AAU-NLP/hifi-kpi-lite

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