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"""MTEB Results""" |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """@article{muennighoff2022mteb, |
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doi = {10.48550/ARXIV.2210.07316}, |
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url = {https://arxiv.org/abs/2210.07316}, |
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author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, |
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title = {MTEB: Massive Text Embedding Benchmark}, |
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publisher = {arXiv}, |
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journal={arXiv preprint arXiv:2210.07316}, |
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year = {2022} |
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} |
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""" |
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_DESCRIPTION = """Results on MTEB""" |
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URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json" |
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VERSION = datasets.Version("1.0.1") |
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EVAL_LANGS = ['af', 'afr-eng', 'am', "amh", 'amh-eng', 'ang-eng', 'ar', 'ar-ar', 'ara-eng', 'arq-eng', 'arz-eng', 'ast-eng', 'awa-eng', 'az', 'aze-eng', 'bel-eng', 'ben-eng', 'ber-eng', 'bn', 'bos-eng', 'bre-eng', 'bul-eng', 'cat-eng', 'cbk-eng', 'ceb-eng', 'ces-eng', 'cha-eng', 'cmn-eng', 'cor-eng', 'csb-eng', 'cy', 'cym-eng', 'da', 'dan-eng', 'de', 'de-fr', 'de-pl', 'deu-eng', 'dsb-eng', 'dtp-eng', 'el', 'ell-eng', 'en', 'en-ar', 'en-de', 'en-en', 'en-tr', 'eng', 'epo-eng', 'es', 'es-en', 'es-es', 'es-it', 'est-eng', 'eus-eng', 'fa', 'fao-eng', 'fi', 'fin-eng', 'fr', 'fr-en', 'fr-pl', 'fra', 'fra-eng', 'fry-eng', 'gla-eng', 'gle-eng', 'glg-eng', 'gsw-eng', 'hau', 'he', 'heb-eng', 'hi', 'hin-eng', 'hrv-eng', 'hsb-eng', 'hu', 'hun-eng', 'hy', 'hye-eng', 'ibo', 'id', 'ido-eng', 'ile-eng', 'ina-eng', 'ind-eng', 'is', 'isl-eng', 'it', 'it-en', 'ita-eng', 'ja', 'jav-eng', 'jpn-eng', 'jv', 'ka', 'kab-eng', 'kat-eng', 'kaz-eng', 'khm-eng', 'km', 'kn', 'ko', 'ko-ko', 'kor-eng', 'kur-eng', 'kzj-eng', 'lat-eng', 'lfn-eng', 'lit-eng', 'lin', 'lug', 'lv', 'lvs-eng', 'mal-eng', 'mar-eng', 'max-eng', 'mhr-eng', 'mkd-eng', 'ml', 'mn', 'mon-eng', 'ms', 'my', 'nb', 'nds-eng', 'nl', 'nl-ende-en', 'nld-eng', 'nno-eng', 'nob-eng', 'nov-eng', 'oci-eng', 'orm', 'orv-eng', 'pam-eng', 'pcm', 'pes-eng', 'pl', 'pl-en', 'pms-eng', 'pol-eng', 'por-eng', 'pt', 'ro', 'ron-eng', 'ru', 'run', 'rus-eng', 'sl', 'slk-eng', 'slv-eng', 'spa-eng', 'sna', 'som', 'sq', 'sqi-eng', 'srp-eng', 'sv', 'sw', 'swa', 'swe-eng', 'swg-eng', 'swh-eng', 'ta', 'tam-eng', 'tat-eng', 'te', 'tel-eng', 'tgl-eng', 'th', 'tha-eng', 'tir', 'tl', 'tr', 'tuk-eng', 'tur-eng', 'tzl-eng', 'uig-eng', 'ukr-eng', 'ur', 'urd-eng', 'uzb-eng', 'vi', 'vie-eng', 'war-eng', 'wuu-eng', 'xho', 'xho-eng', 'yid-eng', 'yor', 'yue-eng', 'zh', 'zh-CN', 'zh-TW', 'zh-en', 'zsm-eng'] |
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SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures", "scores_per_experiment"] |
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TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"] |
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VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "LEMBSummScreenFDRetrieval", "MSMARCO", "MSMARCO-PL", "MultilingualSentiment", "Ocnli", "TNews"] |
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DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval", "TERRa",] |
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TESTFULL_SPLIT = ["OpusparcusPC"] |
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STANDARD_SPLIT = ["BrightRetrieval"] |
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DEVTEST_SPLIT = ["FloresBitextMining"] |
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TEST_AVG_SPLIT = { |
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"LEMBNeedleRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"], |
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"LEMBPasskeyRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"], |
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} |
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MODELS = [ |
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"Baichuan-text-embedding", |
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"Cohere-embed-english-v3.0", |
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"Cohere-embed-english-v3.0-instruct", |
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"Cohere-embed-multilingual-light-v3.0", |
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"Cohere-embed-multilingual-v3.0", |
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"DanskBERT", |
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"FollowIR-7B", |
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"GritLM-7B", |
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"GritLM-7B-noinstruct", |
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"LASER2", |
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"LLM2Vec-Llama-2-supervised", |
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"LLM2Vec-Llama-2-unsupervised", |
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"LLM2Vec-Meta-Llama-3-supervised", |
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"LLM2Vec-Meta-Llama-3-unsupervised", |
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"LLM2Vec-Mistral-supervised", |
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"LLM2Vec-Mistral-unsupervised", |
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"LLM2Vec-Sheared-Llama-supervised", |
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"LLM2Vec-Sheared-Llama-unsupervised", |
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"LaBSE", |
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"OpenSearch-text-hybrid", |
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"SFR-Embedding-Mistral", |
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"all-MiniLM-L12-v2", |
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"all-MiniLM-L6-v2", |
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"all-MiniLM-L6-v2-instruct", |
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"all-mpnet-base-v2", |
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"all-mpnet-base-v2-instruct", |
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"allenai-specter", |
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"bert-base-10lang-cased", |
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"bert-base-15lang-cased", |
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"bert-base-25lang-cased", |
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"bert-base-multilingual-cased", |
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"bert-base-multilingual-uncased", |
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"bert-base-swedish-cased", |
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"bert-base-uncased", |
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"bge-base-en-v1.5", |
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"bge-base-en-v1.5-instruct", |
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"bge-base-en", |
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"bge-base-zh", |
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"bge-base-zh-v1.5", |
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"bge-large-en", |
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"bge-large-en-v1.5", |
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"bge-large-en-v1.5-instruct", |
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"bge-large-zh", |
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"bge-large-zh-noinstruct", |
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"bge-large-zh-v1.5", |
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"bge-m3", |
