Sentence Similarity
Safetensors
sentence-transformers
PyLate
bert
ColBERT
feature-extraction
multilingual
late-interaction
retrieval
bright
loss:Distillation
text-embeddings-inference
Instructions to use VAGOsolutions/SauerkrautLM-Reason-Multi-ColBERT-15m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use VAGOsolutions/SauerkrautLM-Reason-Multi-ColBERT-15m 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-Reason-Multi-ColBERT-15m") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- f2a5adca16b2f9860bd12fa14a214e3089a16e02cd027bb55639e6070d58041f
- Size of remote file:
- 101 kB
- SHA256:
- a9d37f84d79de37250f9839393947802d78981c33aa68583653213848227b62d
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