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 Dockerfile from MegrurNiftiyev/MyGuard-Prompt-Injection-Detector: direct link, hf CLI and curl.
- Browser
- Download file 295 Bytes
-
https://huggingface.co/MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/resolve/main/Dockerfile
- Command line
-
hf download hf://MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/Dockerfile
-
curl -L -o Dockerfile https://huggingface.co/MegrurNiftiyev/MyGuard-Prompt-Injection-Detector/resolve/main/Dockerfile
295 Bytes
| FROM python:3.11-slim | |
| WORKDIR /app | |
| # Install dependencies | |
| COPY requirements.txt . | |
| RUN pip install --no-cache-dir -r requirements.txt | |
| # Copy application code | |
| COPY . . | |
| # Expose service port | |
| EXPOSE 8000 | |
| # Run with uvicorn | |
| CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] | |