| --- |
| license_name: ludov.1.0 |
| license_link: LICENSE |
| task_categories: |
| - text-classification |
| language: |
| - fr |
| tags: |
| - legal |
| --- |
| **Task details** |
|
|
| Document structuring plays a crucial role in various natural language processing (NLP) tasks, such as information retrieval, and document understanding. |
| It also helps readers to effectively navigate into a structured document with a large amount of textual data. |
| In the legal domain, document structuring is particularly important for creating inter- and intra-document links. |
|
|
| The dataset provides documents segmented into lines. |
| Each document was collected in HTML format or PDF format. |
| Then PDFs were converted to HTML with basic formatting tags like bold or italics. |
| Each line includes layout information, raw text (HTML), and a label indicating whether it's a title. |
| Common tasks using this data include predicting titles and reconstructing the Table of Contents (TOC) for each document. |
|
|
| While information about the hierarchical structure of each line is not currently available, we plan to incorporate it in future releases. |
|
|
| **Usage** |
|
|
| Using Hugging Face datasets: |
| ``` |
| from datasets import load_dataset |
| dataset = load_dataset("DoctrineAI/legal_document_structuring") |
| ``` |
|
|
|
|
| **Source data** |
|
|
| The original data comes from public French institution data : |
| - https://www.assemblee-nationale.fr/ |
| - https://www.senat.fr/ |
| - https://www-impots-gouv-fr/ |
|
|
| **License** |
|
|
| License: [Ludo v.1.0](https://datasets.doctrine.fr/Open%20data%20Use%20Licence.pdf) |
