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 training_args.bin from bolu61/logsegmenter: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/bolu61/logsegmenter/resolve/main/training_args.bin
- Command line
-
hf download hf://bolu61/logsegmenter/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bolu61/logsegmenter/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 7b52407f4584e198088f2024906275f3c77d3cbc9d9ea0c1b337f35fd5848cc0
- Size of remote file:
- 5.37 kB
- SHA256:
- 150ce6bde3a19a482cafd5c15141a14f738ac9a04bdbed97e7ef436f67f95fc9
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