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File size: 106,913 Bytes
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from functools import partial, reduce
import json
import os
import re
from datasets import load_dataset
import gradio as gr
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load
import pandas as pd
from tqdm.autonotebook import tqdm
from utils.model_size import get_model_parameters_memory
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_DA = ["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'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.split("/")[-1]}</a>'
)
# Models without metadata, thus we cannot fetch their results naturally
EXTERNAL_MODELS = [
"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-law-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-law-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-law-2": 1024,
"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-law-2": 4000,
"voyage-lite-01-instruct": 4000,
"voyage-lite-02-instruct": 4000,
"xlm-roberta-base": 514,
"xlm-roberta-large": 514,
}
EXTERNAL_MODEL_TO_SIZE = {
"allenai-specter": 110,
"all-MiniLM-L12-v2": 33,
"all-MiniLM-L6-v2": 23,
"all-mpnet-base-v2": 110,
"bert-base-10lang-cased": 138,
"bert-base-15lang-cased": 138,
"bert-base-25lang-cased": 138,
"bert-base-multilingual-cased": 179,
"bert-base-multilingual-uncased": 168,
"bert-base-uncased": 110,
"bert-base-swedish-cased": 125,
"bge-base-zh-v1.5": 102,
"bge-large-zh-v1.5": 326,
"bge-large-zh-noinstruct": 326,
"bge-small-zh-v1.5": 24,
"camembert-base": 111,
"camembert-large": 338,
"cross-en-de-roberta-sentence-transformer": 278,
"contriever-base-msmarco": 110,
"distilbert-base-25lang-cased": 110,
"distilbert-base-en-fr-cased": 110,
"distilbert-base-en-fr-es-pt-it-cased": 110,
"distilbert-base-fr-cased": 110,
"distilbert-base-uncased": 110,
"DanskBERT": 125,
"distiluse-base-multilingual-cased-v2": 135,
"dfm-encoder-large-v1": 355,
"dfm-sentence-encoder-large-1": 355,
"e5-base": 110,
"e5-large": 335,
"e5-mistral-7b-instruct": 7111,
"e5-small": 33,
"electra-small-nordic": 23,
"electra-small-swedish-cased-discriminator": 16,
"flaubert_base_cased": 138,
"flaubert_base_uncased": 138,
"flaubert_large_cased": 372,
"gbert-base": 110,
"gbert-large": 337,
"gelectra-base": 110,
"gelectra-large": 335,
"glove.6B.300d": 120,
"google-gecko.text-embedding-preview-0409": 1200,
"google-gecko-256.text-embedding-preview-0409": 1200,
"gottbert-base": 127,
"gtr-t5-base": 110,
"gtr-t5-large": 168,
"gtr-t5-xl": 1240,
"gtr-t5-xxl": 4865,
"herbert-base-retrieval-v2": 125,
"komninos": 134,
"luotuo-bert-medium": 328,
"LASER2": 43,
"LaBSE": 471,
"m3e-base": 102,
"m3e-large": 102,
"msmarco-bert-co-condensor": 110,
"multi-qa-MiniLM-L6-cos-v1": 23,
"multilingual-e5-base": 278,
"multilingual-e5-small": 118,
"multilingual-e5-large": 560,
"nb-bert-base": 179,
"nb-bert-large": 355,
"nomic-embed-text-v1.5-64": 138,
"nomic-embed-text-v1.5-128": 138,
"nomic-embed-text-v1.5-256": 138,
"nomic-embed-text-v1.5-512": 138,
"norbert3-base": 131,
"norbert3-large": 368,
"paraphrase-multilingual-mpnet-base-v2": 278,
"paraphrase-multilingual-MiniLM-L12-v2": 118,
"sentence-camembert-base": 110,
"sentence-camembert-large": 337,
"sentence-croissant-llm-base": 1280,
"sentence-bert-swedish-cased": 125,
"sentence-t5-base": 110,
"sentence-t5-large": 168,
"sentence-t5-xl": 1240,
"sentence-t5-xxl": 4865,
"silver-retriever-base-v1": 125,
"sup-simcse-bert-base-uncased": 110,
"st-polish-paraphrase-from-distilroberta": 125,
"st-polish-paraphrase-from-mpnet": 125,
"text2vec-base-chinese": 102,
"text2vec-large-chinese": 326,
"unsup-simcse-bert-base-uncased": 110,
"use-cmlm-multilingual": 472,
#"voyage-law-2": 1220,
"voyage-lite-02-instruct": 1220,
"xlm-roberta-base": 279,
"xlm-roberta-large": 560,
}
PROPRIETARY_MODELS = {
"Cohere-embed-english-v3.0",
"Cohere-embed-multilingual-v3.0",
"Cohere-embed-multilingual-light-v3.0",
"Baichuan-text-embedding",
"mistral-embed",
"OpenSearch-text-hybrid",
"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-query-001",
"text-search-ada-001",
"text-search-curie-001",
"text-search-babbage-001",
"text-search-davinci-001",
"titan-embed-text-v1",
"voyage-2",
