Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use HelgeKn/BEA2019-multi-class-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use HelgeKn/BEA2019-multi-class-10 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HelgeKn/BEA2019-multi-class-10") - sentence-transformers
How to use HelgeKn/BEA2019-multi-class-10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HelgeKn/BEA2019-multi-class-10") 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:
- 7ccecad493d621dd087ab6a5f33f5470554785c9bd0a736c97c2815ec3c0ba9c
- Size of remote file:
- 26.1 kB
- SHA256:
- 0a2a30fcd3af3ece171675113cd6b2934e1c60bc21550082aabf24baa7d639ee
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