adoamesh/PyABSA_Cybersecurity_ATE_Polarity_Classification

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 Fast Local Content Focus-Aspect Term Extraction Polarity Classification

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

Daniel Amemba Odhiambo