---
license: mit
language:
- en
base_model:
- google-bert/bert-base-uncased
tags:
- cybersecurity
---
# 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
```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
Daniel Amemba Odhiambo