Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use onyx-dot-app/information-content-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use onyx-dot-app/information-content-model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("onyx-dot-app/information-content-model") - sentence-transformers
How to use onyx-dot-app/information-content-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("onyx-dot-app/information-content-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:
- cc5d119213e5e68985ab726f995730bf765ece01df970802b0759c90437436bb
- Size of remote file:
- 438 MB
- SHA256:
- 75cf0cf21eb8e08d06f7e93fc87214d741b31a5c1520b896bde685cf619bdb31
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