Feature Extraction
Safetensors
Model2Vec
sentence-transformers
code
distiller
code-search
code-embeddings
distillation
static-embeddings
tokenlearn
Instructions to use sarthak1/codemalt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use sarthak1/codemalt with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("sarthak1/codemalt") - sentence-transformers
How to use sarthak1/codemalt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sarthak1/codemalt") 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
| # Python-generated files | |
| __pycache__/ | |
| *.py[oc] | |
| build/ | |
| dist/ | |
| wheels/ | |
| *.egg-info | |
| # Virtual environments | |
| .venv | |
| # Cache | |
| .ruff_cache | |
| .mypy_cache | |
| # Environment variables | |
| .env | |
| # Beam | |
| .beamignore | |
| # Sync Dir | |
| gte_qwen2_m2v | |
| code_model2vec | |
| logs | |
| # codemap | |
| documentation | |
| *.bak |