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
File size: 847 Bytes
9cb6b94 | 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 30 31 32 33 34 35 36 37 38 39 40 41 42 | {
"model_version": "model_v1",
"base_model": "bert-large-uncased",
"metrics": {
"eval_loss": 0.03218596801161766,
"eval_precision": 0.974822974036192,
"eval_recall": 0.9872509960159362,
"eval_f1": 0.980997624703088,
"eval_accuracy": 0.9961780575539568,
"eval_runtime": 2.0737,
"eval_samples_per_second": 128.272,
"eval_steps_per_second": 8.198,
"epoch": 10.0
},
"labels": [
"B-duration",
"B-error_message",
"B-event",
"B-hostname",
"B-ip",
"B-level",
"B-method",
"B-path",
"B-service",
"B-status_code",
"B-timestamp",
"B-useragent",
"I-duration",
"I-error_message",
"I-event",
"I-hostname",
"I-ip",
"I-level",
"I-method",
"I-path",
"I-service",
"I-status_code",
"I-timestamp",
"I-useragent",
"O"
]
} |