Sentence Similarity
sentence-transformers
Safetensors
code
bert
feature-extraction
code-retrieval
code-search
linux-kernel
c
text-embeddings-inference
Instructions to use nethunter2023/kernel-code-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nethunter2023/kernel-code-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nethunter2023/kernel-code-embed") 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.json from nethunter2023/kernel-code-embed: direct link, hf CLI and curl.
- Browser
- Download file 2.22 MB
-
https://huggingface.co/nethunter2023/kernel-code-embed/resolve/main/tokenizer.json
- Command line
-
hf download hf://nethunter2023/kernel-code-embed/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nethunter2023/kernel-code-embed/resolve/main/tokenizer.json
2.22 MB
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