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") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - 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
Commit History
-
chore: update README and REPORT with performance insights and dataset changes
SarthakThis commit adds a warning in the README regarding the performance degradation observed with C4 fine-tuning, recommending the use of basic distillation for optimal results. The REPORT has been updated to reflect revised performance metrics for the fine-tuned model, adjustments in average performance statistics, and the inclusion of new radar charts for model comparisons. Additionally, the dataset configuration has been modified to utilize the C4 dataset for tokenlearn featurization.0dbb356 -
chore: updated eval charts
Sarthak37196da
-
chore(analysis): update image files
Sarthak4afb5eb -
chore: remove unused scripts and update dependencies
SarthakThis commit removes the MTEB evaluation script, distillation script, and evaluation script as they are no longer needed. Additionally, updates the pyproject.toml file to remove dependencies related to the removed scripts and adds typing-extensions to the dependencies.1bc7e54