Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use dappradar/setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use dappradar/setfit-model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("dappradar/setfit-model") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use dappradar/setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dappradar/setfit-model") 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
Download model_head.pkl from dappradar/setfit-model: direct link, hf CLI and curl.
- Browser
- Download file 56.6 kB
-
https://huggingface.co/dappradar/setfit-model/resolve/main/model_head.pkl
- Command line
-
hf download hf://dappradar/setfit-model/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/dappradar/setfit-model/resolve/main/model_head.pkl
56.6 kB
- Xet hash:
- 9ad99640c978f0774565278b8c7048f0ab6069c2bf68f191c7ef4b53bb9ba481
- Size of remote file:
- 56.6 kB
- SHA256:
- a775f40df49a93648e20bee14b57a9bdca499c72756942d97962ec0a5ff72d5c
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