Instructions to use BDRC/Bo-Multilayer-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BDRC/Bo-Multilayer-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BDRC/Bo-Multilayer-Detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BDRC/Bo-Multilayer-Detection") model = AutoModelForTokenClassification.from_pretrained("BDRC/Bo-Multilayer-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BDRC/Bo-Multilayer-Detection: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/BDRC/Bo-Multilayer-Detection/resolve/main/training_args.bin
- Command line
-
hf download hf://BDRC/Bo-Multilayer-Detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BDRC/Bo-Multilayer-Detection/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 71f39b80a18cafca6f7717fab22414c11d696d92b6f24837f345c9bf03c9e088
- Size of remote file:
- 5.27 kB
- SHA256:
- c8f72be86b25da36f103d75c464384bbc44f6c88cd45f948140d71c7c323200b
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