Text Classification
sentence-transformers
PyTorch
setfit
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use germla/satoken with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use germla/satoken with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("germla/satoken") 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 germla/satoken with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("germla/satoken") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from germla/satoken: direct link, hf CLI and curl.
- Browser
- Download file 3.92 kB
-
https://huggingface.co/germla/satoken/resolve/main/model_head.pkl
- Command line
-
hf download hf://germla/satoken/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/germla/satoken/resolve/main/model_head.pkl
3.92 kB
- Xet hash:
- b91d30e1831a2a71c2be3a99bc9e62731f700c7c40ee62e6f16cdf14bd53975a
- Size of remote file:
- 3.92 kB
- SHA256:
- 5f652a87e1bce150bc9b315196f05805c48d5bcfba306c2b8a489b3083ee270b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.