Text Classification
setfit
ONNX
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use serdarcaglar/primary-school-math-question with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use serdarcaglar/primary-school-math-question with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("serdarcaglar/primary-school-math-question") - sentence-transformers
How to use serdarcaglar/primary-school-math-question with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("serdarcaglar/primary-school-math-question") 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:
- 622361e74c8df2d0d7eb42208c096ac8cf70b5f8ec5001dccbd9fc25b141e352
- Size of remote file:
- 3.97 kB
- SHA256:
- 429ea5b4f0ed3110e9268c8fe2f2e6de0c91c07821cdeceef6f8722e304647f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.