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:
- 823fde44e65df0e14be969a6da11a90d87bcf4d4691e62e1f70bdea584249f51
- Size of remote file:
- 438 MB
- SHA256:
- 06a2e908bdc7e7636b76bc2e7f543555655672f10a092caa16d0e75d860d5f89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.