README.md

---
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}]
```
here how to use it for sentence similarity 
```python
import seaborn as sns
import matplotlib.pyplot as pl
from textwrap import wrap

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
    return sum_embeddings / sum_mask


# gettting Sentence Embeddings
def sentence_embeddings(sentences, model_name, max_length=512):
    # load models
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModel.from_pretrained(model_name)
    
    #Tokenize sentences
    encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=max_length, return_tensors='pt')

    #Compute token embeddings
    with torch.no_grad():
        model_output = model(**encoded_input)

    #Perform pooling. In this case, mean pooling
    return mean_pooling(model_output, encoded_input['attention_mask']).detach().numpy()


def plot_similarity(sentences, model_name):
    # Get sentence embeddings produced by the model
    embeddings = sentence_embeddings(sentences, model_name)
    # Perfom similarity score using cosine similarity
    corr = cosine_similarity(embeddings, embeddings)
    
    # Plot heatmap similarity
    sns.set(font_scale=1.2)    
    labels = [ '\n'.join(wrap(l, 40)) for l in sentences] # for text axis labels wrapping
    g = sns.heatmap(
      corr,
      xticklabels=labels,
      yticklabels=labels,
      vmax=1,
      cmap="YlOrRd")
    g.set_xticklabels(labels, rotation=90)
    model_short_name = model_name.split('/')[-1]
    g.set_title(f"Semantic Textual Similarity ({model_short_name})")
    plt.show()

# Sentences to be compared    
sent = [
    # 1. "The court shall pronounce the judgment for the dissolution or termination of the civil effects of marriage."
    "Il tribunale pronuncia la sentenza per lo scioglimento o la cessazione degli effetti civili del matrimonio",
    
    # 2. "having regard to Articles 1, 2, 3 No. 2(b) and 4 Paragraph 13 of Law No. 898 of December 1, 1970, as later amended."
    # NOTE: Law Dec. 1, 1970 No. 898 is on divorce
    "visti gli articoli 1, 2, 3 n. 2 lett. b) e 4 comma 13 della legge 1 dicembre 1970 n. 898 e successive modifiche",
    
    # 3. "The plaintiff has lost the case."
    "Il ricorrente ha perso la causa"
    ]

# Perform Semantic Textual Similarity using 'Italian-Legal-BERT'
model_name = "dlicari/Italian-Legal-BERT"
plot_similarity(sent, model_name)

# Perform Semantic Textual Similarity using 'bert-base-italian-xxl-cased'
model_name =  'dbmdz/bert-base-italian-xxl-cased'
plot_similarity(sent, model_name)
```
The similarity is shown in a heat map. The final graph is a 3x3 matrix in which each entry [i, j] is colored according to the cosine similarity of the encodings for sentences i and j
<img  src="https://huggingface.co/dlicari/Italian-Legal-BERT/resolve/main/semantic_text_similarity.jpg" width="700"/>