Feature Extraction
Transformers
Safetensors
sentence-transformers
English
nvembed
mteb
custom_code
Eval Results (legacy)
Instructions to use nvidia/NV-Embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NV-Embed-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/NV-Embed-v2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/NV-Embed-v2", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use nvidia/NV-Embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/NV-Embed-v2", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Commit History
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Add the Sentence Transformers tag
Verified signatureHello! This PR adds the Sentence Transformers tags, allowing people to find this model more easily when searching for embedding models under https://huggingface.co/models?library=sentence-transformers&sort=trending - Tom Aarsen5cb301c
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Update metadata: Add library_name to Transformers (#7)
Verified signature- Update metadata: Add library_name to Transformers (1da2cb865d4f367cd66cb725a0f58ca8031a91a3) Co-authored-by: Tiezhen WANG <xianbao@users.noreply.huggingface.co>26db444
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Super-squash branch 'main' using huggingface_hub
Verified signatureCo-authored-by: tomaarsen <tomaarsen@users.noreply.huggingface.co>2c0dadb