Feature Extraction
sentence-transformers
ONNX
Safetensors
modernbert
code-search
code-embedding
retrieval
dense
text-embeddings-inference
Instructions to use Shuu12121/NightOwl-CodeEmbedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shuu12121/NightOwl-CodeEmbedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shuu12121/NightOwl-CodeEmbedding") 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 onnx/model_quantized.onnx from Shuu12121/NightOwl-CodeEmbedding: direct link, hf CLI and curl.
- Browser
- Download file 152 MB
-
https://huggingface.co/Shuu12121/NightOwl-CodeEmbedding/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://Shuu12121/NightOwl-CodeEmbedding/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/Shuu12121/NightOwl-CodeEmbedding/resolve/main/onnx/model_quantized.onnx
152 MB
- Xet hash:
- 0b8eaf0007bf17b249290df177c0baeb2924619fb6199658c2cb4d2eb29dcdb7
- Size of remote file:
- 152 MB
- SHA256:
- 46297e0726f35ae86bddfb07dcf6a8d7f27d57a8c270d05215876930ef91c279
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