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
GGUF format second
#6
by kalle07 - opened
you closed the discussion ;)
all doo use gguf-format for embedding documents
- nomic (implementet in gpt4all)
these you can use in LM studio in combination with anything LM
- mxbai
- bge
- snowflake-arctic
or is that not what your model doo?