Instructions to use djsull/logs_rand with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djsull/logs_rand with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="djsull/logs_rand")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("djsull/logs_rand") model = AutoModelForTokenClassification.from_pretrained("djsull/logs_rand", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4f1525c810290bb0c1f77a4984e62e7a36dbcd0500d2de98c7f981f238a5100
- Size of remote file:
- 4.6 kB
- SHA256:
- 6819eecb81bf149b50496875bcaea08e199cd4aef7545a5e4be9da94e12f9ab2
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