Sentence Similarity
sentence-transformers
Safetensors
Transformers
German
English
ministral3
feature-extraction
retrieval
german
mteb
text
text-embeddings
semantic-search
rag
vllm
Instructions to use malteos/most-embed-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use malteos/most-embed-de with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("malteos/most-embed-de") 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 malteos/most-embed-de with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/most-embed-de") model = AutoModel.from_pretrained("malteos/most-embed-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle