{"data":{"slug":"embeddings-benchmark-mteb","name":"mteb","tagline":"State-of-the-art evaluation of embeddings across languages and modalities","github_url":"https://github.com/embeddings-benchmark/mteb","owner":"embeddings-benchmark","repo":"mteb","owner_avatar_url":"https://avatars.githubusercontent.com/u/103029531?v=4","primary_language":"Python","stars":3400,"forks":670,"topics":["benchmark","bitext-mining","clustering","embeddings","evaluation","information-retrieval","low-resource-nlp","mteb","multilingual-nlp","multimodal","neural-search","reranking","retrieval","sbert","semantic-search","sentence-transformers","sts","text-classification","text-embedding"],"archived":false,"github_pushed_at":"2026-08-21T21:26:00+00:00","maintenance_label":"Very active","stars_delta_30d":36,"url":"https://www.graphcanon.com/tools/embeddings-benchmark-mteb","markdown_url":"https://www.graphcanon.com/tools/embeddings-benchmark-mteb.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/embeddings-benchmark-mteb","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=embeddings-benchmark-mteb","description":"MTEB: State-of-the-art evaluation of embeddings across languages and modalities","homepage_url":"https://docs.mteb.org","license":"Apache-2.0","open_issues":340,"watchers":17,"ai_summary":"MTEB is designed for evaluating the performance of various embedding models across different tasks and languages. It supports a broad spectrum of applications including semantic search, clustering, and information retrieval.","readme_excerpt":"## Installation\n\nYou can install mteb simply using pip or uv. For more on installation please see the [documentation](https://embeddings-benchmark.github.io/mteb/installation/).\n\n```bash\npip install mteb\n```\n\nFor faster installation, you can also use [uv](https://docs.astral.sh/uv/):\n```bash\nuv add mteb\n```","github_created_at":"2022-04-05T08:25:47+00:00","created_at":"2026-07-11T11:29:10.616749+00:00","updated_at":"2026-08-22T06:01:04.823181+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"benchmark","name":"benchmark"},{"slug":"bitext-mining","name":"bitext-mining"},{"slug":"clustering","name":"clustering"},{"slug":"embeddings","name":"embeddings"},{"slug":"information-retrieval","name":"information-retrieval"},{"slug":"low-resource-nlp","name":"low-resource-nlp"},{"slug":"multilingual-nlp","name":"multilingual-nlp"},{"slug":"multimodal","name":"multimodal"}],"trust":{"provenance":{"is_fork":false,"github_id":478037973,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-22T06:01:04.101Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":30,"days_since_push":0,"last_release_at":"2026-08-20T20:31:21Z","stars_delta_30d":36,"open_issues_delta_30d":31},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:29:11.838Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-22T06:01:04.520Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-22T06:01:04.520Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-22T06:01:04.520Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-22T06:01:04.520Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-22T06:01:04.520Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-22T06:01:04.520Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts.","Evaluations spanning multiple tasks like semantic search, clustering, and information retrieval are needed."],"when_not_to_use":["Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope.","You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system."],"source":"enrich:decision_facts","observed_at":"2026-07-12T12:40:34.897Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license."}]}}