Create README.md

README.md

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+---
+tags:
+  - financial NLP
+  - named entity recognition
+  - sequence labeling
+  - structured extraction
+  - hierarchical taxonomy
+  - XBRL
+task_categories:
+  - token-classification
+  - text-classification
+  - structured-data-extraction
+task_ids:
+  - named-entity-recognition
+  - financial-information-extraction
+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)**: Includes all extracted entities see github for an example in how to get granular labels [GitHub Repository](https://github.com/rasmus393/HiFi-KPI)
+In the dataset there is also calculationMasterTaxonomy.json and presentationMasterTaxonomy.json that describes the master hiearchy for the presentation and calculation layer 
+
+## 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)                      | 0.7182   |
+| Calculation (1000 Most Common) (SL)         | 0.7121   |
+| Presentation (1000 Most Common) (SL)        | 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={Rasmus Aavang and Giovanni Rizzi and Rasmus Bøggild and Alexandre Iolov and Mike Zhang and Johannes Bjerva}
+  year={2025}
+}
+```
+
+## Access
+More info is avalible at 
+[GitHub Repository](https://github.com/rasmus393/HiFi-KPI)
+