Feature Extraction
Transformers
Safetensors
bert
urls
domain names
cybersecurity
multilingual
domain generation algorithm
dga
phishing
malware
DNS tunneling
urls classification
domain names classification
BERT
Encoder
text-embeddings-inference
Instructions to use amahdaouy/DomURLs_BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amahdaouy/DomURLs_BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="amahdaouy/DomURLs_BERT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("amahdaouy/DomURLs_BERT") model = AutoModel.from_pretrained("amahdaouy/DomURLs_BERT") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |