
<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.



<b>ITALIAN-LEGAL-BERT variants [NEW!!!]</b>
<img  src="https://huggingface.co/dlicari/Italian-Legal-BERT-SC/resolve/main/ITALIAN_LEGAL_BERT-SC.jpg" width="600"/> 

* <a href="https://huggingface.co/dlicari/Italian-Legal-BERT-SC">FROM SCRATCH</a>, It is the ITALIAN-LEGAL-BERT variant pre-trained from scratch on Italian legal documents (<a href="https://huggingface.co/dlicari/Italian-Legal-BERT-SC">ITA-LEGAL-BERT-SC</a>) based on the CamemBERT architecture

<img  src="https://huggingface.co/dlicari/distil-ita-legal-bert/resolve/main/ITALIAN_LEGAL_BERT-DI.jpg" width="600"/> 

* <a href="https://huggingface.co/dlicari/distil-ita-legal-bert">DISTILLED</a>, a distilled version of ITALIAN-LEGAL-BERT ( <a href="https://huggingface.co/dlicari/distil-ita-legal-bert">DISTIL-ITA-LEGAL-BERT</a>)

<img  src="https://huggingface.co/dlicari/lsg16k-Italian-Legal-BERT/resolve/main/ITALIAN_LEGAL_BERT-LSG.jpg" width="600"/>
 
For long documents
  * [LSG ITA LEGAL BERT](https://huggingface.co/dlicari/lsg16k-Italian-Legal-BERT), Local-Sparse-Global version of ITALIAN-LEGAL-BERT (FURTHER PRETRAINED)
  * [LSG ITA LEGAL BERT-SC](https://huggingface.co/dlicari/lsg16k-Italian-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*

<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/1ZOWaWnLaagT_PX6MmXMP2m3MAOVXkyRK?usp=sharing)
 how to use it for sentence similarity, sentence classification, and named entity recognition
 - https://colab.research.google.com/drive/1ZOWaWnLaagT_PX6MmXMP2m3MAOVXkyRK?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},
}

```
{"language": "it", "license": "afl-3.0", "widget": [{"text": "Il [MASK] ha chiesto revocarsi l'obbligo di pagamento"}]}
transformers pytorch safetensors bert fill-mask it license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #it #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 


<img  src="URL width="600"/> 
<h1> ITALIAN-LEGAL-BERT:A pre-trained Transformer Language Model for Italian Law </h1>

ITALIAN-LEGAL-BERT is based on <a href="URL 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.



<b>ITALIAN-LEGAL-BERT variants [NEW!!!]</b>
<img  src="URL width="600"/> 

* <a href="URL SCRATCH</a>, It is the ITALIAN-LEGAL-BERT variant pre-trained from scratch on Italian legal documents (<a href="URL based on the CamemBERT architecture

<img  src="URL width="600"/> 

* <a href="URL a distilled version of ITALIAN-LEGAL-BERT ( <a href="URL

<img  src="URL width="600"/>
 
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*

<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:



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


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
 - URL

<img  src="URL width="700"/> 



<h2> Citation </h2>
If you find our resource or paper is useful, please consider including the following citation in your paper.


TAGS
#transformers #pytorch #safetensors #bert #fill-mask #it #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #bert #fill-mask #it #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
