archful = false
archive_name = "mteb-2.22.2.tar.gz"
automode = true
extras = [
    "api",
    "arctic-embed-m-v2",
    "ark",
    "audio",
    "blip2",
    "bm25s",
    "boto3",
    "codecarbon",
    "cohere",
    "colnanovdr",
    "colpali-engine",
    "colqwen3",
    "colvec1-1",
    "cosmos-embed1",
    "dasheng",
    "e5-v",
    "eager-embed",
    "embeddinggemma",
    "evie",
    "faiss-cpu",
    "flagembedding",
    "flash-attention",
    "fusion-embedding",
    "github",
    "gme",
    "google-genai",
    "gritlm",
    "image",
    "imagebind",
    "internvideo2",
    "jina",
    "jina-clip",
    "jina-v4",
    "languagebind",
    "leaderboard",
    "llama-embed-nemotron",
    "llama-nemotron-colembed-vl",
    "llama-nemotron-embed-vl-1b-v2",
    "llm2vec",
    "moca",
    "model2vec",
    "modernbert",
    "msclap",
    "multimodal-sbert",
    "muq",
    "nemotron-3-embed",
    "nemotron-colembed-vl-v2",
    "nemotron-rerank",
    "nomic",
    "nvembed",
    "octen",
    "og",
    "omniretriever",
    "open-clip-torch",
    "openai",
    "peft",
    "ps3",
    "pylate",
    "qwen-omni-utils",
    "qwen-vl",
    "radio",
    "sauerkrautlm-colpali",
    "siglip",
    "sparse-encoder",
    "speechbrain",
    "timm",
    "tipsv2",
    "torch-vggish-yamnet",
    "transformers-v4",
    "transformers-v5",
    "uembed",
    "vertexai",
    "video",
    "visrag-ret",
    "vllm",
    "voyage-v",
    "voyageai",
    "wav2clip",
    "wemm",
    "xet",
    "xformers",
    "youtu",
]
license = "Apache-2.0"
license_files_present = true
name = "mteb"
python_name = "python-mteb"
scripts = [
    "mteb",
]
source = "PyPI"
summary = "Massive Text Embedding Benchmark"
top_level_modules = [
    "mteb",
]
url = "https://github.com/embeddings-benchmark/mteb"
version = "2.22.2"