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")# 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 tokenizer.json from BDRC/Bo-Multilayer-Detection: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/BDRC/Bo-Multilayer-Detection/resolve/main/tokenizer.json
- Command line
-
hf download hf://BDRC/Bo-Multilayer-Detection/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BDRC/Bo-Multilayer-Detection/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 8fdbdb0307b65058d3b81a88cba0082cd1bbe8e85d34295d2271d6fa85a31c7a
- Size of remote file:
- 34.4 MB
- SHA256:
- 609d8f4c067cd3950f88594c5a802616cea245823836ef5848ee4fc40aab5b6f
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