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Sebastien Campion commited on
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update split from previous cellar dataset

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README.md CHANGED
@@ -1,6 +1,248 @@
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  ---
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  license: eupl-1.1
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  configs:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - config_name: 2006-04
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  data_files: "files/2006-04.jsonl.gz"
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  - config_name: 2006-05
@@ -422,10 +664,9 @@ configs:
422
 
423
  ---
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426
- # Eurovoc dataset
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-
428
- This dataset contains more that 2,000,000 documents with associated eurovoc labels.
429
 
430
 
431
  ## What's Cellar ?
@@ -440,7 +681,7 @@ https://op.europa.eu/fr/web/cellar
440
 
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  https://medium.com/datapy-ai/extreme-multi-label-classification-for-eurovoc-b51d74623820
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- ## How this dataset was created ?
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  The source code is available, check `cellar.py`
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@@ -448,9 +689,46 @@ The source code is available, check `cellar.py`
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  14 July 2023
450
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
451
  ## Bibliography
452
 
453
  - 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.
454
  - 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.
455
  - 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..
456
  - SHAHEEN, Zein, WOHLGENANNT, Gerhard, et FILTZ, Erwin. Large scale legal text classification using transformer models. arXiv preprint arXiv:2010.12871, 2020.
 
 
1
  ---
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  license: eupl-1.1
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  configs:
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  - config_name: 2006-05
 
664
 
665
  ---
666
 
667
+ # 🇪🇺 🏷️ EuroVoc dataset
668
 
669
+ This dataset contains more that 3,700,000 documents in 39 languages with associated EuroVoc labels.
 
 
670
 
671
 
672
  ## What's Cellar ?
 
681
 
682
  https://medium.com/datapy-ai/extreme-multi-label-classification-for-eurovoc-b51d74623820
683
 
684
+ ## How was dataset this created ?
685
 
686
  The source code is available, check `cellar.py`
687
 
 
689
 
690
  14 July 2023
691
 
692
+ ## What are the main characteristics of this dataset ?
693
+
694
+ There are a total of 39 different languages present in this dataset, of which some are EU languages and some are not. As the following graph illustrates, most of the documents of the dataset are written in EU languages (English being the most present language in the dataset), and the non-EU languages are very poorly represented (for example Arabic, Japanese,...). Note that since the Irish language (`gle`) was granted full official and working status in the EU in 2022, there are very few documents in that language. Additionally, Croatian (`hrv`) is also less represented in the dataset as Croatia is the latest country to have joined the EU in 2013.
695
+
696
+ ![language graph](images/nb_documents.png)
697
+
698
+ The lengths of the documents also varies depending on the language it is written in. The document lengths are quite variable, especially in English. There is therefore a quite large disparity in document lengths in this dataset. Note that this boxplot does not present the outliers, since the length of certain documents can contain up to 86 million characters. The red lines in the boxplot indicates the median length of the documents for each language.
699
+
700
+ ![boxplot](images/boxplot.png)
701
+
702
+ We notice that the documents in Irish have a very wide variability in document lengths, due to the fact it has very few documents. Therefore, we present the same boxplot without the Irish language in order to visualize with more detail the document length distribution in the other languages.
703
+
704
+ ![boxplot](images/boxplot2.png)
705
+
706
+ ## How is the data structured ?
707
+
708
+ An example of a sample of this dataset is the following :
709
+ ```json
710
+ {
711
+ "title": "Commission information notice...",
712
+ "date": "2023-09-29",
713
+ "eurovoc_concepts": ["air transport", "intra-EU transport"],
714
+ "url": "http://publications.europa.eu/resource/cellar/ec99987f-5e69-11ee-9220-01aa75ed71a1",
715
+ "lang": "eng",
716
+ "formats": ["fmx4", "pdfa2a", "xhtml"],
717
+ "text": "To ensure ownership by the relevant actors,..."
718
+ }
719
+ ```
720
+ - `title` : title of the document
721
+ - `date` : publication date of the document
722
+ - `eurovoc_concepts` : list of the EuroVoc concepts related to this document
723
+ - `url` : URL to access the document
724
+ - `formats` : list of formats in which the original document is available
725
+ - `text` : text content of the document
726
+
727
+
728
  ## Bibliography
729
 
730
  - 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.
731
  - 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.
732
  - 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..
733
  - SHAHEEN, Zein, WOHLGENANNT, Gerhard, et FILTZ, Erwin. Large scale legal text classification using transformer models. arXiv preprint arXiv:2010.12871, 2020.
734
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