task_categories:text-classification size_categories:10K<n<100K language:en license:mit legal doi:10.57967/hf/1193 region:us
---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- legal
pretty_name: US 117th Congress Bills
size_categories:
- 10K<n<100K
---

# Dataset Card for Dataset US 117th Congress Bills

## Dataset Description

- **Homepage:** https://hunterheidenreich.com/posts/us-117th-congress-data-exploration/
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** Hunter Heidenreich

### Dataset Summary

The US 117th Congress Bills dataset is a collection of all of the House Resolutions, House Joint Resolutions, 
Senate Resolutions, and Senate Joint Resolutions introduced during the 117th Congress (2021-2022). 
The task is to classify each bill into one of thirty-three major policy areas.
There are 11,389 bills in the training split and 3,797 bills in the testing split.

### Supported Tasks and Leaderboards

- `text-classification`: The goal is to classify each bill into one of thirty-three major policy areas. The dataset contains both a text label (`policy_areas`) and a class integer (`y`).

These classes correspond to:
  - 0: Agriculture and Food
  - 1: Animals
  - 2: Armed Forces and National Security
  - 3: Arts, Culture, Religion
  - 4: Civil Rights and Liberties, Minority Issues
  - 5: Commerce
  - 6: Congress
  - 7: Crime and Law Enforcement
  - 8: Economics and Public Finance
  - 9: Education
  - 10: Emergency Management
  - 11: Energy
  - 12: Environmental Protection
  - 13: Families
  - 14: Finance and Financial Sector
  - 15: Foreign Trade and International Finance
  - 16: Government Operations and Politics
  - 17: Health
  - 18: Housing and Community Development
  - 19: Immigration
  - 20: International Affairs
  - 21: Labor and Employment
  - 22: Law
  - 23: Native Americans
  - 24: Private Legislation
  - 25: Public Lands and Natural Resources
  - 26: Science, Technology, Communications
  - 27: Social Sciences and History
  - 28: Social Welfare
  - 29: Sports and Recreation
  - 30: Taxation
  - 31: Transportation and Public Works
  - 32: Water Resources Development

There is no leaderboard currently.

### Languages

English

## Dataset Structure

### Data Instances

```
index                                                          11047
id                                                          H.R.4536
policy_areas                                          Social Welfare
cur_summary        Welfare for Needs not Weed Act\nThis bill proh...
cur_text           To prohibit assistance provided under the prog...
title                                 Welfare for Needs not Weed Act
titles_official    To prohibit assistance provided under the prog...
titles_short                          Welfare for Needs not Weed Act
sponsor_name                                          Rep. Rice, Tom
sponsor_party                                                      R
sponsor_state                                                     SC
Name: 0, dtype: object
```

### Data Fields

- `index`: A numeric index
- `id`: The unique bill ID as a string
- `policy_areas`: The key policy area as a string. This is the classification label.
- `cur_summary`: The latest summary of the bill as a string.
- `cur_text`: The latest text of the bill as a string.
- `title`: The core title of the bill, as labeled on [Congress.gov](congress.gov), as a string.
- `titles_official`: All official titles of the bill (or nested legislation) as a string.
- `titles_short`: All short titles of the bill (or nested legislation) as a string.
- `sponsor_name`: The name of the primary representative sponsoring the legislation as a string.
- `sponsor_party`: The party of the primary sponsor as a string.
- `sponsor_state`: The home state of the primary sponsor as a string. 

### Data Splits

The dataset was split into a training and testing split using a stratefied sampling, due to the class imbalance in the dataset. 

Using scikit-learn, a quarter of the data (by class) is reserved for testing: 
```
train_ix, test_ix = train_test_split(ixs, test_size=0.25, stratify=df['y'], random_state=1234567)
```

## Dataset Creation

### Curation Rationale

This dataset was created to provide a new dataset at the intersection of NLP and legislation. 
Using this data for a simple major topic classification seemed like a practical first step.

### Source Data

#### Initial Data Collection and Normalization

Data was collected from [congress.gov](congress.gov) with minimal pre-processing. 
Additional information about this datasets collection is discussed [here](https://hunterheidenreich.com/posts/us-117th-congress-data-exploration/#data---how-it-was-obtained).

#### Who are the source language producers?

Either [Congressional Research Service](https://www.congress.gov/help/legislative-glossary#glossary_crs) or other congressional staffers.

### Annotations

#### Who are the annotators?

Congressional Staff

### Personal and Sensitive Information

None, this is publicly available text through [congress.gov](congress.gov).

## Additional Information

### Licensing Information

MIT License