Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use edugargar/modality_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use edugargar/modality_model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("edugargar/modality_model") - sentence-transformers
How to use edugargar/modality_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("edugargar/modality_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
- Xet hash:
- f654ba1b74d2d8186ea1760e89bcccc859976860d317f371e0bb3e6e21ffff6b
- Size of remote file:
- 439 MB
- SHA256:
- a697480ae0b51f1677cca75aa3d97b8f788361cf8a224040ea6204398a03fb8f
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