Instructions to use vdmxd/bge-m3-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vdmxd/bge-m3-quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vdmxd/bge-m3-quantized") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c8d4f649c7004b2196609c08e6b51468b8b15b89915ee20f57f9f6d04b35a75
- Size of remote file:
- 1.36 GB
- SHA256:
- 12ee23094191f7c7474ac8c2c7187a9a0eb25a1000451e40364a6b8429999a91
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