Instructions to use Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit") 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] - setfit
How to use Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Linkthat/mdcsi-moebel-einrichtungshaeuser-setfit/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- f9a5341fbcf9726cafd7c74855f614a85fa9fd741093e5334d151ea6b096a5d5
- Size of remote file:
- 499 MB
- SHA256:
- c52e42e2dea4ba34b537a7e8cea3c630e6490816bfd5433c4f9efae1eeee8868
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.