Sentence Similarity
sentence-transformers
Safetensors
Transformers
ministral3
feature-extraction
text
text-embeddings
retrieval
semantic-search
rag
vllm
Instructions to use nvidia/Nemotron-3-Embed-8B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/Nemotron-3-Embed-8B-BF16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/Nemotron-3-Embed-8B-BF16") 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] - Transformers
How to use nvidia/Nemotron-3-Embed-8B-BF16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-3-Embed-8B-BF16") model = AutoModel.from_pretrained("nvidia/Nemotron-3-Embed-8B-BF16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b52da05abaf03eeef07e9d892fc36cf0aac639f72814f6e470df48dcf2f81e9
- Size of remote file:
- 17.1 MB
- SHA256:
- 797410dfb649a5b9ba92bc4fef7dbf4022d00e73de6867c4ac199a8846439421
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