from functools import partial
import json
import os
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_DA = [
"AngryTweetsClassification",
"DanishPoliticalCommentsClassification",
"DKHateClassification",
"LccSentimentClassification",
"MassiveIntentClassification (da)",
"MassiveScenarioClassification (da)",
"NordicLangClassification",
"ScalaDaClassification",
]
TASK_LIST_CLASSIFICATION_FR = [
"AmazonReviewsClassification (fr)",
"MasakhaNEWSClassification (fra)",
"MassiveIntentClassification (fr)",
"MassiveScenarioClassification (fr)",
"MTOPDomainClassification (fr)",
"MTOPIntentClassification (fr)",
]
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_FR = [
"AlloProfClusteringP2P",
"AlloProfClusteringS2S",
"HALClusteringS2S",
"MLSUMClusteringP2P",
"MLSUMClusteringS2S",
"MasakhaNEWSClusteringP2P (fra)",
"MasakhaNEWSClusteringS2S (fra)",
]
TASK_LIST_CLUSTERING_PL = [
"8TagsClustering",
]
TASK_LIST_CLUSTERING_ZH = [
"CLSClusteringP2P",
"CLSClusteringS2S",
"ThuNewsClusteringP2P",
"ThuNewsClusteringS2S",
]
TASK_LIST_PAIR_CLASSIFICATION = [
"SprintDuplicateQuestions",
"TwitterSemEval2015",
"TwitterURLCorpus",
]
TASK_LIST_PAIR_CLASSIFICATION_FR = [
"OpusparcusPC (fr)",
"PawsX (fr)",
]
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_FR = [
"AlloprofReranking",
"SyntecReranking",
]
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_FR = [
"AlloprofRetrieval",
"BSARDRetrieval",
"MintakaRetrieval (fr)",
# "MultiLongDocRetrieval",
"SyntecRetrieval",
"XPQARetrieval (fr)",
]
TASK_LIST_RETRIEVAL_LAW = [
"AILACasedocs",
"AILAStatutes",
"GerDaLIRSmall",
"LeCaRDv2",
"LegalBenchConsumerContractsQA",
"LegalBenchCorporateLobbying",
"LegalQuAD",
"LegalSummarization",
]
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_FR = [
"STS22 (fr)",
"STSBenchmarkMultilingualSTS (fr)",
"SICKFr",
]
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_SUMMARIZATION = ["SummEval",]
TASK_LIST_SUMMARIZATION_FR = ["SummEvalFr"]
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_FR = TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLUSTERING_FR + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_RERANKING_FR + TASK_LIST_RETRIEVAL_FR + TASK_LIST_STS_FR + TASK_LIST_SUMMARIZATION_FR
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 = [
"Baichuan-text-embedding",
"Cohere-embed-english-v3.0",
"Cohere-embed-multilingual-v3.0",
"Cohere-embed-multilingual-light-v3.0",
"DanskBERT",
"LASER2",
"LaBSE",
"OpenSearch-text-hybrid",
"all-MiniLM-L12-v2",
"all-MiniLM-L6-v2",
"all-mpnet-base-v2",
"allenai-specter",
"bert-base-10lang-cased",
"bert-base-15lang-cased",
"bert-base-25lang-cased",
"bert-base-multilingual-cased",
"bert-base-multilingual-uncased",
"bert-base-swedish-cased",
"bert-base-uncased",
"bge-base-zh-v1.5",
"bge-large-en-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",
"e5-base",
"e5-large",
"e5-mistral-7b-instruct",
"e5-small",
"electra-small-nordic",
"electra-small-swedish-cased-discriminator",
"flaubert_base_cased",
"flaubert_base_uncased",
"flaubert_large_cased",
"gbert-base",
"gbert-large",
"gelectra-base",
"gelectra-large",
"glove.6B.300d",
"google-gecko.text-embedding-preview-0409",
"google-gecko-256.text-embedding-preview-0409",
"gottbert-base",
"gtr-t5-base",
"gtr-t5-large",
"gtr-t5-xl",
"gtr-t5-xxl",
"herbert-base-retrieval-v2",
"komninos",
"luotuo-bert-medium",
"m3e-base",
"m3e-large",
"mistral-embed",
"msmarco-bert-co-condensor",
"multi-qa-MiniLM-L6-cos-v1",
"multilingual-e5-base",
"multilingual-e5-large",
"multilingual-e5-small",
"nb-bert-base",
"nb-bert-large",
"nomic-embed-text-v1.5-64",
"nomic-embed-text-v1.5-128",
"nomic-embed-text-v1.5-256",
"nomic-embed-text-v1.5-512",
"norbert3-base",
"norbert3-large",
"paraphrase-multilingual-MiniLM-L12-v2",
"paraphrase-multilingual-mpnet-base-v2",
"sentence-bert-swedish-cased",
"sentence-camembert-base",
"sentence-camembert-large",
"sentence-croissant-llm-base",
"sentence-t5-base",
"sentence-t5-large",
"sentence-t5-xl",
"sentence-t5-xxl",
"silver-retriever-base-v1",
"sup-simcse-bert-base-uncased",
"st-polish-paraphrase-from-distilroberta",
"st-polish-paraphrase-from-mpnet",
"text2vec-base-chinese",
"text2vec-base-multilingual",
"text2vec-large-chinese",
"text-embedding-3-small",
"text-embedding-3-large",
"text-embedding-3-large-256",
"text-embedding-ada-002",
"text-similarity-ada-001",
"text-similarity-babbage-001",
"text-similarity-curie-001",
"text-similarity-davinci-001",
"text-search-ada-doc-001",
"text-search-ada-001",
"text-search-babbage-001",
"text-search-curie-001",
"text-search-davinci-001",
"titan-embed-text-v1",
"udever-bloom-1b1",
"udever-bloom-560m",
"universal-sentence-encoder-multilingual-3",
"universal-sentence-encoder-multilingual-large-3",
"unsup-simcse-bert-base-uncased",
"use-cmlm-multilingual",
"voyage-2",
"voyage-code-2",
"voyage-lite-01-instruct",
"voyage-lite-02-instruct",
"xlm-roberta-base",
"xlm-roberta-large",
]
EXTERNAL_MODEL_TO_LINK = {
"Cohere-embed-english-v3.0": "https://huggingface.co/Cohere/Cohere-embed-english-v3.0",
"Cohere-embed-multilingual-v3.0": "https://huggingface.co/Cohere/Cohere-embed-multilingual-v3.0",
"Cohere-embed-multilingual-light-v3.0": "https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0",
"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-10lang-cased": "https://huggingface.co/Geotrend/bert-base-10lang-cased",
"bert-base-15lang-cased": "https://huggingface.co/Geotrend/bert-base-15lang-cased",
"bert-base-25lang-cased": "https://huggingface.co/Geotrend/bert-base-25lang-cased",
"bert-base-multilingual-cased": "https://huggingface.co/google-bert/bert-base-multilingual-cased",
"bert-base-multilingual-uncased": "https://huggingface.co/google-bert/bert-base-multilingual-uncased",
"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-en-v1.5": "https://huggingface.co/BAAI/bge-large-en-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",
"camembert-base": "https://huggingface.co/almanach/camembert-base",
"camembert-large": "https://huggingface.co/almanach/camembert-large",
"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",
"distilbert-base-25lang-cased": "https://huggingface.co/Geotrend/distilbert-base-25lang-cased",
"distilbert-base-en-fr-cased": "https://huggingface.co/Geotrend/distilbert-base-en-fr-cased",
"distilbert-base-en-fr-es-pt-it-cased": "https://huggingface.co/Geotrend/distilbert-base-en-fr-es-pt-it-cased",
"distilbert-base-fr-cased": "https://huggingface.co/Geotrend/distilbert-base-fr-cased",
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased",
"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-mistral-7b-instruct": "https://huggingface.co/intfloat/e5-mistral-7b-instruct",
"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",
"flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased",
"flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased",
"flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased",
"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",
"google-gecko.text-embedding-preview-0409": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models",
"google-gecko-256.text-embedding-preview-0409": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models",
"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",
"mistral-embed": "https://docs.mistral.ai/guides/embeddings",
"msmarco-bert-co-condensor": "https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor",
"multi-qa-MiniLM-L6-cos-v1": "https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
"multilingual-e5-base": "https://huggingface.co/intfloat/multilingual-e5-base",
"multilingual-e5-large": "https://huggingface.co/intfloat/multilingual-e5-large",
"multilingual-e5-small": "https://huggingface.co/intfloat/multilingual-e5-small",
"nb-bert-base": "https://huggingface.co/NbAiLab/nb-bert-base",
"nb-bert-large": "https://huggingface.co/NbAiLab/nb-bert-large",
"nomic-embed-text-v1.5-64": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"nomic-embed-text-v1.5-128": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"nomic-embed-text-v1.5-256": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"nomic-embed-text-v1.5-512": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"norbert3-base": "https://huggingface.co/ltg/norbert3-base",
"norbert3-large": "https://huggingface.co/ltg/norbert3-large",
"OpenSearch-text-hybrid": "https://help.aliyun.com/zh/open-search/vector-search-edition/hybrid-retrieval",
"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-camembert-base": "https://huggingface.co/dangvantuan/sentence-camembert-base",
"sentence-camembert-large": "https://huggingface.co/dangvantuan/sentence-camembert-large",
"sentence-croissant-llm-base": "https://huggingface.co/Wissam42/sentence-croissant-llm-base",
"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",
"silver-retriever-base-v1": "https://huggingface.co/ipipan/silver-retriever-base-v1",
"sup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased",
"st-polish-paraphrase-from-distilroberta": "https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta",
"st-polish-paraphrase-from-mpnet": "https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet",
"text2vec-base-chinese": "https://huggingface.co/shibing624/text2vec-base-chinese",
"text2vec-large-chinese": "https://huggingface.co/GanymedeNil/text2vec-large-chinese",
"text-embedding-3-small": "https://openai.com/blog/new-embedding-models-and-api-updates",
"text-embedding-3-large": "https://openai.com/blog/new-embedding-models-and-api-updates",
"text-embedding-3-large-256": "https://openai.com/blog/new-embedding-models-and-api-updates",
"text-embedding-ada-002": "https://openai.com/blog/new-and-improved-embedding-model",
"text-similarity-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-similarity-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-similarity-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-similarity-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-ada-doc-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-ada-query-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"titan-embed-text-v1": "https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html",
"udever-bloom-1b1": "https://huggingface.co/izhx/udever-bloom-1b1",
"udever-bloom-560m": "https://huggingface.co/izhx/udever-bloom-560m",
"universal-sentence-encoder-multilingual-3": "https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3",
"universal-sentence-encoder-multilingual-large-3": "https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-large-3",
"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-2": "https://docs.voyageai.com/embeddings/",
"voyage-code-2": "https://docs.voyageai.com/embeddings/",
"voyage-lite-01-instruct": "https://docs.voyageai.com/embeddings/",
"voyage-lite-02-instruct": "https://docs.voyageai.com/embeddings/",
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large",
}
EXTERNAL_MODEL_TO_DIM = {
"Cohere-embed-english-v3.0": 1024,
"Cohere-embed-multilingual-v3.0": 1024,
"Cohere-embed-multilingual-light-v3.0": 384,
"all-MiniLM-L12-v2": 384,
"all-MiniLM-L6-v2": 384,
"all-mpnet-base-v2": 768,
"allenai-specter": 768,
"Baichuan-text-embedding": 1024,
"bert-base-10lang-cased": 768,
"bert-base-15lang-cased": 768,
"bert-base-25lang-cased": 768,
"bert-base-multilingual-cased": 768,
"bert-base-multilingual-uncased": 768,
"bert-base-swedish-cased": 768,
"bert-base-uncased": 768,
"bge-base-zh-v1.5": 768,
"bge-large-en-v1.5": 1024,
"bge-large-zh-v1.5": 1024,
"bge-large-zh-noinstruct": 1024,
"bge-small-zh-v1.5": 512,
"camembert-base": 512,
"camembert-large": 768,
"contriever-base-msmarco": 768,
"cross-en-de-roberta-sentence-transformer": 768,
"DanskBERT": 768,
"distilbert-base-25lang-cased": 768,
"distilbert-base-en-fr-cased": 768,
"distilbert-base-en-fr-es-pt-it-cased": 768,
"distilbert-base-fr-cased": 768,
"distilbert-base-uncased": 768,
"distiluse-base-multilingual-cased-v2": 512,
"dfm-encoder-large-v1": 1024,
"dfm-sentence-encoder-large-1": 1024,
"e5-base": 768,
"e5-large": 1024,
"e5-mistral-7b-instruct": 4096,
"e5-small": 384,
"electra-small-nordic": 256,
"electra-small-swedish-cased-discriminator": 256,
"flaubert_base_cased": 768,
"flaubert_base_uncased": 768,
"flaubert_large_cased": 1024,
"luotuo-bert-medium": 768,
"LASER2": 1024,
"LaBSE": 768,
"gbert-base": 768,
"gbert-large": 1024,
"gelectra-base": 768,
"gelectra-large": 1024,
"glove.6B.300d": 300,
"google-gecko.text-embedding-preview-0409": 768,
"google-gecko-256.text-embedding-preview-0409": 256,
"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,
"mistral-embed": 1024,
"msmarco-bert-co-condensor": 768,
"multi-qa-MiniLM-L6-cos-v1": 384,
"multilingual-e5-base": 768,
"multilingual-e5-small": 384,
"multilingual-e5-large": 1024,
"nb-bert-base": 768,
"nb-bert-large": 1024,
"nomic-embed-text-v1.5-64": 64,
"nomic-embed-text-v1.5-128": 128,
"nomic-embed-text-v1.5-256": 256,
"nomic-embed-text-v1.5-512": 512,
"norbert3-base": 768,
"norbert3-large": 1024,
"OpenSearch-text-hybrid": 1792,
"paraphrase-multilingual-MiniLM-L12-v2": 384,
"paraphrase-multilingual-mpnet-base-v2": 768,
"sentence-camembert-base": 768,
"sentence-camembert-large": 1024,
"sentence-croissant-llm-base": 2048,
"sentence-bert-swedish-cased": 768,
"sentence-t5-base": 768,
"sentence-t5-large": 768,
"sentence-t5-xl": 768,
"sentence-t5-xxl": 768,
"silver-retriever-base-v1": 768,
"sup-simcse-bert-base-uncased": 768,
"st-polish-paraphrase-from-distilroberta": 768,
"st-polish-paraphrase-from-mpnet": 768,
"text2vec-base-chinese": 768,
"text2vec-large-chinese": 1024,
"text-embedding-3-large": 3072,
