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  The successor of German_Semantic_STS_V2 is here!
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- ## Major updates:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- - **Sequence length: 8192, (16 times more than V2 and other models) => thanks to the alibi implementation of Jina-Team!**
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- - **Matryoshka Embeddings: Your embeddings can be sized from 1024 down to 64**
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- - **License: Apache 2.0**
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  ## Model Details
 
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  The successor of German_Semantic_STS_V2 is here!
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+ ## Major updates and USPs:
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+
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+ - **Sequence length:** 8192, (16 times more than V2 and other models) => thanks to the ALiBi implementation of Jina-Team!
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+ - **Matryoshka Embeddings:** The model is trained for embedding sizes from 1024 down to 64, allowing you to store much smaller embeddings with little quality loss.
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+ - **License:** Apache 2.0
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+ - **German only:** This model is German-only, causing the model to learn more efficient and deal better with shorter queries.
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+
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+ ## Usage:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+
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+ matryoshka_dim = 1024 # How big your embeddings should be, choose from: 64, 128, 256, 512, 1024
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+ model = SentenceTransformer("aari1995/German_Semantic_V3", trust_remote_code=True, truncate_dim=matryoshka_dim)
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+
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+ # Run inference
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+ sentences = [
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+ 'Eine Flagge weht.',
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+ 'Die Flagge bewegte sich in der Luft.',
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+ 'Zwei Personen beobachten das Wasser.',
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+ ]
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+ embeddings = model.encode(sentences)
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ ```
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  ## Model Details