AAU-NLP/Cal-BERT-SL1000

Cal-BERT-SL1000

Model Description

Cal-BERT-SL1000 is a BERT-based sequence labeling model fine-tuned on the HiFi-KPI dataset for extracting financial key performance indicators (KPIs) from SEC earnings filings (10-K & 10-Q). It specializes in identifying entities that are one level up the calculation taxonomy ($n=1$), such as revenueAbstract, earnings, and financial ratios, using token classification.

This model was introduced in the paper HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings by Rasmus Aavang, Giovanni Rizzi, Rasmus Bøggild, Alexandre Iolov, Mike Zhang (@jjzha), and Johannes Bjerva.

Use Cases

• Extracting financial KPIs using iXBRL calculation taxonomy
• Financial document parsing with entity recognition

Performance

• Trained on 1,000 most frequent labels from the HiFi-KPI dataset with $n=1$ in the calculation taxonomy.

Dataset & Code

• Dataset: HiFi-KPI on Hugging Face
• Official Code: HiFi-KPI GitHub Repository

Citation

@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."
}