metadata

dataset_info:
  features:
    - name: query
      dtype: string
    - name: answer
      dtype: string
    - name: label
      sequence: string
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 609219
      num_examples: 320
    - name: validation
      num_bytes: 166639
      num_examples: 80
    - name: test
      num_bytes: 219566
      num_examples: 100
  download_size: 268192
  dataset_size: 995424
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: apache-2.0
language:
  - gr
tags:
  - finance
  - numeric
pretty_name: Plutus Finner Numeric
size_categories:
  - n<1K
task_categories:
  - token-classification

Dataset Card for Plutus Finner Numeric

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://huggingface.co./collections/TheFinAI/plutus-benchmarking-greek-financial-llms-67bc718fb8d897c65f1e87db
Repository: https://huggingface.co./datasets/TheFinAI/plutus-finner-numeric
Paper: Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance
Leaderboard: https://huggingface.co./spaces/TheFinAI/Open-Greek-Financial-LLM-Leaderboard#/
Model: https://huggingface.co./spaces/TheFinAI/plutus-8B-instruct

Dataset Summary

Plutus Finner Numeric is a specialized dataset created for numeric named entity recognition within financial texts. It combines financial queries with associated answers, labels, and additional textual context, serving as a benchmark for identifying numeric entities in Greek language financial documents.

Supported Tasks and Leaderboards

Task: Numeric Named Entity Recognition
Evaluation Metrics: Entity F1 Score
Test Size: 100

Languages

Greek

Dataset Structure

Data Instances

Each data instance in the dataset includes the following fields:

query: A financial query or prompt containing numeric expressions.
answer: The expected answer corresponding to the query.
label: A sequence field containing labels that may represent numeric entities or related categories.
text: Supplementary context or explanation accompanying each query.

Data Fields

query: String – The input prompt centered on financial numeric content.
answer: String – The answer intended for the corresponding query.
label: Sequence of strings – Additional labels or categorical descriptors for each instance.
text: String – Extra textual context or commentary.

Data Splits

The dataset is divided into three splits:

Train: 320 instances (609,219 bytes)
Validation: 80 instances (166,639 bytes)
Test: 100 instances (219,566 bytes)

Dataset Creation

Curation Rationale

The dataset was developed to support the evaluation of models in accurately identifying and reasoning with numeric entities in financial texts, specifically tailored for the Greek language. This is an essential resource for advancing research in numeric named entity recognition combined with financial text analysis in low-resource settings.

Source Data

Initial Data Collection and Normalization

The source data was derived from a diverse collection of Greek financial annual reports containing numeric information.

Who are the Source Language Producers?

Greek financial annual reports.

Annotations

Annotation Process

Annotations were performed by domain experts with backgrounds in finance and linguistics. The process involved identifying and labeling numeric entities based on financial context to facilitate accurate named entity recognition.

Who are the Annotators?

The dataset was annotated by a team of financial analysts, data scientists, and linguists collaborating to ensure both numeric accuracy and linguistic coherence.

Personal and Sensitive Information

This dataset has been curated to exclude any personally identifiable information (PII). It focuses solely on financial content and numeric reasoning without sensitive personal data.

Considerations for Using the Data

Social Impact of Dataset

By enhancing the capabilities of numeric named entity recognition in the financial domain, particularly in Greek, this dataset supports improved financial analysis, informed decision-making, and automated information processing. It is a valuable resource for both academic research and practical financial applications.

Discussion of Biases

Potential biases include:

Domain-specific language and numeric formats that might be less applicable outside Greek financial texts.
The curation process may favor specific numeric presentation styles or financial topics.
Annotation variability inherent in domain-specific text processing.

Other Known Limitations

The dataset is specifically designed for Greek financial texts, which may limit its applicability to other domains or languages.
Variations in numeric expressions and formatting may require additional pre-processing before analysis.

Additional Information

Dataset Curators

Xueqing Peng
Triantafillos Papadopoulos
Efstathia Soufleri
Polydoros Giannouris
Ruoyu Xiang
Yan Wang
Lingfei Qian
Jimin Huang
Qianqian Xie
Sophia Ananiadou

The research is supported by NaCTeM, Archimedes RC, and The Fin AI.

Licensing Information

License: Apache License 2.0

Citation Information

If you use this dataset in your research, please consider citing it as follows:

@misc{peng2025plutusbenchmarkinglargelanguage,
      title={Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance}, 
      author={Xueqing Peng and Triantafillos Papadopoulos and Efstathia Soufleri and Polydoros Giannouris and Ruoyu Xiang and Yan Wang and Lingfei Qian and Jimin Huang and Qianqian Xie and Sophia Ananiadou},
      year={2025},
      eprint={2502.18772},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.18772}, 
}