answerai-colbert-small-v1 EU AI Act Compliance Profile

answerai-colbert-small-v1

answerai-colbert-small-v1 is a new, proof-of-concept model by Answer.AI, showing the strong performance multi-vector models with the new JaColBERTv2.5 training recipe and some extra tweaks can reach, even with just 33 million parameters.

While being MiniLM-sized, it outperforms all previous similarly-sized models on common benchmarks, and even outperforms much larger popular models such as e5-large-v2 or bge-base-en-v1.5.

For more information about this model or how it was trained, head over to the announcement blogpost.

Usage

Installation

This model was designed with the upcoming RAGatouille overhaul in mind. However, it's compatible with all recent ColBERT implementations!

To use it, you can either use the Stanford ColBERT library, or RAGatouille. You can install both or either by simply running.

pip install --upgrade ragatouille
pip install --upgrade colbert-ai

If you're interested in using this model as a re-ranker (it vastly outperforms cross-encoders its size!), you can do so via the rerankers library:

pip install --upgrade rerankers[transformers]

Rerankers

from rerankers import Reranker

ranker = Reranker("answerdotai/answerai-colbert-small-v1", model_type='colbert')
docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
query = 'Who directed spirited away?'
ranker.rank(query=query, docs=docs)

RAGatouille

from ragatouille import RAGPretrainedModel

RAG = RAGPretrainedModel.from_pretrained("answerdotai/answerai-colbert-small-v1")

docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]

RAG.index(docs, index_name="ghibli")

query = 'Who directed

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