Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ashercn97/code-y-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ashercn97/code-y-v3 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ashercn97/code-y-v3") - sentence-transformers
How to use ashercn97/code-y-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ashercn97/code-y-v3") 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:
- c191f1cb19f5347453add80297a13f0d86c0ae128ef47c8b5cb2ce4f9a0bad9e
- Size of remote file:
- 7.06 kB
- SHA256:
- e8990e4411285ca4cc91ada89d9b6d945b31ddeb444d56417b7b66ca725cab48
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.