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:
- 4a7b1c2971c6d87b1159ca32a16ff04d79385e814c98fddf5cd722d838ab7608
- Size of remote file:
- 193 kB
- SHA256:
- 3a63eefc044329f66b19e3ec6f289d41863949c769642f195ec340456ad0ced2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.