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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/NV-Embed-v2", trust_remote_code=True, dtype="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
Is this multilingual and has this been tested on non-English only benchmarks?
#16
by Zoahmed1 - opened
Wondering if this model can be used for embedding non-English documents.
Have you any news about this?
This model "NV-Embed-v2" is only for English only!
No multi-lingual support. For that you either can train your own model using the methods specified in the paper linked here: https://arxiv.org/pdf/2405.17428
Or consider some alternative multi-lingual embedding models like jina-embeddings-v3.
I hope this answers your question.