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
| from distiller.model2vec.utils import get_package_extras, importable | |
| _REQUIRED_EXTRA = "inference" | |
| for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): | |
| importable(extra_dependency, _REQUIRED_EXTRA) | |
| from distiller.model2vec.inference.model import StaticModelPipeline, evaluate_single_or_multi_label | |
| __all__ = ["StaticModelPipeline", "evaluate_single_or_multi_label"] | |