Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text-classification
+inference: false
+datasets:
+- sid321axn/malicious-urls-dataset
+tags:
+- malicious-urls
+- url
+---
+
+# ONNX version of DunnBC22/codebert-base-Malicious_URLs
+
+**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.
+
+## Model Architecture
+
+**Base Model**: CodeBERT-base, a robust model for programming and natural languages.
+
+**Dataset**: [https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset](https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset).
+
+**Modifications**: Details of any modifications or fine-tuning done to tailor the model for malicious URL detection.
+
+## Usage
+
+Loading the model requires the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library installed.
+
+```python
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer, pipeline
+
+
+tokenizer = AutoTokenizer.from_pretrained("laiyer/codebert-base-Malicious_URLs-onnx")
+model = ORTModelForSequenceClassification.from_pretrained("laiyer/codebert-base-Malicious_URLs-onnx")
+classifier = pipeline(
+    task="text-classification",
+    model=model,
+    tokenizer=tokenizer,
+    top_k=None,
+)
+
+classifier_output = classifier("https://google.com")
+print(classifier_output)
+```