| --- |
| 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 |
| ``` |
|
|