sentence-transformers
ONNX
Safetensors
English
bert
ColBERT
multi-vector
RAGatouille
passage-retrieval
Instructions to use answerdotai/answerai-colbert-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use answerdotai/answerai-colbert-small-v1 with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("answerdotai/answerai-colbert-small-v1") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
minor typo
#10
by nbroad - opened
README.md
CHANGED
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@@ -54,7 +54,7 @@ RAG = RAGPretrainedModel.from_pretrained("answerdotai/answerai-colbert-small-v1"
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docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
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RAG.index(
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query = 'Who directed spirited away?'
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results = RAG.search(query)
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docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
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RAG.index(docs, index_name="ghibli")
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query = 'Who directed spirited away?'
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results = RAG.search(query)
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