metadata
annotations_creators:
- expert-annotated
language:
- ita
license: apache-2.0
multilinguality: monolingual
task_categories:
- text-classification
task_ids: []
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 3766932
num_examples: 792
- name: validation
num_bytes: 446285
num_examples: 88
- name: test
num_bytes: 959412
num_examples: 221
download_size: 2728060
dataset_size: 5172629
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
tags:
- mteb
- text
An Italian Dataset consisting of 1101 pairs of judgments and their official holdings between the years 2019 and 2022 from the archives of Italian Administrative Justice categorized with 64 subjects.
| Task category | t2c |
| Domains | Legal, Government, Written |
| Reference | https://doi.org/10.1145/3594536.3595177 |
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["ItaCaseholdClassification"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repitory.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@inproceedings{10.1145/3594536.3595177,
abstract = {Legal holdings are used in Italy as a critical component of the legal system, serving to establish legal precedents, provide guidance for future legal decisions, and ensure consistency and predictability in the interpretation and application of the law. They are written by domain experts who describe in a clear and concise manner the principle of law applied in the judgments.We introduce a legal holding extraction method based on Italian-LEGAL-BERT to automatically extract legal holdings from Italian cases. In addition, we present ITA-CaseHold, a benchmark dataset for Italian legal summarization. We conducted several experiments using this dataset, as a valuable baseline for future research on this topic.},
address = {New York, NY, USA},
author = {Licari, Daniele and Bushipaka, Praveen and Marino, Gabriele and Comand\'{e}, Giovanni and Cucinotta, Tommaso},
booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law},
doi = {10.1145/3594536.3595177},
isbn = {9798400701979},
keywords = {Italian-LEGAL-BERT, Holding Extraction, Extractive Text Summarization, Benchmark Dataset},
location = {<conf-loc>, <city>Braga</city>, <country>Portugal</country>, </conf-loc>},
numpages = {9},
pages = {148–156},
publisher = {Association for Computing Machinery},
series = {ICAIL '23},
title = {Legal Holding Extraction from Italian Case Documents using Italian-LEGAL-BERT Text Summarization},
url = {https://doi.org/10.1145/3594536.3595177},
year = {2023},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
Dataset Statistics
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("ItaCaseholdClassification")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 221,
"number_of_characters": 929965,
"number_texts_intersect_with_train": 0,
"min_text_length": 468,
"average_text_length": 4207.986425339366,
"max_text_length": 17878,
"unique_text": 221,
"unique_labels": 64,
"labels": {
"Processo amministrativo": {
"count": 39
},
"Militari, forze armate e di polizia": {
"count": 9
},
"Urbanistica": {
"count": 3
},
"Edilizia": {
"count": 10
},
"Professioni e mestieri": {
"count": 3
},
"Farmaci": {
"count": 1
},
"Contratti della Pubblica amministrazione": {
"count": 28
},
"Circolazione stradale": {
"count": 1
},
"Risarcimento danni": {
"count": 4
},
"Enti locali": {
"count": 2
},
"Covid-19": {
"count": 29
},
"Concessione amministrativa": {
"count": 1
},
"Beni culturali": {
"count": 2
},
"Autorit\u00e0 amministrative indipendenti": {
"count": 3
},
"Magistrati": {
"count": 3
},
"Energia elettrica": {
"count": 2
},
"Contributi e finanziamenti": {
"count": 2
},
"Atto amministrativo": {
"count": 2
},
"Sicurezza pubblica": {
"count": 1
},
"Agricoltura": {
"count": 2
},
"Farmacia": {
"count": 1
},
"Giurisdizione": {
"count": 7
},
"Silenzio della P.A.": {
"count": 1
},
"Accesso ai documenti": {
"count": 4
},
"Ambiente": {
"count": 2
},
"Universit\u00e0 degli studi": {
"count": 2
},
"Amministrazione dello Stato": {
"count": 1
},
"Pesca": {
"count": 1
},
"Ricorso straordinario al Capo dello Stato": {
"count": 2
},
"Cittadinanza": {
"count": 1
},
"Pubblico impiego privatizzato": {
"count": 1
},
"Ordinanza contingibile ed urgente": {
"count": 1
},
"Caccia": {
"count": 1
},
"Animali": {
"count": 2
