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---
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}
}
``` |