"text-embedding-3-large-256": 256,
"text-embedding-3-small": 1536,
"text-embedding-ada-002": 1536,
"text-similarity-ada-001": 1024,
"text-similarity-babbage-001": 2048,
"text-similarity-curie-001": 4096,
"text-similarity-davinci-001": 12288,
"text-search-ada-doc-001": 1024,
"text-search-ada-query-001": 1024,
"text-search-ada-001": 1024,
"text-search-babbage-001": 2048,
"text-search-curie-001": 4096,
"text-search-davinci-001": 12288,
"titan-embed-text-v1": 1536,
"udever-bloom-1b1": 1536,
"udever-bloom-560m": 1024,
"universal-sentence-encoder-multilingual-3": 512,
"universal-sentence-encoder-multilingual-large-3": 512,
"unsup-simcse-bert-base-uncased": 768,
"use-cmlm-multilingual": 768,
"voyage-2": 1024,
"voyage-code-2": 1536,
"voyage-lite-01-instruct": 1024,
"voyage-lite-02-instruct": 1024,
"xlm-roberta-base": 768,
"xlm-roberta-large": 1024,
}
EXTERNAL_MODEL_TO_SEQLEN = {
"Cohere-embed-english-v3.0": 512,
"Cohere-embed-multilingual-v3.0": 512,
"Cohere-embed-multilingual-light-v3.0": 512,
"all-MiniLM-L12-v2": 512,
"all-MiniLM-L6-v2": 512,
"all-mpnet-base-v2": 514,
"allenai-specter": 512,
"Baichuan-text-embedding": 512,
"bert-base-10lang-cased": 512,
"bert-base-15lang-cased": 512,
"bert-base-25lang-cased": 512,
"bert-base-multilingual-cased": 512,
"bert-base-multilingual-uncased": 512,
"bert-base-swedish-cased": 512,
"bert-base-uncased": 512,
"bge-base-zh-v1.5": 512,
"bge-large-en-v1.5": 512,
"bge-large-zh-v1.5": 512,
"bge-large-zh-noinstruct": 512,
"bge-small-zh-v1.5": 512,
"camembert-base": 512,
"camembert-large": 512,
"contriever-base-msmarco": 512,
"cross-en-de-roberta-sentence-transformer": 514,
"distilbert-base-25lang-cased": 512,
"distilbert-base-en-fr-cased": 512,
"distilbert-base-en-fr-es-pt-it-cased": 512,
"distilbert-base-fr-cased": 512,
"distilbert-base-uncased": 512,
"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-mistral-7b-instruct": 32768,
"e5-small": 512,
"electra-small-nordic": 512,
"electra-small-swedish-cased-discriminator": 512,
"flaubert_base_cased": 512,
"flaubert_base_uncased": 512,
"flaubert_large_cased": 512,
"gbert-base": 512,
"gbert-large": 512,
"gelectra-base": 512,
"gelectra-large": 512,
"google-gecko.text-embedding-preview-0409": 2048,
"google-gecko-256.text-embedding-preview-0409": 2048,
"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,
# "mistral-embed": "?",
"msmarco-bert-co-condensor": 512,
"multi-qa-MiniLM-L6-cos-v1": 512,
"multilingual-e5-base": 514,
"multilingual-e5-large": 514,
"multilingual-e5-small": 512,
"nb-bert-base": 512,
"nb-bert-large": 512,
"nomic-embed-text-v1.5-64": 8192,
"nomic-embed-text-v1.5-128": 8192,
"nomic-embed-text-v1.5-256": 8192,
"nomic-embed-text-v1.5-512": 8192,
"norbert3-base": 512,
"norbert3-large": 512,
"OpenSearch-text-hybrid": 512,
"paraphrase-multilingual-MiniLM-L12-v2": 512,
"paraphrase-multilingual-mpnet-base-v2": 514,
"sentence-camembert-base": 512,
"sentence-camembert-large": 512,
"sentence-croissant-llm-base": 2048,
"sentence-bert-swedish-cased": 512,
"sentence-t5-base": 512,
"sentence-t5-large": 512,
"sentence-t5-xl": 512,
"sentence-t5-xxl": 512,
"silver-retriever-base-v1": 514,
"sup-simcse-bert-base-uncased": 512,
"st-polish-paraphrase-from-distilroberta": 514,
"st-polish-paraphrase-from-mpnet": 514,
"text2vec-base-chinese": 512,
"text2vec-large-chinese": 512,
"text-embedding-3-large": 8191,
"text-embedding-3-large-256": 8191,
"text-embedding-3-small": 8191,
"text-embedding-ada-002": 8191,
"text-similarity-ada-001": 2046,
"text-similarity-babbage-001": 2046,
"text-similarity-curie-001": 2046,
"text-similarity-davinci-001": 2046,
"text-search-ada-doc-001": 2046,
"text-search-ada-query-001": 2046,
"text-search-ada-001": 2046,
"text-search-babbage-001": 2046,
"text-search-curie-001": 2046,
"text-search-davinci-001": 2046,
"titan-embed-text-v1": 8000,
"udever-bloom-1b1": 2048,
"udever-bloom-560m": 2048,
"universal-sentence-encoder-multilingual-3": 512,
"universal-sentence-encoder-multilingual-large-3": 512,
"use-cmlm-multilingual": 512,
"unsup-simcse-bert-base-uncased": 512,
"voyage-2": 1024,
"voyage-code-2": 16000,
"voyage-lite-01-instruct": 4000,
"voyage-lite-02-instruct": 4000,
"xlm-roberta-base": 514,
"xlm-roberta-large": 514,
}
EXTERNAL_MODEL_TO_SIZE = {
"allenai-specter": 0.44,
"all-MiniLM-L12-v2": 0.13,
"all-MiniLM-L6-v2": 0.09,
"all-mpnet-base-v2": 0.44,
"bert-base-10lang-cased": 0.61,
"bert-base-15lang-cased": 0.61,
"bert-base-25lang-cased": 0.61,
"bert-base-multilingual-cased": 0.71,
"bert-base-multilingual-uncased": 0.67,
"bert-base-uncased": 0.44,
"bert-base-swedish-cased": 0.50,
"bge-base-zh-v1.5": 0.41,
"bge-large-en-v1.5": 1.30,
"bge-large-zh-v1.5": 1.30,
"bge-large-zh-noinstruct": 1.30,
"bge-small-zh-v1.5": 0.10,
"camembert-base": 0.45,
"camembert-large": 1.35,
"cross-en-de-roberta-sentence-transformer": 1.11,
"contriever-base-msmarco": 0.44,
"distilbert-base-25lang-cased": 0.44,
"distilbert-base-en-fr-cased": 0.44,
"distilbert-base-en-fr-es-pt-it-cased": 0.44,
"distilbert-base-fr-cased": 0.44,
"distilbert-base-uncased": 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-large": 1.34,
"e5-mistral-7b-instruct": 14.22,
"e5-small": 0.13,
"electra-small-nordic": 0.09,
"electra-small-swedish-cased-discriminator": 0.06,
"flaubert_base_cased": 0.55,
"flaubert_base_uncased": 0.55,
"flaubert_large_cased": 1.49,
"gbert-base": 0.44,
"gbert-large": 1.35,
"gelectra-base": 0.44,
"gelectra-large": 1.34,
"glove.6B.300d": 0.48,
"google-gecko.text-embedding-preview-0409": 2.29,
"google-gecko-256.text-embedding-preview-0409": 2.29,
"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,
"multi-qa-MiniLM-L6-cos-v1": 0.09,
"multilingual-e5-base": 1.11,
"multilingual-e5-small": 0.47,
"multilingual-e5-large": 2.24,
"nb-bert-base": 0.71,
"nb-bert-large": 1.42,
"nomic-embed-text-v1.5-64": 0.55,
"nomic-embed-text-v1.5-128": 0.55,
"nomic-embed-text-v1.5-256": 0.55,
"nomic-embed-text-v1.5-512": 0.55,
"norbert3-base": 0.52,
"norbert3-large": 1.47,
"paraphrase-multilingual-mpnet-base-v2": 1.11,
"paraphrase-multilingual-MiniLM-L12-v2": 0.47,
"sentence-camembert-base": 0.44,
"sentence-camembert-large": 1.35,
"sentence-croissant-llm-base": 5.12,
"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,
"silver-retriever-base-v1": 0.50,
"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,
"voyage-lite-02-instruct": 2.45,
"xlm-roberta-base": 1.12,
"xlm-roberta-large": 2.24,
}
MODELS_TO_SKIP = {
"baseplate/instructor-large-1", # Duplicate
"radames/e5-large", # Duplicate
"gentlebowl/instructor-large-safetensors", # Duplicate
"Consensus/instructor-base", # Duplicate
"GovCompete/instructor-xl", # Duplicate
"GovCompete/e5-large-v2", # Duplicate
"t12e/instructor-base", # Duplicate
"michaelfeil/ct2fast-e5-large-v2",
"michaelfeil/ct2fast-e5-large",
