AAU-NLP/HiFi-KPI
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How to use AAU-NLP/BERT-SL1000 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="AAU-NLP/BERT-SL1000") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("AAU-NLP/BERT-SL1000")
model = AutoModelForTokenClassification.from_pretrained("AAU-NLP/BERT-SL1000")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 etc.
This model was introduced in the paper HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings by Rasmus Aavang, Giovanni Rizzi, Rasmus Bøggild, Alexandre Iolov, Mike Zhang, and Johannes Bjerva.
@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}
}
Base model
google-bert/bert-base-uncased