Instructions to use Aliph0th/logtheus-ml-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aliph0th/logtheus-ml-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Aliph0th/logtheus-ml-large")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Aliph0th/logtheus-ml-large") model = AutoModelForTokenClassification.from_pretrained("Aliph0th/logtheus-ml-large") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "B-duration", | |
| "1": "B-error_message", | |
| "2": "B-event", | |
| "3": "B-hostname", | |
| "4": "B-ip", | |
| "5": "B-level", | |
| "6": "B-method", | |
| "7": "B-path", | |
| "8": "B-service", | |
| "9": "B-status_code", | |
| "10": "B-timestamp", | |
| "11": "B-useragent", | |
| "12": "I-duration", | |
| "13": "I-error_message", | |
| "14": "I-event", | |
| "15": "I-hostname", | |
| "16": "I-ip", | |
| "17": "I-level", | |
| "18": "I-method", | |
| "19": "I-path", | |
| "20": "I-service", | |
| "21": "I-status_code", | |
| "22": "I-timestamp", | |
| "23": "I-useragent", | |
| "24": "O" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "is_decoder": false, | |
| "label2id": { | |
| "B-duration": 0, | |
| "B-error_message": 1, | |
| "B-event": 2, | |
| "B-hostname": 3, | |
| "B-ip": 4, | |
| "B-level": 5, | |
| "B-method": 6, | |
| "B-path": 7, | |
| "B-service": 8, | |
| "B-status_code": 9, | |
| "B-timestamp": 10, | |
| "B-useragent": 11, | |
| "I-duration": 12, | |
| "I-error_message": 13, | |
| "I-event": 14, | |
| "I-hostname": 15, | |
| "I-ip": 16, | |
| "I-level": 17, | |
| "I-method": 18, | |
| "I-path": 19, | |
| "I-service": 20, | |
| "I-status_code": 21, | |
| "I-timestamp": 22, | |
| "I-useragent": 23, | |
| "O": 24 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.4.0", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 30522 | |
| } | |