Sentence Similarity
sentence-transformers
Safetensors
Transformers
German
bert
feature-extraction
text-embeddings-inference
Instructions to use JoBeer/german-semantic-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JoBeer/german-semantic-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JoBeer/german-semantic-base") 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 JoBeer/german-semantic-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JoBeer/german-semantic-base") model = AutoModel.from_pretrained("JoBeer/german-semantic-base") - Notebooks
- Google Colab
- Kaggle