{
 "body": "\nPart of the [MTEB](mteb.md) family.\n\n## What it measures\n\nmteb_reranking starts from a query and a pre-assembled list of candidate passages that mix\nrelevant and irrelevant items. The model embeds the query and each candidate, and the\ncandidates are reordered by similarity to the query embedding. It covers 4 datasets in the\noriginal MTEB task set, primarily English, and is meant to mirror the second-stage reranking\nstep in a retrieve-then-rerank search pipeline.\n\n## Reading the numbers\n\nMAP on reranking shows how well a model reorders an already-narrowed candidate list, which is\na different skill from mteb_retrieval's job of finding those candidates in a huge corpus in\nthe first place \u2014 a model can be strong at one and weak at the other. Because the candidate\nlists are fixed and small (only 4 datasets), reranking scores are more sensitive to a handful\nof hard examples than retrieval scores are, so treat a narrow gap between two models with more\ncaution than the same gap on a larger task type.\n",
 "build": {
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  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
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  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
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 ],
 "page": {
  "aliases": [],
  "category": "embedding",
  "contamination": {
   "note": "Candidate lists and relevance labels are public; small dataset count (4) also means individual examples carry more weight in the score.",
   "risk": "medium"
  },
  "dataset": {
   "languages": [],
   "license": "Varies by dataset.",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 4,
   "size_note": "4 reranking datasets in the original MTEB task set, primarily English.",
   "splits": "",
   "url": "https://huggingface.co/spaces/mteb/leaderboard"
  },
  "freshness": {
   "researched": "2026-09-07",
   "researched_by": "sonnet-5 agent, batch 1, slice D",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "Run the Reranking task group in the mteb Python package (pip install mteb; mteb run -t <reranking-task-name>)."
  },
  "id": "mteb_reranking",
  "last_updated": "",
  "leaderboard_url": "https://huggingface.co/spaces/mteb/leaderboard",
  "lineage": {
   "family": "mteb",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "Starting from a query and a pre-assembled list of candidate passages that mix relevant and irrelevant items, the model embeds the query and each candidate and the candidates are reordered by similarity to the query embedding.\n",
  "metric": {
   "baseline_note": "",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "MAP",
   "random_baseline": null,
   "unit": "%"
  },
  "name": "MTEB Reranking",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2210.07316",
   "title": "MTEB: Massive Text Embedding Benchmark",
   "url": "https://aclanthology.org/2023.eacl-main.148/",
   "year": 2023
  },
  "publisher": {
   "authors": [
    "Niklas Muennighoff",
    "Nouamane Tazi",
    "Lo\u00efc Magne",
    "Nils Reimers"
   ],
   "org": "Hugging Face and Cohere (original authors); maintained today by the open embeddings-benchmark community",
   "url": "https://github.com/embeddings-benchmark"
  },
  "released": "2022-10",
  "repo_url": "https://github.com/embeddings-benchmark/mteb",
  "saturation": {
   "as_of": "",
   "note": "Not established from a source read for this page; see the mteb family page for the general watch on public test-set exposure.",
   "status": "unknown",
   "top_score": null
  },
  "sources": [
   {
    "accessed": "2026-09-07",
    "title": "MTEB: Massive Text Embedding Benchmark (arXiv preprint)",
    "url": "https://arxiv.org/abs/2210.07316"
   },
   {
    "accessed": "2026-09-07",
    "title": "embeddings-benchmark/mteb GitHub repository",
    "url": "https://github.com/embeddings-benchmark/mteb"
   },
   {
    "accessed": "2026-09-07",
    "title": "MTEB Leaderboard (Hugging Face Space)",
    "url": "https://huggingface.co/spaces/mteb/leaderboard"
   }
  ],
  "status": "active",
  "subcategory": "reranking",
  "summary": "Reorders a fixed candidate list of passages for a query by embedding similarity and scores the reordering by MAP, across 4 mostly-English datasets.",
  "tags": [
   "embedding",
   "reranking"
  ],
  "task_format": "Given a query and a fixed candidate list, embed query and candidates and reorder the list by cosine similarity; score with MAP."
 }
}