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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve metadata, language name, small typos, code snippet output formatting
#1
by tomaarsen HF Staff - opened
Hello!
This PR mirrors https://huggingface.co/nvidia/Nemotron-3-Embed-1B-BF16/discussions/1, with slight differences:
- A different
base_model, obviously - The code snippet prints + expected outputs were already fine, but I've now matched them with the 1B PR. Before this PR, this model has 1 less space between each of the scores. Doesn't really matter, but this normalizes the two models.
As usual, feel free to make any edits or accept parts of the PR if you prefer.
- Tom Aarsen
ybabakhin changed pull request status to merged