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Available models

Tip

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

from pylate import models

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

Note that some models can use non-default parameters (e.g, prefixes) and so you might have to specify them, e.g for the jinaai/jina-colbert-v2 model, you need to specify the prefixes and allow the query tokens to attend to expansion tokens: 

model = models.ColBERT(
    model_name_or_path="jinaai/jina-colbert-v2",
    query_prefix="[QueryMarker]",
    document_prefix="[DocumentMarker]",
    attend_to_expansion_tokens=True,
    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:

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
Note

lightonai/colbertv2.0 is the original ColBERTv2 model made compatible with PyLate. We thank Omar Khattab for allowing us to share the model on PyLate.

We are planning to release various strong models in the near future, but feel free to contact us if you want to make your existing ColBERT compatible with PyLate.