Text Classification
Transformers
Joblib
English
cybersecurity
industrial-control-systems
bert
from-scratch
synthetic-data
Instructions to use ARotting/protocol-guardian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARotting/protocol-guardian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ARotting/protocol-guardian")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ARotting/protocol-guardian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model": "Protocol Guardian MiniLM Linear", | |
| "base_model": "sentence-transformers/all-MiniLM-L6-v2", | |
| "embedding_dimensions": 384, | |
| "linear_head_parameters": 385, | |
| "selected_c": 0.3, | |
| "validation": { | |
| "accuracy": 0.8175, | |
| "precision": 0.7343173431734318, | |
| "recall": 0.995, | |
| "f1": 0.8450106157112527, | |
| "confusion_matrix": [ | |
| [ | |
| 256, | |
| 144 | |
| ], | |
| [ | |
| 2, | |
| 398 | |
| ] | |
| ], | |
| "examples": 800 | |
| }, | |
| "test": { | |
| "accuracy": 0.8858333333333334, | |
| "precision": 1.0, | |
| "recall": 0.7716666666666666, | |
| "f1": 0.8711194731890874, | |
| "confusion_matrix": [ | |
| [ | |
| 600, | |
| 0 | |
| ], | |
| [ | |
| 137, | |
| 463 | |
| ] | |
| ], | |
| "examples": 1200 | |
| }, | |
| "test_split": "1200 examples from a third, unseen template family" | |
| } |