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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",
	"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-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",
	"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",
	"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-ada-002": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-similarity-ada-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-similarity-babbage-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-similarity-curie-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-similarity-davinci-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-search-ada-doc-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-search-ada-query-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-search-ada-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-search-curie-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-search-babbage-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"text-search-davinci-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
	"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/",
	"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,
	"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-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,
	"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,
	"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-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": 4096,
	"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,
	"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",
	"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",
	}

	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)
	# 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", "Sequence Length"]]
	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["Sequence Length"] = 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:
	out["Embedding Dimensions"], out["Sequence Length"], out["Model Size (GB)"] = get_dim_seq_size(model)
	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", "Sequence Length", 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", "Sequence Length", 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", "Sequence Length", 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))

	block = gr.Blocks()
	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.

	- Total Datasets: {NUM_DATASETS}
	- Total Languages: 113
	- Total Scores: {NUM_SCORES}
	- Total Models: {NUM_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",
	wrap=True,
	)
	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",
	wrap=True,
	)
	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",
	wrap=True,
	)
	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(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/