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| from functools import partial | |
| import json | |
| from datasets import load_dataset | |
| import gradio as gr | |
| from huggingface_hub import get_hf_file_metadata, HfApi, hf_hub_download, hf_hub_url | |
| from huggingface_hub.repocard import metadata_load | |
| import pandas as pd | |
| from tqdm.autonotebook import tqdm | |
| TASKS = [ | |
| "BitextMining", | |
| "Classification", | |
| "Clustering", | |
| "PairClassification", | |
| "Reranking", | |
| "Retrieval", | |
| "STS", | |
| "Summarization", | |
| ] | |
| TASK_LIST_BITEXT_MINING = ['BUCC (de-en)', 'BUCC (fr-en)', 'BUCC (ru-en)', 'BUCC (zh-en)', 'Tatoeba (afr-eng)', 'Tatoeba (amh-eng)', 'Tatoeba (ang-eng)', 'Tatoeba (ara-eng)', 'Tatoeba (arq-eng)', 'Tatoeba (arz-eng)', 'Tatoeba (ast-eng)', 'Tatoeba (awa-eng)', 'Tatoeba (aze-eng)', 'Tatoeba (bel-eng)', 'Tatoeba (ben-eng)', 'Tatoeba (ber-eng)', 'Tatoeba (bos-eng)', 'Tatoeba (bre-eng)', 'Tatoeba (bul-eng)', 'Tatoeba (cat-eng)', 'Tatoeba (cbk-eng)', 'Tatoeba (ceb-eng)', 'Tatoeba (ces-eng)', 'Tatoeba (cha-eng)', 'Tatoeba (cmn-eng)', 'Tatoeba (cor-eng)', 'Tatoeba (csb-eng)', 'Tatoeba (cym-eng)', 'Tatoeba (dan-eng)', 'Tatoeba (deu-eng)', 'Tatoeba (dsb-eng)', 'Tatoeba (dtp-eng)', 'Tatoeba (ell-eng)', 'Tatoeba (epo-eng)', 'Tatoeba (est-eng)', 'Tatoeba (eus-eng)', 'Tatoeba (fao-eng)', 'Tatoeba (fin-eng)', 'Tatoeba (fra-eng)', 'Tatoeba (fry-eng)', 'Tatoeba (gla-eng)', 'Tatoeba (gle-eng)', 'Tatoeba (glg-eng)', 'Tatoeba (gsw-eng)', 'Tatoeba (heb-eng)', 'Tatoeba (hin-eng)', 'Tatoeba (hrv-eng)', 'Tatoeba (hsb-eng)', 'Tatoeba (hun-eng)', 'Tatoeba (hye-eng)', 'Tatoeba (ido-eng)', 'Tatoeba (ile-eng)', 'Tatoeba (ina-eng)', 'Tatoeba (ind-eng)', 'Tatoeba (isl-eng)', 'Tatoeba (ita-eng)', 'Tatoeba (jav-eng)', 'Tatoeba (jpn-eng)', 'Tatoeba (kab-eng)', 'Tatoeba (kat-eng)', 'Tatoeba (kaz-eng)', 'Tatoeba (khm-eng)', 'Tatoeba (kor-eng)', 'Tatoeba (kur-eng)', 'Tatoeba (kzj-eng)', 'Tatoeba (lat-eng)', 'Tatoeba (lfn-eng)', 'Tatoeba (lit-eng)', 'Tatoeba (lvs-eng)', 'Tatoeba (mal-eng)', 'Tatoeba (mar-eng)', 'Tatoeba (max-eng)', 'Tatoeba (mhr-eng)', 'Tatoeba (mkd-eng)', 'Tatoeba (mon-eng)', 'Tatoeba (nds-eng)', 'Tatoeba (nld-eng)', 'Tatoeba (nno-eng)', 'Tatoeba (nob-eng)', 'Tatoeba (nov-eng)', 'Tatoeba (oci-eng)', 'Tatoeba (orv-eng)', 'Tatoeba (pam-eng)', 'Tatoeba (pes-eng)', 'Tatoeba (pms-eng)', 'Tatoeba (pol-eng)', 'Tatoeba (por-eng)', 'Tatoeba (ron-eng)', 'Tatoeba (rus-eng)', 'Tatoeba (slk-eng)', 'Tatoeba (slv-eng)', 'Tatoeba (spa-eng)', 'Tatoeba (sqi-eng)', 'Tatoeba (srp-eng)', 'Tatoeba (swe-eng)', 'Tatoeba (swg-eng)', 'Tatoeba (swh-eng)', 'Tatoeba (tam-eng)', 'Tatoeba (tat-eng)', 'Tatoeba (tel-eng)', 'Tatoeba (tgl-eng)', 'Tatoeba (tha-eng)', 'Tatoeba (tuk-eng)', 'Tatoeba (tur-eng)', 'Tatoeba (tzl-eng)', 'Tatoeba (uig-eng)', 'Tatoeba (ukr-eng)', 'Tatoeba (urd-eng)', 'Tatoeba (uzb-eng)', 'Tatoeba (vie-eng)', 'Tatoeba (war-eng)', 'Tatoeba (wuu-eng)', 'Tatoeba (xho-eng)', 'Tatoeba (yid-eng)', 'Tatoeba (yue-eng)', 'Tatoeba (zsm-eng)'] | |
| TASK_LIST_BITEXT_MINING_OTHER = ["BornholmBitextMining"] | |
| TASK_LIST_CLASSIFICATION = [ | |
| "AmazonCounterfactualClassification (en)", | |
| "AmazonPolarityClassification", | |
| "AmazonReviewsClassification (en)", | |
| "Banking77Classification", | |
| "EmotionClassification", | |
| "ImdbClassification", | |
| "MassiveIntentClassification (en)", | |
| "MassiveScenarioClassification (en)", | |
| "MTOPDomainClassification (en)", | |
| "MTOPIntentClassification (en)", | |
| "ToxicConversationsClassification", | |
| "TweetSentimentExtractionClassification", | |
| ] | |
| TASK_LIST_CLASSIFICATION_NORM = [x.replace(" (en)", "") for x in TASK_LIST_CLASSIFICATION] | |
| TASK_LIST_CLASSIFICATION_DA = [ | |
| "AngryTweetsClassification", | |
| "DanishPoliticalCommentsClassification", | |
| "DKHateClassification", | |
| "LccSentimentClassification", | |
| "MassiveIntentClassification (da)", | |
| "MassiveScenarioClassification (da)", | |
| "NordicLangClassification", | |
| "ScalaDaClassification", | |
| ] | |
| TASK_LIST_CLASSIFICATION_NB = [ | |
| "NoRecClassification", | |
| "NordicLangClassification", | |
| "NorwegianParliament", | |
| "MassiveIntentClassification (nb)", | |
| "MassiveScenarioClassification (nb)", | |
| "ScalaNbClassification", | |
| ] | |
| TASK_LIST_CLASSIFICATION_PL = [ | |
| "AllegroReviews", | |
| "CBD", | |
| "MassiveIntentClassification (pl)", | |
| "MassiveScenarioClassification (pl)", | |
| "PAC", | |
| "PolEmo2.0-IN", | |
| "PolEmo2.0-OUT", | |
| ] | |
| TASK_LIST_CLASSIFICATION_SV = [ | |
| "DalajClassification", | |
| "MassiveIntentClassification (sv)", | |
| "MassiveScenarioClassification (sv)", | |
| "NordicLangClassification", | |
| "ScalaSvClassification", | |
| "SweRecClassification", | |
| ] | |
| TASK_LIST_CLASSIFICATION_ZH = [ | |
| "AmazonReviewsClassification (zh)", | |
| "IFlyTek", | |
| "JDReview", | |
| "MassiveIntentClassification (zh-CN)", | |
| "MassiveScenarioClassification (zh-CN)", | |
| "MultilingualSentiment", | |
| "OnlineShopping", | |
| "TNews", | |
| "Waimai", | |
| ] | |
| TASK_LIST_CLASSIFICATION_OTHER = ['AmazonCounterfactualClassification (de)', 'AmazonCounterfactualClassification (ja)', 'AmazonReviewsClassification (de)', 'AmazonReviewsClassification (es)', 'AmazonReviewsClassification (fr)', 'AmazonReviewsClassification (ja)', 'AmazonReviewsClassification (zh)', 'MTOPDomainClassification (de)', 'MTOPDomainClassification (es)', 'MTOPDomainClassification (fr)', 'MTOPDomainClassification (hi)', 'MTOPDomainClassification (th)', 'MTOPIntentClassification (de)', 'MTOPIntentClassification (es)', 'MTOPIntentClassification (fr)', 'MTOPIntentClassification (hi)', 'MTOPIntentClassification (th)', 'MassiveIntentClassification (af)', 'MassiveIntentClassification (am)', 'MassiveIntentClassification (ar)', 'MassiveIntentClassification (az)', 'MassiveIntentClassification (bn)', 'MassiveIntentClassification (cy)', 'MassiveIntentClassification (de)', 'MassiveIntentClassification (el)', 'MassiveIntentClassification (es)', 'MassiveIntentClassification (fa)', 'MassiveIntentClassification (fi)', 'MassiveIntentClassification (fr)', 'MassiveIntentClassification (he)', 'MassiveIntentClassification (hi)', 'MassiveIntentClassification (hu)', 'MassiveIntentClassification (hy)', 'MassiveIntentClassification (id)', 'MassiveIntentClassification (is)', 'MassiveIntentClassification (it)', 'MassiveIntentClassification (ja)', 'MassiveIntentClassification (jv)', 'MassiveIntentClassification (ka)', 'MassiveIntentClassification (km)', 'MassiveIntentClassification (kn)', 'MassiveIntentClassification (ko)', 'MassiveIntentClassification (lv)', 'MassiveIntentClassification (ml)', 'MassiveIntentClassification (mn)', 'MassiveIntentClassification (ms)', 'MassiveIntentClassification (my)', 'MassiveIntentClassification (nl)', 'MassiveIntentClassification (pt)', 'MassiveIntentClassification (ro)', 'MassiveIntentClassification (ru)', 'MassiveIntentClassification (sl)', 'MassiveIntentClassification (sq)', 'MassiveIntentClassification (sw)', 'MassiveIntentClassification (ta)', 'MassiveIntentClassification (te)', 'MassiveIntentClassification (th)', 'MassiveIntentClassification (tl)', 'MassiveIntentClassification (tr)', 'MassiveIntentClassification (ur)', 'MassiveIntentClassification (vi)', 'MassiveIntentClassification (zh-TW)', 'MassiveScenarioClassification (af)', 'MassiveScenarioClassification (am)', 'MassiveScenarioClassification (ar)', 'MassiveScenarioClassification (az)', 'MassiveScenarioClassification (bn)', 'MassiveScenarioClassification (cy)', 'MassiveScenarioClassification (de)', 'MassiveScenarioClassification (el)', 'MassiveScenarioClassification (es)', 'MassiveScenarioClassification (fa)', 'MassiveScenarioClassification (fi)', 'MassiveScenarioClassification (fr)', 'MassiveScenarioClassification (he)', 'MassiveScenarioClassification (hi)', 'MassiveScenarioClassification (hu)', 'MassiveScenarioClassification (hy)', 'MassiveScenarioClassification (id)', 'MassiveScenarioClassification (is)', 'MassiveScenarioClassification (it)', 'MassiveScenarioClassification (ja)', 'MassiveScenarioClassification (jv)', 'MassiveScenarioClassification (ka)', 'MassiveScenarioClassification (km)', 'MassiveScenarioClassification (kn)', 'MassiveScenarioClassification (ko)', 'MassiveScenarioClassification (lv)', 'MassiveScenarioClassification (ml)', 'MassiveScenarioClassification (mn)', 'MassiveScenarioClassification (ms)', 'MassiveScenarioClassification (my)', 'MassiveScenarioClassification (nl)', 'MassiveScenarioClassification (pt)', 'MassiveScenarioClassification (ro)', 'MassiveScenarioClassification (ru)', 'MassiveScenarioClassification (sl)', 'MassiveScenarioClassification (sq)', 'MassiveScenarioClassification (sw)', 'MassiveScenarioClassification (ta)', 'MassiveScenarioClassification (te)', 'MassiveScenarioClassification (th)', 'MassiveScenarioClassification (tl)', 'MassiveScenarioClassification (tr)', 'MassiveScenarioClassification (ur)', 'MassiveScenarioClassification (vi)', 'MassiveScenarioClassification (zh-TW)'] | |
| TASK_LIST_CLUSTERING = [ | |
| "ArxivClusteringP2P", | |
| "ArxivClusteringS2S", | |
| "BiorxivClusteringP2P", | |
| "BiorxivClusteringS2S", | |
| "MedrxivClusteringP2P", | |
| "MedrxivClusteringS2S", | |
| "RedditClustering", | |
| "RedditClusteringP2P", | |
| "StackExchangeClustering", | |
| "StackExchangeClusteringP2P", | |
| "TwentyNewsgroupsClustering", | |
| ] | |
| TASK_LIST_CLUSTERING_DE = [ | |
| "BlurbsClusteringP2P", | |
| "BlurbsClusteringS2S", | |
| "TenKGnadClusteringP2P", | |
| "TenKGnadClusteringS2S", | |
| ] | |
| TASK_LIST_CLUSTERING_PL = [ | |
| "8TagsClustering", | |
| ] | |
| TASK_LIST_CLUSTERING_ZH = [ | |
| "CLSClusteringP2P", | |
| "CLSClusteringS2S", | |
| "ThuNewsClusteringP2P", | |
| "ThuNewsClusteringS2S", | |
| ] | |
| TASK_LIST_PAIR_CLASSIFICATION = [ | |
| "SprintDuplicateQuestions", | |
| "TwitterSemEval2015", | |
| "TwitterURLCorpus", | |
| ] | |
| TASK_LIST_PAIR_CLASSIFICATION_PL = [ | |
| "CDSC-E", | |
| "PPC", | |
| "PSC", | |
