Text Classification
setfit
ONNX
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use aadiausa/Set_Fit_Ausa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use aadiausa/Set_Fit_Ausa with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("aadiausa/Set_Fit_Ausa") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use aadiausa/Set_Fit_Ausa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aadiausa/Set_Fit_Ausa") 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
Commit History
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Refresh pytorch/ to current 39-label model (adds messages.read; matches flat top-level)
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Retrain to 38 labels (+meal_time.read, +meal_time.update, +summary.read); fill model card [pytorch mirror]
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chore: retrain — summary.read + system.oos + meal_time (38 classes)
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