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README.md

@@ -1,13 +1,40 @@
----
-license: mit
-metrics:
-- APC_F1: 41.24,
-- ATE_F1: 90.57,
-- APC_Accuracy: 65.23
-base_model:
-- google-bert/bert-base-uncased
-pipeline_tag: text-classification
-model_architecture: 
-- FAST_LCF_ATEPC
-
----
+# 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)