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AAU-NLP
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hifi-kpi-lite

Tasks:
Token Classification
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
financial NLP
named entity recognition
XBRL
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metadata
tags:
  - financial NLP
  - named entity recognition
  - XBRL
task_categories:
  - token-classification
  - text-classification
task_ids:
  - named-entity-recognition
pretty_name: 'HiFi-KPI Lite: Expert-Curated Financial KPI Extraction'
dataset_name: HiFi-KPI Lite
size_categories:
  - 10K<n<100K
language:
  - en

HiFi-KPI Lite: Expert-Curated Financial KPI Extraction

Dataset Summary

HiFi-KPI Lite is a manually curated subset of the HiFi-KPI dataset, designed for evaluating structured financial KPI extraction. Unlike the full HiFi-KPI dataset, HiFi-KPI Lite maps financial entities to a much reduced, expert-defined label space. The dataset consists of ∼8K paragraphs and ∼25K entities, making it suitable for rapid model evaluation.

Supported Tasks

The dataset is optimized for:

  • Named Entity Recognition (NER): Identifying financial KPIs from text.
  • Structured Data Extraction: Extracting numerical values, currencies, and corresponding time periods from financial statements.
  • Text Classification: Associating financial statements with expert-mapped KPI labels.

Languages

The dataset is in English, sourced from SEC 10-K and 10-Q filings.

Dataset Structure

Data Fields

Each entry in HiFi-KPI Lite includes:

  • form_type: "10-K" or "10-Q"
  • accession_number: Unique filing identifier
  • filing_date: Timestamp of the filing
  • company_name: Name of the reporting entity
  • text: Extracted paragraph from the filing
  • entities (list of extracted entities):
    • label: Expert-defined financial KPI category(Ebit, revnues ..)
    • start_date_for_period / end_date_for_period: Time period of the financial figure
    • currency/unit: Currency (e.g., USD, EUR)
    • value: Extracted numerical figure

Dataset Statistics

Split # Paragraphs # Entities
Train 6,359 19,749
Dev 768 2,601
Test 856 2,437

Baseline Model Performance

We establish baselines using:

  • Sequence Labeling: fine-tuning BERT (bert-base-uncased) with a token classification head.
  • LLM-based Structured Extraction: Few-shot prompting for NuExtract, Qwen-2.5-14B, and DeepSeek-V3.

Macro F1 Performance on HiFi-KPI Lite

Model Precision Recall Micro F1
BERT (SL) 89.2 91.8 89.1
Qwen-2.5-14B 63.7 60.2 49.5
DeepSeek-V3 67.8 65.9 46.4

Uses and Applications

HiFi-KPI Lite is useful for:

  • Benchmarking KPI Extraction: Evaluating model performance on structured financial data extraction.
  • Fine-Grained Financial NLP Tasks: Training models on structured financial entity recognition.
  • Evaluation of Large Language Models (LLMs): Testing how well LLMs generalize to financial entity extraction.

Citation

If you use HiFi-KPI Lite in your research, please cite:

@article{aavang2025hifi,
  title={HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings},
  author={Aavang, Rasmus and Rizzi, Giovanni and Bøggild, Rasmus and Iolov, Alexandre and Zhang, Mike and Bjerva, Johannes},
  journal={arXiv preprint arXiv:2502.15411},
  year={2025}
}

Access

Example code and repo with links to all models at GitHub Repository