README.md

---
license: mit
task_categories:
- text-classification
- object-detection
language:
- en
size_categories:
- 10K<n<100K
---

# Mathematical Documents Dataset

This dataset contains 36,661 scientific documents with OCR-extracted text and mathematical content probability scores.
Documents were filtered from the **CommonCrawl PDF corpus** based on mathematical content probability.

## Quick Start

```python
from datasets import load_dataset
import json

# Load metadata
with open("metadata.jsonl") as f:
    for line in f:
        doc = json.loads(line)
        doc_id = doc['doc_id']
        
        # Read extracted text for each page
        # texts/{doc_id}/page_1.md, page_2.md, ...
        with open(f"texts/{doc_id}/page_1.md") as page:
            text = page.read()
            print(text)
        break
```

## Dataset Structure

```
math-docs-dataset/
├── metadata.jsonl           # Document metadata with probability scores
├── metadata_updated.jsonl   # Updated metadata (if applicable)
├── token_counts.jsonl       # Token counts per document
├── token_stats.json         # Aggregate token statistics
├── texts/                   # OCR-extracted text (2.5GB)
│   ├── {doc_id}/
│   │   ├── page_1.md
│   │   ├── page_2.md
│   │   └── ...
└── samples/                 # 50 sample documents for preview
    ├── pdfs/
    │   └── {doc_id}.pdf
    ├── texts/
    │   └── {doc_id}/
    └── sample_metadata.jsonl
```

## Statistics

- **Total documents**: 36,661
- **Total pages**: 885,333
- **Average pages per document**: 24.1
- **Mean probability range**: [0.8007, 1.0000]

### Token Statistics

- **Total tokens**: 756,843,504
- **Average tokens per document**: 20,644
- **Average tokens per page**: 854

Token counts calculated using tiktoken (cl100k_base encoding, GPT-4 tokenizer).

## Accessing Full PDFs

Due to size constraints, full PDF files (30+ GB) are hosted on Wasabi S3 storage.

### Download All PDFs

```bash
# Install AWS CLI if needed
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
./aws/install -i ~/.local/aws-cli -b ~/.local/bin

# Download PDFs (no authentication required)
aws s3 sync s3://igor-bucket/math_docs_dataset/pdfs/ ./pdfs/ \
  --endpoint-url=https://s3.eu-central-1.wasabisys.com \
  --no-sign-request
```

### Download Specific PDF

```bash
# Download single document
aws s3 cp s3://igor-bucket/math_docs_dataset/pdfs/{doc_id}.pdf ./pdfs/ \
  --endpoint-url=https://s3.eu-central-1.wasabisys.com \
  --no-sign-request
```

### Preview Samples

50 sample PDFs are included in the `samples/` directory for preview without downloading the full dataset.

## Metadata Fields

Each entry in `metadata.jsonl` contains:

- `doc_id`: Unique document identifier
- `pdf_path`: Relative path to PDF file
- `num_pages`: Number of pages in the document
- `mean_proba`: Mean probability that document contains mathematical content

## Data Collection

1. **Source**: CommonCrawl PDF corpus
2. **Filtering**: Documents classified by mathematical content probability
3. **Text Extraction**: [doct.ocr](https://github.com/parse-data/doct.ocr)

## Usage Examples

### Load and Process Documents

```python
import json
from pathlib import Path

# Load metadata
docs = []
with open("metadata.jsonl") as f:
    for line in f:
        docs.append(json.loads(line))

# Filter high-quality math documents
high_quality = [d for d in docs if d['mean_proba'] > 0.95]
print(f"Found {len(high_quality)} high-quality documents")

# Read document text
def read_document(doc_id):
    text_dir = Path(f"texts/{doc_id}")
    full_text = []
    
    for page_file in sorted(text_dir.glob("page_*.md")):
        with open(page_file) as f:
            full_text.append(f.read())
    
    return "\n\n".join(full_text)

# Example usage
doc = high_quality[0]
text = read_document(doc['doc_id'])
print(f"Document {doc['doc_id']}: {len(text)} characters")
```

### Token Analysis

```python
import json

# Load token statistics
with open("token_stats.json") as f:
    stats = json.load(f)
    print(f"Total tokens: {stats['total_tokens']:,}")
    print(f"Avg tokens/doc: {stats['avg_tokens_per_doc']:.0f}")

# Load per-document token counts
with open("token_counts.jsonl") as f:
    for line in f:
        doc_tokens = json.loads(line)
        # Process individual document token counts
        break
```

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{math_docs_dataset,
  title={Mathematical Documents Dataset},
  author={Your Name},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/your-username/math-docs-dataset}
}
```

## License

MIT License

## Contact

For questions or issues, please open an issue on the dataset repository.