Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
visual-document-retrieval
cross-modal-distillation
knowledge-distillation
nanovdr
Eval Results (legacy)
text-embeddings-inference
Instructions to use nanovdr/NanoVDR-M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanovdr/NanoVDR-M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanovdr/NanoVDR-M") 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:
- 7110ff34e0e0a44e720479a9c2f7b779938405c80fde856d51cbca1681f42364
- Size of remote file:
- 6.3 MB
- SHA256:
- d6a4e7c46859198aefedfb9b1aed5f079ca8562abc4ae84412a50b4815c7efbd
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