Instructions to use protectai/codebert-base-Malicious_URLs-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use protectai/codebert-base-Malicious_URLs-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="protectai/codebert-base-Malicious_URLs-onnx")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("protectai/codebert-base-Malicious_URLs-onnx") model = AutoModelForSequenceClassification.from_pretrained("protectai/codebert-base-Malicious_URLs-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# ONNX version of DunnBC22/codebert-base-Malicious_URLs
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**This model is a conversion of [DunnBC22/codebert-base-Malicious_URLs](https://huggingface.co/DunnBC22/codebert-base-Malicious_URLs) to ONNX** format. It's based on the CodeBERT architecture, tailored for the specific task of identifying URLs that may pose security threats. The model was converted to ONNX using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.
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> [!WARNING]
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> **THIS PROJECT HAS BEEN ARCHIVED.**
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> This project and its associated code on [GitHub](https://github.com/protectai/llm-guard) are no longer under active development or maintained.
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# ONNX version of DunnBC22/codebert-base-Malicious_URLs
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**This model is a conversion of [DunnBC22/codebert-base-Malicious_URLs](https://huggingface.co/DunnBC22/codebert-base-Malicious_URLs) to ONNX** format. It's based on the CodeBERT architecture, tailored for the specific task of identifying URLs that may pose security threats. The model was converted to ONNX using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.
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