Text Classification
setfit
Safetensors
sentence-transformers
modernbert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use juliusherzig/setfit-modernbert-studien4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use juliusherzig/setfit-modernbert-studien4 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("juliusherzig/setfit-modernbert-studien4") - sentence-transformers
How to use juliusherzig/setfit-modernbert-studien4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("juliusherzig/setfit-modernbert-studien4") 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:
- 7aa7ae8f058cebc01dd8ddba6fa1de5b2e738565c8e26bfe8f34c40a68f304d8
- Size of remote file:
- 7.01 kB
- SHA256:
- 72b9818a85b30e3f347fb0740f5949b4ef10b1ca562bbe6703abb9b2988d8c70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.