README.md

metadata

base_model: bert-base-uncased
datasets:
  - AAU-NLP/HiFi-KPI
language:
  - en
library_name: transformers
license: apache-2.0
model_name: BERT-SL1000
pipeline_tag: text-classification
tags:
  - financial NLP
  - named entity recognition
  - sequence labeling
  - structured extraction
  - hierarchical taxonomy
  - XBRL
  - iXBRL
  - SEC filings
  - financial-information-extraction
task_categories:
  - text-classification
  - token-classification
task_ids:
  - named-entity-recognition
  - financial-information-extraction
pretty_name: 'BERT-SL1000: Sequence Labeling for Financial KPI Extraction'
size_categories: 1M<n<10M
languages:
  - en
dataset_name: HiFi-KPI
model_description: >
  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, such as revenue, earnings, and financial ratios, using **token
  classification**.


  This model is part of the **HiFi-KPI benchmark** and is optimized for
  **hierarchical label consistency**.
dataset_link: https://huggingface.co/datasets/AAU-NLP/HiFi-KPI
repo_link: https://github.com/aaunlp/HiFi-KPI

BERT-SL1000

Model Description

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, such as revenue, earnings etc.

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, and Johannes Bjerva.

Use Cases

• Extracting financial KPIs from SEC 10-K and 10-Q reports
• Financial document parsing with iXBRL-based entity recognition

Performance

• Trained on 1,000 most frequent labels from the HiFi-KPI dataset

Dataset & Code

• Dataset: HiFi-KPI on Hugging Face
• 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."
}