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"bge-m3-instruct", |
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"bge-small-en-v1.5", |
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"bge-small-en-v1.5-instruct", |
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"bge-small-zh", |
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"bge-small-zh-v1.5", |
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"bm25", |
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"bm25s", |
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"camembert-base", |
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"camembert-large", |
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"contriever", |
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"contriever-instruct", |
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"contriever-base-msmarco", |
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"cross-en-de-roberta-sentence-transformer", |
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"dfm-encoder-large-v1", |
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"dfm-sentence-encoder-large-1", |
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"distilbert-base-25lang-cased", |
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"distilbert-base-en-fr-cased", |
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"distilbert-base-en-fr-es-pt-it-cased", |
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"distilbert-base-fr-cased", |
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"distilbert-base-uncased", |
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"distiluse-base-multilingual-cased-v2", |
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"dragon-plus", |
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"dragon-plus-instruct", |
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"e5-base", |
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"e5-base-4k", |
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"e5-base-v2", |
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"e5-large", |
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"e5-large-v2", |
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"e5-mistral-7b-instruct", |
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"e5-mistral-7b-instruct-noinstruct", |
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"e5-small", |
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"e5-small-v2", |
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"electra-small-nordic", |
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"electra-small-swedish-cased-discriminator", |
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"elser-v2", |
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"embedder-100p", |
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"facebook-dpr-ctx_encoder-multiset-base", |
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"flan-t5-base", |
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"flan-t5-large", |
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"flaubert_base_cased", |
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"flaubert_base_uncased", |
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"flaubert_large_cased", |
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"gbert-base", |
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"gbert-large", |
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"gelectra-base", |
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"gelectra-large", |
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"glove.6B.300d", |
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"google-gecko-256.text-embedding-preview-0409", |
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"google-gecko.text-embedding-preview-0409", |
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"gottbert-base", |
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"gte-Qwen1.5-7B-instruct", |
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"gte-Qwen2-7B-instruct", |
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"gtr-t5-base", |
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"gtr-t5-large", |
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"gtr-t5-xl", |
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"gtr-t5-xxl", |
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"herbert-base-retrieval-v2", |
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"instructor-base", |
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"instructor-large", |
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"instructor-xl", |
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"jina-embeddings-v2-base-en", |
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"komninos", |
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"llama-2-7b-chat", |
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"luotuo-bert-medium", |
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"m3e-base", |
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"m3e-large", |
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"mistral-7b-instruct-v0.2", |
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"mistral-embed", |
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"monobert-large-msmarco", |
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"monot5-3b-msmarco-10k", |
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"monot5-base-msmarco-10k", |
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"msmarco-bert-co-condensor", |
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"multi-qa-MiniLM-L6-cos-v1", |
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"multilingual-e5-base", |
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"multilingual-e5-large", |
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"multilingual-e5-large-instruct", |
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"multilingual-e5-small", |
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"mxbai-embed-large-v1", |
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"nb-bert-base", |
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"nb-bert-large", |
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"nomic-embed-text-v1", |
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"nomic-embed-text-v1.5-128", |
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"nomic-embed-text-v1.5-256", |
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"nomic-embed-text-v1.5-512", |
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"nomic-embed-text-v1.5-64", |
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"norbert3-base", |
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"norbert3-large", |
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"paraphrase-multilingual-MiniLM-L12-v2", |
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"paraphrase-multilingual-mpnet-base-v2", |
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"rubert-tiny", |
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"rubert-tiny2", |
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"sbert_large_mt_nlu_ru", |
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"sbert_large_nlu_ru", |
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"sentence-bert-swedish-cased", |
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"sentence-camembert-base", |
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"sentence-camembert-large", |
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"sentence-croissant-llm-base", |
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"sentence-t5-base", |
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"sentence-t5-large", |
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"sentence-t5-xl", |
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"sentence-t5-xxl", |
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"sgpt-bloom-1b7-nli", |
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"sgpt-bloom-7b1-msmarco", |
