Text Classification
setfit
Safetensors
sentence-transformers
English
mpnet
mtg
multilabel
magic
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use joshuasundance/mtg-coloridentity-multilabel-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use joshuasundance/mtg-coloridentity-multilabel-classification with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("joshuasundance/mtg-coloridentity-multilabel-classification") - sentence-transformers
How to use joshuasundance/mtg-coloridentity-multilabel-classification with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("joshuasundance/mtg-coloridentity-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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 810402de1f413308836db34044333f14964d0e87e520451fb3dcb2c238aabcd5
- Size of remote file:
- 438 MB
- SHA256:
- aa4d0acfcb0680f0dd982e21113998ba45d7ff0d2379444b328d20904e69853d
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