task_categories:text-generation language:pt license:apache-2.0 size_categories:n<1K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: apache-2.0
task_categories:
- text-generation
language:
- pt
pretty_name: Alpaca Translated Pt-Br
size_categories:
- 1K<n<10K
---
# Legal Data Analysis Dataset

This dataset contains legal statements, analyses, and judgments primarily related to labor law and contract law, drawn from various cases and legal interpretations. It includes text entries with factual descriptions, legal arguments, and conclusions based on judicial decisions, as well as instructions related to interpreting those facts.

## Dataset Overview

The dataset is structured as a series of legal paragraphs and corresponding instructions that delve into legal principles, case-specific details, and interpretations of labor contracts and employment laws. It is designed to aid in the development of models for legal text analysis, specifically for reasoning, explanation, and question-answering tasks.

### Key Sections of the Dataset:

- **Legal Facts**: Statements detailing the specifics of legal cases and situations.
- **Judicial Decisions**: Text describing court rulings, including the application of law and legal interpretations.
- **Instructions**: Corresponding queries (categorized as closed QA, explanation, or reasoning) to test and evaluate understanding of the legal facts and judicial decisions.

## Structure of the Dataset

Each data entry consists of the following fields:

1. **ID**: A unique identifier for the data entry.
2. **Legal Text**: A paragraph describing the facts, decisions, and context of a legal case.
3. **Instruction**: An instruction that serves as a question or task for the user to interpret or respond to based on the legal text.

The instructions are divided into three categories:

- **Closed QA**: Specific factual questions that have definitive answers based on the legal text.
- **Explanation**: Requests that require the user to explain the reasoning or interpretation of the legal facts.
- **Reasoning**: Queries that assess the logical process behind judicial decisions or legal conclusions.

### Example Entry:

```json
{
    "id": 1,
    "legal_text": "O Tribunal a quo erradamente conclui que o facto de o docente ter sido pago durante as férias letivas é indiciador de laboralidade...",
    "instruction": {
        "closed_qa": "O que o texto sugere em relação ao pagamento durante as férias letivas?",
        "explanation": "Explique por que o pagamento durante as férias letivas é considerado irrelevante para o vínculo laboral.",
        "reasoning": "Qual é a implicação do pagamento de férias no entendimento do vínculo contratual?"
    }
}
```

### Categories of Instructions:
- **Closed QA**: Questions that require a direct, factual answer based on the provided legal text.
- **Explanation**: Requests that require the user to explain the underlying legal concept or argument.
- **Reasoning**: Questions that involve evaluating the logical connections or implications of legal arguments presented.

## Data Usage

This dataset is ideal for training and evaluating models related to:

- Legal document summarization
- Legal question-answering systems
- Legal text reasoning and argumentation analysis
- Text classification in the legal domain

## Requirements

- **Format**: JSON
- **Data fields**: id, legal_text, instruction (subfields: closed_qa, explanation, reasoning)

## License

This dataset is provided for research purposes only, and may not be used for commercial purposes. Please ensure proper citation and attribution if you decide to use it for academic or research work.