| "SICK-E-PL", | |
| ] | |
| TASK_LIST_PAIR_CLASSIFICATION_ZH = [ | |
| "Cmnli", | |
| "Ocnli", | |
| ] | |
| TASK_LIST_RERANKING = [ | |
| "AskUbuntuDupQuestions", | |
| "MindSmallReranking", | |
| "SciDocsRR", | |
| "StackOverflowDupQuestions", | |
| ] | |
| TASK_LIST_RERANKING_ZH = [ | |
| "CMedQAv1", | |
| "CMedQAv2", | |
| "MMarcoReranking", | |
| "T2Reranking", | |
| ] | |
| TASK_LIST_RETRIEVAL = [ | |
| "ArguAna", | |
| "ClimateFEVER", | |
| "CQADupstackRetrieval", | |
| "DBPedia", | |
| "FEVER", | |
| "FiQA2018", | |
| "HotpotQA", | |
| "MSMARCO", | |
| "NFCorpus", | |
| "NQ", | |
| "QuoraRetrieval", | |
| "SCIDOCS", | |
| "SciFact", | |
| "Touche2020", | |
| "TRECCOVID", | |
| ] | |
| TASK_LIST_RETRIEVAL_PL = [ | |
| "ArguAna-PL", | |
| "DBPedia-PL", | |
| "FiQA-PL", | |
| "HotpotQA-PL", | |
| "MSMARCO-PL", | |
| "NFCorpus-PL", | |
| "NQ-PL", | |
| "Quora-PL", | |
| "SCIDOCS-PL", | |
| "SciFact-PL", | |
| "TRECCOVID-PL", | |
| ] | |
| TASK_LIST_RETRIEVAL_ZH = [ | |
| "CmedqaRetrieval", | |
| "CovidRetrieval", | |
| "DuRetrieval", | |
| "EcomRetrieval", | |
| "MedicalRetrieval", | |
| "MMarcoRetrieval", | |
| "T2Retrieval", | |
| "VideoRetrieval", | |
| ] | |
| TASK_LIST_RETRIEVAL_NORM = TASK_LIST_RETRIEVAL + [ | |
| "CQADupstackAndroidRetrieval", | |
| "CQADupstackEnglishRetrieval", | |
| "CQADupstackGamingRetrieval", | |
| "CQADupstackGisRetrieval", | |
| "CQADupstackMathematicaRetrieval", | |
| "CQADupstackPhysicsRetrieval", | |
| "CQADupstackProgrammersRetrieval", | |
| "CQADupstackStatsRetrieval", | |
| "CQADupstackTexRetrieval", | |
| "CQADupstackUnixRetrieval", | |
| "CQADupstackWebmastersRetrieval", | |
| "CQADupstackWordpressRetrieval" | |
| ] | |
| TASK_LIST_STS = [ | |
| "BIOSSES", | |
| "SICK-R", | |
| "STS12", | |
| "STS13", | |
| "STS14", | |
| "STS15", | |
| "STS16", | |
| "STS17 (en-en)", | |
| "STS22 (en)", | |
| "STSBenchmark", | |
| ] | |
| TASK_LIST_STS_PL = [ | |
| "CDSC-R", | |
| "SICK-R-PL", | |
| "STS22 (pl)", | |
| ] | |
| TASK_LIST_STS_ZH = [ | |
| "AFQMC", | |
| "ATEC", | |
| "BQ", | |
| "LCQMC", | |
| "PAWSX", | |
| "QBQTC", | |
| "STS22 (zh)", | |
| "STSB", | |
| ] | |
| TASK_LIST_STS_OTHER = ["STS17 (ar-ar)", "STS17 (en-ar)", "STS17 (en-de)", "STS17 (en-tr)", "STS17 (es-en)", "STS17 (es-es)", "STS17 (fr-en)", "STS17 (it-en)", "STS17 (ko-ko)", "STS17 (nl-en)", "STS22 (ar)", "STS22 (de)", "STS22 (de-en)", "STS22 (de-fr)", "STS22 (de-pl)", "STS22 (es)", "STS22 (es-en)", "STS22 (es-it)", "STS22 (fr)", "STS22 (fr-pl)", "STS22 (it)", "STS22 (pl)", "STS22 (pl-en)", "STS22 (ru)", "STS22 (tr)", "STS22 (zh-en)", "STSBenchmark",] | |
| TASK_LIST_STS_NORM = [x.replace(" (en)", "").replace(" (en-en)", "") for x in TASK_LIST_STS] | |
| TASK_LIST_SUMMARIZATION = ["SummEval",] | |
| TASK_LIST_EN = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION | |
| TASK_LIST_PL = TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLUSTERING_PL + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_RETRIEVAL_PL + TASK_LIST_STS_PL | |
| TASK_LIST_ZH = TASK_LIST_CLASSIFICATION_ZH + TASK_LIST_CLUSTERING_ZH + TASK_LIST_PAIR_CLASSIFICATION_ZH + TASK_LIST_RERANKING_ZH + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_STS_ZH | |
| TASK_TO_METRIC = { | |
| "BitextMining": "f1", | |
| "Clustering": "v_measure", | |
| "Classification": "accuracy", | |
| "PairClassification": "cos_sim_ap", | |
| "Reranking": "map", | |
| "Retrieval": "ndcg_at_10", | |
| "STS": "cos_sim_spearman", | |
| "Summarization": "cos_sim_spearman", | |
| } | |
| def make_clickable_model(model_name, link=None): | |
| if link is None: | |
| link = "https://huggingface.co/" + model_name | |
| # Remove user from model name | |
| return ( | |
| f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.split("/")[-1]}</a>' | |
| ) | |
| # Models without metadata, thus we cannot fetch their results naturally | |
| EXTERNAL_MODELS = [ | |
| "all-MiniLM-L12-v2", | |
| "all-MiniLM-L6-v2", | |
| "all-mpnet-base-v2", | |
| "allenai-specter", | |
| "Baichuan-text-embedding", | |
| "bert-base-swedish-cased", | |
| "bert-base-uncased", | |
| "bge-base-zh-v1.5", | |
| "bge-large-zh-v1.5", | |
| "bge-large-zh-noinstruct", | |
| "bge-small-zh-v1.5", | |
| "contriever-base-msmarco", | |
| "cross-en-de-roberta-sentence-transformer", | |
| "dfm-encoder-large-v1", | |
| "dfm-sentence-encoder-large-1", | |
| "distiluse-base-multilingual-cased-v2", | |
| "DanskBERT", | |
| "e5-base", | |
| "e5-large", | |
| "e5-small", | |
| "electra-small-nordic", | |
| "electra-small-swedish-cased-discriminator", | |
| "gbert-base", | |
| "gbert-large", | |
| "gelectra-base", | |
| "gelectra-large", | |
| "gottbert-base", | |
| "glove.6B.300d", | |
| "gtr-t5-base", | |
| "gtr-t5-large", | |
| "gtr-t5-xl", | |
| "gtr-t5-xxl", | |
| "herbert-base-retrieval-v2", | |
| "komninos", | |
| "luotuo-bert-medium", | |
| "LASER2", | |
| "LaBSE", | |
| "m3e-base", | |
| "m3e-large", | |
| "msmarco-bert-co-condensor", | |
| "multilingual-e5-base", | |
| "multilingual-e5-large", | |
| "multilingual-e5-small", | |
| "nb-bert-base", | |
| "nb-bert-large", | |
| "nomic-embed-text-v1.5-64", | |
| "nomic-embed-text-v1.5-128", | |
| "nomic-embed-text-v1.5-256", | |
| "nomic-embed-text-v1.5-512", | |
| "norbert3-base", | |
| "norbert3-large", | |
| "paraphrase-multilingual-MiniLM-L12-v2", | |
| "paraphrase-multilingual-mpnet-base-v2", | |
| "sentence-bert-swedish-cased", | |
| "sentence-t5-base", | |
| "sentence-t5-large", | |
| "sentence-t5-xl", | |
| "sentence-t5-xxl", | |
| "sup-simcse-bert-base-uncased", | |
| "st-polish-paraphrase-from-distilroberta", | |
| "st-polish-paraphrase-from-mpnet", | |
| "text2vec-base-chinese", | |
| "text2vec-large-chinese", | |
| "text-embedding-3-small", | |
| "text-embedding-3-large", | |
| "text-embedding-3-large-256", | |
| "text-embedding-ada-002", | |
| "text-similarity-ada-001", | |
| "text-similarity-babbage-001", | |
| "text-similarity-curie-001", | |
| "text-similarity-davinci-001", | |
| "text-search-ada-doc-001", | |
| "text-search-ada-001", | |
| "text-search-babbage-001", | |
| "text-search-curie-001", | |
| "text-search-davinci-001", | |
| "titan-embed-text-v1", | |
| "unsup-simcse-bert-base-uncased", | |
| "use-cmlm-multilingual", | |
| "voyage-lite-01-instruct", | |
| "voyage-lite-02-instruct", | |
| "xlm-roberta-base", | |
| "xlm-roberta-large", | |
| ] | |
| EXTERNAL_MODEL_TO_LINK = { | |
| "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter", | |
| "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter", | |
| "all-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2", | |
| "all-MiniLM-L6-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2", | |
| "all-mpnet-base-v2": "https://huggingface.co/sentence-transformers/all-mpnet-base-v2", | |
| "Baichuan-text-embedding": "https://platform.baichuan-ai.com/docs/text-Embedding", | |
| "bert-base-swedish-cased": "https://huggingface.co/KB/bert-base-swedish-cased", | |
| "bert-base-uncased": "https://huggingface.co/bert-base-uncased", | |
| "bge-base-zh-v1.5": "https://huggingface.co/BAAI/bge-base-zh-v1.5", | |
| "bge-large-zh-v1.5": "https://huggingface.co/BAAI/bge-large-zh-v1.5", | |
| "bge-large-zh-noinstruct": "https://huggingface.co/BAAI/bge-large-zh-noinstruct", | |
| "bge-small-zh-v1.5": "https://huggingface.co/BAAI/bge-small-zh-v1.5", | |
| "contriever-base-msmarco": "https://huggingface.co/nthakur/contriever-base-msmarco", | |
| "cross-en-de-roberta-sentence-transformer": "https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer", | |
| "DanskBERT": "https://huggingface.co/vesteinn/DanskBERT", | |
| "distiluse-base-multilingual-cased-v2": "https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2", | |
| "dfm-encoder-large-v1": "https://huggingface.co/chcaa/dfm-encoder-large-v1", | |
| "dfm-sentence-encoder-large-1": "https://huggingface.co/chcaa/dfm-encoder-large-v1", | |
| "e5-base": "https://huggingface.co/intfloat/e5-base", | |
| "e5-large": "https://huggingface.co/intfloat/e5-large", | |
| "e5-small": "https://huggingface.co/intfloat/e5-small", | |
| "electra-small-nordic": "https://huggingface.co/jonfd/electra-small-nordic", | |
| "electra-small-swedish-cased-discriminator": "https://huggingface.co/KBLab/electra-small-swedish-cased-discriminator", | |
| "gbert-base": "https://huggingface.co/deepset/gbert-base", | |
| "gbert-large": "https://huggingface.co/deepset/gbert-large", | |
| "gelectra-base": "https://huggingface.co/deepset/gelectra-base", | |
| "gelectra-large": "https://huggingface.co/deepset/gelectra-large", | |
| "glove.6B.300d": "https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d", | |
| "gottbert-base": "https://huggingface.co/uklfr/gottbert-base", | |
| "gtr-t5-base": "https://huggingface.co/sentence-transformers/gtr-t5-base", | |
| "gtr-t5-large": "https://huggingface.co/sentence-transformers/gtr-t5-large", | |
| "gtr-t5-xl": "https://huggingface.co/sentence-transformers/gtr-t5-xl", | |
| "gtr-t5-xxl": "https://huggingface.co/sentence-transformers/gtr-t5-xxl", | |
| "herbert-base-retrieval-v2": "https://huggingface.co/ipipan/herbert-base-retrieval-v2", | |
| "komninos": "https://huggingface.co/sentence-transformers/average_word_embeddings_komninos", | |
| "luotuo-bert-medium": "https://huggingface.co/silk-road/luotuo-bert-medium", | |
| "LASER2": "https://github.com/facebookresearch/LASER", | |
| "LaBSE": "https://huggingface.co/sentence-transformers/LaBSE", | |
| "m3e-base": "https://huggingface.co/moka-ai/m3e-base", | |
| "m3e-large": "https://huggingface.co/moka-ai/m3e-large", | |
| "msmarco-bert-co-condensor": "https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor", | |
| "multilingual-e5-base": "https://huggingface.co/intfloat/multilingual-e5-base", | |
| "multilingual-e5-large": "https://huggingface.co/intfloat/multilingual-e5-large", | |
| "multilingual-e5-small": "https://huggingface.co/intfloat/multilingual-e5-small", | |
| "nb-bert-base": "https://huggingface.co/NbAiLab/nb-bert-base", | |
| "nb-bert-large": "https://huggingface.co/NbAiLab/nb-bert-large", | |
