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-Q-DistilBERT-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-DistilBERT-Qwen3VL2B-2048 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanovdr/NanoVDR-Q-DistilBERT-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
Drop the venue note
Browse files
README.md
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> **Paper**: [NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrieval](https://arxiv.org/abs/2603.12824) | [Blog](https://huggingface.co/blog/Ryenhails/nanovdr)
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> EMNLP 2026, Main Conference.
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# NanoVDR-Q-DistilBERT-Qwen3VL2B-2048
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> **Paper**: [NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrieval](https://arxiv.org/abs/2603.12824) | [Blog](https://huggingface.co/blog/Ryenhails/nanovdr)
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# NanoVDR-Q-DistilBERT-Qwen3VL2B-2048
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