"michaelfeil/ct2fast-e5-small-v2",
"newsrx/instructor-xl-newsrx",
"newsrx/instructor-large-newsrx",
"fresha/e5-large-v2-endpoint",
"ggrn/e5-small-v2",
"michaelfeil/ct2fast-e5-small",
"jncraton/e5-small-v2-ct2-int8",
"anttip/ct2fast-e5-small-v2-hfie",
"newsrx/instructor-large",
"newsrx/instructor-xl",
"dmlls/all-mpnet-base-v2",
"cgldo/semanticClone",
"Malmuk1/e5-large-v2_Sharded",
"jncraton/gte-small-ct2-int8",
"Einas/einas_ashkar",
"gruber/e5-small-v2-ggml",
"jncraton/bge-small-en-ct2-int8",
"vectoriseai/bge-small-en",
"recipe/embeddings",
"dhairya0907/thenlper-get-large",
"Narsil/bge-base-en",
"kozistr/fused-large-en",
"sionic-ai/sionic-ai-v2", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1
"sionic-ai/sionic-ai-v1", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1
"BAAI/bge-large-en", # Deprecated in favor of v1.5
"BAAI/bge-base-en", # Deprecated in favor of v1.5
"BAAI/bge-small-en", # Deprecated in favor of v1.5
"d0rj/e5-large-en-ru",
"d0rj/e5-base-en-ru",
"d0rj/e5-small-en-ru",
"aident-ai/bge-base-en-onnx",
"barisaydin/bge-base-en",
"barisaydin/gte-large",
"barisaydin/gte-base",
"barisaydin/gte-small",
"barisaydin/bge-small-en",
"odunola/e5-base-v2",
"goldenrooster/multilingual-e5-large",
"davidpeer/gte-small",
"barisaydin/bge-large-en",
"jamesgpt1/english-large-v1",
"vectoriseai/bge-large-en-v1.5",
"vectoriseai/bge-base-en-v1.5",
"vectoriseai/instructor-large",
"vectoriseai/instructor-base",
"vectoriseai/gte-large",
"vectoriseai/gte-base",
"vectoriseai/e5-large-v2",
"vectoriseai/bge-small-en-v1.5",
"vectoriseai/e5-base-v2",
"vectoriseai/e5-large",
"vectoriseai/multilingual-e5-large",
"vectoriseai/gte-small",
"vectoriseai/ember-v1",
"vectoriseai/e5-base",
"vectoriseai/e5-small-v2",
"michaelfeil/ct2fast-bge-large-en-v1.5",
"michaelfeil/ct2fast-bge-large-en-v1.5",
"michaelfeil/ct2fast-bge-base-en-v1.5",
"michaelfeil/ct2fast-gte-large",
"michaelfeil/ct2fast-gte-base",
"michaelfeil/ct2fast-bge-small-en-v1.5",
"rizki/bgr-tf",
"ef-zulla/e5-multi-sml-torch",
"cherubhao/yogamodel",
"morgendigital/multilingual-e5-large-quantized",
"jncraton/gte-tiny-ct2-int8",
"Research2NLP/electrical_stella",
"Intel/bge-base-en-v1.5-sts-int8-static",
"Intel/bge-base-en-v1.5-sts-int8-dynamic",
"Intel/bge-base-en-v1.5-sst2",
"Intel/bge-base-en-v1.5-sst2-int8-static",
"Intel/bge-base-en-v1.5-sst2-int8-dynamic",
"Intel/bge-small-en-v1.5-sst2",
"Intel/bge-small-en-v1.5-sst2-int8-dynamic",
"Intel/bge-small-en-v1.5-sst2-int8-static",
"binqiangliu/EmbeddingModlebgelargeENv1.5",
"DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext",
"woody72/multilingual-e5-base",
"Severian/embed",
"Frazic/udever-bloom-3b-sentence",
"jamesgpt1/zzz",
"karrar-alwaili/UAE-Large-V1",
"odunola/UAE-Large-VI",
"shubham-bgi/UAE-Large",
"retrainai/instructor-xl",
"weakit-v/bge-base-en-v1.5-onnx",
"ieasybooks/multilingual-e5-large-onnx",
"gizmo-ai/Cohere-embed-multilingual-v3.0",
"jingyeom/korean_embedding_model",
"barisaydin/text2vec-base-multilingual",
"mlx-community/multilingual-e5-large-mlx",
"mlx-community/multilingual-e5-base-mlx",
"mlx-community/multilingual-e5-small-mlx",
"maiyad/multilingual-e5-small",
"khoa-klaytn/bge-base-en-v1.5-angle",
"khoa-klaytn/bge-small-en-v1.5-angle",
"mixamrepijey/instructor-small",
"mixamrepijey/instructor-models",
"lsf1000/bge-evaluation", # Empty
"giulio98/placeholder", # Empty
"Severian/nomic", # Copy
"atian-chapters/Chapters-SFR-Embedding-Mistral", # Copy
"rlsChapters/Chapters-SFR-Embedding-Mistral", # Copy
"TitanML/jina-v2-base-en-embed", # Copy
"MaziyarPanahi/GritLM-8x7B-GGUF", # GGUF variant
"Geolumina/instructor-xl", # Duplicate
"krilecy/e5-mistral-7b-instruct",
"beademiguelperez/sentence-transformers-multilingual-e5-small",
"arcdev/SFR-Embedding-Mistral",
"arcdev/e5-mistral-7b-instruct",
}
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 norm(names): return set([name.split(" ")[0] for name in names])
def add_task(examples):
# Could be added to the dataset loading script instead
if examples["mteb_dataset_name"] in norm(TASK_LIST_CLASSIFICATION + TASK_LIST_CLASSIFICATION_DA + TASK_LIST_CLASSIFICATION_FR + 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 norm(TASK_LIST_CLUSTERING + TASK_LIST_CLUSTERING_DE + TASK_LIST_CLUSTERING_FR + TASK_LIST_CLUSTERING_PL + TASK_LIST_CLUSTERING_ZH):
examples["mteb_task"] = "Clustering"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_PAIR_CLASSIFICATION_ZH):
examples["mteb_task"] = "PairClassification"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_RERANKING + TASK_LIST_RERANKING_FR + TASK_LIST_RERANKING_ZH):
examples["mteb_task"] = "Reranking"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_RETRIEVAL_NORM + TASK_LIST_RETRIEVAL_FR + TASK_LIST_RETRIEVAL_PL + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_RETRIEVAL_LAW):
examples["mteb_task"] = "Retrieval"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_STS + TASK_LIST_STS_FR + TASK_LIST_STS_PL + TASK_LIST_STS_ZH):
examples["mteb_task"] = "STS"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_SUMMARIZATION + TASK_LIST_SUMMARIZATION_FR):
examples["mteb_task"] = "Summarization"
elif examples["mteb_dataset_name"] in norm(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
if os.path.exists("EXTERNAL_MODEL_RESULTS.json"):
with open("EXTERNAL_MODEL_RESULTS.json") as f:
EXTERNAL_MODEL_RESULTS = json.load(f)
# Update with models not contained
models_to_run = []
for model in EXTERNAL_MODELS:
if model not in EXTERNAL_MODEL_RESULTS:
models_to_run.append(model)
EXTERNAL_MODEL_RESULTS[model] = {k: {v: []} for k, v in TASK_TO_METRIC.items()}
else:
EXTERNAL_MODEL_RESULTS = {model: {k: {v: []} for k, v in TASK_TO_METRIC.items()} for model in EXTERNAL_MODELS}
models_to_run = EXTERNAL_MODELS
pbar = tqdm(models_to_run, desc="Fetching external model results")
for model in pbar:
pbar.set_description(f"Fetching external model results for {model!r}")
ds = load_dataset("mteb/results", model, trust_remote_code=True)
ds = ds.map(add_lang)
# 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})
# Save & cache EXTERNAL_MODEL_RESULTS
with open("EXTERNAL_MODEL_RESULTS.json", "w") as f:
json.dump(EXTERNAL_MODEL_RESULTS, f)
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': '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", "Max Tokens"]]
if len(cols_to_rank) == 1:
df.sort_values(cols_to_rank[0], ascending=False, inplace=True)
else:
df.insert(1, "Average", df[cols_to_rank].mean(axis=1, skipna=False))
df.sort_values("Average", ascending=False, inplace=True)
df.insert(0, "Rank", list(range(1, len(df) + 1)))
df = df.round(2)
# Fill NaN after averaging
df.fillna("", inplace=True)
return df
def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_emb_dim=False, task_to_metric=TASK_TO_METRIC, rank=True):
api = HfApi()
