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Lite-BERT-SL

Token Classification
Transformers
Safetensors
English
bert
financial-nlp
named-entity-recognition
sequence-labeling
finance
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metadata 
base_model: bert-base-uncased
datasets:
  - AAU-NLP/hifi-kpi-lite
language:
  - en
library_name: transformers
model_name: Lite-BERT-SL
pipeline_tag: token-classification
tags:
  - financial-nlp
  - named-entity-recognition
  - sequence-labeling
  - finance
license: cc-by-4.0
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: 'Lite-BERT-SL: Sequence Labeling for HiFi-KPI Lite'
size_categories: 10K<n<100K

Lite-BERT-SL: Sequence Labeling for HiFi-KPI Lite

Lite-BERT-SL is a BERT-based sequence labeling model fine-tuned on the HiFi-KPI Lite dataset. This model was introduced in the paper HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings.

Model Description

The model is designed for the hierarchical extraction of Key Performance Indicators (KPIs) from financial earnings filings (SEC 10-K and 10-Q reports). While the full HiFi-KPI dataset contains a massive taxonomy of iXBRL tags, Lite-BERT-SL is fine-tuned on a manually curated subset focusing on four expert-mapped KPI clusters:

  • Revenues

  • Earnings

  • EPS (Earnings Per Share)

  • EBIT (Earnings Before Interest and Taxes)

  • Developed by: Rasmus Aavang, Giovanni Rizzi, Rasmus Bøggild, Alexandre Iolov, Mike Zhang, Johannes Bjerva

  • Model type: Token Classification (Sequence Labeling)

  • Base Model: bert-base-uncased

  • Language: English

Use Cases

  • Identifying and extracting generalized financial KPIs from earnings filings.
  • Automating the parsing of SEC 10-K and 10-Q reports for structured data extraction.
  • Assisting in the alignment of financial text with iXBRL taxonomies.

Performance

According to the paper, encoder-based models achieve over 0.906 macro-F1 on the HiFi-KPI Lite classification task. For detailed performance metrics, please refer to the paper and the HiFi-KPI Lite dataset page.

Dataset & Code

Citation

If you use this model or the dataset in your research, please cite:

@article{aavang2025hifikpi,
  title={HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings},
  author={Aavang, Rasmus and Rizzi, Giovanni and B{\o}ggild, Rasmus and Iolov, Alexandre and Zhang, Mike and Bjerva, Johannes},
  journal={arXiv preprint arXiv:2502.15411},
  year={2025}
}