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
change recall to f1 score
Browse files
README.md
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name: Recall 'Bezeichnung' (macro)
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- type: f1
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value: 0.6674970889256604
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name:
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- type: accuracy
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value: 0.8934426229508197
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name: Accuracy 'Thema'
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name: Recall 'Thema' (macro)
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- type: f1
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value: 0.8768881364249785
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name:
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---
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# Model Card for Musterdatenkatalog Classifier
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name: Recall 'Bezeichnung' (macro)
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- type: f1
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value: 0.6674970889256604
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name: F1 'Bezeichnung' (macro)
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- type: accuracy
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value: 0.8934426229508197
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name: Accuracy 'Thema'
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name: Recall 'Thema' (macro)
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- type: f1
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value: 0.8768881364249785
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name: F1 'Thema' (macro)
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---
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# Model Card for Musterdatenkatalog Classifier
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