char_count': char_count,
'word_count': word_count,
'sentence_count': sentence_count,
'diversity_ratio': diversity_ratio,
'special_char_ratio': special_char_ratio
}
}
# Assess quality across dataset sample
print("Assessing document quality...")
quality_results = []
for item in dataset['train'].take(500):
quality = assess_document_quality(item['text'])
quality_results.append(quality)
# Analyze quality distribution
quality_scores = [result['quality_score'] for result in quality_results]
avg_quality = sum(quality_scores) / len(quality_scores)
high_quality_count = sum(1 for score in quality_scores if score >= 2)
print(f"Average quality score: {avg_quality:.2f}")
print(f"High quality documents: {high_quality_count}/{len(quality_results)}")
print(f"Quality distribution: {Counter(quality_scores)}")
Testing and Validation
Comprehensive Test Script
#!/usr/bin/env python3
"""
FinePDFs Dataset Testing Script
Validates dataset loading, processing, and analysis functionality
"""
import sys
import time
import traceback
from datasets import load_dataset
import pandas as pd
import numpy as np
def test_basic_loading():
"""Test basic dataset loading functionality"""
print("Testing basic dataset loading...")
try:
dataset = load_dataset("HuggingFaceFW/finepdfs", "eng_Latn", split="train")
print(f"✓ Successfully loaded dataset with {len(dataset)} documents")
# Test data access
first_item = dataset[0]
required_fields = ['text', 'language']
for field in required_fields:
if field not in first_item:
raise ValueError(f"Missing required field: {field}")
print("✓ Dataset structure validation passed")
return True
except Exception as e:
print(f"✗ Basic loading test failed: {e}")
return False
def test_processing_functions():
"""Test data processing and analysis functions"""
print("\nTesting processing functions...")
try:
# Load small sample
dataset = load_dataset("HuggingFaceFW/finepdfs", "eng_Latn", split="train")
sample = dataset.take(10)
# Test text analysis
texts = [item['text'] for item in sample]
avg_length = sum(len(text) for text in texts) / len(texts)
if avg_length == 0:
raise ValueError("Invalid text analysis results")
print(f"✓ Text analysis successful (avg length: {avg_length:.0f})")
# Test filtering
filtered = [text for text in texts if len(text) > 100]
print(f"✓ Filtering successful ({len(filtered)}/{len(texts)} documents)")
return True
except Exception as e:
print(f"✗ Processing functions test failed: {e}")
return False
def test_memory_efficiency():
"""Test memory-efficient processing"""
print("\nTesting memory efficiency...")
try:
# Stream processing test
dataset = load_dataset("HuggingFaceFW/finepdfs", "eng_Latn",
streaming=True, split="train")
count = 0
for item in dataset:
count += 1
if count >= 5: # Test first 5 items
break
if count != 5:
raise ValueError("Streaming processing failed")
print("✓ Streaming processing successful")
return True
except Exception as e:
print(f"✗ Memory efficiency test failed: {e}")
return False
def run_all_tests():
"""Run comprehensive test suite"""
print("=" * 50)
print("FinePDFs Dataset Test Suite")
print("=" * 50)
tests = [
test_basic_loading,
test_processing_functions,
test_memory_efficiency
]
results = []
start_time = time.time()
for test in tests:
try:
result = test()
results.append(result)
except Exception as e:
print(f"✗ Test {test.__name__} crashed: {e}")
traceback.print_exc()
results.append(False)
# Summary
print("\n" + "=" * 50)
print("Test Summary")
print("=" * 50)
passed = sum(results)
total = len(results)
print(f"Tests passed: {passed}/{total}")
print(f"Success rate: {passed/total*100:.1f}%")
print(f"Total time: {time.time() - start_time:.1f} seconds")
if passed == total:
print("🎉 All tests passed!")
return True
else:
print("⚠️ Some tests failed. Check output above.")
return False
if __name__ == "__main__":
success = run_all_tests()
sys.exit(0 if success else 1)
Best Practices and Tips
1. Efficient Data Loading
# Best practice: Use appropriate subsets
def load_dataset_efficiently(language_code, max_size=None):
"""Load dataset with optimal settings"""
try:
if max_size and max_size < 10000:
# Small dataset: load fully
dataset = load_dataset("HuggingFaceFW/finepdfs", language_code,
split=f"train[:{max_size}]")
else:
# Large dataset: use streaming
dataset = load_dataset("HuggingFaceFW/finepdfs", language_code,
streaming=True, split="train")
return dataset
except Exception as e:
print(f"Failed to load {language_code}: {e}")
return None
# Example usage
efficient_dataset = load_dataset_efficiently("eng_Latn", max_size=5000)
2. Error Handling and Robustness
def robust_text_processing(text, max_retries=3):
"""Process text with error handling and retries"""
for attempt in range(max_retries):
try:
# Validate input
if not isinstance(text, str):
raise TypeError("Input must be string")
if len(text) == 0:
return {"status": "empty", "result": None}
# Process text
processed = {
"length": len(text),
"word_count": len(text.split()),
"status": "success"
}
return processed
except Exception as e:
print(f"Attempt {attempt + 1} failed: {e}")
if attempt == max_retries - 1:
return {"status": "failed", "error": str(e)}
time.sleep(0.1) # Brief delay before retry
# Example usage with error handling
def process_dataset_safely(dataset, max_items=1000):
"""Process dataset with comprehensive error handling"""
successful = 0
failed = 0
results = []
for i, item in enumerate(dataset.take(max_items)):
try:
result = robust_text_processing(item['text'])
if result['status'] == 'success':
successful += 1
results.append(result)
else:
failed += 1
except Exception as e:
print(f"Item {i} processing failed: {e}")
failed += 1
print(f"Processing complete: {successful} successful, {failed} failed")
return results
Conclusion
FinePDFs represents a paradigm shift in document-based machine learning, offering unprecedented access to high-quality, diverse textual content. This tutorial has covered:
Key Takeaways
- Dataset Mastery: Understanding FinePDFs’ structure and capabilities
- Efficient Processing: Memory-aware techniques for large-scale operations
- Practical Applications: Real-world use cases from training to analysis
- Quality Assessment: Methods to evaluate and filter content
- Multi-language Support: Leveraging the dataset’s linguistic diversity
Next Steps
- Experiment: Try different language configurations and domain filters
- Scale Up: Apply techniques to larger subsets as your infrastructure allows
- Integrate: Incorporate FinePDFs into your existing ML pipelines
- Contribute: Share improvements and findings with the community
Resources
The future of document-based AI is here with FinePDFs. Start building today! 🚀
Have questions or feedback? Join the discussion in the Hugging Face community forums or contribute to the dataset’s ongoing development.