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

- Xet hash:
- a0d99eab36d6b5d13380e8d34d5d3219e8a4a2e700de7c5080764882455d9769
- Size of remote file:
- 190 kB
- SHA256:
- 2b229a777e69b37402b2a0ac77b878436d3a6321912767c20da84bfb6ab893f1
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