Stanford Future Data Systems

weights

open(ungated MIT safetensors and ONNX on colbert-ir/colbertv2.0)

data

open(MS MARCO passage-ranking triples plus the released 64-way distillation examples at colbert-ir/colbertv2.0_msmarco_64way)

code

open(the repository ships the ColBERTv2-style Trainer, the indexer and the evaluation utilities, not just inference)

license

MIT(OSI)

ColBERT ships MIT weights on the Hub, trains on a public benchmark corpus plus a published distillation file, and documents its training entry point rather than leaving only inference code. That combination is what a fully open score requires.

• https://huggingface.co/colbert-ir/colbertv2.0/raw/main/README.md recorded 2026-10-06

  `license: mit` in the card front matter; card describes late interaction and links the ColBERTv2 paper.

• https://raw.githubusercontent.com/stanford-futuredata/ColBERT/main/LICENSE recorded 2026-10-06

  'MIT License / Copyright (c) 2019, 2020 Stanford Future Data Systems'.

• https://raw.githubusercontent.com/stanford-futuredata/ColBERT/main/README.md recorded 2026-10-06

  'This checkpoint has been trained on the MS MARCO Passage Ranking task'; a Data section naming triples.train.small.tar.gz, collection.tar.gz and qrels; and 'Basic Training (ColBERTv1-style)' plus 'Advanced Training (ColBERTv2-style)' sections that call `colbert.Trainer` with the published 64-way examples.json from colbert-ir/colbertv2.0_msmarco_64way.

Adoption is measured as Hugging Face downloads across the colbert-ir organization, almost all of it for the colbertv2.0 checkpoint. Third-party re-uploads such as lightonai/colbertv2.0 are excluded as mirrors.

• https://huggingface.co/api/models?author=colbert-ir&limit=100&sort=downloads&direction=-1 recorded 2026-09-11

  six colbert-ir repos with `downloads` (trailing 30 days) summing to 2,542,542; colbert-ir/colbertv2.0 alone at 2,539,633.

ColBERT is measured against Harrier-OSS, the category's benchmark reference, as the one pure text retriever in the group. Its paper reports state-of-the-art BEIR and LoTTE results at publication, but those are numbers from a generation of models now behind the frontier.

• https://arxiv.org/abs/2112.01488 recorded 2026-09-11

  'We evaluate ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 6--10x.'

• https://raw.githubusercontent.com/stanford-futuredata/ColBERT/main/README.md recorded 2026-09-11

  links the ColBERTv2 (NAACL'22) paper and documents downloading 'our new LoTTE benchmark,' an out-of-domain benchmark introduced with the checkpoint.