Italian-Legal-BERT-SC

<img src="https://huggingface.co/dlicari/Italian-Legal-BERT-SC/resolve/main/ITALIAN_LEGAL_BERT-SC.jpg" width="600"/>

ITALIAN-LEGAL-BERT-SC

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

Training procedure

It was trained from scratch using a larger training dataset, 6.6GB of civil and criminal cases. We used CamemBERT architecture with a language modeling head on top, AdamW Optimizer, initial learning rate 2e-5 (with linear learning rate decay), sequence length 512, batch size 18, 1 million training steps, device 8*NVIDIA A100 40GB using distributed data parallel (each step performs 8 batches). It uses SentencePiece tokenization trained from scratch on a subset of training set (5 milions sentences) and vocabulary size of 32000

<h2> Usage </h2>

ITALIAN-LEGAL-BERT model can be loaded like:

from transformers import AutoModel, AutoTokenizer
model_name = "dlicari/Italian-Legal-BERT-SC"
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.

# %pip install sentencepiece 
# %pip install transformers

from transformers import pipeline
model_name = "dlicari/Italian-Legal-BERT-SC"
fill_mask = pipeline("fill-mask", model_name)
fill_mask("Il  <mask> ha chiesto revocarsi l'obbligo di pagamento")
# [{'score': 0.6529251933097839,'token_str': 'ricorrente',
#  {'score': 0.0380014143884182, 'token_str': 'convenuto',
#  {'score': 0.0360226035118103,  'token_str': 'richiedente',
#  {'score': 0.023908283561468124,'token_str': 'Condominio',  
#  {'score': 0.020863816142082214, 'token_str': 'lavoratore'}]