Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use Catchy1282/GradientBoosting_model_5_samples_per_label with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Catchy1282/GradientBoosting_model_5_samples_per_label with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Catchy1282/GradientBoosting_model_5_samples_per_label") - sentence-transformers
How to use Catchy1282/GradientBoosting_model_5_samples_per_label with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Catchy1282/GradientBoosting_model_5_samples_per_label") 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:
- 065d12df2a18628a20da530892e8c634a5c754d8e833c4539959943d0ff41cec
- Size of remote file:
- 438 MB
- SHA256:
- 5664c17154a48a8becb67f9761075efa7a63b1b7defeed7f52bedd7bf6953fa6
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