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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |