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
File size: 325 Bytes
c3ebc49 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"model": {
"n_queries": 200,
"n_documents": 2200,
"recall": {
"@1": 0.22,
"@5": 0.325,
"@10": 0.385
},
"mrr": 0.2679
},
"bm25": {
"n_queries": 200,
"n_documents": 2200,
"recall": {
"@1": 0.225,
"@5": 0.275,
"@10": 0.31
},
"mrr": 0.2546
}
} |