| "nomic-embed-text-v1.5-64": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", | |
| "nomic-embed-text-v1.5-128": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", | |
| "nomic-embed-text-v1.5-256": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", | |
| "nomic-embed-text-v1.5-512": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", | |
| "norbert3-base": "https://huggingface.co/ltg/norbert3-base", | |
| "norbert3-large": "https://huggingface.co/ltg/norbert3-large", | |
| "paraphrase-multilingual-mpnet-base-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2", | |
| "paraphrase-multilingual-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", | |
| "sentence-bert-swedish-cased": "https://huggingface.co/KBLab/sentence-bert-swedish-cased", | |
| "sentence-t5-base": "https://huggingface.co/sentence-transformers/sentence-t5-base", | |
| "sentence-t5-large": "https://huggingface.co/sentence-transformers/sentence-t5-large", | |
| "sentence-t5-xl": "https://huggingface.co/sentence-transformers/sentence-t5-xl", | |
| "sentence-t5-xxl": "https://huggingface.co/sentence-transformers/sentence-t5-xxl", | |
| "sup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased", | |
| "st-polish-paraphrase-from-distilroberta": "https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta", | |
| "st-polish-paraphrase-from-mpnet": "https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet", | |
| "text2vec-base-chinese": "https://huggingface.co/shibing624/text2vec-base-chinese", | |
| "text2vec-large-chinese": "https://huggingface.co/GanymedeNil/text2vec-large-chinese", | |
| "text-embedding-3-small": "https://openai.com/blog/new-embedding-models-and-api-updates", | |
| "text-embedding-3-large": "https://openai.com/blog/new-embedding-models-and-api-updates", | |
| "text-embedding-3-large-256": "https://openai.com/blog/new-embedding-models-and-api-updates", | |
| "text-embedding-ada-002": "https://openai.com/blog/new-and-improved-embedding-model", | |
| "text-similarity-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-similarity-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-similarity-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-similarity-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-search-ada-doc-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-search-ada-query-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-search-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-search-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-search-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "text-search-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings", | |
| "titan-embed-text-v1": "https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html", | |
| "unsup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased", | |
| "use-cmlm-multilingual": "https://huggingface.co/sentence-transformers/use-cmlm-multilingual", | |
| "voyage-lite-01-instruct": "https://docs.voyageai.com/embeddings/", | |
| "voyage-lite-02-instruct": "https://docs.voyageai.com/embeddings/", | |
| "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base", | |
| "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large", | |
| } | |
| EXTERNAL_MODEL_TO_DIM = { | |
| "all-MiniLM-L12-v2": 384, | |
| "all-MiniLM-L6-v2": 384, | |
| "all-mpnet-base-v2": 768, | |
| "allenai-specter": 768, | |
| "Baichuan-text-embedding": 1024, | |
| "bert-base-swedish-cased": 768, | |
| "bert-base-uncased": 768, | |
| "bge-base-zh-v1.5": 768, | |
| "bge-large-zh-v1.5": 1024, | |
| "bge-large-zh-noinstruct": 1024, | |
| "bge-small-zh-v1.5": 512, | |
| "contriever-base-msmarco": 768, | |
| "cross-en-de-roberta-sentence-transformer": 768, | |
| "DanskBERT": 768, | |
| "distiluse-base-multilingual-cased-v2": 512, | |
| "dfm-encoder-large-v1": 1024, | |
| "dfm-sentence-encoder-large-1": 1024, | |
| "e5-base": 768, | |
| "e5-small": 384, | |
| "e5-large": 1024, | |
| "electra-small-nordic": 256, | |
| "electra-small-swedish-cased-discriminator": 256, | |
| "luotuo-bert-medium": 768, | |
| "LASER2": 1024, | |
| "LaBSE": 768, | |
| "gbert-base": 768, | |
| "gbert-large": 1024, | |
| "gelectra-base": 768, | |
| "gelectra-large": 1024, | |
| "glove.6B.300d": 300, | |
| "gottbert-base": 768, | |
| "gtr-t5-base": 768, | |
| "gtr-t5-large": 768, | |
| "gtr-t5-xl": 768, | |
| "gtr-t5-xxl": 768, | |
| "herbert-base-retrieval-v2": 768, | |
| "komninos": 300, | |
| "m3e-base": 768, | |
| "m3e-large": 768, | |
| "msmarco-bert-co-condensor": 768, | |
| "multilingual-e5-base": 768, | |
| "multilingual-e5-small": 384, | |
| "multilingual-e5-large": 1024, | |
| "nb-bert-base": 768, | |
| "nb-bert-large": 1024, | |
| "nomic-embed-text-v1.5-64": 64, | |
| "nomic-embed-text-v1.5-128": 128, | |
| "nomic-embed-text-v1.5-256": 256, | |
| "nomic-embed-text-v1.5-512": 512, | |
| "norbert3-base": 768, | |
| "norbert3-large": 1024, | |
| "paraphrase-multilingual-MiniLM-L12-v2": 384, | |
| "paraphrase-multilingual-mpnet-base-v2": 768, | |
| "sentence-bert-swedish-cased": 768, | |
| "sentence-t5-base": 768, | |
| "sentence-t5-large": 768, | |
| "sentence-t5-xl": 768, | |
| "sentence-t5-xxl": 768, | |
| "sup-simcse-bert-base-uncased": 768, | |
| "st-polish-paraphrase-from-distilroberta": 768, | |
| "st-polish-paraphrase-from-mpnet": 768, | |
| "text2vec-base-chinese": 768, | |
| "text2vec-large-chinese": 1024, | |
| "text-embedding-3-large": 3072, | |
| "text-embedding-3-large-256": 256, | |
| "text-embedding-3-small": 1536, | |
| "text-embedding-ada-002": 1536, | |
| "text-similarity-ada-001": 1024, | |
| "text-similarity-babbage-001": 2048, | |
| "text-similarity-curie-001": 4096, | |
| "text-similarity-davinci-001": 12288, | |
| "text-search-ada-doc-001": 1024, | |
| "text-search-ada-query-001": 1024, | |
| "text-search-ada-001": 1024, | |
| "text-search-babbage-001": 2048, | |
| "text-search-curie-001": 4096, | |
| "text-search-davinci-001": 12288, | |
| "titan-embed-text-v1": 1536, | |
| "unsup-simcse-bert-base-uncased": 768, | |
| "use-cmlm-multilingual": 768, | |
| "voyage-lite-01-instruct": 1024, | |
| "voyage-lite-02-instruct": 1024, | |
| "xlm-roberta-base": 768, | |
| "xlm-roberta-large": 1024, | |
| } | |
| EXTERNAL_MODEL_TO_SEQLEN = { | |
| "all-MiniLM-L12-v2": 512, | |
| "all-MiniLM-L6-v2": 512, | |
| "all-mpnet-base-v2": 514, | |
| "allenai-specter": 512, | |
| "Baichuan-text-embedding": 512, | |
| "bert-base-swedish-cased": 512, | |
| "bert-base-uncased": 512, | |
| "bge-base-zh-v1.5": 512, | |
| "bge-large-zh-v1.5": 512, | |
| "bge-large-zh-noinstruct": 512, | |
| "bge-small-zh-v1.5": 512, | |
| "contriever-base-msmarco": 512, | |
| "cross-en-de-roberta-sentence-transformer": 514, | |
| "DanskBERT": 514, | |
| "dfm-encoder-large-v1": 512, | |
| "dfm-sentence-encoder-large-1": 512, | |
| "distiluse-base-multilingual-cased-v2": 512, | |
| "e5-base": 512, | |
| "e5-large": 512, | |
| "e5-small": 512, | |
| "electra-small-nordic": 512, | |
| "electra-small-swedish-cased-discriminator": 512, | |
| "gbert-base": 512, | |
| "gbert-large": 512, | |
| "gelectra-base": 512, | |
| "gelectra-large": 512, | |
| "gottbert-base": 512, | |
| "glove.6B.300d": "N/A", | |
| "gtr-t5-base": 512, | |
| "gtr-t5-large": 512, | |
| "gtr-t5-xl": 512, | |
| "gtr-t5-xxl": 512, | |
| "herbert-base-retrieval-v2": 514, | |
| "komninos": "N/A", | |
| "luotuo-bert-medium": 512, | |
| "LASER2": "N/A", | |
| "LaBSE": 512, | |
| "m3e-base": 512, | |
| "m3e-large": 512, | |
| "msmarco-bert-co-condensor": 512, | |
| "multilingual-e5-base": 514, | |
| "multilingual-e5-large": 514, | |
| "multilingual-e5-small": 512, | |
| "nb-bert-base": 512, | |
| "nb-bert-large": 512, | |
| "nomic-embed-text-v1.5-64": 8192, | |
| "nomic-embed-text-v1.5-128": 8192, | |
| "nomic-embed-text-v1.5-256": 8192, | |
| "nomic-embed-text-v1.5-512": 8192, | |
| "norbert3-base": 512, | |
| "norbert3-large": 512, | |
| "paraphrase-multilingual-MiniLM-L12-v2": 512, | |
| "paraphrase-multilingual-mpnet-base-v2": 514, | |
| "sentence-bert-swedish-cased": 512, | |
| "sentence-t5-base": 512, | |
| "sentence-t5-large": 512, | |
| "sentence-t5-xl": 512, | |
| "sentence-t5-xxl": 512, | |
| "sup-simcse-bert-base-uncased": 512, | |
| "st-polish-paraphrase-from-distilroberta": 514, | |
| "st-polish-paraphrase-from-mpnet": 514, | |
| "text2vec-base-chinese": 512, | |
| "text2vec-large-chinese": 512, | |
| "text-embedding-3-large": 8191, | |
| "text-embedding-3-large-256": 8191, | |
| "text-embedding-3-small": 8191, | |
| "text-embedding-ada-002": 8191, | |
| "text-similarity-ada-001": 2046, | |
| "text-similarity-babbage-001": 2046, | |
| "text-similarity-curie-001": 2046, | |
| "text-similarity-davinci-001": 2046, | |
| "text-search-ada-doc-001": 2046, | |
| "text-search-ada-query-001": 2046, | |
| "text-search-ada-001": 2046, | |
| "text-search-babbage-001": 2046, | |
| "text-search-curie-001": 2046, | |
| "text-search-davinci-001": 2046, | |
| "titan-embed-text-v1": 8000, | |
| "use-cmlm-multilingual": 512, | |
| "unsup-simcse-bert-base-uncased": 512, | |
| "voyage-lite-01-instruct": 4000, | |
| "voyage-lite-02-instruct": 4000, | |
| "xlm-roberta-base": 514, | |
| "xlm-roberta-large": 514, | |
| } | |
| EXTERNAL_MODEL_TO_SIZE = { | |
| "allenai-specter": 0.44, | |
| "all-MiniLM-L12-v2": 0.13, | |
| "all-MiniLM-L6-v2": 0.09, | |
| "all-mpnet-base-v2": 0.44, | |
| "bert-base-uncased": 0.44, | |
| "bert-base-swedish-cased": 0.50, | |
| "bge-base-zh-v1.5": 0.41, | |
| "bge-large-zh-v1.5": 1.30, | |
| "bge-large-zh-noinstruct": 1.30, | |
| "bge-small-zh-v1.5": 0.10, | |
| "cross-en-de-roberta-sentence-transformer": 1.11, | |
| "contriever-base-msmarco": 0.44, | |
| "DanskBERT": 0.50, | |
| "distiluse-base-multilingual-cased-v2": 0.54, | |
| "dfm-encoder-large-v1": 1.42, | |
| "dfm-sentence-encoder-large-1": 1.63, | |
| "e5-base": 0.44, | |
| "e5-small": 0.13, | |
| "e5-large": 1.34, | |
| "electra-small-nordic": 0.09, | |
| "electra-small-swedish-cased-discriminator": 0.06, | |
| "gbert-base": 0.44, | |
| "gbert-large": 1.35, | |
| "gelectra-base": 0.44, | |
