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-multi-lang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use serdarcaglar/primary-school-math-question-multi-lang with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("serdarcaglar/primary-school-math-question-multi-lang") - sentence-transformers
How to use serdarcaglar/primary-school-math-question-multi-lang with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("serdarcaglar/primary-school-math-question-multi-lang") 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:
- 18224073dc412458d77afb4874d1c2f159bf90441105531e2c7fb0765efe9df5
- Size of remote file:
- 17.1 MB
- SHA256:
- fa685fc160bbdbab64058d4fc91b60e62d207e8dc60b9af5c002c5ab946ded00
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