allegro/allRank

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1009 stars · 131 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

allRank is a PyTorch-based framework for training neural learning-to-rank (LTR) models. It provides pointwise, pairwise, and listwise loss functions, Transformer and fully-connected scoring architectures, ranking metrics like NDCG and MRR, and click models for simulated click-through experiments.

Use cases

• train a neural learning-to-rank model in pytorch
• experiment with listwise ranking losses like lambdaloss or approxndcg
• evaluate ranking models with ndcg and mrr metrics
• simulate click-through data with click models for LTR research
• train a transformer scoring function on libsvm ranking data

When to choose

• you need a ready-made, configurable framework for neural LTR research or experimentation
• you want to compare many ranking loss functions under one training pipeline
• you work in PyTorch and want NDCG/MRR evaluation built in

When to avoid

• you need a production search/ranking service rather than a training framework
• you want non-neural or gradient-boosted-tree LTR (e.g. XGBoost/LightGBM rankers)
• you need active feature development or frequent updates

Facets

framework · maturity maintenance

machine-learning deep-learning search-engine benchmarking machine-learning deep-learning python learning-to-rank pytorch ndcg ranking loss-functions transformer click-models libsvm information-retrieval search docker gpu linux

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markdown · JSON · MCP: product_card(name="allegro/allRank")