colbertv2.0 is a high-speed, high-accuracy retrieval model that enables scalable neural search over large text corpora using BERT-based embeddings. It introduces a “late interaction” mechanism where passages and queries are encoded into matrices of token-level embeddings. These are compared efficiently at search time using MaxSim operations, preserving contextual richness without sacrificing speed. Trained on datasets like MS MARCO, it significantly outperforms single-vector retrieval approaches and supports indexing and querying millions of documents with sub-second latency. ColBERTv2 builds on previous ColBERT versions with improved training, lightweight server options, and support for integration into end-to-end LLM pipelines.

Features

  • Late interaction using token-level BERT embeddings
  • Fast and scalable search over large corpora
  • Efficient MaxSim-based similarity computation
  • Pretrained on MS MARCO Passage Ranking
  • Supports custom indexing and fine-tuning
  • API and Colab notebook available for quick use
  • Lightweight server script for live querying
  • MIT-licensed and compatible with PyTorch & ONNX

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Registered

2025-07-01