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-Q-BERT-Qwen3VL2B-2048 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanovdr/NanoVDR-Q-BERT-Qwen3VL2B-2048 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanovdr/NanoVDR-Q-BERT-Qwen3VL2B-2048") 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
Download banner.png from nanovdr/NanoVDR-Q-BERT-Qwen3VL2B-2048: direct link, hf CLI and curl.
- Browser
- Download file 7.47 MB
-
https://huggingface.co/nanovdr/NanoVDR-Q-BERT-Qwen3VL2B-2048/resolve/main/banner.png
- Command line
-
hf download hf://nanovdr/NanoVDR-Q-BERT-Qwen3VL2B-2048/banner.png
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curl -L -o banner.png https://huggingface.co/nanovdr/NanoVDR-Q-BERT-Qwen3VL2B-2048/resolve/main/banner.png
7.47 MB

- Xet hash:
- 3eff0cd9ddaeb45dda9036d5a1c902962cb07ddc64d2557923bf8b39854625ff
- Size of remote file:
- 7.47 MB
- SHA256:
- caec09fa91e56deeb2d53e7ca2613573fc67c4e5af50817437f67a606d8fe200
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