Text Classification
Keras
security
web-attack-detection
sql-injection
xss
command-injection
rasp
cnn
gru
Instructions to use YangYang-Research/web-attack-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use YangYang-Research/web-attack-detection 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://YangYang-Research/web-attack-detection") - Notebooks
- Google Colab
- Kaggle
Download metadata.json from YangYang-Research/web-attack-detection: direct link, hf CLI and curl.
- Browser
- Download file 402 Bytes
-
https://huggingface.co/YangYang-Research/web-attack-detection/resolve/main/metadata.json
- Command line
-
hf download hf://YangYang-Research/web-attack-detection/metadata.json
-
curl -L -o metadata.json https://huggingface.co/YangYang-Research/web-attack-detection/resolve/main/metadata.json
402 Bytes
| { | |
| "keras_version": "legacy-hdf5", | |
| "date_saved": "2025-03-11", | |
| "format": "model.h5", | |
| "note": "Legacy single-file Keras HDF5 export. Load with keras.saving.load_model after hf_hub_download('model.h5'). Not the Keras 3 Hub layout (config.json + model.weights.h5).", | |
| "repo_id": "YangYang-Research/web-attack-detection", | |
| "encoder": "sentence-transformers/all-MiniLM-L6-v2", | |
| "input_dim": 384 | |
| } | |