ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.
Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage.
As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings (shown above in blue). Then at search time, it embeds every query into another matrix (shown in green) and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators.
These rich interactions allow ColBERT to surpass the quality of single-vector representation models, while scaling efficiently to large corpora. You can read more in our papers:
(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.
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.
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 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
NEW: We have an experimental notebook on Google Colab that you can use with free GPUs. Indexing 10,000 on the free Colab T4 GPU takes six minutes.
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.
You can optionally specify the ncells, centroid_score_threshold, and ndocs search hyperparameters to trade off between speed and result quality. Defaults for different values of k are listed in colbert/searcher.py.
We can evaluate the MSMARCO rankings using the following command:
We provide a pre-trained model checkpoint, but we also detail how to train from scratch here.
Note that this example demonstrates the ColBERTv1 style of training, but the provided checkpoint was trained with ColBERTv2.
Training requires a JSONL triples file with a [qid, pid+, pid-] list per line. The query IDs and passage IDs correspond to the specified queries.tsv and collection.tsv files respectively.
We provide a script to run a lightweight server which serves k (upto 100) results in ranked order for a given search query, in JSON format. This script can be used to power DSP programs.
To run the server, update the environment variables INDEX_ROOT and INDEX_NAME in the .env file to point to the appropriate ColBERT index. The run the following command:
python server.py
A sample query:
http://localhost:8893/api/search?query=Who won the 2022 FIFA world cup&k=25
binarization: ColBERT with a baseline binarization-based compression strategy (as opposed to ColBERTv2's residual compression, which we found to be more robust).
