README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: query
    dtype: string
  - name: answer
    dtype: string
  - name: text
    dtype: string
  - name: choices
    sequence: string
  - name: gold
    dtype: int64
  splits:
  - name: test
    num_bytes: 384180
    num_examples: 496
  download_size: 140144
  dataset_size: 384180
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- finance
pretty_name: FinBen FOMC
size_categories:
- n<1K
---

---

# Dataset Card for FinBen-FOMC

## Table of Contents

- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://huggingface.co/datasets/TheFinAI/finben-fomc
- **Repository:** https://huggingface.co/datasets/TheFinAI/finben-fomc
- **Paper:** FinBen: An Holistic Financial Benchmark for Large Language Models
- **Leaderboard:** https://huggingface.co/spaces/finosfoundation/Open-Financial-LLM-Leaderboard

### Dataset Summary

FinBen-FOMC is a financial sentiment classification dataset adapted from **FOMC (Shah et al., 2023a)**. The dataset is designed for training and evaluating large language models (LLMs) on classifying central bank policy stances as **Hawkish, Dovish, or Neutral**.

### Supported Tasks and Leaderboards

- **Task:** Hawkish-Dovish Classification
- **Evaluation Metric:** F1 Score, Accuracy
- **Test Size:** 496 instances

### Languages

- English

## Dataset Structure

### Data Instances

Each instance consists of a structured format with the following fields:

- **id**: A unique identifier for each data instance.
- **query**: An excerpt from a central bank’s release.
- **answer**: The classification label (`HAWKISH`, `DOVISH`, or `NEUTRAL`).

### Data Fields

- **id**: Unique string identifier for the data instance.
- **query**: The input text containing an excerpt from a central bank statement.
- **answer**: The classification label (`HAWKISH`, `DOVISH`, or `NEUTRAL`).

### Data Splits

The dataset is split into:

- **Test:** 496 instances

## Dataset Creation

### Curation Rationale

The dataset is adapted from **FOMC (Shah et al., 2023a)** to improve its suitability for LLM-based classification tasks in central bank policy analysis.

### Source Data

#### Initial Data Collection and Normalization

The dataset originates from Federal Open Market Committee (FOMC) statements and other central bank releases.

#### Who are the source language producers?

Central bank officials and policy documents.

### Annotations

#### Annotation Process

Annotations follow a structured classification framework to label monetary policy stances.

#### Who are the annotators?

Financial experts and researchers.

### Personal and Sensitive Information

No personally identifiable information (PII) is included.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset enhances financial NLP capabilities, allowing more accurate analysis of monetary policy signals.

### Discussion of Biases

Potential biases may exist due to:

- Interpretation differences in policy statements.
- Variability in central bank language across periods.

### Other Known Limitations

- Requires financial domain expertise for best model performance.
- May not generalize well to non-FOMC policy documents.

## Additional Information

### Dataset Curators

- The Fin AI Team

### Licensing Information

- **License:** CC BY-NC 4.0

### Citation Information

**Original Dataset:**

```bibtex
@inproceedings{shah2023trillion,
  title={Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis},
  author={Shah, Agam and Paturi, Suvan and Chava, Sudheer},
  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  editor={Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki},
  pages={6664--6679},
  year={2023},
  organization={Association for Computational Linguistics},
  address={Toronto, Canada},
  doi={10.18653/v1/2023.acl-long.368}
}
```

**Adapted Version (FinBen-FOMC):**

```bibtex
@article{xie2024finben,
  title={FinBen: A Holistic Financial Benchmark for Large Language Models},
  author={Xie, Qianqian and others},
  journal={arXiv preprint arXiv:2402.12659},
  year={2024}
}
```