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
| { | |
| "generated_at": 1786855640.2194777, | |
| "files": [ | |
| { | |
| "path": "LINEAGE.md", | |
| "bytes": 960, | |
| "sha256": "ee3ca897bc77c62b0a4026391ead4a17079b649b648d41c4e78cfb11bea58ff8" | |
| }, | |
| { | |
| "path": "MODEL_CARD.md", | |
| "bytes": 3833, | |
| "sha256": "c5be295b5887f59b9f0eb3fae47fa09b487c0e99c6b262653d04943298d5c676" | |
| }, | |
| { | |
| "path": "jobspec.json", | |
| "bytes": 2797, | |
| "sha256": "2395a1b6421addc9613df3bf1fd7c191deca4e3a889f86c36d1db8ddb009aed0" | |
| }, | |
| { | |
| "path": "metrics_summary.json", | |
| "bytes": 21018, | |
| "sha256": "fe4877acc42369a8a30574102129e8f93fa6471da5ad79dc1be7844da476dcb5" | |
| }, | |
| { | |
| "path": "reports.json", | |
| "bytes": 16339, | |
| "sha256": "aa143bc9fa9368472433b0559a02670f08f6305d3d6ac14601518cdf82d51b5d" | |
| }, | |
| { | |
| "path": "run_state.json", | |
| "bytes": 3394, | |
| "sha256": "cc0434b796c195adecfd9dc1dd99256d2d2ee56267eb05e7f2e7029395af6493" | |
| } | |
| ], | |
| "produced_by": "ThinkingDBx Pvt. Ltd." | |
| } |