Sentence Similarity
sentence-transformers
PyTorch
German
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use and-effect/musterdatenkatalog_clf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use and-effect/musterdatenkatalog_clf with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("and-effect/musterdatenkatalog_clf") sentences = [ "Bebauungspläne, vorhabenbezogene Bebauungspläne (Geltungsbereiche)", "Fachkräfte für Glücksspielsuchtprävention und -beratung", "Tagespflege Altenhilfe", "Bebauungsplan der Innenentwicklung gem. § 13a BauGB - Ortskern Rütenbrock" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fabc7299d9a2f368132bd2d5daa8fa9f04f53179a486818d1460778e5d6a4af0
- Size of remote file:
- 436 MB
- SHA256:
- c214c8dc99352a71934c0a2e5d67c690454426eea836c42fa6849b99a1cf8d62
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