Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mann2107/BCMPIIRAB_MiniLM_HTTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use mann2107/BCMPIIRAB_MiniLM_HTTest with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_HTTest") - sentence-transformers
How to use mann2107/BCMPIIRAB_MiniLM_HTTest with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mann2107/BCMPIIRAB_MiniLM_HTTest") 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:
- 2bfe1c98e4ebff73cb7baa634fa954fe5d72d37be9c43a5274260d1249720cdc
- Size of remote file:
- 90.9 MB
- SHA256:
- 0a68742cfafc8025db0eccba7ff051f03ad641a293221da02377ec8d074bacbb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.