Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use zainabawan229/LogisticRegression_model_5_samples_per_label with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use zainabawan229/LogisticRegression_model_5_samples_per_label with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("zainabawan229/LogisticRegression_model_5_samples_per_label") - sentence-transformers
How to use zainabawan229/LogisticRegression_model_5_samples_per_label with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zainabawan229/LogisticRegression_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:
- 04a81237ce5cb4a5ca0f1a5bfca633748c5f0b6e655b0f1e10f0f599978dbd20
- Size of remote file:
- 19.3 kB
- SHA256:
- 3f2fc6f24ac8d868a94da3981467bfb84912d0c1df70d6b81ef4a0792dfce71a
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