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": "1 of 2: masked language model pretraining, from scratch", | |
| "documents_kept": 1980241, | |
| "documents_seen": 2047089, | |
| "permissive_fraction": 0.9673, | |
| "licences_kept": 171, | |
| "licences_excluded": 139, | |
| "unlicensed_kept": 0, | |
| "pii_redacted": 1384479, | |
| "tokens_trained": 3973189632, | |
| "steps": 30402, | |
| "final_loss": 4.3267, | |
| "vocab_size": 32000, | |
| "parameters": 110700000 | |
| } |