| "gelectra-large": 1.34, | |
| "glove.6B.300d": 0.48, | |
| "gottbert-base": 0.51, | |
| "gtr-t5-base": 0.22, | |
| "gtr-t5-large": 0.67, | |
| "gtr-t5-xl": 2.48, | |
| "gtr-t5-xxl": 9.73, | |
| "herbert-base-retrieval-v2": 0.50, | |
| "komninos": 0.27, | |
| "luotuo-bert-medium": 1.31, | |
| "LASER2": 0.17, | |
| "LaBSE": 1.88, | |
| "m3e-base": 0.41, | |
| "m3e-large": 0.41, | |
| "msmarco-bert-co-condensor": 0.44, | |
| "multilingual-e5-base": 1.11, | |
| "multilingual-e5-small": 0.47, | |
| "multilingual-e5-large": 2.24, | |
| "nb-bert-base": 0.71, | |
| "nb-bert-large": 1.42, | |
| "nomic-embed-text-v1.5-64": 0.55, | |
| "nomic-embed-text-v1.5-128": 0.55, | |
| "nomic-embed-text-v1.5-256": 0.55, | |
| "nomic-embed-text-v1.5-512": 0.55, | |
| "norbert3-base": 0.52, | |
| "norbert3-large": 1.47, | |
| "paraphrase-multilingual-mpnet-base-v2": 1.11, | |
| "paraphrase-multilingual-MiniLM-L12-v2": 0.47, | |
| "sentence-bert-swedish-cased": 0.50, | |
| "sentence-t5-base": 0.22, | |
| "sentence-t5-large": 0.67, | |
| "sentence-t5-xl": 2.48, | |
| "sentence-t5-xxl": 9.73, | |
| "sup-simcse-bert-base-uncased": 0.44, | |
| "st-polish-paraphrase-from-distilroberta": 0.50, | |
| "st-polish-paraphrase-from-mpnet": 0.50, | |
| "text2vec-base-chinese": 0.41, | |
| "text2vec-large-chinese": 1.30, | |
| "unsup-simcse-bert-base-uncased": 0.44, | |
| "use-cmlm-multilingual": 1.89, | |
| "xlm-roberta-base": 1.12, | |
| "xlm-roberta-large": 2.24, | |
| } | |
| MODELS_TO_SKIP = { | |
| "baseplate/instructor-large-1", # Duplicate | |
| "radames/e5-large", # Duplicate | |
| "gentlebowl/instructor-large-safetensors", # Duplicate | |
| "Consensus/instructor-base", # Duplicate | |
| "GovCompete/instructor-xl", # Duplicate | |
| "GovCompete/e5-large-v2", # Duplicate | |
| "t12e/instructor-base", # Duplicate | |
| "michaelfeil/ct2fast-e5-large-v2", | |
| "michaelfeil/ct2fast-e5-large", | |
| "michaelfeil/ct2fast-e5-small-v2", | |
| "newsrx/instructor-xl-newsrx", | |
| "newsrx/instructor-large-newsrx", | |
| "fresha/e5-large-v2-endpoint", | |
| "ggrn/e5-small-v2", | |
| "michaelfeil/ct2fast-e5-small", | |
| "jncraton/e5-small-v2-ct2-int8", | |
| "anttip/ct2fast-e5-small-v2-hfie", | |
| "newsrx/instructor-large", | |
| "newsrx/instructor-xl", | |
| "dmlls/all-mpnet-base-v2", | |
| "cgldo/semanticClone", | |
| "Malmuk1/e5-large-v2_Sharded", | |
| "jncraton/gte-small-ct2-int8", | |
| "Einas/einas_ashkar", | |
| "gruber/e5-small-v2-ggml", | |
| "jncraton/bge-small-en-ct2-int8", | |
| "vectoriseai/bge-small-en", | |
| "recipe/embeddings", | |
| "dhairya0907/thenlper-get-large", | |
| "Narsil/bge-base-en", | |
| "kozistr/fused-large-en", | |
| "sionic-ai/sionic-ai-v2", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1 | |
| "sionic-ai/sionic-ai-v1", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1 | |
| "BAAI/bge-large-en", # Deprecated in favor of v1.5 | |
| "BAAI/bge-base-en", # Deprecated in favor of v1.5 | |
| "BAAI/bge-small-en", # Deprecated in favor of v1.5 | |
| "d0rj/e5-large-en-ru", | |
| "d0rj/e5-base-en-ru", | |
| "d0rj/e5-small-en-ru", | |
| "aident-ai/bge-base-en-onnx", | |
| "barisaydin/bge-base-en", | |
| "barisaydin/gte-large", | |
| "barisaydin/gte-base", | |
| "barisaydin/gte-small", | |
| "barisaydin/bge-small-en", | |
| "odunola/e5-base-v2", | |
| "goldenrooster/multilingual-e5-large", | |
| "davidpeer/gte-small", | |
| "barisaydin/bge-large-en", | |
| "jamesgpt1/english-large-v1", | |
| "vectoriseai/bge-large-en-v1.5", | |
| "vectoriseai/bge-base-en-v1.5", | |
| "vectoriseai/instructor-large", | |
| "vectoriseai/instructor-base", | |
| "vectoriseai/gte-large", | |
| "vectoriseai/gte-base", | |
| "vectoriseai/e5-large-v2", | |
| "vectoriseai/bge-small-en-v1.5", | |
| "vectoriseai/e5-base-v2", | |
| "vectoriseai/e5-large", | |
| "vectoriseai/multilingual-e5-large", | |
| "vectoriseai/gte-small", | |
| "vectoriseai/ember-v1", | |
| "vectoriseai/e5-base", | |
| "vectoriseai/e5-small-v2", | |
| "michaelfeil/ct2fast-bge-large-en-v1.5", | |
| "michaelfeil/ct2fast-bge-large-en-v1.5", | |
| "michaelfeil/ct2fast-bge-base-en-v1.5", | |
| "michaelfeil/ct2fast-gte-large", | |
| "michaelfeil/ct2fast-gte-base", | |
| "michaelfeil/ct2fast-bge-small-en-v1.5", | |
| "rizki/bgr-tf", | |
| "ef-zulla/e5-multi-sml-torch", | |
| "cherubhao/yogamodel", | |
| "morgendigital/multilingual-e5-large-quantized", | |
| "jncraton/gte-tiny-ct2-int8", | |
| "Research2NLP/electrical_stella", | |
| "Intel/bge-base-en-v1.5-sts-int8-static", | |
| "Intel/bge-base-en-v1.5-sts-int8-dynamic", | |
| "Intel/bge-base-en-v1.5-sst2", | |
| "Intel/bge-base-en-v1.5-sst2-int8-static", | |
| "Intel/bge-base-en-v1.5-sst2-int8-dynamic", | |
| "Intel/bge-small-en-v1.5-sst2", | |
| "Intel/bge-small-en-v1.5-sst2-int8-dynamic", | |
| "Intel/bge-small-en-v1.5-sst2-int8-static", | |
| "binqiangliu/EmbeddingModlebgelargeENv1.5", | |
| "DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext", | |
| "woody72/multilingual-e5-base", | |
| "Severian/embed", | |
| "Frazic/udever-bloom-3b-sentence", | |
| "jamesgpt1/zzz", | |
| "karrar-alwaili/UAE-Large-V1", | |
| "odunola/UAE-Large-VI", | |
| "shubham-bgi/UAE-Large", | |
| "retrainai/instructor-xl", | |
| "weakit-v/bge-base-en-v1.5-onnx", | |
| "ieasybooks/multilingual-e5-large-onnx", | |
| "gizmo-ai/Cohere-embed-multilingual-v3.0", | |
| "jingyeom/korean_embedding_model", | |
| "barisaydin/text2vec-base-multilingual", | |
| "mlx-community/multilingual-e5-large-mlx", | |
| "mlx-community/multilingual-e5-base-mlx", | |
| "mlx-community/multilingual-e5-small-mlx", | |
| "maiyad/multilingual-e5-small", | |
| "khoa-klaytn/bge-base-en-v1.5-angle", | |
| "khoa-klaytn/bge-small-en-v1.5-angle", | |
| "mixamrepijey/instructor-small", | |
| "mixamrepijey/instructor-models", | |
| "lsf1000/bge-evaluation", # Empty | |
| "giulio98/placeholder", # Empty | |
| "Severian/nomic", # Copy | |
| } | |
| EXTERNAL_MODEL_RESULTS = {model: {k: {v: []} for k, v in TASK_TO_METRIC.items()} for model in EXTERNAL_MODELS} | |
| def add_lang(examples): | |
| if not(examples["eval_language"]): | |
| examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] | |
| else: | |
| examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] + f' ({examples["eval_language"]})' | |
| return examples | |
| def add_task(examples): | |
| # Could be added to the dataset loading script instead | |
| if examples["mteb_dataset_name"] in TASK_LIST_CLASSIFICATION_NORM + TASK_LIST_CLASSIFICATION_DA + TASK_LIST_CLASSIFICATION_NB + TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLASSIFICATION_SV + TASK_LIST_CLASSIFICATION_ZH: | |
| examples["mteb_task"] = "Classification" | |
| elif examples["mteb_dataset_name"] in TASK_LIST_CLUSTERING + TASK_LIST_CLUSTERING_DE + TASK_LIST_CLUSTERING_PL + TASK_LIST_CLUSTERING_ZH: | |
| examples["mteb_task"] = "Clustering" | |
| elif examples["mteb_dataset_name"] in TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_PAIR_CLASSIFICATION_ZH: | |
| examples["mteb_task"] = "PairClassification" | |
| elif examples["mteb_dataset_name"] in TASK_LIST_RERANKING + TASK_LIST_RERANKING_ZH: | |
| examples["mteb_task"] = "Reranking" | |
| elif examples["mteb_dataset_name"] in TASK_LIST_RETRIEVAL_NORM + TASK_LIST_RETRIEVAL_PL + TASK_LIST_RETRIEVAL_ZH: | |
| examples["mteb_task"] = "Retrieval" | |
| elif examples["mteb_dataset_name"] in TASK_LIST_STS_NORM + TASK_LIST_STS_PL + TASK_LIST_STS_ZH: | |
| examples["mteb_task"] = "STS" | |
| elif examples["mteb_dataset_name"] in TASK_LIST_SUMMARIZATION: | |
| examples["mteb_task"] = "Summarization" | |
| elif examples["mteb_dataset_name"] in [x.split(" ")[0] for x in TASK_LIST_BITEXT_MINING + TASK_LIST_BITEXT_MINING_OTHER]: | |
| examples["mteb_task"] = "BitextMining" | |
| else: | |
| print("WARNING: Task not found for dataset", examples["mteb_dataset_name"]) | |
| examples["mteb_task"] = "Unknown" | |
| return examples | |
| pbar = tqdm(EXTERNAL_MODELS, desc="Fetching external model results") | |
| for model in pbar: | |
| pbar.set_description(f"Fetching external model results for {model!r}") | |
| ds = load_dataset("mteb/results", model, trust_remote_code=True) | |
| # For local debugging: | |
| #, download_mode='force_redownload', verification_mode="no_checks") | |
| ds = ds.map(add_lang) | |
| ds = ds.map(add_task) | |
| base_dict = {"Model": make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard"))} | |
| # For now only one metric per task - Could add more metrics lateron | |
| for task, metric in TASK_TO_METRIC.items(): | |
| ds_dict = ds.filter(lambda x: (x["mteb_task"] == task) and (x["metric"] == metric))["test"].to_dict() | |
| ds_dict = {k: round(v, 2) for k, v in zip(ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"])} | |
| EXTERNAL_MODEL_RESULTS[model][task][metric].append({**base_dict, **ds_dict}) | |
| def get_dim_seq_size(model): | |
| filenames = [sib.rfilename for sib in model.siblings] | |
| dim, seq, size = "", "", "" | |
| if "1_Pooling/config.json" in filenames: | |
| st_config_path = hf_hub_download(model.modelId, filename="1_Pooling/config.json") | |
| dim = json.load(open(st_config_path)).get("word_embedding_dimension", "") | |
| elif "2_Pooling/config.json" in filenames: | |
| st_config_path = hf_hub_download(model.modelId, filename="2_Pooling/config.json") | |
| dim = json.load(open(st_config_path)).get("word_embedding_dimension", "") | |
| if "config.json" in filenames: | |
| config_path = hf_hub_download(model.modelId, filename="config.json") | |
| config = json.load(open(config_path)) | |
| if not dim: | |
| dim = config.get("hidden_dim", config.get("hidden_size", config.get("d_model", ""))) | |
| seq = config.get("n_positions", config.get("max_position_embeddings", config.get("n_ctx", config.get("seq_length", "")))) | |
| # Get model file size without downloading | |
| if "pytorch_model.bin" in filenames: | |
| url = hf_hub_url(model.modelId, filename="pytorch_model.bin") | |
| meta = get_hf_file_metadata(url) | |
| size = round(meta.size / 1e9, 2) | |
| elif "pytorch_model.bin.index.json" in filenames: | |
