Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DunnBC22/codebert-base-mlm-Malicious_URLs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/codebert-base-mlm-Malicious_URLs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/codebert-base-mlm-Malicious_URLs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/codebert-base-mlm-Malicious_URLs") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/codebert-base-mlm-Malicious_URLs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f287b917b095a2562a51814ea629040405c400c0010816100cd9d46b41bc2cf
- Size of remote file:
- 499 MB
- SHA256:
- 304be74b2f6ad6d282754cf4a0cffb7a0388a1e3841e5a9b0fdbad263ac4e15d
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