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
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- ru
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metrics:
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- accuracy
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library_name:
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pipeline_tag: text-classification
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tags:
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- sentiment
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- ru
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metrics:
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library_name: tensorflowtts
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pipeline_tag: text-classification
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tags:
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- sentiment
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