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

  This model is trained specifically on n=1 with the **presentation taxonomy labels** from **HiFi-KPI**, focusing on entity identification.
  
dataset_link: "https://huggingface.co/datasets/AAU-NLP/HiFi-KPI"
repo_link: "https://github.com/rasmus393/HiFi-KPI"
---

## **Pre-BERT-SL1000**

### **Model Description**  
Pre-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)  and is focused on the **presentation layer taxonomy** with **n=1**.

### **Use Cases**
- Extracting **financial KPIs** using **iXBRL presentation taxonomy**  
- **Financial document parsing** with entity recognition  

### **Performance**
- Trained on **1,000 most frequent labels** from the **[HiFi-KPI dataset](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI)** with n=1 in the **presentation taxonomy**

### **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)