Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
visual-document-retrieval
cross-modal-distillation
knowledge-distillation
nanovdr
Eval Results (legacy)
text-embeddings-inference
Instructions to use nanovdr/NanoVDR-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanovdr/NanoVDR-S with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanovdr/NanoVDR-S") 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:
- ed6e8922421aa3c2f7178672e45d8a243db45916153ef2cd18743c2c1379a664
- Size of remote file:
- 265 MB
- SHA256:
- 94a6e24feffa39d1bb68301894d7df0d8f79303246e2b2f41ce2442145164d7d
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