Instructions to use ccaug/albert-network-attack-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ccaug/albert-network-attack-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ccaug/albert-network-attack-classifier")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ccaug/albert-network-attack-classifier") model = AutoModel.from_pretrained("ccaug/albert-network-attack-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 672 Bytes
cc27356 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"architectures": [
"AlbertModel"
],
"attention_probs_dropout_prob": 0,
"bos_token_id": 2,
"classifier_dropout_prob": 0.1,
"dtype": "float32",
"embedding_size": 128,
"eos_token_id": 3,
"hidden_act": "gelu_new",
"hidden_dropout_prob": 0,
"hidden_size": 4096,
"initializer_range": 0.02,
"inner_group_num": 1,
"intermediate_size": 16384,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "albert",
"num_attention_heads": 64,
"num_hidden_groups": 1,
"num_hidden_layers": 12,
"pad_token_id": 0,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"type_vocab_size": 2,
"vocab_size": 30000
}
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