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name: reranking description: "Rerank BEIR retrieval results with cross-encoder, MonoT5, SBERT, or custom pair-scoring models and re-evaluate top-k changes." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

BEIR Reranking

Use this sub-skill when you already have BEIR-style corpus, queries, and retrieval results, and need to rescore a candidate set with a pair-scoring model.

Route Here

• Rerank top-k BM25, dense, sparse, or lexical retrieval candidates with beir.reranking.Rerank.
• Adapt a custom model that exposes predict(sentence_pairs, batch_size=...).
• Diagnose candidate truncation, missing corpus documents, score-shape mismatches, or re-evaluation after reranking.

Route Elsewhere

• Initial retrieval, BM25/Elasticsearch setup, dense retrieval, and EvaluateRetrieval.rerank routing belong to retrieval-evaluation.
• Dataset file layout, corpus/query/qrels loading, and schema repair belong to data-loading.
• Training rerankers or retrievers belongs to training.

Start Points

• Read references/api-reference.md for Rerank, model protocol, pair construction, and result shape.
• Read references/workflows.md for BM25/dense + reranker recipes and evaluation handoff.
• Read references/troubleshooting.md for common reranking failures.
• Run scripts/rerank_smoke.py for a no-download deterministic check of top-k truncation and score replacement.

Minimal Pattern

from beir.reranking import Rerank
from beir.reranking.models import CrossEncoder
from beir.retrieval.evaluation import EvaluateRetrieval

reranker = Rerank(CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2"), batch_size=128)
rerank_results = reranker.rerank(corpus, queries, results, top_k=100)
ndcg, _map, recall, precision = EvaluateRetrieval.evaluate(qrels, rerank_results, k_values)

rerank_results contains only the candidates passed into reranking, scored by the reranker; it is not merged back with non-reranked retrieval hits.