{
 "body": "\nPart of the [MTEB](mteb.md) family.\n\n## What it measures\n\nmteb_clustering embeds a set of texts and fits a mini-batch k-means model (batch size 32, k\nset to the number of ground-truth categories) directly on those embeddings, with no\nsupervision. The resulting cluster assignments are then compared to the true category labels.\nIt spans 11 datasets in the original MTEB task set, mostly English, covering things such as\nclustering research paper titles or news articles by topic.\n\n## Reading the numbers\n\nV-measure rewards embeddings whose geometry naturally separates the underlying categories\nwithout ever seeing the labels, which makes it a reasonable proxy for topic modelling and\ndeduplication-by-category use cases. It says less about performance on categories the dataset\ndoes not cover, and k-means' sensitivity to the chosen k and to embedding dimensionality means\ntwo models can score differently for reasons that have little to do with embedding quality\nfor your own clustering task.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "mteb_clustering",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
 "models_covered": [
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "NV Embed v2",
   "model_id": "nvidia/nv-embed-v2",
   "provider": "nvidia",
   "provider_display": "NVIDIA",
   "score": 53.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge m3",
   "model_id": "baai/bge-m3",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 52.3,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 Embedding 8B",
   "model_id": "qwen/qwen3-embedding-8b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 52.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 VL Embedding 8B",
   "model_id": "qwen/qwen3-vl-embedding-8b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 52.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v4",
   "model_id": "jina/jina-embeddings-v4",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 51.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v4 vllm retrieval",
   "model_id": "jina/jina-embeddings-v4-vllm-retrieval",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 51.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage 3",
   "model_id": "voyage/voyage-3",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 51.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 mistral 7B instruct",
   "model_id": "intfloat/e5-mistral-7b-instruct",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 50.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge multilingual gemma2",
   "model_id": "baai/bge-multilingual-gemma2",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 50.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemini Embedding 001",
   "model_id": "google/gemini-embedding-001",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 50.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v3",
   "model_id": "jina/jina-embeddings-v3",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 50.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed l v2.0",
   "model_id": "snowflake/snowflake-arctic-embed-l-v2-0",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 49.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v2 moe",
   "model_id": "nomic/nomic-embed-text-v2-moe",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 49.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v2 moe GGUF",
   "model_id": "nomic/nomic-embed-text-v2-moe-gguf",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 49.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 Embedding 4B",
   "model_id": "qwen/qwen3-embedding-4b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 49.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "text-embedding-3-large",
   "model_id": "openai/text-embedding-3-large",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 49.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "SFR Embedding 2 R",
   "model_id": "salesforce/sfr-embedding-2-r",
   "provider": "salesforce",
   "provider_display": "Salesforce",
   "score": 49.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large zh v1.5",
   "model_id": "baai/bge-large-zh-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 48.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multilingual e5 large instruct",
   "model_id": "intfloat/multilingual-e5-large-instruct",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 48.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large en v1.5",
   "model_id": "baai/bge-large-en-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 48.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large zh",
   "model_id": "baai/bge-large-zh",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 47.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding english r2",
   "model_id": "ibm/granite-embedding-english-r2",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 47.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding small english r2",
   "model_id": "ibm/granite-embedding-small-english-r2",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 47.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed l",
   "model_id": "snowflake/snowflake-arctic-embed-l",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 47.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large en",
   "model_id": "baai/bge-large-en",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 47.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Code 3",
   "model_id": "voyage/voyage-code-3",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 47.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v1.5",
   "model_id": "nomic/nomic-embed-text-v1-5",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 46.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v1.5 GGUF",
   "model_id": "nomic/nomic-embed-text-v1-5-gguf",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 46.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Finance 2",
   "model_id": "voyage/voyage-finance-2",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 46.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 large v2",
   "model_id": "intfloat/e5-large-v2",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 46.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m v2.0",
   "model_id": "snowflake/snowflake-arctic-embed-m-v2-0",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 46.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Multilingual 2",
