Colbert

• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (SIGIR'20).
• Relevance-guided Supervision for OpenQA with ColBERT (TACL'21).
• Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval (NeurIPS'21).
• ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (NAACL'22).
• PLAID: An Efficient Engine for Late Interaction Retrieval (CIKM'22).

🚨 Announcements

• (1/29/23) We have merged a new index updater feature and support for additional Hugging Face models! These are in beta so please give us feedback as you try them out.
• (1/24/23) If you're looking for the DSP framework for composing ColBERTv2 and LLMs, it's at: https://github.com/stanfordnlp/dsp

ColBERTv1

The ColBERTv1 code from the SIGIR'20 paper is in the colbertv1 branch. See here for more information on other branches.

Installation

ColBERT requires Python 3.7+ and Pytorch 1.9+ and uses the Hugging Face Transformers library.

We strongly recommend creating a conda environment using the commands below. (If you don't have conda, follow the official conda installation guide.)

We have also included a new environment file specifically for CPU-only environments (conda_env_cpu.yml), but note that if you are testing CPU execution on a machine that includes GPUs you might need to specify CUDA_VISIBLE_DEVICES="" as part of your command. Note that a GPU is required for training and indexing.

conda env create -f conda_env[_cpu].yml
conda activate colbert

If you face any problems, please open a new issue and we'll help you promptly!

Overview

Using ColBERT on a dataset typically involves the following steps.

Step 0: Preprocess your collection. At its simplest, ColBERT works with tab-separated (TSV) files: a file (e.g., collection.tsv) will contain all passages and another (e.g., queries.tsv) will contain a set of queries for searching the collection.

Step 1: Download the pre-trained ColBERTv2 checkpoint. This checkpoint has been trained on the MS MARCO Passage Ranking task. You can also optionally train your own ColBERT model.

Step 2: Index your collection. Once you have a trained ColBERT model, you need to index your collection to permit fast retrieval. This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search.

Step 3: Search the collection with your queries. Given the model and index, you can issue queries over the collection to retrieve the top-k passages for each query.

Below, we illustrate these steps via an example run on the MS MARCO Passage Ranking task.

API Usage Notebook

This Jupyter notebook docs/intro.ipynb notebook illustrates using the key features of ColBERT with the new Python API.

It includes how to download the ColBERTv2 model checkpoint trained on MS MARCO Passage Ranking and how to download our new LoTTE benchmark.

Data

This repository works directly with a simple tab-separated file format to store queries, passages, and top-k ranked lists.

• Queries: each line is qid \t query text.
• Collection: each line is pid \t passage text.
• Top-k Ranking: each line is qid \t pid \t rank.

This works directly with the data format of the MS MARCO Passage Ranking dataset. You will need the training triples (triples.train.small.tar.gz), the official top-1000 ranked lists for the dev set queries (top1000.dev), and the dev set relevant passages (qrels.dev.small.tsv). For indexing the full collection, you will also need the list of passages (collection.tar.gz).

Indexing

For fast retrieval, indexing precomputes the ColBERT representations of passages.

Example usage:

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

if __name__=='__main__':
    with Run().context(RunConfig(nranks=1, experiment="msmarco")):

        config = ColBERTConfig(
            nbits=2,
            root="/path/to/experiments",
        )
        indexer = Indexer(checkpoint="/path/to/checkpoint", config=config)
        indexer.index(name="msmarco.nbits=2", collection="/path/to/MSMARCO/collection.tsv")

Retrieval

We typically recommend that you use ColBERT for end-to-end retrieval, where it directly finds its top-k passages from the full collection:

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

if __name__=='__main__':
    with Run().context(RunConfig(nranks=1, experiment="msmarco")):

        config = ColBERTConfig(
            root="/path/to/experiments",