---
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.

```
@article{ita_legalbert_2022,
     author = {Daniele Licari and Giovanni Comandè},
     title = {ITALIAN-LEGAL-BERT: A Pre-trained Transformer
     Language Model for Italian Law},
     booktitle = {Proceedings of The Knowledge Management for Law Workshop (KM4LAW)}
     note = {Accepted for publication},
     year = {2022}
}

```