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 spirited away?'
results = RAG.search(query)

Stanford ColBERT

Indexing

from colbert import Indexer
from colbert.infra import Run, RunConfig, ColBERTConfig

INDEX_NAME = "DEFINE_HERE" 

if __name__ == "__main__":
    config = ColBERTConfig(
        doc_maxlen=512,
        nbits=2
    )
    indexer = Indexer(
        checkpoint="answerdotai/answerai-colbert-small-v1",
        config=config,
    )
    docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]

    indexer.index(name=INDEX_NAME, collection=docs)

Querying

from colbert import Searcher
from colbert.infra import Run, RunConfig, ColBERTConfig

INDEX_NAME = "THE_INDEX_YOU_CREATED" 
k = 10 

if __name__ == "__main__":
    config = ColBERTConfig(
        query_maxlen=32 # Adjust as needed, we recommend the nearest higher multiple of 16 to your query
    )
    searcher = Searcher(
        index=index_name, 
        config=config
    ) 
    query = 'Who directed spirited away?'
    results = searcher.search(query, k=k)

Extracting Vectors

Finally, if you want to extract individula vectors, you can use the model this way:

from colbert.modeling.checkpoint import Checkpoint

ckpt = Checkpoint("answerdotai/answerai-colbert-small-v1", colbert_config=ColBERTConfig())
embedded_query = ckpt.queryFromText(["Who dubs Howl's in English?"], bsize=16)

Results

Against single-vector models

Dataset / Model	answer-colbert-s	snowflake-s	bge-small-en	bge-base-en
Size	33M (1x)	33M (1x)	33M (1x)	109M (3.3x)
BEIR AVG	53.79	51.99	51.68	53.25
FiQA2018	41.15	40.65	40.34	40.65
HotpotQA	76.11	66.54	69.94	72.6
MSMARCO	43.5	40.23	40.83	41.35
NQ	59.1	50.9	50.18	54.15
TRECCOVID	84.59	80.12	75.9	78.07
ArguAna	50.09	57.59	59.55	63.61
ClimateFEVER	33.07	35.2	31.84	31.17
CQADupstackRetrieval	38.75	39.65	39.05	42.35
DBPedia	45.58	41.02	40.03	40.77
FEVER	90.96	87.13	86.64	86.29
NFCorpus	37.3	34.92	34.3	37.39
QuoraRetrieval	87.72	88.41	88.78	88.9
SCIDOCS	18.42	21.82	20.52	21.73
SciFact	74.77	72.22	71.28	74.04
Touche2020	25.69	23.48	26.04	25.7

Against ColBERTv2.0

Dataset / Model	answerai-colbert-small-v1	ColBERTv2.0
BEIR AVG	53.79	50.02
DBPedia	45.58	44.6
FiQA2018	41.15	35.6
NQ	59.1	56.2
HotpotQA	76.11	66.7
NFCorpus	37.3	33.8
TRECCOVID	84.59	73.3
Touche2020	25.69	26.3
ArguAna	50.09	46.3
ClimateFEVER	33.07	17.6
FEVER	90.96	78.5
QuoraRetrieval	87.72	85.2
SCIDOCS	18.42	15.4
SciFact	74.77	69.3

Referencing

We'll most likely eventually release a technical report. In the meantime, if you use this model or other models following the JaColBERTv2.5 recipe and would like to give us credit, please cite the JaColBERTv2.5 journal pre-print:

@article{clavie2024jacolbertv2,
  title={JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources},
  author={Clavi{\'e}, Benjamin},
  journal={arXiv preprint arXiv:2407.20750},
  year={2024}
}