Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
feature-extraction
late-interaction
reasoning-retrieval
edge
Generated from Trainer
loss:CachedContrastive
text-embeddings-inference
Instructions to use DataScience-UIBK/Reason-mxbai-colbert-v0-32m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DataScience-UIBK/Reason-mxbai-colbert-v0-32m 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="DataScience-UIBK/Reason-mxbai-colbert-v0-32m") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -102,7 +102,7 @@ scores = retriever.retrieve(queries_embeddings=query_embs, k=10)
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```python
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from pylate import rank, models
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model = models.ColBERT(model_name_or_path="
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queries = ["query A", "query B"]
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documents = [["document A", "document B"], ["document 1", "document C", "document B"]]
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```python
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from pylate import rank, models
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model = models.ColBERT(model_name_or_path="DataScience-UIBK/Reason-mxbai-colbert-v0-32m")
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queries = ["query A", "query B"]
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documents = [["document A", "document B"], ["document 1", "document C", "document B"]]
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