Sentence Similarity
sentence-transformers
Safetensors
mistralbidirectional
swe-bench
code-similarity
code-retrieval
code-search
code-explanation
custom_code
Instructions to use nvidia/NV-EmbedCode-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/NV-EmbedCode-7b-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/NV-EmbedCode-7b-v1", 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
Download tokenizer.model from nvidia/NV-EmbedCode-7b-v1: direct link, hf CLI and curl.
- Browser
- Download file 493 kB
-
https://huggingface.co/nvidia/NV-EmbedCode-7b-v1/resolve/main/tokenizer.model
- Command line
-
hf download hf://nvidia/NV-EmbedCode-7b-v1/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/nvidia/NV-EmbedCode-7b-v1/resolve/main/tokenizer.model
493 kB
- Xet hash:
- 1e090c2d2774ea7875da72d682c12600bd69085e9c28674b917a49fe82ccffe2
- Size of remote file:
- 493 kB
- SHA256:
- dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.