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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://YangYang-Research/web-attack-detection") - Notebooks
- Google Colab
- Kaggle
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README.md
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- precision
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- recall
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library_name: keras
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---
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# Model Card for Model ID
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- **Developed by:** [
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper
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- **Demo [optional]:** [More Information Needed]
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## Uses
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- recall
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library_name: keras
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pipeline_tag: text-classification
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# Model Card for Model ID
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- **Developed by:** [noobpk](https://github.com/noobpk/)
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper :** [Research and Development of a Smart Solution for Runtime Web Application Self-Protection](https://doi.org/10.1145/3628797.3628901)
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- **Demo [optional]:** [More Information Needed]
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## Uses
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