{
 "body": "\nPart of the [MTEB](mteb.md) family.\n\n## What it measures\n\nmteb_classification embeds a labelled train split, fits a logistic regression classifier\n(scikit-learn, 100 maximum iterations) on those embeddings, and scores accuracy on the\nembedded test split. It covers 12 datasets in the original MTEB task set, spanning sentiment,\nintent and topic labelling, in English and several other languages depending on the dataset.\nThe embedding model is never fine-tuned; only the small linear probe on top of it is trained.\n\n## Reading the numbers\n\nA strong score says a model's embedding space linearly separates that dataset's label\ncategories well; it is a proxy for how useful the embeddings would be for your own downstream\nclassifier, not a guarantee, since a simple 100-iteration linear probe can flatter or penalise\nembeddings differently than a production classifier would. Because the probe is retrained per\ndataset, this task type is somewhat less sensitive to pretraining exposure than retrieval or\nSTS, but the label sets themselves (Banking77's intents, for example) are public and could\nstill be memorised.\n",
 "build": {
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  "eligibility_as_of": "2026-09-09"
 },
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  "status": "unassessed",
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 ],
 "page": {
  "aliases": [],
  "category": "embedding",
  "contamination": {
   "note": "Labels and text for datasets such as Banking77 are public, so they can appear in pretraining data; the linear-probe protocol is somewhat less exposed than retrieval or STS since the probe itself is retrained per dataset.",
   "risk": "medium"
  },
  "dataset": {
   "languages": [],
   "license": "Varies by dataset.",
   "modalities": [
    "text"
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   "public_test_set": true,
   "size": 12,
   "size_note": "12 classification datasets in the original MTEB task set, covering sentiment, intent and topic labelling; language coverage varies by dataset.",
   "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": ""
  },
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   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "Run the Classification task group in the mteb Python package (pip install mteb; mteb run -t <classification-task-name>)."
  },
  "id": "mteb_classification",
  "last_updated": "",
  "leaderboard_url": "https://huggingface.co/spaces/mteb/leaderboard",
  "lineage": {
   "family": "mteb",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "A labelled train split is embedded, a logistic regression classifier is fit on those embeddings, and accuracy is measured on the embedded test split. The embedding model itself is never fine-tuned; only the small linear probe on top of it is trained.\n",
  "metric": {
   "baseline_note": "Random baseline depends on the number of classes in each dataset and is not a single number.",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "accuracy",
   "random_baseline": null,
   "unit": "%"
  },
  "name": "MTEB Classification",
  "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": "classification",
  "summary": "Trains a logistic regression probe on a model's embeddings and scores accuracy on 12 classification datasets in English and other languages.",
  "tags": [
   "embedding",
   "classification"
  ],
  "task_format": "Embed train and test splits; fit a scikit-learn logistic regression classifier (max_iter=100) on train embeddings; score accuracy on test embeddings."
 }
}