models = api.list_models(filter="mteb")
# Initialize list to models that we cannot fetch metadata from
df_list = []
for model in EXTERNAL_MODEL_RESULTS:
results_list = [res for task in tasks for res in EXTERNAL_MODEL_RESULTS[model][task][task_to_metric[task]]]
if len(datasets) > 0:
res = {k: v for d in results_list for k, v in d.items() if (k == "Model") or any([x in k for x in datasets])}
elif langs:
# Would be cleaner to rely on an extra language column instead
langs_format = [f"({lang})" for lang in langs]
res = {k: v for d in results_list for k, v in d.items() if any([k.split(" ")[-1] in (k, x) for x in langs_format])}
else:
res = {k: v for d in results_list for k, v in d.items()}
# Model & at least one result
if len(res) > 1:
if add_emb_dim:
res["Model Size (GB)"] = EXTERNAL_MODEL_TO_SIZE.get(model, "")
res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "")
res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "")
df_list.append(res)
for model in models:
if model.modelId in MODELS_TO_SKIP: continue
print("MODEL", model)
readme_path = hf_hub_download(model.modelId, filename="README.md")
meta = metadata_load(readme_path)
if "model-index" not in meta:
continue
# meta['model-index'][0]["results"] is list of elements like:
# {
# "task": {"type": "Classification"},
# "dataset": {
# "type": "mteb/amazon_massive_intent",
# "name": "MTEB MassiveIntentClassification (nb)",
# "config": "nb",
# "split": "test",
# },
# "metrics": [
# {"type": "accuracy", "value": 39.81506388702084},
# {"type": "f1", "value": 38.809586587791664},
# ],
# },
# Use "get" instead of dict indexing to skip incompat metadata instead of erroring out
if len(datasets) > 0:
task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and any([x in sub_res.get("dataset", {}).get("name", "") for x in datasets])]
elif langs:
task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and (sub_res.get("dataset", {}).get("config", "default") in ("default", *langs))]
else:
task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks)]
out = [{res["dataset"]["name"].replace("MTEB ", ""): [round(score["value"], 2) for score in res["metrics"] if score["type"] == task_to_metric.get(res["task"]["type"])][0]} for res in task_results]
out = {k: v for d in out for k, v in d.items()}
out["Model"] = make_clickable_model(model.modelId)
# Model & at least one result
if len(out) > 1:
if add_emb_dim:
try:
# Fails on gated repos, so we only include scores for them
out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (GB)"] = get_dim_seq_size(model)
except:
pass
df_list.append(out)
df = pd.DataFrame(df_list)
# If there are any models that are the same, merge them
# E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one
df = df.groupby("Model", as_index=False).first()
# Put 'Model' column first
cols = sorted(list(df.columns))
cols.insert(0, cols.pop(cols.index("Model")))
df = df[cols]
if rank:
df = add_rank(df)
if fillna:
df.fillna("", inplace=True)
return df
def get_mteb_average():
global DATA_OVERALL, DATA_CLASSIFICATION_EN, DATA_CLUSTERING, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS_EN, DATA_SUMMARIZATION
DATA_OVERALL = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
"Summarization",
],
datasets=TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL.to_csv("overall.csv")
DATA_OVERALL.insert(1, f"Average ({len(TASK_LIST_EN)} datasets)", DATA_OVERALL[TASK_LIST_EN].mean(axis=1, skipna=False))
DATA_OVERALL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_CLASSIFICATION].mean(axis=1, skipna=False))
DATA_OVERALL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", DATA_OVERALL[TASK_LIST_CLUSTERING].mean(axis=1, skipna=False))
DATA_OVERALL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_PAIR_CLASSIFICATION].mean(axis=1, skipna=False))
DATA_OVERALL.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", DATA_OVERALL[TASK_LIST_RERANKING].mean(axis=1, skipna=False))
DATA_OVERALL.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", DATA_OVERALL[TASK_LIST_RETRIEVAL].mean(axis=1, skipna=False))
DATA_OVERALL.insert(7, f"STS Average ({len(TASK_LIST_STS)} datasets)", DATA_OVERALL[TASK_LIST_STS].mean(axis=1, skipna=False))
DATA_OVERALL.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)", DATA_OVERALL[TASK_LIST_SUMMARIZATION].mean(axis=1, skipna=False))
DATA_OVERALL.sort_values(f"Average ({len(TASK_LIST_EN)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1)))
DATA_OVERALL = DATA_OVERALL.round(2)
DATA_CLASSIFICATION_EN = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_CLASSIFICATION])
# Only keep rows with at least one score in addition to the "Model" & rank column
DATA_CLASSIFICATION_EN = DATA_CLASSIFICATION_EN[DATA_CLASSIFICATION_EN.iloc[:, 2:].ne("").any(axis=1)]
DATA_CLUSTERING = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_CLUSTERING])
DATA_CLUSTERING = DATA_CLUSTERING[DATA_CLUSTERING.iloc[:, 2:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_PAIR_CLASSIFICATION])
DATA_PAIR_CLASSIFICATION = DATA_PAIR_CLASSIFICATION[DATA_PAIR_CLASSIFICATION.iloc[:, 2:].ne("").any(axis=1)]
DATA_RERANKING = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_RERANKING])
DATA_RERANKING = DATA_RERANKING[DATA_RERANKING.iloc[:, 2:].ne("").any(axis=1)]
DATA_RETRIEVAL = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_RETRIEVAL])
DATA_RETRIEVAL = DATA_RETRIEVAL[DATA_RETRIEVAL.iloc[:, 2:].ne("").any(axis=1)]
DATA_STS_EN = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_STS])
DATA_STS_EN = DATA_STS_EN[DATA_STS_EN.iloc[:, 2:].ne("").any(axis=1)]
DATA_SUMMARIZATION = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_SUMMARIZATION])
DATA_SUMMARIZATION = DATA_SUMMARIZATION[DATA_SUMMARIZATION.iloc[:, 1:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL.fillna("", inplace=True)
DATA_OVERALL = DATA_OVERALL[["Rank", "Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_EN)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", f"STS Average ({len(TASK_LIST_STS)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)"]]
DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL
def get_mteb_average_zh():
global DATA_OVERALL_ZH, DATA_CLASSIFICATION_ZH, DATA_CLUSTERING_ZH, DATA_PAIR_CLASSIFICATION_ZH, DATA_RERANKING_ZH, DATA_RETRIEVAL_ZH, DATA_STS_ZH
DATA_OVERALL_ZH = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
],
datasets=TASK_LIST_CLASSIFICATION_ZH + TASK_LIST_CLUSTERING_ZH + TASK_LIST_PAIR_CLASSIFICATION_ZH + TASK_LIST_RERANKING_ZH + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_STS_ZH,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL_ZH.to_csv("overall.csv")
