Instructions to use njprogrammer/setfit-goemotions-multilabel-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use njprogrammer/setfit-goemotions-multilabel-classification with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("njprogrammer/setfit-goemotions-multilabel-classification") 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] - setfit
How to use njprogrammer/setfit-goemotions-multilabel-classification with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("njprogrammer/setfit-goemotions-multilabel-classification") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from njprogrammer/setfit-goemotions-multilabel-classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/njprogrammer/setfit-goemotions-multilabel-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://njprogrammer/setfit-goemotions-multilabel-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/njprogrammer/setfit-goemotions-multilabel-classification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 5fd50463325a053fefb40a2df1d13e2dfe9fde32214361941d172781d3ec67c7
- Size of remote file:
- 438 MB
- SHA256:
- 84b44cb5a12bf132170a7f39ecc8f9585ff5c579ff5bccf0a90f99dd6c6c45bc
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