Add link to paper

README.md

@@ -1,25 +1,27 @@
 ---
-tags:
-  - financial NLP
-  - named entity recognition
-  - sequence labeling
-  - structured extraction
-  - hierarchical taxonomy
-  - XBRL
+language:
+- en
+size_categories: 1M<n<10M
 task_categories:
-  - token-classification
-  - text-classification
+- token-classification
+- text-classification
 task_ids:
-  - named-entity-recognition
-pretty_name: "HiFi-KPI: Hierarchical Financial KPI Extraction"
-dataset_name: "HiFi-KPI"
-size_categories: "1M<n<10M"
-language:
-  - en
+- named-entity-recognition
+pretty_name: 'HiFi-KPI: Hierarchical Financial KPI Extraction'
+tags:
+- financial NLP
+- named entity recognition
+- sequence labeling
+- structured extraction
+- hierarchical taxonomy
+- XBRL
+dataset_name: HiFi-KPI
 ---
 
 # HiFi-KPI: Hierarchical Financial KPI Extraction
 
+**Paper:** [HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings](https://huggingface.co/papers/2502.15411)
+
 ## 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
@@ -108,5 +110,4 @@ If you use HiFi-KPI in your research, please cite:
 
 ## Access
 More info is avalible at 
-[GitHub Repository](https://github.com/rasmus393/HiFi-KPI)
-
+[GitHub Repository](https://github.com/rasmus393/HiFi-KPI)