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"silver-retriever-base-v1", |
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"st-polish-paraphrase-from-distilroberta", |
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"st-polish-paraphrase-from-mpnet", |
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"sup-simcse-bert-base-uncased", |
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"tart-dual-contriever-msmarco", |
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"tart-full-flan-t5-xl", |
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"text-embedding-3-large", |
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"text-embedding-3-large-instruct", |
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"text-embedding-3-large-256", |
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"text-embedding-3-small", |
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"text-embedding-3-small-instruct", |
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"text-embedding-ada-002", |
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"text-embedding-ada-002-instruct", |
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"text-search-ada-001", |
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"text-search-ada-doc-001", |
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"text-search-babbage-001", |
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"text-search-curie-001", |
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"text-search-davinci-001", |
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"text-similarity-ada-001", |
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"text-similarity-babbage-001", |
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"text-similarity-curie-001", |
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"text-similarity-davinci-001", |
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"text2vec-base-chinese", |
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"text2vec-base-multilingual", |
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"text2vec-large-chinese", |
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"titan-embed-text-v1", |
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"udever-bloom-1b1", |
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"udever-bloom-560m", |
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"universal-sentence-encoder-multilingual-3", |
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"universal-sentence-encoder-multilingual-large-3", |
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"unsup-simcse-bert-base-uncased", |
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"use-cmlm-multilingual", |
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"voyage-2", |
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"voyage-code-2", |
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"voyage-large-2-instruct", |
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"voyage-law-2", |
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"voyage-lite-01-instruct", |
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"voyage-lite-02-instruct", |
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"voyage-multilingual-2", |
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"xlm-roberta-base", |
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"xlm-roberta-large", |
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"deberta-v1-base", |
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"USER-bge-m3", |
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"USER-base", |
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"rubert-tiny-turbo", |
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"LaBSE-ru-turbo", |
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"distilrubert-small-cased-conversational", |
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"rubert-base-cased", |
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"rubert-base-cased-sentence", |
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"LaBSE-en-ru", |
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] |
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def get_paths(): |
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import collections, json, os |
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files = collections.defaultdict(list) |
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for model_dir in os.listdir("results"): |
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results_model_dir = os.path.join("results", model_dir) |
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if not os.path.isdir(results_model_dir): |
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print(f"Skipping {results_model_dir}") |
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continue |
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for revision_folder in os.listdir(results_model_dir): |
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if not os.path.isdir(os.path.join(results_model_dir, revision_folder)): |
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continue |
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for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)): |
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if (res_file.endswith(".json")) and not(res_file.endswith(("overall_results.json", "model_meta.json"))): |
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results_model_file = os.path.join(results_model_dir, revision_folder, res_file) |
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files[model_dir].append(results_model_file) |
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with open("paths.json", "w") as f: |
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json.dump(files, f, indent=2) |
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return files |
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class MTEBResults(datasets.GeneratorBasedBuilder): |
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"""MTEBResults""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=model, |
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description=f"{model} MTEB results", |
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version=VERSION, |
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) |
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for model in MODELS |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"mteb_dataset_name": datasets.Value("string"), |
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"eval_language": datasets.Value("string"), |
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"metric": datasets.Value("string"), |
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"score": datasets.Value("float"), |
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} |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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path_file = dl_manager.download_and_extract(URL) |
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with open(path_file) as f: |
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files = json.load(f) |
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downloaded_files = dl_manager.download_and_extract(files[self.config.name]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={'filepath': downloaded_files} |
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) |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info(f"Generating examples from {filepath}") |
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out = [] |
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for path in filepath: |
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with open(path, encoding="utf-8") as f: |
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res_dict = json.load(f) |
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ds_name = res_dict.get("mteb_dataset_name", res_dict.get("task_name")) |
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res_dict = res_dict.get("scores", res_dict) |
