Instructions to use keisuke-miyako/bge-m3-doc-r3-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use keisuke-miyako/bge-m3-doc-r3-adapter with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("keisuke-miyako/bge-m3-doc-R2-merged") model = PeftModel.from_pretrained(base_model, "keisuke-miyako/bge-m3-doc-r3-adapter") - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from keisuke-miyako/bge-m3-doc-r3-adapter: direct link, hf CLI and curl.
- Browser
- Download file 5.07 MB
-
https://huggingface.co/keisuke-miyako/bge-m3-doc-r3-adapter/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://keisuke-miyako/bge-m3-doc-r3-adapter/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/keisuke-miyako/bge-m3-doc-r3-adapter/resolve/main/sentencepiece.bpe.model
5.07 MB
- Xet hash:
- ad3e6aac23a8a87f79c4164f1ce17d9d5bbbd51e8edd05bd00e3d06dd9e802a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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