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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://MegrurNiftiyev/MyGuard-Prompt-Injection-Detector") - Notebooks
- Google Colab
- Kaggle
Download push_to_firebase.py from MegrurNiftiyev/MyGuard-Prompt-Injection-Detector: direct link, hf CLI and curl.
- Browser
- Download file 171 Bytes
-
https://huggingface.co/MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/resolve/main/push_to_firebase.py
- Command line
-
hf download hf://MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/push_to_firebase.py
-
curl -L -o push_to_firebase.py https://huggingface.co/MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/resolve/main/push_to_firebase.py
171 Bytes
| """ | |
| Root CLI entrypoint — delegates execution to app.scripts.push_to_firebase. | |
| """ | |
| from app.scripts.push_to_firebase import main | |
| if __name__ == "__main__": | |
| main() | |