Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use edwsiew/phantom-dispatch-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use edwsiew/phantom-dispatch-01 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("edwsiew/phantom-dispatch-01") - sentence-transformers
How to use edwsiew/phantom-dispatch-01 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("edwsiew/phantom-dispatch-01") 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:
- dc9c3598f0944aef7b2d58bf00c7cfa06c7f66c6e6381d22052b4d411ed9941b
- Size of remote file:
- 438 MB
- SHA256:
- b98b1065de2c8053f68a35565a1a93540ea372c509362a078094cb893543b0b9
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