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split = "test" |
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if (ds_name in TRAIN_SPLIT) and ("train" in res_dict): |
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split = "train" |
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elif (ds_name in VALIDATION_SPLIT) and ("validation" in res_dict): |
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split = "validation" |
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elif (ds_name in DEV_SPLIT) and ("dev" in res_dict): |
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split = "dev" |
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elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict): |
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split = "test.full" |
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elif (ds_name in STANDARD_SPLIT) and ("standard" in res_dict): |
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split = "standard" |
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elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict): |
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split = "devtest" |
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elif (ds_name in TEST_AVG_SPLIT): |
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res_dict["test_avg"] = {} |
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for split in TEST_AVG_SPLIT[ds_name]: |
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if isinstance(res_dict.get(split), dict): |
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for k, v in res_dict.get(split, {}).items(): |
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v /= len(TEST_AVG_SPLIT[ds_name]) |
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if k not in res_dict["test_avg"]: |
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res_dict["test_avg"][k] = v |
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else: |
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res_dict["test_avg"][k] += v |
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elif isinstance(res_dict.get(split), list): |
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assert len(res_dict[split]) == 1, "Only single-lists supported for now" |
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for k, v in res_dict[split][0].items(): |
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if not isinstance(v, float): continue |
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v /= len(TEST_AVG_SPLIT[ds_name]) |
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if k not in res_dict["test_avg"]: |
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res_dict["test_avg"][k] = v |
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else: |
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res_dict["test_avg"][k] += v |
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split = "test_avg" |
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elif "test" not in res_dict: |
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print(f"Skipping {ds_name} as split {split} not present.") |
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continue |
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res_dict = res_dict.get(split) |
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if isinstance(res_dict, list): |
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for res in res_dict: |
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lang = res.pop("languages", [""]) |
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subset = res.pop("hf_subset", "") |
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if len(lang) == 1: |
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lang = lang[0].replace("eng-Latn", "") |
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else: |
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lang = "_".join(lang) |
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if not lang: |
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lang = subset |
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for metric, score in res.items(): |
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if metric in SKIP_KEYS: continue |
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if isinstance(score, dict): |
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for k, v in score.items(): |
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if not isinstance(v, float): |
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print(f'WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}') |
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continue |
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if metric in SKIP_KEYS: continue |
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out.append({ |
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"mteb_dataset_name": ds_name, |
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"eval_language": lang, |
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"metric": metric + "_" + k, |
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"score": v * 100, |
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}) |
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else: |
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if not isinstance(score, float): |
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print(f'WARNING: Expected float, got {score} for {ds_name} {lang} {metric}') |
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continue |
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out.append({ |
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"mteb_dataset_name": ds_name, |
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"eval_language": lang, |
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"metric": metric, |
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"score": score * 100, |
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}) |
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else: |
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is_multilingual = any(x in res_dict for x in EVAL_LANGS) |
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langs = res_dict.keys() if is_multilingual else ["en"] |
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for lang in langs: |
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if lang in SKIP_KEYS: continue |
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test_result_lang = res_dict.get(lang) if is_multilingual else res_dict |
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for metric, score in test_result_lang.items(): |
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if not isinstance(score, dict): |
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score = {metric: score} |
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for sub_metric, sub_score in score.items(): |
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if any(x in sub_metric for x in SKIP_KEYS): continue |
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if isinstance(sub_score, dict): continue |
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out.append({ |
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"mteb_dataset_name": ds_name, |
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"eval_language": lang if is_multilingual else "", |
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"metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric, |
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"score": sub_score * 100, |
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}) |
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for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])): |
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yield idx, row |
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if __name__ == "__main__": |
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get_paths() |
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