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:
- c8585f74b1a3fbfa1bf22bfac31e9eff7cd7eb946226ab88224fff82c592b88a
- Size of remote file:
- 33.3 kB
- SHA256:
- a28a31117915c2b5713e87f99b4f25908f4848ee57df2d4707db9ef228eaaf51
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.