Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use rovargasc/modelopruebaIA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use rovargasc/modelopruebaIA with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("rovargasc/modelopruebaIA") - sentence-transformers
How to use rovargasc/modelopruebaIA with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rovargasc/modelopruebaIA") 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:
- 2e042daf744a5d2b3908a8eeb45a6dcadff693a6693d04bdab862b84bc75e3dc
- Size of remote file:
- 7.01 kB
- SHA256:
- 70a9a6a15efa249de624958621d71c86ce04ddd958589e8bd8a35810e14b9fd7
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