Text Classification
setfit
Safetensors
sentence-transformers
German
distilbert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use mbley/german-webtext-quality-classifier-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use mbley/german-webtext-quality-classifier-base with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("mbley/german-webtext-quality-classifier-base") - sentence-transformers
How to use mbley/german-webtext-quality-classifier-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mbley/german-webtext-quality-classifier-base") 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:
- 157af0bcb56575f799bad28117cef837efa006c33c44b49ac87323f19f20d582
- Size of remote file:
- 23.1 kB
- SHA256:
- 6f9987dfa2480c2d3617f3e78596ba8d98f7c3717e54b76287cea083f4e89b4d
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