DATA_OVERALL_ZH.insert(1, f"Average ({len(TASK_LIST_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLASSIFICATION_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLUSTERING_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_PAIR_CLASSIFICATION_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RERANKING_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RETRIEVAL_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(7, f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_STS_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.sort_values(f"Average ({len(TASK_LIST_ZH)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL_ZH.insert(0, "Rank", list(range(1, len(DATA_OVERALL_ZH) + 1)))
DATA_OVERALL_ZH = DATA_OVERALL_ZH.round(2)
DATA_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_CLASSIFICATION_ZH])
# Only keep rows with at least one score in addition to the "Model" & rank column
DATA_CLASSIFICATION_ZH = DATA_CLASSIFICATION_ZH[DATA_CLASSIFICATION_ZH.iloc[:, 2:].ne("").any(axis=1)]
DATA_CLUSTERING_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_CLUSTERING_ZH])
DATA_CLUSTERING_ZH = DATA_CLUSTERING_ZH[DATA_CLUSTERING_ZH.iloc[:, 2:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_PAIR_CLASSIFICATION_ZH])
DATA_PAIR_CLASSIFICATION_ZH = DATA_PAIR_CLASSIFICATION_ZH[DATA_PAIR_CLASSIFICATION_ZH.iloc[:, 2:].ne("").any(axis=1)]
DATA_RERANKING_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_RERANKING_ZH])
DATA_RERANKING_ZH = DATA_RERANKING_ZH[DATA_RERANKING_ZH.iloc[:, 2:].ne("").any(axis=1)]
DATA_RETRIEVAL_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_RETRIEVAL_ZH])
DATA_RETRIEVAL_ZH = DATA_RETRIEVAL_ZH[DATA_RETRIEVAL_ZH.iloc[:, 2:].ne("").any(axis=1)]
DATA_STS_ZH = add_rank(DATA_OVERALL_ZH[["Model"] + TASK_LIST_STS_ZH])
DATA_STS_ZH = DATA_STS_ZH[DATA_STS_ZH.iloc[:, 2:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_ZH.fillna("", inplace=True)
DATA_OVERALL_ZH = DATA_OVERALL_ZH[["Rank", "Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_ZH)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)"]]
DATA_OVERALL_ZH = DATA_OVERALL_ZH[DATA_OVERALL_ZH.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL_ZH
def get_mteb_average_fr():
global DATA_OVERALL_FR, DATA_CLASSIFICATION_FR, DATA_CLUSTERING_FR, DATA_PAIR_CLASSIFICATION_FR, DATA_RERANKING_FR, DATA_RETRIEVAL_FR, DATA_STS_FR, DATA_SUMMARIZATION_FR
DATA_OVERALL_FR = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
"Summarization"
],
datasets=TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLUSTERING_FR + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_RERANKING_FR + TASK_LIST_RETRIEVAL_FR + TASK_LIST_STS_FR + TASK_LIST_SUMMARIZATION_FR,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL_FR.to_csv("overall.csv")
DATA_OVERALL_FR.insert(1, f"Average ({len(TASK_LIST_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_CLASSIFICATION_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_CLUSTERING_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_PAIR_CLASSIFICATION_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_RERANKING_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_RETRIEVAL_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(7, f"STS Average ({len(TASK_LIST_STS_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_STS_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION_FR)} dataset)", DATA_OVERALL_FR[TASK_LIST_SUMMARIZATION_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.sort_values(f"Average ({len(TASK_LIST_FR)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL_FR.insert(0, "Rank", list(range(1, len(DATA_OVERALL_FR) + 1)))
DATA_OVERALL_FR = DATA_OVERALL_FR.round(2)
DATA_CLASSIFICATION_FR = add_rank(DATA_OVERALL_FR[["Model"] + TASK_LIST_CLASSIFICATION_FR])
DATA_CLASSIFICATION_FR = DATA_CLASSIFICATION_FR[DATA_CLASSIFICATION_FR.iloc[:, 2:].ne("").any(axis=1)]
DATA_CLUSTERING_FR = add_rank(DATA_OVERALL_FR[["Model"] + TASK_LIST_CLUSTERING_FR])
DATA_CLUSTERING_FR = DATA_CLUSTERING_FR[DATA_CLUSTERING_FR.iloc[:, 2:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_FR = add_rank(DATA_OVERALL_FR[["Model"] + TASK_LIST_PAIR_CLASSIFICATION_FR])
DATA_PAIR_CLASSIFICATION_FR = DATA_PAIR_CLASSIFICATION_FR[DATA_PAIR_CLASSIFICATION_FR.iloc[:, 2:].ne("").any(axis=1)]
DATA_RERANKING_FR = add_rank(DATA_OVERALL_FR[["Model"] + TASK_LIST_RERANKING_FR])
DATA_RERANKING_FR = DATA_RERANKING_FR[DATA_RERANKING_FR.iloc[:, 2:].ne("").any(axis=1)]
DATA_RETRIEVAL_FR = add_rank(DATA_OVERALL_FR[["Model"] + TASK_LIST_RETRIEVAL_FR])
DATA_RETRIEVAL_FR = DATA_RETRIEVAL_FR[DATA_RETRIEVAL_FR.iloc[:, 2:].ne("").any(axis=1)]
DATA_STS_FR = add_rank(DATA_OVERALL_FR[["Model"] + TASK_LIST_STS_FR])
DATA_STS_FR = DATA_STS_FR[DATA_STS_FR.iloc[:, 2:].ne("").any(axis=1)]
DATA_SUMMARIZATION_FR = add_rank(DATA_OVERALL_FR[["Model"] + TASK_LIST_SUMMARIZATION_FR])
DATA_SUMMARIZATION_FR = DATA_SUMMARIZATION_FR[DATA_SUMMARIZATION_FR.iloc[:, 1:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_FR.fillna("", inplace=True)
DATA_OVERALL_FR = DATA_OVERALL_FR[["Rank", "Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_FR)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_FR)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_FR)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_FR)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING_FR)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_FR)} datasets)", f"STS Average ({len(TASK_LIST_STS_FR)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION_FR)} dataset)"]]
DATA_OVERALL_FR = DATA_OVERALL_FR[DATA_OVERALL_FR.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL_FR
def get_mteb_average_pl():
global DATA_OVERALL_PL, DATA_CLASSIFICATION_PL, DATA_CLUSTERING_PL, DATA_PAIR_CLASSIFICATION_PL, DATA_RETRIEVAL_PL, DATA_STS_PL
DATA_OVERALL_PL = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Retrieval",
"STS",
],
datasets=TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLUSTERING_PL + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_RETRIEVAL_PL + TASK_LIST_STS_PL,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL_PL.to_csv("overall.csv")
DATA_OVERALL_PL.insert(1, f"Average ({len(TASK_LIST_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLASSIFICATION_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLUSTERING_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PAIR_CLASSIFICATION_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(5, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_RETRIEVAL_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(6, f"STS Average ({len(TASK_LIST_STS_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_STS_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.sort_values(f"Average ({len(TASK_LIST_PL)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL_PL.insert(0, "Rank", list(range(1, len(DATA_OVERALL_PL) + 1)))
DATA_OVERALL_PL = DATA_OVERALL_PL.round(2)
DATA_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_CLASSIFICATION_PL])
