Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
visual-document-retrieval
cross-modal-distillation
knowledge-distillation
document-retrieval
multilingual
nanovdr
Eval Results (legacy)
text-embeddings-inference
Instructions to use nanovdr/NanoVDR-S-Multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanovdr/NanoVDR-S-Multi with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanovdr/NanoVDR-S-Multi") 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:
- 791fc64b20dd196c3576cbadf03b9c5a8cf33426db68fc2fe58870c003da13d3
- Size of remote file:
- 6.3 MB
- SHA256:
- 93b4e4ddf2abd0b96bb573f55fb57559d28a056dc7a3658e7ddd1a6eca000dc4
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