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
Training in progress, epoch 1
Browse files- config.json +24 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
config.json
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{
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.51.3",
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"vocab_size": 30522
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c759093eaa9b392ef93d1a81ddd269ab3fd5490734d0612087dbd425114051c1
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size 267832560
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:1dc15cdd22ce8aecfdfb64d94e8faefb7b398cde9537334ff11543d91669bcc1
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size 5304
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