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 | [ | |
| {"idx": 0, "name": "0", "path": "", "type": "sentence_transformers.models.Transformer"}, | |
| {"idx": 1, "name": "1", "path": "1_Pooling", "type": "sentence_transformers.models.Pooling"}, | |
| {"idx": 2, "name": "2", "path": "", "type": "sentence_transformers.models.Normalize"} | |
| ] | |