Instructions to use bolu61/logsegmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bolu61/logsegmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bolu61/logsegmenter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bolu61/logsegmenter") model = AutoModelForTokenClassification.from_pretrained("bolu61/logsegmenter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from bolu61/logsegmenter: direct link, hf CLI and curl.
- Browser
- Download file 183 Bytes
-
https://huggingface.co/bolu61/logsegmenter/resolve/main/eval_results.json
- Command line
-
hf download hf://bolu61/logsegmenter/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/bolu61/logsegmenter/resolve/main/eval_results.json
183 Bytes
| { | |
| "eval_loss": 2.775719404220581, | |
| "eval_model_preparation_time": 0.0022, | |
| "eval_runtime": 1.19, | |
| "eval_samples_per_second": 75.629, | |
| "eval_steps_per_second": 10.084 | |
| } |