readme: ---
  language: it
  license: apache-2.0
  widget:
  - text: "Il [MASK] ha chiesto revocarsi l'obbligo di pagamento"
  ---
  
  <img  src="https://huggingface.co/dlicari/Italian-Legal-BERT/resolve/main/ITALIAN_LEGAL_BERT.jpg" width="600"/> 
  <h1> ITALIAN-LEGAL-BERT:A pre-trained Transformer Language Model for Italian Law </h1>
  
  ITALIAN-LEGAL-BERT is based on <a href="https://huggingface.co/dbmdz/bert-base-italian-xxl-cased">bert-base-italian-xxl-cased</a> 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.
  
  <h2>Training procedure</h2> 
  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
  <p />
  <h2> Usage </h2> 
  
  ITALIAN-LEGAL-BERT model can be loaded like:
  
  ```python
  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. 
  ```python
  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](https://colab.research.google.com/drive/1aXOmqr70fjm8lYgIoGJMZDsK0QRIL4Lt?usp=sharing)
   how to use it for sentence similarity, sentence classification, and named entity recognition
   - https://colab.research.google.com/drive/1aXOmqr70fjm8lYgIoGJMZDsK0QRIL4Lt?usp=sharing
  
  <img  src="https://huggingface.co/dlicari/Italian-Legal-BERT/resolve/main/semantic_text_similarity.jpg" width="700"/> 
  
  
  
  <h2> Citation </h2>
  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},
  }
  
  ```