Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use SOUMYADEEPSAR/Setfit_designed_sample_random_forest_head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use SOUMYADEEPSAR/Setfit_designed_sample_random_forest_head with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("SOUMYADEEPSAR/Setfit_designed_sample_random_forest_head") - sentence-transformers
How to use SOUMYADEEPSAR/Setfit_designed_sample_random_forest_head with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SOUMYADEEPSAR/Setfit_designed_sample_random_forest_head") 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:
- 5c47e0cb20e7ae5090014823ab98e1a1d111d236e724333c11af11e2fc03bc0a
- Size of remote file:
- 438 MB
- SHA256:
- 3eab060696fdf81860778e95c8c97bc926986e1bf3d9521ee032494e70f63036
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