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 unigram.json from germla/satoken: direct link, hf CLI and curl.
- Browser
- Download file 14.8 MB
-
https://huggingface.co/germla/satoken/resolve/main/unigram.json
- Command line
-
hf download hf://germla/satoken/unigram.json
-
curl -L -o unigram.json https://huggingface.co/germla/satoken/resolve/main/unigram.json
14.8 MB
- Xet hash:
- 303c2c1342c3411d08077fdbae9b292c26f1d198acc653311731c7b7d9734f38
- Size of remote file:
- 14.8 MB
- SHA256:
- 71b44701d7efd054205115acfa6ef126c5d2f84bd3affe0c59e48163674d19a6
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