# Only keep rows with at least one score in addition to the "Model" & rank column
DATA_CLASSIFICATION_PL = DATA_CLASSIFICATION_PL[DATA_CLASSIFICATION_PL.iloc[:, 2:].ne("").any(axis=1)]
DATA_CLUSTERING_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_CLUSTERING_PL])
DATA_CLUSTERING_PL = DATA_CLUSTERING_PL[DATA_CLUSTERING_PL.iloc[:, 2:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_PAIR_CLASSIFICATION_PL])
DATA_PAIR_CLASSIFICATION_PL = DATA_PAIR_CLASSIFICATION_PL[DATA_PAIR_CLASSIFICATION_PL.iloc[:, 2:].ne("").any(axis=1)]
DATA_RETRIEVAL_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_RETRIEVAL_PL])
DATA_RETRIEVAL_PL = DATA_RETRIEVAL_PL[DATA_RETRIEVAL_PL.iloc[:, 2:].ne("").any(axis=1)]
DATA_STS_PL = add_rank(DATA_OVERALL_PL[["Model"] + TASK_LIST_STS_PL])
DATA_STS_PL = DATA_STS_PL[DATA_STS_PL.iloc[:, 2:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_PL.fillna("", inplace=True)
DATA_OVERALL_PL = DATA_OVERALL_PL[["Rank", "Model", "Model Size (GB)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_PL)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", f"STS Average ({len(TASK_LIST_STS_PL)} datasets)"]]
DATA_OVERALL_PL = DATA_OVERALL_PL[DATA_OVERALL_PL.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL_PL
get_mteb_average()
get_mteb_average_fr()
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)
DATA_RETRIEVAL_LAW = get_mteb_data(["Retrieval"], [], TASK_LIST_RETRIEVAL_LAW)
# 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_FR,
DATA_CLASSIFICATION_NB,
DATA_CLASSIFICATION_PL,
DATA_CLASSIFICATION_SV,
DATA_CLASSIFICATION_ZH,
DATA_CLASSIFICATION_OTHER,
DATA_CLUSTERING,
DATA_CLUSTERING_DE,
DATA_CLUSTERING_FR,
DATA_CLUSTERING_PL,
DATA_CLUSTERING_ZH,
DATA_PAIR_CLASSIFICATION,
DATA_PAIR_CLASSIFICATION_FR,
DATA_PAIR_CLASSIFICATION_PL,
DATA_PAIR_CLASSIFICATION_ZH,
DATA_RERANKING,
DATA_RERANKING_FR,
DATA_RERANKING_ZH,
DATA_RETRIEVAL,
DATA_RETRIEVAL_FR,
DATA_RETRIEVAL_PL,
DATA_RETRIEVAL_ZH,
DATA_RETRIEVAL_LAW,
DATA_STS_EN,
DATA_STS_FR,
DATA_STS_PL,
DATA_STS_ZH,
DATA_STS_OTHER,
DATA_SUMMARIZATION,
DATA_SUMMARIZATION_FR,
]:
# NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum()
cols_to_ignore = 3 if "Average" in d.columns else 2
# Count number of scores including only non-nan floats & excluding the rank column
NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum()
# Exclude rank & model name column (first two); Do not count different language versions as different datasets
DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]]
# LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]]
MODELS += d["Model"].tolist()
NUM_DATASETS = len(set(DATASETS))
# NUM_LANGUAGES = len(set(LANGUAGES))
NUM_MODELS = len(set(MODELS))
# 1. Force headers to wrap
# 2. Force model column (maximum) width
# 3. Prevent model column from overflowing, scroll instead
css = """
table > thead {
white-space: normal
}
table {
--cell-width-1: 250px
}
table > tbody > tr > td:nth-child(2) > div {
overflow-x: auto
}
"""
block = gr.Blocks(css=css)
with block:
gr.Markdown(f"""
Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the MTEB GitHub repository ๐ค Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models.
""")
with gr.Tabs():
with gr.TabItem("Overall"):
with gr.TabItem("English"):
with gr.Row():
gr.Markdown("""
**Overall MTEB English leaderboard** ๐ฎ
- **Metric:** Various, refer to task tabs
- **Languages:** English
""")
with gr.Row():
data_overall = gr.components.Dataframe(
DATA_OVERALL,
datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL.columns),
type="pandas",
height=600,
)
with gr.Row():
data_run_overall = gr.Button("Refresh")
data_run_overall.click(get_mteb_average, inputs=None, outputs=data_overall)
with gr.TabItem("Chinese"):
with gr.Row():
gr.Markdown("""
**Overall MTEB Chinese leaderboard (C-MTEB)** ๐ฎ๐จ๐ณ
- **Metric:** Various, refer to task tabs
- **Languages:** Chinese
- **Credits:** [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)
""")
with gr.Row():
data_overall_zh = gr.components.Dataframe(
DATA_OVERALL_ZH,
datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL_ZH.columns),
type="pandas",
height=600,
)
with gr.Row():
data_run_overall_zh = gr.Button("Refresh")
data_run_overall_zh.click(get_mteb_average_zh, inputs=None, outputs=data_overall_zh)
with gr.TabItem("French"):
with gr.Row():
gr.Markdown("""
**Overall MTEB French leaderboard (F-MTEB)** ๐ฎ๐ซ๐ท
- **Metric:** Various, refer to task tabs
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [Wissam Siblini](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_overall_fr = gr.components.Dataframe(
DATA_OVERALL_FR,
datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL_FR.columns),
type="pandas",
height=600,
)
with gr.Row():
data_overall_fr = gr.Button("Refresh")
data_overall_fr.click(get_mteb_average_fr, inputs=None, outputs=data_overall_fr)
with gr.TabItem("Polish"):
with gr.Row():
gr.Markdown("""
**Overall MTEB Polish leaderboard (PL-MTEB)** ๐ฎ๐ต๐ฑ
- **Metric:** Various, refer to task tabs
- **Languages:** Polish
- **Credits:** [Rafaล Poลwiata](https://github.com/rafalposwiata), [Konrad Wojtasik](https://github.com/kwojtasi) & [BEIR-PL](https://arxiv.org/abs/2305.19840)
""")
with gr.Row():
data_overall_pl = gr.components.Dataframe(
DATA_OVERALL_PL,
datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL_PL.columns),
type="pandas",
height=600,
)
with gr.Row():
data_run_overall_pl = gr.Button("Refresh")
data_run_overall_pl.click(get_mteb_average_pl, inputs=None, outputs=data_overall_pl)
with gr.TabItem("Bitext Mining"):
with gr.TabItem("English-X"):
with gr.Row():
gr.Markdown("""
**Bitext Mining English-X Leaderboard** ๐
- **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** ๐๐ฉ๐ฐ
- **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** โค๏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** ๐งก๐จ๐ณ
- **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** ๐ค๐ฉ๐ฐ
- **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("French"):
with gr.Row():
gr.Markdown("""
**Classification French Leaderboard** ๐๐ซ๐ท
- **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [wissam-sib](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_classification_fr = gr.components.Dataframe(
DATA_CLASSIFICATION_FR,
datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_FR.columns),
type="pandas",
)
with gr.Row():
data_run_classification_fr = gr.Button("Refresh")
data_run_classification_fr.click(
partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_FR),
outputs=data_run_classification_fr,
)
with gr.TabItem("Norwegian"):
with gr.Row():
gr.Markdown("""
**Classification Norwegian Leaderboard** ๐๐ณ๐ด
- **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** ๐ค๐ต๐ฑ
- **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
- **Languages:** Polish
