Text Classification
setfit
Safetensors
sentence-transformers
English
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use CrisisNarratives/setfit-13classes-single_label with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use CrisisNarratives/setfit-13classes-single_label with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("CrisisNarratives/setfit-13classes-single_label") - sentence-transformers
How to use CrisisNarratives/setfit-13classes-single_label with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CrisisNarratives/setfit-13classes-single_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:
- f7825dedfc3d7f320843b5c8a3e10d96c73d9c8e9d92ebb5f8f7da5d18ab4a97
- Size of remote file:
- 80.9 kB
- SHA256:
- ea7f06274c697946edd5e3849db28a08d84d6665adb1b1353e067fc64ef0b728
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