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 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'{model_name.split("/")[-1]}' ) # 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", } 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 for model in EXTERNAL_MODELS: 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) 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': 'BornholmBitextMining',}) 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) # 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""" SeaEval Leaderboard. To submit, refer to the SeaEval Website Refer to the [SeaEval paper](https://arxiv.org/abs/2309.04766) for details on metrics, tasks and models. - **Total Datasets**: 31 - **Total Languages**: 8 - **Total Models**: 5 """) with gr.Tabs(): # dataset 1: cross-mmlu with gr.TabItem("Cross-MMLU"): with gr.Row(): gr.Markdown(""" **Overall Cross-MMLU Leaderboard** ๐Ÿ”ฎ - **Metric:** Cross-Lingual Consistency, Accuracy, AC3 - **Languages:** English, Chinese, Malay, Indonesian, Spanish, Vietnamese, Filipino """) with gr.TabItem("Zero-Shot"): with gr.TabItem("Overall"): 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("Detailed Consistency"): 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("Five-Shot"): with gr.TabItem("Overall"): 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("Detailed Consistency"): 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("Classification"): with gr.TabItem("English"): with gr.Row(): gr.Markdown(""" **Classification English Leaderboard** โค๏ธ - **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** ๐Ÿงก๐Ÿ‡จ๐Ÿ‡ณ - **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** ๐Ÿค๐Ÿ‡ฉ๐Ÿ‡ฐ - **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** ๐Ÿ’™๐Ÿ‡ณ๐Ÿ‡ด - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) - **Languages:** Norwegian Bokmรฅl - **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** ๐Ÿค๐Ÿ‡ต๐Ÿ‡ฑ - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) - **Languages:** Polish - **Credits:** [Rafaล‚ Poล›wiata](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** ๐Ÿ’›๐Ÿ‡ธ๐Ÿ‡ช - **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** ๐Ÿ’œ๐Ÿ’š๐Ÿ’™ - **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** โœจ - **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** โœจ๐Ÿ‡จ๐Ÿ‡ณ - **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** โœจ๐Ÿ‡ฉ๐Ÿ‡ช - **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** โœจ๐Ÿ‡ต๐Ÿ‡ฑ - **Metric:** Validity Measure (v_measure) - **Languages:** Polish - **Credits:** [Rafaล‚ Poล›wiata](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** ๐ŸŽญ - **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** ๐ŸŽญ๐Ÿ‡จ๐Ÿ‡ณ - **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** ๐ŸŽญ๐Ÿ‡ต๐Ÿ‡ฑ - **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap) - **Languages:** Polish - **Credits:** [Rafaล‚ Poล›wiata](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** ๐Ÿฅˆ - **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** ๐Ÿฅˆ๐Ÿ‡จ๐Ÿ‡ณ - **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** ๐Ÿ”Ž - **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** ๐Ÿ”Ž๐Ÿ‡จ๐Ÿ‡ณ - **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** ๐Ÿ”Ž๐Ÿ‡ต๐Ÿ‡ฑ - **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** ๐Ÿค– - **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** ๐Ÿค–๐Ÿ‡จ๐Ÿ‡ณ - **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** ๐Ÿค–๐Ÿ‡ต๐Ÿ‡ฑ - **Metric:** Spearman correlation based on cosine similarity - **Languages:** Polish - **Credits:** [Rafaล‚ Poล›wiata](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** ๐Ÿ‘ฝ - **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** ๐Ÿ“œ - **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""" If this work is useful to you, please consider citing: ```bibtex @article{SeaEval2023, title={SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning}, author={Wang, Bin and Liu, Zhengyuan and Huang, Xin and Jiao, Fangkai and Ding, Yang and Aw, Ai Ti and Chen, Nancy F.}, journal={arXiv preprint arXiv:2309.04766}, year={2023} } ``` """) # 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(server_name="0.0.0.0", share=True) # 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/