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:
- 4786592ea7559ad79ac9adb5350927747fb7d0e28b608784f8cf9cd36906c228
- Size of remote file:
- 17.1 MB
- SHA256:
- 5df1f55d60c9705a501ab9a75550728625740741fe4be308dac4806c16b7d51d
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