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: 390 Bytes
c3ebc49 ed02651 c3ebc49 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"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
} |