{
 "body": "\nPart of the [MTEB](mteb.md) family.\n\n## What it measures\n\nmteb_retrieval scores an embedding model on 15 mostly-English retrieval datasets from the\noriginal MTEB task set, drawn largely from the BEIR collection (sources such as MS MARCO,\nNatural Questions, HotpotQA, FiQA and SciFact). For each query, the model embeds the query and\nevery candidate passage in a corpus, and passages are ranked by similarity with no\ntask-specific fine-tuning. This is the MTEB task type closest to production semantic search\nand retrieval-augmented generation, where an embedding model's job is exactly this.\n\n## Reading the numbers\n\nnDCG@10 on retrieval is the number to check before choosing an embedding model for search or\nRAG; a several-point gap here is more meaningful than the same gap on mteb_overall, because it\nisolates the one skill your system needs. A high score does not guarantee good results on your\nown corpus, since MTEB's retrieval sets skew toward web, news and QA-style English text \u2014\nspot-check with your own documents and queries. The beta RTEB track keeps some retrieval test\ndata private precisely because the public sets are old enough to have leaked into pretraining.\n",
 "build": {
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  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
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  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
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 ],
 "page": {
  "aliases": [],
  "category": "embedding",
  "contamination": {
   "note": "Most retrieval datasets are public with public relevance judgements and predate 2023, so their text can plausibly enter later models' pretraining data.",
   "risk": "medium"
  },
  "dataset": {
   "languages": [],
   "license": "Varies by dataset.",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 15,
   "size_note": "15 retrieval datasets in the original MTEB task set, mostly drawn from the BEIR collection (e.g. MS MARCO, Natural Questions, HotpotQA, FiQA, SciFact, TREC-COVID); 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 Retrieval task group in the mteb Python package (pip install mteb; mteb run -t <retrieval-task-name>)."
  },
  "id": "mteb_retrieval",
  "last_updated": "",
  "leaderboard_url": "https://huggingface.co/spaces/mteb/leaderboard",
  "lineage": {
   "family": "mteb",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "Given a short query, the model embeds the query and every candidate passage in a fixed corpus, and passages are ranked by similarity with no task-specific fine-tuning. It is the MTEB task type closest to production semantic search and retrieval-augmented generation.\n",
  "metric": {
   "baseline_note": "",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "nDCG@10",
   "random_baseline": null,
   "unit": "%"
  },
  "name": "MTEB Retrieval",
  "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": "MTEB's beta RTEB track keeps some retrieval test data private specifically because the public retrieval sets are old and public enough to leak into pretraining.",
   "status": "watch",
   "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": "retrieval",
  "summary": "Ranks passages in a corpus by relevance to a query using embedding similarity, scored by nDCG@10 across 15 mostly-English MTEB datasets.",
  "tags": [
   "embedding",
   "retrieval",
   "semantic-search"
  ],
  "task_format": "Query-to-corpus ranking: embed a query and a candidate pool, rank the pool by cosine or dot-product similarity, score against relevance judgements."
 }
}