Sentence Similarity
Safetensors
sentence-transformers
PyLate
lfm2
liquid
lfm2.5
edge
ColBERT
feature-extraction
custom_code
Instructions to use LiquidAI/LFM2.5-ColBERT-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LiquidAI/LFM2.5-ColBERT-350M 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="LiquidAI/LFM2.5-ColBERT-350M") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Update README example to bf16 MaxSim scores
Browse files
README.md
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@@ -130,7 +130,7 @@ print(query_embeddings[0].shape, document_embeddings[0].shape)
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# MaxSim late-interaction scoring (the Mars document ranks highest)
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scores = model.similarity(query_embeddings, document_embeddings)
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print(scores)
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# tensor([[27.
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```
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### Using PyLate
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# MaxSim late-interaction scoring (the Mars document ranks highest)
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scores = model.similarity(query_embeddings, document_embeddings)
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print(scores)
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# tensor([[27.1621, 28.2578, 27.7266, 28.1992]])
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```
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### Using PyLate
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