task_categories:text-classification language:en license:eupl-1.1 eurovoc legal multilabel-classification region:us
---
language:
- en
pretty_name: Cellar_Eurovoc
tags:
- eurovoc
- legal
- multilabel-classification
task_categories:
- text-classification
license: eupl-1.1
configs:
- config_name: default
  data_files: "cellar.jsonl.gz"
- config_name: en-GB
  data_files: "cellar_en.jsonl.gz"
- config_name: fr-FR
  data_files: "cellar_fr.jsonl.gz"
---


# Cellar datasets 

This dataset contains more that 200,000 documents with associated eurovoc labels in english or in french.


Number of documents :
```bash
zcat cellar.jsonl.gz | jq -s ".[].url" | sort -u | wc -l
293408
```

- cellar_en.jsonl.gz  203 367 rows
- cellar_fr.jsonl.gz 200 426 rows
- cellar.jsonl.gz 3 725 395 rows



## What's Cellar ? 

Cellar is the common data repository of the Publications Office of the European Union. Digital publications and metadata are stored in and disseminated via Cellar, in order to be used by humans and machines. Aiming to transparently serve users, Cellar stores multilingual publications and metadata, it is open to all EU citizens and provides machine-readable data.

https://op.europa.eu/fr/web/cellar

## Why was this dataset created ?

"Extreme classification come with challenges of scalability due to large label spaces, data sparsity issues due to insufficient training samples."

https://medium.com/datapy-ai/extreme-multi-label-classification-for-eurovoc-b51d74623820

## How this dataset was created ? 

The source code is available, check `cellar.py` 

## When this dataset was created ? 

14 July 2023

## 📚 Bibliography

- Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2019. Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 78–87, Minneapolis, Minnesota. Association for Computational Linguistics.
- I. Chalkidis, M. Fergadiotis, P. Malakasiotis and I. Androutsopoulos, Large-Scale Multi-Label Text Classification on EU Legislation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, (short papers), 2019.
- Andrei-Marius Avram, Vasile Pais, and Dan Ioan Tufis. 2021. PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 92–101, Held Online. INCOMA Ltd..
- SHAHEEN, Zein, WOHLGENANNT, Gerhard, et FILTZ, Erwin. Large scale legal text classification using transformer models. arXiv preprint arXiv:2010.12871, 2020.
