Update README.md

README.md

@@ -1,40 +1,49 @@
-# 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
+---
+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
+
+## 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)