metadata

language: it
license: afl-3.0
widget:
  - text: Il [MASK] ha chiesto revocarsi l'obbligo di pagamento

ITALIAN-LEGAL-BERT:A pre-trained Transformer Language Model for Italian Law

ITALIAN-LEGAL-BERT is based on bert-base-italian-xxl-cased with additional pre-training of the Italian BERT model on Italian civil law corpora. It achieves better results than the ‘general-purpose’ Italian BERT in different domain-specific tasks.

ITALIAN-LEGAL-BERT variants [NEW!!!]

FROM SCRATCH, It is the ITALIAN-LEGAL-BERT variant pre-trained from scratch on Italian legal documents (ITA-LEGAL-BERT-SC) based on the CamemBERT architecture

DISTILLED, a distilled version of ITALIAN-LEGAL-BERT ( DISTIL-ITA-LEGAL-BERT)

For long documents

LSG ITA LEGAL BERT, Local-Sparse-Global version of ITALIAN-LEGAL-BERT (FURTHER PRETRAINED)
LSG ITA LEGAL BERT-SC, Local-Sparse-Global version of ITALIAN-LEGAL-BERT-SC (FROM SCRATCH)

Note: We are working on the extended version of the paper with more details and the results of these new models. We will update you soon

Training procedure

We initialized ITALIAN-LEGAL-BERT with ITALIAN XXL BERT and pretrained for an additional 4 epochs on 3.7 GB of preprocessed text from the National Jurisprudential Archive using the Huggingface PyTorch-Transformers library. We used BERT architecture with a language modeling head on top, AdamW Optimizer, initial learning rate 5e-5 (with linear learning rate decay, ends at 2.525e-9), sequence length 512, batch size 10 (imposed by GPU capacity), 8.4 million training steps, device 1*GPU V100 16GB

Usage

ITALIAN-LEGAL-BERT model can be loaded like:

from transformers import AutoModel, AutoTokenizer
model_name = "dlicari/Italian-Legal-BERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

You can use the Transformers library fill-mask pipeline to do inference with ITALIAN-LEGAL-BERT.

from transformers import pipeline
model_name = "dlicari/Italian-Legal-BERT"
fill_mask = pipeline("fill-mask", model_name)
fill_mask("Il [MASK] ha chiesto revocarsi l'obbligo di pagamento")
#[{'sequence': "Il ricorrente ha chiesto revocarsi l'obbligo di pagamento",'score': 0.7264330387115479},
# {'sequence': "Il convenuto ha chiesto revocarsi l'obbligo di pagamento",'score': 0.09641049802303314},
# {'sequence': "Il resistente ha chiesto revocarsi l'obbligo di pagamento",'score': 0.039877112954854965},
# {'sequence': "Il lavoratore ha chiesto revocarsi l'obbligo di pagamento",'score': 0.028993653133511543},
# {'sequence': "Il Ministero ha chiesto revocarsi l'obbligo di pagamento", 'score': 0.025297977030277252}]

In this COLAB: ITALIAN-LEGAL-BERT: Minimal Start for Italian Legal Downstream Tasks how to use it for sentence similarity, sentence classification, and named entity recognition

https://colab.research.google.com/drive/1ZOWaWnLaagT_PX6MmXMP2m3MAOVXkyRK?usp=sharing

Citation

If you find our resource or paper is useful, please consider including the following citation in your paper.

@inproceedings{licari_italian-legal-bert_2022,
    address = {Bozen-Bolzano, Italy},
    series = {{CEUR} {Workshop} {Proceedings}},
    title = {{ITALIAN}-{LEGAL}-{BERT}: {A} {Pre}-trained {Transformer} {Language} {Model} for {Italian} {Law}},
    volume = {3256},
    shorttitle = {{ITALIAN}-{LEGAL}-{BERT}},
    url = {https://ceur-ws.org/Vol-3256/#km4law3},
    language = {en},
    urldate = {2022-11-19},
    booktitle = {Companion {Proceedings} of the 23rd {International} {Conference} on {Knowledge} {Engineering} and {Knowledge} {Management}},
    publisher = {CEUR},
    author = {Licari, Daniele and Comandè, Giovanni},
    editor = {Symeonidou, Danai and Yu, Ran and Ceolin, Davide and Poveda-Villalón, María and Audrito, Davide and Caro, Luigi Di and Grasso, Francesca and Nai, Roberto and Sulis, Emilio and Ekaputra, Fajar J. and Kutz, Oliver and Troquard, Nicolas},
    month = sep,
    year = {2022},
    note = {ISSN: 1613-0073},
    file = {Full Text PDF:https://ceur-ws.org/Vol-3256/km4law3.pdf},
}