Cybersecurity Aspect Term Extraction and Polarity Classification
Model Description
This model is trained to extract aspect terms and classify their sentiment polarity in cybersecurity texts.
Model Architecture
- Base Model: BERT-base-uncased
- Architecture: FAST_LCF_ATEPC
Performance Metrics
- APC F1: 41.24%
- ATE F1: 90.57%
- APC Accuracy: 65.23%
Usage
from pyabsa import AspectTermExtraction as ATEPC
# Load the model
aspect_extractor = ATEPC.AspectExtractor(checkpoint="adoamesh/PyABSA_Cybersecurity_ATE_Polarity_Classification")
# Predict aspects and sentiments
result = aspect_extractor.predict("A ransomware attack targeted the hospital's patient records system.")
print(result)
Training Data
The model was trained on a custom cybersecurity dataset with IOB format annotations.
Limitations
This model is trained on cybersecurity texts and may not perform well on other domains.
Biases
The model may reflect biases present in the training data.
License
MIT
Author
Ado Amesh (adoamesh@example.com)