Text Classification
Keras
English
Azerbaijani
prompt-injection
security
llm-security
document-security
retvec
cnn
tensorflow
fastapi
Eval Results (legacy)
Instructions to use MegrurNiftiyev/MyGuard-Prompt-Injection-Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MegrurNiftiyev/MyGuard-Prompt-Injection-Detector with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://MegrurNiftiyev/MyGuard-Prompt-Injection-Detector") - Notebooks
- Google Colab
- Kaggle
Download train_model.py from MegrurNiftiyev/MyGuard-Prompt-Injection-Detector: direct link, hf CLI and curl.
- Browser
- Download file 161 Bytes
-
https://huggingface.co/MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/resolve/main/train_model.py
- Command line
-
hf download hf://MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/train_model.py
-
curl -L -o train_model.py https://huggingface.co/MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/resolve/main/train_model.py
161 Bytes
| """ | |
| Root CLI entrypoint — delegates execution to app.scripts.train_model. | |
| """ | |
| from app.scripts.train_model import main | |
| if __name__ == "__main__": | |
| main() | |