---
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
```