- **Credits:** [Rafaล Poลwiata](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** ๐๐ธ๐ช
- **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** ๐๐๐
- **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** โจ
- **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** โจ๐จ๐ณ
- **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("French"):
with gr.Row():
gr.Markdown("""
**Clustering French Leaderboard** โจ๐ซ๐ท
- **Metric:** Validity Measure (v_measure)
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [wissam-sib](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_clustering_fr = gr.components.Dataframe(
DATA_CLUSTERING_FR,
datatype=["number", "markdown"] + ["number"] * len(DATA_CLUSTERING_FR.columns),
type="pandas",
)
with gr.Row():
data_run_clustering_fr = gr.Button("Refresh")
data_run_clustering_fr.click(
partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_FR),
outputs=data_clustering_fr,
)
with gr.TabItem("German"):
with gr.Row():
gr.Markdown("""
**Clustering German Leaderboard** โจ๐ฉ๐ช
- **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** โจ๐ต๐ฑ
- **Metric:** Validity Measure (v_measure)
- **Languages:** Polish
- **Credits:** [Rafaล Poลwiata](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** ๐ญ
- **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** ๐ญ๐จ๐ณ
- **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("French"):
with gr.Row():
gr.Markdown("""
**Pair Classification French Leaderboard** ๐ญ๐ซ๐ท
- **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap)
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [wissam-sib](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_pair_classification_fr = gr.components.Dataframe(
DATA_PAIR_CLASSIFICATION_FR,
datatype=["number", "markdown"] + ["number"] * len(DATA_PAIR_CLASSIFICATION_FR.columns),
type="pandas",
)
with gr.Row():
data_run_pair_classification_fr = gr.Button("Refresh")
data_run_pair_classification_fr.click(
partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_FR),
outputs=data_pair_classification_fr,
)
with gr.TabItem("Polish"):
with gr.Row():
gr.Markdown("""
**Pair Classification Polish Leaderboard** ๐ญ๐ต๐ฑ
- **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap)
- **Languages:** Polish
- **Credits:** [Rafaล Poลwiata](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** ๐ฅ
- **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** ๐ฅ๐จ๐ณ
- **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("French"):
with gr.Row():
gr.Markdown("""
**Reranking French Leaderboard** ๐ฅ๐ซ๐ท
- **Metric:** Mean Average Precision (MAP)
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [wissam-sib](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_reranking_fr = gr.components.Dataframe(
DATA_RERANKING_FR,
datatype=["number", "markdown"] + ["number"] * len(DATA_RERANKING_FR.columns),
type="pandas",
)
with gr.Row():
data_run_reranking_fr = gr.Button("Refresh")
data_run_reranking_fr.click(
partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_FR),
outputs=data_reranking_fr,
)
with gr.TabItem("Retrieval"):
with gr.TabItem("English"):
with gr.Row():
gr.Markdown("""
**Retrieval English Leaderboard** ๐
- **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** ๐๐จ๐ณ
- **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("French"):
with gr.Row():
gr.Markdown("""
**Retrieval French Leaderboard** ๐๐ซ๐ท
- **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10)
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [wissam-sib](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_retrieval_fr = gr.components.Dataframe(
DATA_RETRIEVAL_FR,
# 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_FR.columns) * 2,
type="pandas",
)
with gr.Row():
data_run_retrieval_fr = gr.Button("Refresh")
data_run_retrieval_fr.click(
partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_FR),
outputs=data_retrieval_fr,
)
with gr.TabItem("Law"):
with gr.Row():
gr.Markdown("""
**Retrieval Law Leaderboard** ๐โ๏ธ
- **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10)
- **Languages:** English, German, Chinese
- **Credits:** [Voyage AI](https://www.voyageai.com/)
""")
with gr.Row():
data_retrieval_law = gr.components.Dataframe(
DATA_RETRIEVAL_LAW,
# 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_LAW.columns) * 2,
type="pandas",
)
with gr.Row():
data_run_retrieval_law = gr.Button("Refresh")
data_run_retrieval_law.click(
partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_LAW),
outputs=data_retrieval_law,
)
with gr.TabItem("Polish"):
with gr.Row():
gr.Markdown("""
**Retrieval Polish Leaderboard** ๐๐ต๐ฑ
- **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** ๐ค
- **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** ๐ค๐จ๐ณ
- **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("French"):
with gr.Row():
gr.Markdown("""
**STS French Leaderboard** ๐ค๐ซ๐ท
- **Metric:** Spearman correlation based on cosine similarity
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [wissam-sib](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_sts_fr = gr.components.Dataframe(
DATA_STS_FR,
datatype=["number", "markdown"] + ["number"] * len(DATA_STS_FR.columns),
type="pandas",
)
with gr.Row():
data_run_sts_fr = gr.Button("Refresh")
data_run_sts_fr.click(
partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_FR),
outputs=data_sts_fr,
)
with gr.TabItem("Polish"):
with gr.Row():
gr.Markdown("""
**STS Polish Leaderboard** ๐ค๐ต๐ฑ
- **Metric:** Spearman correlation based on cosine similarity
- **Languages:** Polish
- **Credits:** [Rafaล Poลwiata](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** ๐ฝ
- **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.TabItem("English"):
with gr.Row():
gr.Markdown("""
**Summarization Leaderboard** ๐
- **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=TASK_LIST_SUMMARIZATION),
outputs=data_summarization,
)
with gr.TabItem("French"):
with gr.Row():
gr.Markdown("""
**Summarization Leaderboard** ๐
- **Metric:** Spearman correlation based on cosine similarity
- **Languages:** French
- **Credits:** [Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [wissam-sib](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)
""")
with gr.Row():
data_summarization_fr = gr.components.Dataframe(
DATA_SUMMARIZATION_FR,
datatype=["number", "markdown"] + ["number"] * 2,
type="pandas",
)
with gr.Row():
data_run_summarization_fr = gr.Button("Refresh")
data_run_summarization_fr.click(
partial(get_mteb_data, tasks=TASK_LIST_SUMMARIZATION_FR),
outputs=data_run_summarization_fr,
)
gr.Markdown(f"""
- **Total Datasets**: {NUM_DATASETS}
- **Total Languages**: 113
- **Total Scores**: {NUM_SCORES}
- **Total Models**: {NUM_MODELS}
""" + r"""
Made with โค๏ธ 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/