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197baf3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # 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
```python
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) |