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
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Long texts should use the same window settings shown in the training parameters.
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```
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Long texts should use the same window settings shown in the training parameters.
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## License and attribution
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Base model license follows [`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base) (Apache-2.0). Source texts were digitized and made available by the Buddhist Digital Resource Center (BDRC). Annotations were prepared through OpenPecha with support from the Tsadra Foundation.
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