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---
license: apache-2.0
language:
- it
base_model:
- dbmdz/bert-base-italian-uncased
tags:
- legal
- italian
- infocube
metrics:
- perplexity
pipeline_tag: fill-mask
library_name: transformers
---
# Model Card for Model ID
This model is a BERT-based Masked Language Model fine-tuned on Italian legal texts. It is designed to predict masked tokens in legal documents and capture domain-specific semantic and syntactic structures.
### Model Description
This model is fine-tuned from `dbmdz/bert-base-italian-uncased` using **Masked Language Modeling (MLM) with Whole Word Masking (WWM)**.
WWM ensures that all subword tokens of a selected word are masked together, encouraging the model to learn deeper contextual representations, especially for complex legal terminology.
- **Developed by:** [Mohammad Mahdi Heydari Asl](https://huggingface.co/HYDARIM7) / infocube]
- **Model type:** Transformer, BERT-based Masked Language Model
- **Language(s):** Italian
- **License:** Apache-2.0
- **Finetuned from model:** `dbmdz/bert-base-italian-uncased`
## Uses
### Direct Use
The model can be used for:
- Predicting masked tokens in Italian legal texts (`[MASK]` prediction)
- Embedding legal text for downstream NLP tasks
- Transfer learning for other Italian legal NLP applications
## Bias, Risks, and Limitations
- Not suitable for general-purpose Italian NLP outside legal text.
### Recommendations
Users should verify outputs and avoid relying on predictions for legal decision-making without expert supervision.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
model_name = "InfocubeSrl/LexCube"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
text = "La legge [MASK] approvata dal parlamento."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
mask_index = (inputs["input_ids"][0] == tokenizer.mask_token_id).nonzero()[0]
predicted_id = outputs.logits[0, mask_index].argmax()
predicted_token = tokenizer.decode(predicted_id)
print("Prediction:", predicted_token)
```
### Training Data
- **Source:** Provided by *Infocube*,
- **Size:** 15,646 documents
- **Language:** Italian
- **Domain:** Legal and administrative texts
- Formal and technical legal language
- Frequent references to laws, decrees, and legislative articles
- Structured format with numbered provisions and cross-citations
- Avg. length: ~909 words (≈2,193 tokens per document); some documents exceed 11k tokens
- **Confidentiality:** Raw dataset cannot be shared due to contractual agreements, but it has been statistically and linguistically analyzed for research
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