Instructions to use Aliph0th/logtheus-ml-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aliph0th/logtheus-ml-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Aliph0th/logtheus-ml-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Aliph0th/logtheus-ml-base") model = AutoModelForTokenClassification.from_pretrained("Aliph0th/logtheus-ml-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_version": "model_base", | |
| "base_model": "google-bert/bert-base-uncased", | |
| "metrics": { | |
| "eval_loss": 0.07173649966716766, | |
| "eval_precision": 0.9525547445255474, | |
| "eval_recall": 0.9709821428571429, | |
| "eval_f1": 0.9616801768607222, | |
| "eval_accuracy": 0.9900185111248231, | |
| "eval_runtime": 2.0007, | |
| "eval_samples_per_second": 141.453, | |
| "eval_steps_per_second": 8.997, | |
| "epoch": 10.0 | |
| }, | |
| "labels": [ | |
| "B-duration", | |
| "B-environment", | |
| "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-environment", | |
| "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" | |
| ] | |
| } |