| # 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) |