| index_path = hf_hub_download(model.modelId, filename="pytorch_model.bin.index.json") | |
| """ | |
| { | |
| "metadata": { | |
| "total_size": 28272820224 | |
| },.... | |
| """ | |
| size = json.load(open(index_path)) | |
| if ("metadata" in size) and ("total_size" in size["metadata"]): | |
| size = round(size["metadata"]["total_size"] / 1e9, 2) | |
| elif "model.safetensors" in filenames: | |
| url = hf_hub_url(model.modelId, filename="model.safetensors") | |
| meta = get_hf_file_metadata(url) | |
| size = round(meta.size / 1e9, 2) | |
| elif "model.safetensors.index.json" in filenames: | |
| index_path = hf_hub_download(model.modelId, filename="model.safetensors.index.json") | |
| """ | |
| { | |
| "metadata": { | |
| "total_size": 14483464192 | |
| },.... | |
| """ | |
| size = json.load(open(index_path)) | |
| if ("metadata" in size) and ("total_size" in size["metadata"]): | |
| size = round(size["metadata"]["total_size"] / 1e9, 2) | |
| return dim, seq, size | |
| def make_datasets_clickable(df): | |
| """Does not work""" | |
| if "BornholmBitextMining" in df.columns: | |
| link = "https://huggingface.co/datasets/strombergnlp/bornholmsk_parallel" | |
| df = df.rename( | |
| columns={f'BornholmBitextMining': '<a target="_blank" style="text-decoration: underline" href="{link}">BornholmBitextMining</a>',}) | |
| return df | |
| def add_rank(df): | |
| cols_to_rank = [col for col in df.columns if col not in ["Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens"]] | |
| if len(cols_to_rank) == 1: | |
| df.sort_values(cols_to_rank[0], ascending=False, inplace=True) | |
| else: | |
| df.insert(1, "Average", df[cols_to_rank].mean(axis=1, skipna=False)) | |
| df.sort_values("Average", ascending=False, inplace=True) | |
| df.insert(0, "Rank", list(range(1, len(df) + 1))) | |
| df = df.round(2) | |
| # Fill NaN after averaging | |
| df.fillna("", inplace=True) | |
| return df | |
| def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_emb_dim=False, task_to_metric=TASK_TO_METRIC, rank=True): | |
| api = HfApi() | |
| models = api.list_models(filter="mteb") | |
| # Initialize list to models that we cannot fetch metadata from | |
| df_list = [] | |
| for model in EXTERNAL_MODEL_RESULTS: | |
| results_list = [res for task in tasks for res in EXTERNAL_MODEL_RESULTS[model][task][task_to_metric[task]]] | |
| if len(datasets) > 0: | |
| res = {k: v for d in results_list for k, v in d.items() if (k == "Model") or any([x in k for x in datasets])} | |
| elif langs: | |
| # Would be cleaner to rely on an extra language column instead | |
| langs_format = [f"({lang})" for lang in langs] | |
| res = {k: v for d in results_list for k, v in d.items() if any([k.split(" ")[-1] in (k, x) for x in langs_format])} | |
| else: | |
| res = {k: v for d in results_list for k, v in d.items()} | |
| # Model & at least one result | |
| if len(res) > 1: | |
| if add_emb_dim: | |
| res["Model Size (GB)"] = EXTERNAL_MODEL_TO_SIZE.get(model, "") | |
| res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "") | |
| res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "") | |
| df_list.append(res) | |
| for model in models: | |
| if model.modelId in MODELS_TO_SKIP: continue | |
| print("MODEL", model) | |
| readme_path = hf_hub_download(model.modelId, filename="README.md") | |
| meta = metadata_load(readme_path) | |
| if "model-index" not in meta: | |
| continue | |
| # meta['model-index'][0]["results"] is list of elements like: | |
| # { | |
| # "task": {"type": "Classification"}, | |
| # "dataset": { | |
| # "type": "mteb/amazon_massive_intent", | |
| # "name": "MTEB MassiveIntentClassification (nb)", | |
| # "config": "nb", | |
| # "split": "test", | |
| # }, | |
| # "metrics": [ | |
| # {"type": "accuracy", "value": 39.81506388702084}, | |
| # {"type": "f1", "value": 38.809586587791664}, | |
| # ], | |
| # }, | |
| # Use "get" instead of dict indexing to skip incompat metadata instead of erroring out | |
| if len(datasets) > 0: | |
| task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and any([x in sub_res.get("dataset", {}).get("name", "") for x in datasets])] | |
| elif langs: | |
| task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and (sub_res.get("dataset", {}).get("config", "default") in ("default", *langs))] | |
| else: | |
| task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks)] | |
| out = [{res["dataset"]["name"].replace("MTEB ", ""): [round(score["value"], 2) for score in res["metrics"] if score["type"] == task_to_metric.get(res["task"]["type"])][0]} for res in task_results] | |
| out = {k: v for d in out for k, v in d.items()} | |
| out["Model"] = make_clickable_model(model.modelId) | |
| # Model & at least one result | |
| if len(out) > 1: | |
| if add_emb_dim: | |
| try: | |
| # Fails on gated repos, so we only include scores for them | |
| out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (GB)"] = get_dim_seq_size(model) | |
| except: | |
| pass | |
| df_list.append(out) | |
| df = pd.DataFrame(df_list) | |
| # If there are any models that are the same, merge them | |
| # E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one | |
| df = df.groupby("Model", as_index=False).first() | |
| # Put 'Model' column first | |
| cols = sorted(list(df.columns)) | |
| cols.insert(0, cols.pop(cols.index("Model"))) | |
| df = df[cols] | |
| if rank: | |
| df = add_rank(df) | |
| if fillna: | |
| df.fillna("", inplace=True) | |
| return df | |
| def get_mteb_average(): | |
| global DATA_OVERALL, DATA_CLASSIFICATION_EN, DATA_CLUSTERING, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS_EN, DATA_SUMMARIZATION | |
| DATA_OVERALL = get_mteb_data( | |
| tasks=[ | |
| "Classification", | |
| "Clustering", | |
| "PairClassification", | |
| "Reranking", | |
| "Retrieval", | |
| "STS", | |
| "Summarization", | |
| ], | |
| datasets=TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION, | |
| fillna=False, | |
| add_emb_dim=True, | |
| rank=False, | |
| ) | |
| # Debugging: | |
| # DATA_OVERALL.to_csv("overall.csv") | |
| DATA_OVERALL.insert(1, f"Average ({len(TASK_LIST_EN)} datasets)", DATA_OVERALL[TASK_LIST_EN].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_CLASSIFICATION].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", DATA_OVERALL[TASK_LIST_CLUSTERING].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_PAIR_CLASSIFICATION].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", DATA_OVERALL[TASK_LIST_RERANKING].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", DATA_OVERALL[TASK_LIST_RETRIEVAL].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.insert(7, f"STS Average ({len(TASK_LIST_STS)} datasets)", DATA_OVERALL[TASK_LIST_STS].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)", DATA_OVERALL[TASK_LIST_SUMMARIZATION].mean(axis=1, skipna=False)) | |
| DATA_OVERALL.sort_values(f"Average ({len(TASK_LIST_EN)} datasets)", ascending=False, inplace=True) | |
| # Start ranking from 1 | |
| DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1))) | |
| DATA_OVERALL = DATA_OVERALL.round(2) | |
| DATA_CLASSIFICATION_EN = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_CLASSIFICATION]) | |
| # Only keep rows with at least one score in addition to the "Model" & rank column | |
| DATA_CLASSIFICATION_EN = DATA_CLASSIFICATION_EN[DATA_CLASSIFICATION_EN.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_CLUSTERING = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_CLUSTERING]) | |
| DATA_CLUSTERING = DATA_CLUSTERING[DATA_CLUSTERING.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_PAIR_CLASSIFICATION = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_PAIR_CLASSIFICATION]) | |
| DATA_PAIR_CLASSIFICATION = DATA_PAIR_CLASSIFICATION[DATA_PAIR_CLASSIFICATION.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_RERANKING = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_RERANKING]) | |
| DATA_RERANKING = DATA_RERANKING[DATA_RERANKING.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_RETRIEVAL = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_RETRIEVAL]) | |
| DATA_RETRIEVAL = DATA_RETRIEVAL[DATA_RETRIEVAL.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_STS_EN = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_STS]) | |
| DATA_STS_EN = DATA_STS_EN[DATA_STS_EN.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_SUMMARIZATION = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_SUMMARIZATION]) | |
| DATA_SUMMARIZATION = DATA_SUMMARIZATION[DATA_SUMMARIZATION.iloc[:, 1:].ne("").any(axis=1)] | |
| # Fill NaN after averaging | |
| DATA_OVERALL.fillna("", inplace=True) | |
| DATA_OVERALL = DATA_OVERALL[["Rank", "Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_EN)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", f"STS Average ({len(TASK_LIST_STS)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)"]] | |
| DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)] | |
| return DATA_OVERALL | |
| def get_mteb_average_zh(): | |
| global DATA_OVERALL_ZH, DATA_CLASSIFICATION_ZH, DATA_CLUSTERING_ZH, DATA_PAIR_CLASSIFICATION_ZH, DATA_RERANKING_ZH, DATA_RETRIEVAL_ZH, DATA_STS_ZH | |
| DATA_OVERALL_ZH = get_mteb_data( | |
| tasks=[ | |
| "Classification", | |
| "Clustering", | |
| "PairClassification", | |
| "Reranking", | |
| "Retrieval", | |
| "STS", | |
| ], | |
| datasets=TASK_LIST_CLASSIFICATION_ZH + TASK_LIST_CLUSTERING_ZH + TASK_LIST_PAIR_CLASSIFICATION_ZH + TASK_LIST_RERANKING_ZH + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_STS_ZH, | |
| fillna=False, | |
| add_emb_dim=True, | |
| rank=False, | |
| ) | |
| # Debugging: | |
| # DATA_OVERALL_ZH.to_csv("overall.csv") | |
