Sentence Similarity
Safetensors
sentence-transformers
PyLate
modernbert
multi-vector
ColBERT
feature-extraction
multilingual
late-interaction
retrieval
pretrained
loss:Distillation
text-embeddings-inference
Instructions to use VAGOsolutions/SauerkrautLM-Multi-ModernColBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use VAGOsolutions/SauerkrautLM-Multi-ModernColBERT with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="VAGOsolutions/SauerkrautLM-Multi-ModernColBERT") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 399509da82cf2b0f9e08cacfa6c0ac0e0eb4e257b6ab4cb2603363e88077f758
- Size of remote file:
- 298 MB
- SHA256:
- a2fe4ad3379371a5c20b9ae45a72a5c37684a25c7b0d7dc757106ac6bf4e8ad2
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