---
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](https://github.com/rasmus393/HiFi-KPI) 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)](https://huggingface.co/AAU-NLP/BERT-SL1000) | 0.7182 |
| [Calculation (1000 Most Common) (SL)](https://huggingface.co/AAU-NLP/Cal-BERT-SL1000) | 0.7121 |
| [Presentation (1000 Most Common) (SL)](https://huggingface.co/AAU-NLP/Pre-BERT-SL1000) | 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](https://github.com/rasmus393/HiFi-KPI)