transformers safetensors bert token-classification financial NLP named entity recognition sequence labeling structured extraction hierarchical taxonomy XBRL iXBRL SEC filings financial-information-extraction en dataset:AAU-NLP/HiFi-KPI base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- AAU-NLP/HiFi-KPI
language:
- en
library_name: transformers
model_name: BERT-SL1000
pipeline_tag: token-classification
tags:
- financial NLP
- named entity recognition
- sequence labeling
- structured extraction
- hierarchical taxonomy
- XBRL
- iXBRL
- SEC filings
- financial-information-extraction
task_categories:
- 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 \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/rasmus393/HiFi-KPI
---

## **BERT-SL1000**

### **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)

### **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](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI)**  

### **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)  