A ColBERT-style MaxSim late-interaction re-ranker, trained as the baseline for an independent reproduction of LITE (arXiv:2406.17968). MaxSim scores a query–document pair as Σ_i max_j (q_i · d_j) over token embeddings — the fixed operator that LITE replaces with a learnable scorer.

This model exists to quantify what the learnable interaction adds. The learnable counterpart is jaganadhg/literank-msmarco-distilbert.

Model

Encoder: shared distilbert-base-uncased dual-encoder → token embeddings.
Scorer (MaxSim): S = Q·Dᵀ, then sum over query tokens of the max over (valid) doc tokens. No learned parameters beyond the encoder.

Training

Identical recipe to the LITE model, for a fair comparison:

Objective: Margin-MSE distillation from cross-encoder/ms-marco-MiniLM-L-6-v2.
Data: MS MARCO v2.1 train, ~300k (query, positive, negative) triplets (subset of 500k rows).
Schedule: batch 64, AdamW lr 2.8e-5, AMP, early stopping on held-out dev MRR. This checkpoint is the best.pt; it plateaued and stopped around step 14k.
Compute: Kaggle free T4×2 (trained in parallel with the LITE model).

Results (held-out MS MARCO `dev`/validation, 2000 queries)

Model	MRR@10	nDCG@10
LITE (learnable)	0.724	0.791
MaxSim (this model)	0.664	0.745

The learnable LITE scorer beats this MaxSim baseline by +0.06 MRR@10 (+9%). (A smaller-data run showed a wider +15% gap; with ~5x more data this baseline improved more, narrowing the margin — a reminder that a reported advantage depends on how well-trained the baseline is.)

How to use

bash

git clone https://huggingface.co/jaganadhg/maxsim-msmarco-distilbert
cd maxsim-msmarco-distilbert && pip install torch transformers
python load_example.py

python

import torch
from literank.config import ModelConfig
from literank.model import Ranker
from literank.checkpoint import load_checkpoint

ckpt = torch.load("model.pt", map_location="cpu", weights_only=False)
ranker = Ranker(ModelConfig(**ckpt["config"]))   # config has scorer="maxsim"
load_checkpoint("model.pt", ranker)
ranker.eval()
scores = ranker.score(["query"] * 2, ["a relevant passage", "an irrelevant one"])
print(scores)

Limitations & honest caveats

Absolute scores are NOT comparable to the paper's 0.393 — eval reranks each query's ~10 own passages, not BM25 top-1000. Only the relative LITE-vs-MaxSim gap is claimed.
Subset / fixed-budget training, not paper scale.
This is a baseline, intentionally less expressive than LITE (no learnable output scale), which is why its distillation loss floors higher (~3.6 vs LITE's ~0.74).

Citation

bibtex

@article{ji2024lite,
  title  = {Efficient Document Ranking with Learnable Late Interactions},
  author = {Ji, Ziwei and others},
  journal= {arXiv preprint arXiv:2406.17968},
  year   = {2024}
}

Source code

Full implementation, tests, Kaggle training notebook, and detailed results: https://github.com/jaganadhg/searchandrank — branch feat/paper-faithful.

This HF repo already bundles the literank/ package, so python load_example.py works straight after cloning. To train/evaluate from scratch, use the GitHub branch above.

Independent reproduction; credit for the LITE method and the MaxSim/ColBERT formulation belongs to their original authors.

jaganadhg/maxsim-msmarco-distilbert

MaxSim Re-ranker (MS MARCO, DistilBERT) — baseline for LITE reproduction