Available models

Tip

Following an update, all the models trained using the stanford-nlp ColBERT library or RAGatouille should be compatible with PyLate natively (including their configurations). You can simply load the model in PyLate:

from pylate import models

model = models.ColBERT(
    model_name_or_path="colbert-ir/colbertv2.0",
)

or

model = models.ColBERT(
    model_name_or_path="jinaai/jina-colbert-v2",
    trust_remote_code=True,
)

Here is a list of some of the pre-trained ColBERT models available in PyLate along with their results on BEIR:

Table

Model	BEIR AVG	NFCorpus	SciFact	SCIDOCS	FiQA2018	TRECCOVID	HotpotQA	Touche2020	ArguAna	ClimateFEVER	FEVER	QuoraRetrieval	NQ	DBPedia
lightonai/colbertv2.0	50.02	33.8	69.3	15.4	35.6	73.3	66.7	26.3	46.3	17.6	78.5	85.2	56.2	44.6
answerdotai/answerai-colbert-small-v1	53.79	37.3	74.77	18.42	41.15	84.59	76.11	25.69	50.09	33.07	90.96	87.72	59.1	45.58
jinaai/jina-colbert-v2	53.1	34.6	67.8	18.6	40.8	83.4	76.6	27.4	36.6	23.9	80.05	88.7	64.0	47.1
GTE-ModernColBERT-v1	54.89	37.93	76.34	19.06	48.51	83.59	77.32	31.23	48.51	30.62	87.44	86.61	61.8	48.3

Note

lightonai/colbertv2.0 is the original ColBERTv2 model made compatible with PyLate before we supported loading directly model from Stanford-NLP. We thank Omar Khattab for allowing us to share the model on PyLate.