Link model to paper and official repository

README.md

@@ -1,41 +1,42 @@
 ---
-language: 
-  - en
-tags:
-  - financial NLP
-  - named entity recognition
-  - sequence labeling
-  - structured extraction
-  - hierarchical taxonomy
-  - XBRL
-  - iXBRL
-  - SEC filings
-  - financial-information-extraction
+base_model: bert-base-uncased
 datasets:
-  - AAU-NLP/HiFi-KPI
-model_name: "BERT-SL1000"
-library_name: "transformers"
-pipeline_tag: "token-classification"
-base_model: "bert-base-uncased"
+- 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:
-  - token-classification
+- 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"
+- 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/rasmus393/HiFi-KPI"
+- 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 \n**financial key performance indicators\
+  \ (KPIs)** from **SEC earnings filings (10-K & 10-Q)**. It specializes in identifying\
+  \ \nentities, such as revenue, earnings, and financial ratios, using **token classification**.\n\
+  \nThis model is part of the **HiFi-KPI benchmark** and is optimized for **hierarchical\
+  \ label consistency**.\n"
+dataset_link: https://huggingface.co/datasets/AAU-NLP/HiFi-KPI
+repo_link: https://github.com/aaunlp/HiFi-KPI
 ---
 
 ## **BERT-SL1000**
@@ -43,7 +44,7 @@ repo_link: "https://github.com/rasmus393/HiFi-KPI"
 ### **Model Description**  
 BERT-SL1000 is a **BERT-based sequence labeling model** fine-tuned on the **[HiFi-KPI dataset](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI)** 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 is trained on the [HiFi-KPI dataset](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI)
+This model was introduced in the paper [HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings](https://huggingface.co/papers/2502.15411) 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  
@@ -54,4 +55,15 @@ This model is trained on the [HiFi-KPI dataset](https://huggingface.co/datasets/
 
 ### **Dataset & Code**
 - **Dataset**: [HiFi-KPI on Hugging Face](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI)  
-- **Code Example**: [HiFi-KPI GitHub Repository](https://github.com/rasmus393/HiFi-KPI)  
+- **Code**: [HiFi-KPI GitHub Repository](https://github.com/aaunlp/HiFi-KPI)  
+
+### **Citation**
+
+```bibtex
+@article{aavang2025hifikpi,
+  title={HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings},
+  author={Aavang, Rasmus and Rizzi, Giovanni and B\o{}ggild, Rasmus and Iolov, Alexandre and Zhang, Mike and Bjerva, Johannes},
+  journal={arXiv preprint arXiv:2502.15411},
+  year={2025}
+}
+```