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