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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Change max_seq_length for sentence_bert_config.json
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Update configuration_nvembed.py (transformers + sentence-transformers) and infinity usage (#23)
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Update README.md
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Add the Sentence Transformers tag (#14)
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Update metadata: Add library_name to Transformers (#7)
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Super-squash branch 'main' using huggingface_hub
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