openlegalai/Indian-parliament-bills

---
dataset_info:
features:
- name: text
dtype: string
- name: dataset
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 127981073
num_examples: 5101
download_size: 52950298
dataset_size: 127981073
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---

Dataset from : DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction
https://aclanthology.org/2022.umios-1.8/

repo : https://github.com/monk1337/DeepParliament

```bibtex
@inproceedings{pal-2022-deepparliament,
title = "{D}eep{P}arliament: A Legal domain Benchmark {\&} Dataset for Parliament Bills Prediction",
author = "Pal, Ankit",
editor = "Han, Wenjuan and
Zheng, Zilong and
Lin, Zhouhan and
Jin, Lifeng and
Shen, Yikang and
Kim, Yoon and
Tu, Kewei",
booktitle = "Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.umios-1.8",
doi = "10.18653/v1/2022.umios-1.8",
pages = "73--81",
abstract = "This paper introduces DeepParliament, a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The proposed dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. Data collection, detailed statistics and analyses are provided in the paper. Moreover, we experimented with different types of models ranging from RNN to pretrained and reported the results. We are proposing two new benchmarks: Binary and Multi-Class Bill Status classification. Models developed for bill documents and relevant supportive tasks may assist Members of Parliament (MPs), presidents, and other legal practitioners. It will help review or prioritise bills, thus speeding up the billing process, improving the quality of decisions and reducing the time consumption in both houses. Considering that the foundation of the country{''}s democracy is Parliament and state legislatures, we anticipate that our research will be an essential addition to the Legal NLP community. This work will be the first to present a Parliament bill prediction task. In order to improve the accessibility of legal AI resources and promote reproducibility, we have made our code and dataset publicly accessible at github.com/monk1337/DeepParliament.",
}
```

原始信息汇总

数据集概述

数据特征

• text: 数据类型为字符串。
• dataset: 数据类型为字符串。
• id: 数据类型为字符串。

数据分割

• train: 包含5101个样本，总字节数为127981073。

数据大小

• 下载大小: 52950298字节。
• 数据集大小: 127981073字节。

配置

• default: 包含训练数据文件，路径为data/train-*。