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
Download tokenizer.json from and-effect/musterdatenkatalog_clf: direct link, hf CLI and curl.
- Browser
- Download file 727 kB
-
https://huggingface.co/and-effect/musterdatenkatalog_clf/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://and-effect/musterdatenkatalog_clf@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/and-effect/musterdatenkatalog_clf/resolve/refs%2Fpr%2F1/tokenizer.json
727 kB
File too large to display, you can check the raw version instead.