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
Fine-tuning example
#9
by gabrielchua - opened
Any examples on how to fine-tune this model would be appreciated!
I'll be posting some pretty soon, with the new https://github.com/lightonai/pylate backend for Ragatouille!
@bclavie this repo from your earlier post is private?
Uh that's strange, it seems HF messed up the link... https://github.com/lightonai/pylate should work?