Sentence Similarity
sentence-transformers
ONNX
Safetensors
German
bert
feature-extraction
loss:MatryoshkaLoss
custom_code
text-embeddings-inference
Instructions to use aari1995/German_Semantic_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aari1995/German_Semantic_V3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aari1995/German_Semantic_V3", trust_remote_code=True) sentences = [ "Bundeskanzler.", "Angela Merkel.", "Olaf Scholz.", "Tino Chrupalla." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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README.md
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: JinaBertModel
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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```
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# FAQ
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# FAQ
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