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
| { | |
| "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 | |
| } | |