README.md

---
license: cc-by-sa-4.0
---
Only a small part of the actual dataset for testing purposes uploaded at the moment.
# Law Area Prediction

## Introduction
The main subset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social

A portion of the cases from the main areas Public, Civil and Criminal can be classified further into sub-areas:
```
"public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'],
"civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'],
'criminal': ['Substantive Criminal', 'Criminal Procedure']
```


## Size


## Load datasets
Load the main dataset:
```python
dataset = load_dataset("rcds/law_area_prediction")
```
Load the dataset with the sub-areas of Civil law:
```python
dataset = load_dataset("rcds/law_area_prediction", "civil")
```

## Columns
### Main dataset
- decision_id: unique identifier for the decision
- facts: facts section of the decision
- considerations: considerations section of the decision
- label: label of the decision (main area of law)
- law_sub_area: sub area of law of the decision
- language: language of the decision
- year: year of the decision
- court: court of the decision
- chamber: chamber of the decision
- canton: canton of the decision
- region: region of the decision

### Sub-area dataset
- decision_id: unique identifier for the decision
- facts: facts section of the decision
- considerations: considerations section of the decision
- law_area: label of the decision (main area of law)
- label: sub area of law of the decision
- language: language of the decision
- year: year of the decision
- court: court of the decision
- chamber: chamber of the decision
- canton: canton of the decision
- region: region of the decision