Text Classification
setfit
Safetensors
sentence-transformers
new
generated_from_setfit_trainer
custom_code
text-embeddings-inference
Instructions to use tmp-org/dm_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use tmp-org/dm_v1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("tmp-org/dm_v1") - sentence-transformers
How to use tmp-org/dm_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tmp-org/dm_v1", trust_remote_code=True) 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:
- 6eaab6b6b70411ffb4c522d3ecd689c3608a64a1cb1219d897ee2b515413ecdf
- Size of remote file:
- 1.22 GB
- SHA256:
- b22e97f4fa00d58a3532bd1fa78853f7b724d615dc30ecb1e82cbdd8ecc3bf86
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