Feature Extraction
sentence-transformers
Safetensors
code
bert
code-search
code-retrieval
text-embeddings-inference
Instructions to use thinkingdbx/codebert-permissive-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thinkingdbx/codebert-permissive-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thinkingdbx/codebert-permissive-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
Stage-2 code embedding model: permissive-only corpus, function-level contrastive tuning
c3ebc49 verified - Xet hash:
- eb03b02b996d3e3897b1ab038aaacf0f8f7dc627fa0c2393927170359fcb37d3
- Size of remote file:
- 442 MB
- SHA256:
- c77730b7e72082619b4caa9c9c260d49aa4cfe77222376a46bd1a333bb86d8de
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