   "model_id": "voyage/voyage-multilingual-2",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 46.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage 3 Lite",
   "model_id": "voyage/voyage-3-lite",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 46.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Law 2",
   "model_id": "voyage/voyage-law-2",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 46.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base en v1.5",
   "model_id": "baai/bge-base-en-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 45.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base zh v1.5",
   "model_id": "baai/bge-base-zh-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 45.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding 278M multilingual",
   "model_id": "ibm/granite-embedding-278m-multilingual",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 45.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v1",
   "model_id": "nomic/nomic-embed-text-v1",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 45.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multilingual e5 large",
   "model_id": "intfloat/multilingual-e5-large",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 45.2,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m v1.5",
   "model_id": "snowflake/snowflake-arctic-embed-m-v1-5",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 45.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 large",
   "model_id": "intfloat/e5-large",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 45.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base zh",
   "model_id": "baai/bge-base-zh",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 44.8,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v2 base en",
   "model_id": "jina/jina-embeddings-v2-base-en",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 44.8,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m long",
   "model_id": "snowflake/snowflake-arctic-embed-m-long",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 44.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base en",
   "model_id": "baai/bge-base-en",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 44.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mistral Embed",
   "model_id": "mistral/mistral-embed",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 44.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 large unsupervised",
   "model_id": "intfloat/e5-large-unsupervised",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 44.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v2 base de",
   "model_id": "jina/jina-embeddings-v2-base-de",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 44.2,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m",
   "model_id": "snowflake/snowflake-arctic-embed-m",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 44.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed code",
   "model_id": "nomic/nomic-embed-code",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 44.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 Embedding 0.6B",
   "model_id": "qwen/qwen3-embedding-0-6b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 44.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 VL Embedding 2B",
   "model_id": "qwen/qwen3-vl-embedding-2b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 44.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 base v2",
   "model_id": "intfloat/e5-base-v2",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 43.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge code v1",
   "model_id": "baai/bge-code-v1",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 43.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v2 base code",
   "model_id": "jina/jina-embeddings-v2-base-code",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 43.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "SFR Embedding Code 400M R",
   "model_id": "salesforce/sfr-embedding-code-400m-r",
   "provider": "salesforce",
   "provider_display": "Salesforce",
   "score": 43.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multilingual e5 base",
   "model_id": "intfloat/multilingual-e5-base",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 43.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 base",
   "model_id": "intfloat/e5-base",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 42.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "text-embedding-3-small",
   "model_id": "openai/text-embedding-3-small",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 42.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v2 small en",
   "model_id": "jina/jina-embeddings-v2-small-en",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 42.2,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge small en v1.5",
   "model_id": "baai/bge-small-en-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 42.1,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge small zh v1.5",
   "model_id": "baai/bge-small-zh-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 42,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding 107M multilingual",
   "model_id": "ibm/granite-embedding-107m-multilingual",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 42.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding 125M english",
   "model_id": "ibm/granite-embedding-125m-english",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 42.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed s",
   "model_id": "snowflake/snowflake-arctic-embed-s",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 41.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "all mpnet base v2",
   "model_id": "sentence-transformers/all-mpnet-base-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 41.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 small v2",
   "model_id": "intfloat/e5-small-v2",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 41.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge small zh",
   "model_id": "baai/bge-small-zh",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 41.2,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multilingual e5 small",
   "model_id": "intfloat/multilingual-e5-small",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 41.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "all MiniLM L12 v2",
