Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
German
bert
feature-extraction
gBERT-large
RAG
retrieval augmented generation
STS
MTEB
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use aari1995/German_Semantic_STS_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aari1995/German_Semantic_STS_V2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aari1995/German_Semantic_STS_V2") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use aari1995/German_Semantic_STS_V2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aari1995/German_Semantic_STS_V2") model = AutoModel.from_pretrained("aari1995/German_Semantic_STS_V2") - Inference
- Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#5
by aari1995 - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f18d51e758f1eff8c182378dc2ea2fb68dc64e50bbc21bdf35013d1f250ea806
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size 1342992294
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