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AAU-NLP/Lite-BERT-SL

sourceHugging Facecc-by-4.0updated 1mo agoView on Hugging Face
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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:

bibtex
@inproceedings{aavang-etal-2026-hifi,
    title = "{H}i{F}i-{KPI}: A Dataset for Hierarchical {KPI} Extraction from Earnings Filings",
    author = "Aavang, Rasmus T.  and
      Rizzi, Giovanni  and
      Tjalk-B{\o}ggild, Rasmus  and
      Iolov, Alexandre  and
      Zhang, Mike  and
      Bjerva, Johannes",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.30/",
    doi = "10.63317/2nbsp7zzfb3g",
    pages = "441--455",
    abstract = "Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Business Reporting Language (iXBRL) is mandated for public financial filings. Yet, its complex, fine-grained taxonomy limits the cross-company transferability of tagged Key Performance Indicators (KPIs). To address this, we introduce the Hierarchical Financial Key Performance Indicator (HiFi-KPI) dataset, a large-scale corpus of 1.65M paragraphs and 198k unique, hierarchically organized labels linked to iXBRL taxonomies. HiFi-KPI supports multiple tasks and we evaluate three: KPI classification, KPI extraction, and structured KPI extraction. For rapid evaluation, we also release HiFi-KPI-Lite, a manually curated 2.5K-instance subset. Baselines on HiFi-KPI-Lite show that encoder-based models achieve over 0.906 macro-F1 on classification, while Large Language Models (LLMs) reach 0.440 F1 on structured extraction. Finally, a qualitative analysis reveals that extraction errors primarily relate to dates. We open-source all code and data at Anonymous."
}