| --- |
| language: en |
| license: mit |
| tags: |
| - sentiment-analysis |
| - cybersecurity |
| - bert |
| widget: |
| - text: "Security team successfully prevents major data breach" |
| example_title: "Positive" |
| - text: "Ransomware attack shuts down hospital systems" |
| example_title: "Negative" |
| - text: "New security guidelines published by agency" |
| example_title: "Neutral" |
| --- |
| |
| # Security Sentiment Analyzer |
|
|
| Fine-tuned BERT model that analyzes sentiment of cybersecurity news headlines. |
|
|
| ## Model Details |
|
|
| - **Base Model:** `bert-base-uncased` |
| - **Task:** Text Classification (Sentiment Analysis) |
| - **Labels:** |
| - `0`: Positive 🟢 |
| - `1`: Negative 🔴 |
| - `2`: Neutral 🔵 |
|
|
| ## Accuracy |
|
|
| **87.5%** on test set |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline("text-classification", model="Aikaksh-Singh-Routela/security-sentiment-model") |
| |
| result = classifier("Ransomware attack shuts down hospital systems") |
| print(result) |
| |
