Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
visual-document-retrieval
cross-modal-distillation
knowledge-distillation
nanovdr
Eval Results (legacy)
text-embeddings-inference
Instructions to use nanovdr/NanoVDR-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanovdr/NanoVDR-L with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanovdr/NanoVDR-L") 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:
- 169aad753cb33ad4415ef7e9d981f80aa9dbe468c8ee60c0e8952ed7866c983c
- Size of remote file:
- 6.3 MB
- SHA256:
- a2e1d3ce375cbfeb036d67971e99fbbf58ea0cb2b6685d138ccc914b5c0f8adb
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