metadata

tags:
  - financial NLP
  - named entity recognition
  - sequence labeling
  - structured extraction
  - hierarchical taxonomy
  - XBRL
task_categories:
  - token-classification
  - text-classification
task_ids:
  - named-entity-recognition
pretty_name: 'HiFi-KPI: Hierarchical Financial KPI Extraction'
dataset_name: HiFi-KPI
size_categories: 1M<n<10M
language:
  - en

HiFi-KPI: Hierarchical Financial KPI Extraction

Dataset Summary

HiFi-KPI is a large-scale dataset designed for financial numerical key performance indicator (KPI) extraction from earnings filings. It is derived from iXBRL filings mandated by the SEC, featuring hierarchical labels structured from the XBRL taxonomy. The dataset consists of ∼1.8M paragraphs and ∼5M entities, each linked to labels in the iXBRL calculation and presentation taxonomies.

Languages

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

Dataset Structure

Data Fields

Each entry in HiFi-KPI includes the following fields:

form_type: "10-K" or "10-Q"
accession_number: Unique filing identifier
filing_date: Timestamp of the filing
quarter_ending: Fiscal quarter end date
company_name: Name of the reporting entity
text: Extracted paragraph from the filing
entities (list of extracted entities):
- start_character / end_character: Position of the entity in the text
- label: iXBRL-based tag (e.g., us-gaap:Revenues)
- 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	1.43M	4.04M
Dev	162K	468K
Test	179K	491K

Data Splits

HiFi-KPI (full dataset): Contains all extracted entities. See the GitHub Repository for an example of how to obtain granular labels.
The dataset includes calculationMasterTaxonomy.json and presentationMasterTaxonomy.json, which define the master hierarchical structure for the calculation and presentation layers.

Baselines and Benchmarks

We establish baselines using:

Text Classification: fine-tuning all-MiniLM-L6-v2 to classify entity labels from text snippets.
Sequence Labeling: fine-tuning BERT (bert-base-uncased) with a token classification head.

Text Classification Performance Across Hierarchical Collapsing Levels

Collapsed Levels	Unique Labels	Validation Accuracy	Validation F1 (macro)	Test Accuracy	Test F1 (macro)
3	2241	0.5467	0.0232	0.5156	0.0207
6	1326	0.5956	0.0494	0.5575	0.0400
10	1266	0.6321	0.0249	0.5984	0.0194

Macro F1 Performance on 1000 Most Common Labels

Model	Macro F1
1000 Most Common (n=0)	0.7182
Calculation (1000 Most Common) (SL)	0.7121
Presentation (1000 Most Common) (SL)	0.7124

Uses and Applications

HiFi-KPI can be used for:

Financial Information Extraction: Extracting key financial metrics for downstream applications.
XBRL-based Entity Mapping: Linking textual content to structured financial labels.
Document Understanding: Training models to interpret financial data.

Citation

If you use HiFi-KPI 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

More info is avalible at GitHub Repository