Text Classification
Transformers
PyTorch
TensorBoard
English
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DunnBC22/codebert-base-Malicious_URLs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/codebert-base-Malicious_URLs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/codebert-base-Malicious_URLs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/codebert-base-Malicious_URLs") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/codebert-base-Malicious_URLs") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset
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## Training procedure
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### Training hyperparameters
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| 0.8273 | 1.0 | 6450 | 0.8225 | 0.7279 | 0.6508 | 0.7279 | 0.4611 | 0.7279 | 0.7279 | 0.4422 | 0.6256 | 0.7279 | 0.5436 |
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### Framework versions
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- Transformers 4.27.4
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Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset
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_Input Word Length:_
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_Input Word Length By Class:_
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_Class Distribution:_
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/Images/Class%20Distribution.png)
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## Training procedure
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### Training hyperparameters
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
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| 0.8273 | 1.0 | 6450 | 0.8225 | 0.7279 | 0.6508 | 0.7279 | 0.4611 | 0.7279 | 0.7279 | 0.4422 | 0.6256 | 0.7279 | 0.5436 |
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### Framework versions
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- Transformers 4.27.4
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