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| tags: | |
| - document-processing | |
| - docling | |
| - hierarchical-parsing | |
| - pdf-processing | |
| - generated | |
| # PDF Document Processing with Docling | |
| This dataset contains structured markdown extraction from PDFs in [baobabtech/test-eval-documents](https://huggingface.co/datasets/baobabtech/test-eval-documents) | |
| using Docling with hierarchical parsing. | |
| ## Processing Details | |
| - **Source Dataset**: [baobabtech/test-eval-documents](https://huggingface.co/datasets/baobabtech/test-eval-documents) | |
| - **Number of PDFs**: 20 | |
| - **Processing Time**: 8.4 minutes | |
| - **Processing Date**: 2025-12-02 15:40 UTC | |
| ### Configuration | |
| - **PDF Column**: `pdf_bytes` | |
| - **Dataset Split**: `train` | |
| ## Dataset Structure | |
| The dataset contains all original columns plus: | |
| - `original_md`: Markdown extracted by Docling (before hierarchical restructuring) | |
| - `hierarchical_md`: Markdown with proper heading hierarchy (after hierarchical processing) | |
| - `sections_toc`: Table of contents (one section per line, indented by level) | |
| - `inference_info`: JSON with processing metadata | |
| ## Usage | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("YOUR_DATASET_ID", split="train") | |
| for example in dataset: | |
| print(f"Document: {example.get('file_name', 'unknown')}") | |
| # Original markdown from Docling | |
| print("=== Original Markdown ===") | |
| print(example['original_md'][:500]) | |
| # Hierarchical markdown with proper heading levels | |
| print("\n=== Hierarchical Markdown ===") | |
| print(example['hierarchical_md'][:500]) | |
| # Table of contents | |
| print("\n=== Table of Contents ===") | |
| print(example['sections_toc']) | |
| break | |
| ``` | |