Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use dzinampini/phishing-links-detection-using-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dzinampini/phishing-links-detection-using-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dzinampini/phishing-links-detection-using-transformers")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dzinampini/phishing-links-detection-using-transformers") model = AutoModelForSequenceClassification.from_pretrained("dzinampini/phishing-links-detection-using-transformers") - Notebooks
- Google Colab
- Kaggle
Added: missing critical model support files
Browse files- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{"do_lower_case": true, "model_max_length": 512}
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vocab.txt
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