| DATA_OVERALL_ZH.insert(1, f"Average ({len(TASK_LIST_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_ZH].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_ZH.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLASSIFICATION_ZH].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_ZH.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLUSTERING_ZH].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_ZH.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_PAIR_CLASSIFICATION_ZH].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_ZH.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RERANKING_ZH].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_ZH.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RETRIEVAL_ZH].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_ZH.insert(7, f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_STS_ZH].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_ZH.sort_values(f"Average ({len(TASK_LIST_ZH)} datasets)", ascending=False, inplace=True) | |
| # Start ranking from 1 | |
| DATA_OVERALL_ZH.insert(0, "Rank", list(range(1, len(DATA_OVERALL_ZH) + 1))) | |
| DATA_OVERALL_ZH = DATA_OVERALL_ZH.round(2) | |
| DATA_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_CLASSIFICATION_ZH]) | |
| # Only keep rows with at least one score in addition to the "Model" & rank column | |
| DATA_CLASSIFICATION_ZH = DATA_CLASSIFICATION_ZH[DATA_CLASSIFICATION_ZH.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_CLUSTERING_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_CLUSTERING_ZH]) | |
| DATA_CLUSTERING_ZH = DATA_CLUSTERING_ZH[DATA_CLUSTERING_ZH.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_PAIR_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_PAIR_CLASSIFICATION_ZH]) | |
| DATA_PAIR_CLASSIFICATION_ZH = DATA_PAIR_CLASSIFICATION_ZH[DATA_PAIR_CLASSIFICATION_ZH.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_RERANKING_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_RERANKING_ZH]) | |
| DATA_RERANKING_ZH = DATA_RERANKING_ZH[DATA_RERANKING_ZH.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_RETRIEVAL_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_RETRIEVAL_ZH]) | |
| DATA_RETRIEVAL_ZH = DATA_RETRIEVAL_ZH[DATA_RETRIEVAL_ZH.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_STS_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_STS_ZH]) | |
| DATA_STS_ZH = DATA_STS_ZH[DATA_STS_ZH.iloc[:, 2:].ne("").any(axis=1)] | |
| # Fill NaN after averaging | |
| DATA_OVERALL_ZH.fillna("", inplace=True) | |
| DATA_OVERALL_ZH = DATA_OVERALL_ZH[["Rank", "Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_ZH)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)"]] | |
| DATA_OVERALL_ZH = DATA_OVERALL_ZH[DATA_OVERALL_ZH.iloc[:, 5:].ne("").any(axis=1)] | |
| return DATA_OVERALL_ZH | |
| def get_mteb_average_pl(): | |
| global DATA_OVERALL_PL, DATA_CLASSIFICATION_PL, DATA_CLUSTERING_PL, DATA_PAIR_CLASSIFICATION_PL, DATA_RETRIEVAL_PL, DATA_STS_PL | |
| DATA_OVERALL_PL = get_mteb_data( | |
| tasks=[ | |
| "Classification", | |
| "Clustering", | |
| "PairClassification", | |
| "Retrieval", | |
| "STS", | |
| ], | |
| datasets=TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLUSTERING_PL + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_RETRIEVAL_PL + TASK_LIST_STS_PL, | |
| fillna=False, | |
| add_emb_dim=True, | |
| rank=False, | |
| ) | |
| # Debugging: | |
| # DATA_OVERALL_PL.to_csv("overall.csv") | |
| DATA_OVERALL_PL.insert(1, f"Average ({len(TASK_LIST_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PL].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_PL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLASSIFICATION_PL].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_PL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLUSTERING_PL].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_PL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PAIR_CLASSIFICATION_PL].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_PL.insert(5, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_RETRIEVAL_PL].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_PL.insert(6, f"STS Average ({len(TASK_LIST_STS_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_STS_PL].mean(axis=1, skipna=False)) | |
| DATA_OVERALL_PL.sort_values(f"Average ({len(TASK_LIST_PL)} datasets)", ascending=False, inplace=True) | |
| # Start ranking from 1 | |
| DATA_OVERALL_PL.insert(0, "Rank", list(range(1, len(DATA_OVERALL_PL) + 1))) | |
| DATA_OVERALL_PL = DATA_OVERALL_PL.round(2) | |
| DATA_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_CLASSIFICATION_PL]) | |
| # Only keep rows with at least one score in addition to the "Model" & rank column | |
| DATA_CLASSIFICATION_PL = DATA_CLASSIFICATION_PL[DATA_CLASSIFICATION_PL.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_CLUSTERING_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_CLUSTERING_PL]) | |
| DATA_CLUSTERING_PL = DATA_CLUSTERING_PL[DATA_CLUSTERING_PL.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_PAIR_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_PAIR_CLASSIFICATION_PL]) | |
| DATA_PAIR_CLASSIFICATION_PL = DATA_PAIR_CLASSIFICATION_PL[DATA_PAIR_CLASSIFICATION_PL.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_RETRIEVAL_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_RETRIEVAL_PL]) | |
| DATA_RETRIEVAL_PL = DATA_RETRIEVAL_PL[DATA_RETRIEVAL_PL.iloc[:, 2:].ne("").any(axis=1)] | |
| DATA_STS_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_STS_PL]) | |
| DATA_STS_PL = DATA_STS_PL[DATA_STS_PL.iloc[:, 2:].ne("").any(axis=1)] | |
| # Fill NaN after averaging | |
| DATA_OVERALL_PL.fillna("", inplace=True) | |
| DATA_OVERALL_PL = DATA_OVERALL_PL[["Rank", "Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_PL)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", f"STS Average ({len(TASK_LIST_STS_PL)} datasets)"]] | |
| DATA_OVERALL_PL = DATA_OVERALL_PL[DATA_OVERALL_PL.iloc[:, 5:].ne("").any(axis=1)] | |
| return DATA_OVERALL_PL | |
| get_mteb_average() | |
| get_mteb_average_pl() | |
| get_mteb_average_zh() | |
| DATA_BITEXT_MINING = get_mteb_data(["BitextMining"], [], TASK_LIST_BITEXT_MINING) | |
| DATA_BITEXT_MINING_OTHER = get_mteb_data(["BitextMining"], [], TASK_LIST_BITEXT_MINING_OTHER) | |
| DATA_CLASSIFICATION_DA = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_DA) | |
| DATA_CLASSIFICATION_NB = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_NB) | |
| DATA_CLASSIFICATION_SV = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_SV) | |
| DATA_CLASSIFICATION_OTHER = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_OTHER) | |
| DATA_CLUSTERING_DE = get_mteb_data(["Clustering"], [], TASK_LIST_CLUSTERING_DE) | |
| DATA_STS_OTHER = get_mteb_data(["STS"], [], TASK_LIST_STS_OTHER) | |
| # Exact, add all non-nan integer values for every dataset | |
| NUM_SCORES = 0 | |
| DATASETS = [] | |
| MODELS = [] | |
| # LANGUAGES = [] | |
| for d in [ | |
| DATA_BITEXT_MINING, | |
| DATA_BITEXT_MINING_OTHER, | |
| DATA_CLASSIFICATION_EN, | |
| DATA_CLASSIFICATION_DA, | |
| DATA_CLASSIFICATION_NB, | |
| DATA_CLASSIFICATION_PL, | |
| DATA_CLASSIFICATION_SV, | |
| DATA_CLASSIFICATION_ZH, | |
| DATA_CLASSIFICATION_OTHER, | |
| DATA_CLUSTERING, | |
| DATA_CLUSTERING_DE, | |
| DATA_CLUSTERING_PL, | |
| DATA_CLUSTERING_ZH, | |
| DATA_PAIR_CLASSIFICATION, | |
| DATA_PAIR_CLASSIFICATION_PL, | |
| DATA_PAIR_CLASSIFICATION_ZH, | |
| DATA_RERANKING, | |
| DATA_RERANKING_ZH, | |
| DATA_RETRIEVAL, | |
| DATA_RETRIEVAL_PL, | |
| DATA_RETRIEVAL_ZH, | |
| DATA_STS_EN, | |
| DATA_STS_PL, | |
| DATA_STS_ZH, | |
| DATA_STS_OTHER, | |
| DATA_SUMMARIZATION, | |
| ]: | |
| # NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum() | |
| cols_to_ignore = 3 if "Average" in d.columns else 2 | |
| # Count number of scores including only non-nan floats & excluding the rank column | |
| NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum() | |
| # Exclude rank & model name column (first two); Do not count different language versions as different datasets | |
| DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]] | |
| # LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]] | |
| MODELS += d["Model"].tolist() | |
| NUM_DATASETS = len(set(DATASETS)) | |
| # NUM_LANGUAGES = len(set(LANGUAGES)) | |
| NUM_MODELS = len(set(MODELS)) | |
| # 1. Force headers to wrap | |
| # 2. Force model column (maximum) width | |
| # 3. Prevent model column from overflowing, scroll instead | |
| css = """ | |
| table > thead { | |
| white-space: normal | |
| } | |
| table { | |
| --cell-width-1: 250px | |
| } | |
| table > tbody > tr > td:nth-child(2) > div { | |
| overflow-x: auto | |
| } | |
| """ | |
| block = gr.Blocks(css=css) | |
| with block: | |
| gr.Markdown(f""" | |
| Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the <a href="https://github.com/embeddings-benchmark/mteb#leaderboard" target="_blank" style="text-decoration: underline">MTEB GitHub repository</a> 🤗 Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models. | |
| """) | |
| with gr.Tabs(): | |
| with gr.TabItem("Overall"): | |
| with gr.TabItem("English"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Overall MTEB English leaderboard** 🔮 | |
| - **Metric:** Various, refer to task tabs | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_overall = gr.components.Dataframe( | |
| DATA_OVERALL, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL.columns), | |
| type="pandas", | |
| height=600, | |
| ) | |
| with gr.Row(): | |
| data_run_overall = gr.Button("Refresh") | |
| data_run_overall.click(get_mteb_average, inputs=None, outputs=data_overall) | |
| with gr.TabItem("Chinese"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Overall MTEB Chinese leaderboard (C-MTEB)** 🔮🇨🇳 | |
| - **Metric:** Various, refer to task tabs | |
| - **Languages:** Chinese | |
| - **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | |
| """) | |
| with gr.Row(): | |
| data_overall_zh = gr.components.Dataframe( | |
| DATA_OVERALL_ZH, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL_ZH.columns), | |
| type="pandas", | |
| height=600, | |
| ) | |
| with gr.Row(): | |
| data_run_overall_zh = gr.Button("Refresh") | |
| data_run_overall_zh.click(get_mteb_average_zh, inputs=None, outputs=data_overall_zh) | |
| with gr.TabItem("Polish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Overall MTEB Polish leaderboard (PL-MTEB)** 🔮🇵🇱 | |