   "model_id": "sentence-transformers/all-minilm-l12-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 40.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge small en",
   "model_id": "baai/bge-small-en",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 40.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "all MiniLM L6 v2",
   "model_id": "sentence-transformers/all-minilm-l6-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 40.1,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 small",
   "model_id": "intfloat/e5-small",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 40.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multi qa mpnet base dot v1",
   "model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 39.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "text-embedding-ada-002",
   "model_id": "openai/text-embedding-ada-002",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 39.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "all roberta large v1",
   "model_id": "sentence-transformers/all-roberta-large-v1",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 39.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multi qa mpnet base cos v1",
   "model_id": "sentence-transformers/multi-qa-mpnet-base-cos-v1",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 39.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed xs",
   "model_id": "snowflake/snowflake-arctic-embed-xs",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 39.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "paraphrase mpnet base v2",
   "model_id": "sentence-transformers/paraphrase-mpnet-base-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 39.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "all distilroberta v1",
   "model_id": "sentence-transformers/all-distilroberta-v1",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 39.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "paraphrase multilingual mpnet base v2",
   "model_id": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 38.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multi qa MiniLM L6 cos v1",
   "model_id": "sentence-transformers/multi-qa-minilm-l6-cos-v1",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 38.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding 30M english",
   "model_id": "ibm/granite-embedding-30m-english",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 37.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding 30M sparse",
   "model_id": "ibm/granite-embedding-30m-sparse",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 37.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "paraphrase multilingual MiniLM L12 v2",
   "model_id": "sentence-transformers/paraphrase-multilingual-minilm-l12-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 37.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "paraphrase MiniLM L12 v2",
   "model_id": "sentence-transformers/paraphrase-minilm-l12-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 37.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "paraphrase MiniLM L6 v2",
   "model_id": "sentence-transformers/paraphrase-minilm-l6-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 36.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "msmarco bert base dot v5",
   "model_id": "sentence-transformers/msmarco-bert-base-dot-v5",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 36.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "LaBSE",
   "model_id": "sentence-transformers/labse",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 36.2,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "msmarco MiniLM L12 cos v5",
   "model_id": "sentence-transformers/msmarco-minilm-l12-cos-v5",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 36.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "msmarco MiniLM L6 v3",
   "model_id": "sentence-transformers/msmarco-minilm-l6-v3",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 35.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "paraphrase MiniLM L3 v2",
   "model_id": "sentence-transformers/paraphrase-minilm-l3-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 34.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "distiluse base multilingual cased v2",
   "model_id": "sentence-transformers/distiluse-base-multilingual-cased-v2",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 34.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "distiluse base multilingual cased v1",
   "model_id": "sentence-transformers/distiluse-base-multilingual-cased-v1",
   "provider": "sentence-transformers",
   "provider_display": "Sentence Transformers",
   "score": 32.5,
   "source": "mteb-leaderboard"
  }
 ],
 "page": {
  "aliases": [],
  "category": "embedding",
  "contamination": {
   "note": "Source texts and category labels are public, so they can appear in pretraining data; k-means is unsupervised at scoring time, which does not remove this exposure.",
   "risk": "medium"
  },
  "dataset": {
   "languages": [],
   "license": "Varies by dataset.",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 11,
   "size_note": "11 clustering datasets in the original MTEB task set, mostly English, e.g. clustering research paper titles or news articles by topic.",
   "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 Clustering task group in the mteb Python package (pip install mteb; mteb run -t <clustering-task-name>)."
  },
  "id": "mteb_clustering",
  "last_updated": "",
  "leaderboard_url": "https://huggingface.co/spaces/mteb/leaderboard",
  "lineage": {
   "family": "mteb",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "A set of texts is embedded and a mini-batch k-means model is fit directly on those embeddings, with k set to the number of ground-truth categories. No labels are used during fitting; cluster assignments are then compared to the true category labels.\n",
  "metric": {
   "baseline_note": "",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "V-measure",
   "random_baseline": null,
   "unit": "%"
  },
  "name": "MTEB Clustering",
  "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": "clustering",
  "summary": "Runs mini-batch k-means over a model's embeddings and scores the clusters against ground-truth labels with V-measure, across 11 mostly-English datasets.",
  "tags": [
   "embedding",
   "clustering"
  ],
  "task_format": "Embed a text collection; fit mini-batch k-means (batch size 32, k = number of ground-truth labels); score cluster assignments against labels with V-measure."
 }
}