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README.md

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+---
+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.