Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
bert
ColBERT
feature-extraction
Generated from Trainer
loss:Contrastive
Eval Results (legacy)
text-embeddings-inference
Instructions to use NeuML/pubmedbert-base-colbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-colbert 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="NeuML/pubmedbert-base-colbert") 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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@@ -43,7 +43,7 @@ _Note: txtai 9.0+ is required for late interaction model support_
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import txtai
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embeddings = txtai.Embeddings(
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content=True
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)
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embeddings.index(documents())
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import txtai
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embeddings = txtai.Embeddings(
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path="neuml/pubmedbert-base-colbert",
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content=True
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)
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embeddings.index(documents())
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