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
Download tokenizer.json from answerdotai/answerai-colbert-small-v1: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/answerdotai/answerai-colbert-small-v1/resolve/refs%2Fpr%2F18/tokenizer.json
- Command line
-
hf download hf://answerdotai/answerai-colbert-small-v1@refs/pr/18/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/answerdotai/answerai-colbert-small-v1/resolve/refs%2Fpr%2F18/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.