},
"Sport": {
"count": 1
},
"Ricorso straordinario al Presidente della Regione Siciliana": {
"count": 1
},
"Sanit\u00e0 pubblica": {
"count": 5
},
"Rifiuti": {
"count": 1
},
"Societ\u00e0 in house": {
"count": 1
},
"Paesaggio": {
"count": 2
},
"Lavoro": {
"count": 1
},
"Economia": {
"count": 1
},
"Informativa antimafia": {
"count": 6
},
"Consiglio di Stato e Consiglio di Giustizia per la Regione Siciliana": {
"count": 1
},
"Elezioni": {
"count": 1
},
"Procedimento amministrativo": {
"count": 2
},
"Pubblica istruzione": {
"count": 3
},
"Inquinamento": {
"count": 1
},
"Pubblica amministrazione": {
"count": 1
},
"Straniero": {
"count": 3
},
"Contratti pubblici": {
"count": 1
},
"Telecomunicazione": {
"count": 1
},
"Concorso": {
"count": 1
},
"Commercio": {
"count": 1
},
"Espropriazione per pubblica utilit\u00e0": {
"count": 2
},
"Giustizia amministrativa": {
"count": 2
},
"Imposte e tasse": {
"count": 1
},
"Alimenti": {
"count": 1
},
"Autorizzazione amministrativa": {
"count": 1
},
"Aeroporti": {
"count": 1
},
"Concorrenza": {
"count": 1
},
"Leggi e decreti": {
"count": 1
},
"Giochi": {
"count": 1
},
"Annullamento d\u2019ufficio e revoca": {
"count": 1
}
}
},
"train": {
"num_samples": 792,
"number_of_characters": 3651636,
"number_texts_intersect_with_train": null,
"min_text_length": 322,
"average_text_length": 4610.651515151515,
"max_text_length": 19037,
"unique_text": 792,
"unique_labels": 71,
"labels": {
"Procedimento amministrativo": {
"count": 7
},
"Edilizia": {
"count": 36
},
"Contratti della Pubblica amministrazione": {
"count": 102
},
"Giochi": {
"count": 4
},
"Espropriazione per pubblica utilit\u00e0": {
"count": 5
},
"Covid-19": {
"count": 104
},
"Militari, forze armate e di polizia": {
"count": 31
},
"Processo amministrativo": {
"count": 138
},
"Energia elettrica": {
"count": 9
},
"Alimenti": {
"count": 4
},
"Autorit\u00e0 amministrative indipendenti": {
"count": 13
},
"Ambiente": {
"count": 8
},
"Consiglio di Stato e Consiglio di Giustizia per la Regione Siciliana": {
"count": 4
},
"Magistrati": {
"count": 9
},
"Concorrenza": {
"count": 4
},
"Agricoltura": {
"count": 5
},
"Pubblica istruzione": {
"count": 13
},
"Animali": {
"count": 7
},
"Rifiuti": {
"count": 5
},
"Beni culturali": {
"count": 5
},
"Giurisdizione": {
"count": 25
},
"Societ\u00e0 in house": {
"count": 2
},
"Enti locali": {
"count": 8
},
"Paesaggio": {
"count": 6
},
"Concorso": {
"count": 5
},
"Farmaci": {
"count": 4
},
"Sport": {
"count": 5
},
"Elezioni": {
"count": 5
},
"Sicurezza pubblica": {
"count": 2
},
"Concessione amministrativa": {
"count": 4
},
"Silenzio della P.A.": {
"count": 2
},
"Straniero": {
"count": 9
},
"Informativa antimafia": {
"count": 22
},
"Contributi e finanziamenti": {
"count": 8
},
"Farmacia": {
"count": 5
},
"Risarcimento danni": {
"count": 13
},
"Giustizia amministrativa": {
"count": 6
},
"Ricorso straordinario al Presidente della Regione Siciliana": {
"count": 2
},
"Atto amministrativo": {
"count": 9
},
"Amministrazione dello Stato": {
"count": 3
},
"Ricorso straordinario al Capo dello Stato": {
"count": 8
},
"Urbanistica": {
"count": 11
},
"Inquinamento": {
"count": 3
},
"Cave": {
"count": 2
},
"Piano nazionale di ripresa e resilienza": {
"count": 2
},
"Sanit\u00e0 pubblica": {
"count": 20
},
"Autorizzazione amministrativa": {
"count": 2
},
"Lavoro": {
"count": 2
},
"Imposte e tasse": {
"count": 3
},
"Universit\u00e0 degli studi": {
"count": 7
},
"Societ\u00e0": {
"count": 2
},
"Pubblica amministrazione": {
"count": 5
},
"Ordinanza contingibile ed urgente": {
"count": 3
},
"Annullamento d\u2019ufficio e revoca": {
"count": 2
},
"Criminalit\u00e0 organizzata": {
"count": 2
},
"Mare": {
"count": 2
},
"Circolazione stradale": {
"count": 4
},
"Aeroporti": {
"count": 2
},
"Professioni e mestieri": {
"count": 9
},
"Cittadinanza": {
"count": 4
},
"Accesso ai documenti": {
"count": 14
},
"Pesca": {
"count": 2
},
"Telecomunicazione": {
"count": 2
},
"Unione Europea": {
"count": 2
},
"Caccia": {
"count": 2
},
"Contratti pubblici": {
"count": 4
},
"Economia": {
"count": 2
},
"Commercio": {
"count": 3
},
"Demanio": {
"count": 2
},
"Leggi e decreti": {
"count": 2
},
"Pubblico impiego privatizzato": {
"count": 4
}
}
}
}
This dataset card was automatically generated using MTEB