| - **Metric:** Various, refer to task tabs | |
| - **Languages:** Polish | |
| - **Credits:** [Rafał Poświata](https://github.com/rafalposwiata), [Konrad Wojtasik](https://github.com/kwojtasi) & [BEIR-PL](https://arxiv.org/abs/2305.19840) | |
| """) | |
| with gr.Row(): | |
| data_overall_pl = gr.components.Dataframe( | |
| DATA_OVERALL_PL, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL_PL.columns), | |
| type="pandas", | |
| height=600, | |
| ) | |
| with gr.Row(): | |
| data_run_overall_pl = gr.Button("Refresh") | |
| data_run_overall_pl.click(get_mteb_average_pl, inputs=None, outputs=data_overall_pl) | |
| with gr.TabItem("Bitext Mining"): | |
| with gr.TabItem("English-X"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Bitext Mining English-X Leaderboard** 🎌 | |
| - **Metric:** [F1](https://huggingface.co/spaces/evaluate-metric/f1) | |
| - **Languages:** 117 (Pairs of: English & other language) | |
| """) | |
| with gr.Row(): | |
| data_bitext_mining = gr.components.Dataframe( | |
| DATA_BITEXT_MINING, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_BITEXT_MINING.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_bitext_mining = gr.Button("Refresh") | |
| data_run_bitext_mining.click( | |
| partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING), | |
| outputs=data_bitext_mining, | |
| ) | |
| with gr.TabItem("Danish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Bitext Mining Danish Leaderboard** 🎌🇩🇰 | |
| - **Metric:** [F1](https://huggingface.co/spaces/evaluate-metric/f1) | |
| - **Languages:** Danish & Bornholmsk (Danish Dialect) | |
| - **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/) | |
| """) | |
| with gr.Row(): | |
| data_bitext_mining_da = gr.components.Dataframe( | |
| DATA_BITEXT_MINING_OTHER, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_BITEXT_MINING_OTHER.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_bitext_mining_da = gr.Button("Refresh") | |
| data_run_bitext_mining_da.click( | |
| partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING_OTHER), | |
| outputs=data_bitext_mining_da, | |
| ) | |
| with gr.TabItem("Classification"): | |
| with gr.TabItem("English"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Classification English Leaderboard** ❤️ | |
| - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_classification_en = gr.components.Dataframe( | |
| DATA_CLASSIFICATION_EN, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_EN.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_classification_en = gr.Button("Refresh") | |
| data_run_classification_en.click( | |
| partial(get_mteb_data, tasks=["Classification"], langs=["en"]), | |
| outputs=data_classification_en, | |
| ) | |
| with gr.TabItem("Chinese"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Classification Chinese Leaderboard** 🧡🇨🇳 | |
| - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) | |
| - **Languages:** Chinese | |
| - **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | |
| """) | |
| with gr.Row(): | |
| data_classification_zh = gr.components.Dataframe( | |
| DATA_CLASSIFICATION_ZH, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_ZH.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_classification_zh = gr.Button("Refresh") | |
| data_run_classification_zh.click( | |
| partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_ZH), | |
| outputs=data_classification_zh, | |
| ) | |
| with gr.TabItem("Danish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Classification Danish Leaderboard** 🤍🇩🇰 | |
| - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) | |
| - **Languages:** Danish | |
| - **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/) | |
| """) | |
| with gr.Row(): | |
| data_classification_da = gr.components.Dataframe( | |
| DATA_CLASSIFICATION_DA, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_DA.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_classification_da = gr.Button("Refresh") | |
| data_run_classification_da.click( | |
| partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_DA), | |
| outputs=data_run_classification_da, | |
| ) | |
| with gr.TabItem("Norwegian"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Classification Norwegian Leaderboard** 💙🇳🇴 | |
| - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) | |
| - **Languages:** Norwegian Bokmål | |
| - **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/) | |
| """) | |
| with gr.Row(): | |
| data_classification_nb = gr.components.Dataframe( | |
| DATA_CLASSIFICATION_NB, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_NB.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_classification_nb = gr.Button("Refresh") | |
| data_run_classification_nb.click( | |
| partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_NB), | |
| outputs=data_classification_nb, | |
| ) | |
| with gr.TabItem("Polish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Classification Polish Leaderboard** 🤍🇵🇱 | |
| - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) | |
| - **Languages:** Polish | |
| - **Credits:** [Rafał Poświata](https://github.com/rafalposwiata) | |
| """) | |
| with gr.Row(): | |
| data_classification_pl = gr.components.Dataframe( | |
| DATA_CLASSIFICATION_PL, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_PL.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_classification_pl = gr.Button("Refresh") | |
| data_run_classification_pl.click( | |
| partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_PL), | |
| outputs=data_classification_pl, | |
| ) | |
| with gr.TabItem("Swedish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Classification Swedish Leaderboard** 💛🇸🇪 | |
| - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) | |
| - **Languages:** Swedish | |
| - **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/) | |
| """) | |
| with gr.Row(): | |
| data_classification_sv = gr.components.Dataframe( | |
| DATA_CLASSIFICATION_SV, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_SV.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_classification_sv = gr.Button("Refresh") | |
| data_run_classification_sv.click( | |
| partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_SV), | |
| outputs=data_classification_sv, | |
| ) | |
| with gr.TabItem("Other"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Classification Other Languages Leaderboard** 💜💚💙 | |
| - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) | |
| - **Languages:** 47 (Only languages not included in the other tabs) | |
| """) | |
| with gr.Row(): | |
| data_classification = gr.components.Dataframe( | |
| DATA_CLASSIFICATION_OTHER, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_OTHER) * 10, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_classification = gr.Button("Refresh") | |
| data_run_classification.click( | |
| partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_OTHER), | |
| outputs=data_classification, | |
| ) | |
| with gr.TabItem("Clustering"): | |
| with gr.TabItem("English"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Clustering Leaderboard** ✨ | |
| - **Metric:** Validity Measure (v_measure) | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_clustering = gr.components.Dataframe( | |
| DATA_CLUSTERING, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLUSTERING.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_clustering_en = gr.Button("Refresh") | |
| data_run_clustering_en.click( | |
| partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING), | |
| outputs=data_clustering, | |
| ) | |
| with gr.TabItem("Chinese"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Clustering Chinese Leaderboard** ✨🇨🇳 | |
| - **Metric:** Validity Measure (v_measure) | |
| - **Languages:** Chinese | |
| - **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | |
| """) | |
| with gr.Row(): | |
| data_clustering_zh = gr.components.Dataframe( | |
| DATA_CLUSTERING_ZH, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLUSTERING_ZH.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_clustering_zh = gr.Button("Refresh") | |
| data_run_clustering_zh.click( | |
| partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_ZH), | |
| outputs=data_clustering_zh, | |
| ) | |
| with gr.TabItem("German"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Clustering German Leaderboard** ✨🇩🇪 | |
| - **Metric:** Validity Measure (v_measure) | |
| - **Languages:** German | |
| - **Credits:** [Silvan](https://github.com/slvnwhrl) | |
| """) | |
| with gr.Row(): | |
| data_clustering_de = gr.components.Dataframe( | |
| DATA_CLUSTERING_DE, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLUSTERING_DE.columns) * 2, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_clustering_de = gr.Button("Refresh") | |
| data_run_clustering_de.click( | |
| partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_DE), | |
| outputs=data_clustering_de, | |
| ) | |
| with gr.TabItem("Polish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Clustering Polish Leaderboard** ✨🇵🇱 | |
| - **Metric:** Validity Measure (v_measure) | |
| - **Languages:** Polish | |
| - **Credits:** [Rafał Poświata](https://github.com/rafalposwiata) | |
| """) | |
| with gr.Row(): | |
| data_clustering_pl = gr.components.Dataframe( | |
| DATA_CLUSTERING_PL, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_CLUSTERING_PL.columns) * 2, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_clustering_pl = gr.Button("Refresh") | |
| data_run_clustering_pl.click( | |
| partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_PL), | |
| outputs=data_clustering_pl, | |
| ) | |
| with gr.TabItem("Pair Classification"): | |
| with gr.TabItem("English"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Pair Classification English Leaderboard** 🎭 | |
| - **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap) | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_pair_classification = gr.components.Dataframe( | |
| DATA_PAIR_CLASSIFICATION, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_PAIR_CLASSIFICATION.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_pair_classification = gr.Button("Refresh") | |
| data_run_pair_classification.click( | |
| partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION), | |
| outputs=data_pair_classification, | |
| ) | |
| with gr.TabItem("Chinese"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Pair Classification Chinese Leaderboard** 🎭🇨🇳 | |
| - **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap) | |
| - **Languages:** Chinese | |
| - **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | |
| """) | |
| with gr.Row(): | |
| data_pair_classification_zh = gr.components.Dataframe( | |
| DATA_PAIR_CLASSIFICATION_ZH, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_PAIR_CLASSIFICATION_ZH.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_pair_classification_zh = gr.Button("Refresh") | |
| data_run_pair_classification_zh.click( | |
| partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_ZH), | |
| outputs=data_pair_classification_zh, | |
| ) | |
| with gr.TabItem("Polish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Pair Classification Polish Leaderboard** 🎭🇵🇱 | |
| - **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap) | |
| - **Languages:** Polish | |
| - **Credits:** [Rafał Poświata](https://github.com/rafalposwiata) | |
| """) | |
| with gr.Row(): | |
| data_pair_classification_pl = gr.components.Dataframe( | |
| DATA_PAIR_CLASSIFICATION_PL, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_PAIR_CLASSIFICATION_PL.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_pair_classification_pl = gr.Button("Refresh") | |
| data_run_pair_classification_pl.click( | |
| partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_PL), | |
| outputs=data_pair_classification_pl, | |
| ) | |
| with gr.TabItem("Reranking"): | |
| with gr.TabItem("English"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Reranking English Leaderboard** 🥈 | |
| - **Metric:** Mean Average Precision (MAP) | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_reranking = gr.components.Dataframe( | |
| DATA_RERANKING, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_RERANKING.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_reranking = gr.Button("Refresh") | |
| data_run_reranking.click( | |
| partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING), | |
| outputs=data_reranking, | |
| ) | |
| with gr.TabItem("Chinese"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Reranking Chinese Leaderboard** 🥈🇨🇳 | |
| - **Metric:** Mean Average Precision (MAP) | |
| - **Languages:** Chinese | |
| - **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | |
| """) | |
| with gr.Row(): | |
| data_reranking_zh = gr.components.Dataframe( | |
| DATA_RERANKING_ZH, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_RERANKING_ZH.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_reranking_zh = gr.Button("Refresh") | |
| data_run_reranking_zh.click( | |
| partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_ZH), | |
| outputs=data_reranking_zh, | |
| ) | |
| with gr.TabItem("Retrieval"): | |
| with gr.TabItem("English"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Retrieval English Leaderboard** 🔎 | |
| - **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10) | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_retrieval = gr.components.Dataframe( | |
| DATA_RETRIEVAL, | |
| # Add support for more columns than existing as a buffer for CQADupstack & other Retrieval tasks (e.g. MSMARCOv2) | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_RETRIEVAL.columns) * 2, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_retrieval = gr.Button("Refresh") | |
| data_run_retrieval.click( | |
| partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL), | |
| outputs=data_retrieval, | |
| ) | |
| with gr.TabItem("Chinese"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Retrieval Chinese Leaderboard** 🔎🇨🇳 | |
| - **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10) | |
| - **Languages:** Chinese | |
| - **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | |
| """) | |
| with gr.Row(): | |
| data_retrieval_zh = gr.components.Dataframe( | |
| DATA_RETRIEVAL_ZH, | |
| # Add support for more columns than existing as a buffer for CQADupstack & other Retrieval tasks (e.g. MSMARCOv2) | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_RETRIEVAL_ZH.columns) * 2, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_retrieval_zh = gr.Button("Refresh") | |
| data_run_retrieval_zh.click( | |
| partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_ZH), | |
| outputs=data_retrieval_zh, | |
| ) | |
| with gr.TabItem("Polish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Retrieval Polish Leaderboard** 🔎🇵🇱 | |
| - **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10) | |
| - **Languages:** Polish | |
| - **Credits:** [Konrad Wojtasik](https://github.com/kwojtasi) & [BEIR-PL](https://arxiv.org/abs/2305.19840) | |
| """) | |
| with gr.Row(): | |
| data_retrieval_pl = gr.components.Dataframe( | |
| DATA_RETRIEVAL_PL, | |
| # Add support for more columns than existing as a buffer for CQADupstack & other Retrieval tasks (e.g. MSMARCOv2) | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_RETRIEVAL_PL.columns) * 2, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_retrieval_pl = gr.Button("Refresh") | |
| data_run_retrieval_pl.click( | |
| partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_PL), | |
| outputs=data_retrieval_pl, | |
| ) | |
| with gr.TabItem("STS"): | |
| with gr.TabItem("English"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **STS English Leaderboard** 🤖 | |
| - **Metric:** Spearman correlation based on cosine similarity | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_sts_en = gr.components.Dataframe( | |
| DATA_STS_EN, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_STS_EN.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_sts_en = gr.Button("Refresh") | |
| data_run_sts_en.click( | |
| partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS), | |
| outputs=data_sts_en, | |
| ) | |
| with gr.TabItem("Chinese"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **STS Chinese Leaderboard** 🤖🇨🇳 | |
| - **Metric:** Spearman correlation based on cosine similarity | |
| - **Languages:** Chinese | |
| - **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | |
| """) | |
| with gr.Row(): | |
| data_sts_zh = gr.components.Dataframe( | |
| DATA_STS_ZH, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_STS_ZH.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_sts_zh = gr.Button("Refresh") | |
| data_run_sts_zh.click( | |
| partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_ZH), | |
| outputs=data_sts_zh, | |
| ) | |
| with gr.TabItem("Polish"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **STS Polish Leaderboard** 🤖🇵🇱 | |
| - **Metric:** Spearman correlation based on cosine similarity | |
| - **Languages:** Polish | |
| - **Credits:** [Rafał Poświata](https://github.com/rafalposwiata) | |
| """) | |
| with gr.Row(): | |
| data_sts_pl = gr.components.Dataframe( | |
| DATA_STS_PL, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_STS_PL.columns), | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_sts_pl = gr.Button("Refresh") | |
| data_run_sts_pl.click( | |
| partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_PL), | |
| outputs=data_sts_pl, | |
| ) | |
| with gr.TabItem("Other"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **STS Other Leaderboard** 👽 | |
| - **Metric:** Spearman correlation based on cosine similarity | |
| - **Languages:** Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs) | |
| """) | |
| with gr.Row(): | |
| data_sts_other = gr.components.Dataframe( | |
| DATA_STS_OTHER, | |
| datatype=["number", "markdown"] + ["number"] * len(DATA_STS_OTHER.columns) * 2, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run_sts_other = gr.Button("Refresh") | |
| data_run_sts_other.click( | |
| partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_OTHER), | |
| outputs=data_sts_other, | |
| ) | |
| with gr.TabItem("Summarization"): | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| **Summarization Leaderboard** 📜 | |
| - **Metric:** Spearman correlation based on cosine similarity | |
| - **Languages:** English | |
| """) | |
| with gr.Row(): | |
| data_summarization = gr.components.Dataframe( | |
| DATA_SUMMARIZATION, | |
| datatype=["number", "markdown"] + ["number"] * 2, | |
| type="pandas", | |
| ) | |
| with gr.Row(): | |
| data_run = gr.Button("Refresh") | |
| data_run.click( | |
| partial(get_mteb_data, tasks=["Summarization"]), | |
| outputs=data_summarization, | |
| ) | |
| gr.Markdown(f""" | |
| - **Total Datasets**: {NUM_DATASETS} | |
| - **Total Languages**: 113 | |
| - **Total Scores**: {NUM_SCORES} | |
| - **Total Models**: {NUM_MODELS} | |
| """ + r""" | |
| Made with ❤️ for NLP. If this work is useful to you, please consider citing: | |
| ```bibtex | |
| @article{muennighoff2022mteb, | |
| doi = {10.48550/ARXIV.2210.07316}, | |
| url = {https://arxiv.org/abs/2210.07316}, | |
| author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, | |
| title = {MTEB: Massive Text Embedding Benchmark}, | |
| publisher = {arXiv}, | |
| journal={arXiv preprint arXiv:2210.07316}, | |
| year = {2022} | |
| } | |
| ``` | |
| """) | |
| # Running the functions on page load in addition to when the button is clicked | |
| # This is optional - If deactivated the data loaded at "Build time" is shown like for Overall tab | |
| """ | |
| block.load(get_mteb_data, inputs=[task_bitext_mining], outputs=data_bitext_mining) | |
| """ | |
| block.queue(max_size=10) | |
| block.launch() | |
| # Possible changes: | |
| # Could add graphs / other visual content | |
| # Could add verification marks | |
| # Sources: | |
| # https://huggingface.co/spaces/gradio/leaderboard | |
| # https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard | |
| # https://getemoji.com/ | |