"voyage-code-2",
"voyage-law-2",
"voyage-lite-01-instruct",
"voyage-lite-02-instruct",
"google-gecko.text-embedding-preview-0409",
"google-gecko-256.text-embedding-preview-0409",
}
PROPRIETARY_MODELS = {
make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard"))
for model in PROPRIETARY_MODELS
}
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
"allenai-specter",
"allenai-specter",
"all-MiniLM-L12-v2",
"all-MiniLM-L6-v2",
"all-mpnet-base-v2",
"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-zh-v1.5",
"bge-large-zh-noinstruct",
"bge-small-zh-v1.5",
"camembert-base",
"camembert-large",
"contriever-base-msmarco",
"cross-en-de-roberta-sentence-transformer",
"DanskBERT",
"distilbert-base-25lang-cased",
"distilbert-base-en-fr-cased",
"distilbert-base-en-fr-es-pt-it-cased",
"distilbert-base-fr-cased",
"distilbert-base-uncased",
"distiluse-base-multilingual-cased-v2",
"dfm-encoder-large-v1",
"dfm-sentence-encoder-large-1",
"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",
"gottbert-base",
"gtr-t5-base",
"gtr-t5-large",
"gtr-t5-xl",
"gtr-t5-xxl",
"herbert-base-retrieval-v2",
"komninos",
"luotuo-bert-medium",
"LaBSE",
"m3e-base",
"m3e-large",
"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-mpnet-base-v2",
"paraphrase-multilingual-MiniLM-L12-v2",
"sentence-camembert-base",
"sentence-camembert-large",
"sentence-croissant-llm-base",
"sentence-bert-swedish-cased",
"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-large-chinese",
"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",
"xlm-roberta-base",
"xlm-roberta-large",
}
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard"))
for model in SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS
}
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",
"Koat/gte-tiny",
"SmartComponents/bge-micro-v2",
}
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_DA):
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)
# 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 = "", ""
for filename in filenames:
if re.match("\d+_Pooling/config.json", filename):
st_config_path = hf_hub_download(model.modelId, filename=filename)
dim = json.load(open(st_config_path)).get("word_embedding_dimension", "")
break
for filename in filenames:
if re.match("\d+_Dense/config.json", filename):
st_config_path = hf_hub_download(model.modelId, filename=filename)
dim = json.load(open(st_config_path)).get("out_features", dim)
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. Parameters in million parameters and memory in GB
parameters, memory = get_model_parameters_memory(model)
return dim, seq, parameters, memory
def make_datasets_clickable(df):
"""Does not work"""
if "BornholmBitextMining" in df.columns:
link = "https://huggingface.co/datasets/strombergnlp/bornholmsk_parallel"
df = df.rename(
columns={f'BornholmBitextMining': '<a target="_blank" style="text-decoration: underline" href="{link}">BornholmBitextMining</a>',})
return df
def add_rank(df):
cols_to_rank = [col for col in df.columns if col not in ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"]]
if len(cols_to_rank) == 1:
df.sort_values(cols_to_rank[0], ascending=False, inplace=True)
else:
df.insert(len(df.columns) - len(cols_to_rank), "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=True, 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 (Million Parameters)"] = EXTERNAL_MODEL_TO_SIZE.get(model, "")
res["Memory Usage (GB, fp32)"] = round(res["Model Size (Million Parameters)"] * 1e6 * 4 / 1024**3, 2) if res["Model Size (Million Parameters)"] != "" else ""
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 (Million Parameters)"], out["Memory Usage (GB, fp32)"] = get_dim_seq_size(model)
except:
pass
df_list.append(out)
if model.library_name == "sentence-transformers" or "sentence-transformers" in model.tags or "modules.json" in {file.rfilename for file in model.siblings}:
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"])
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", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + 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[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING])
DATA_CLUSTERING = DATA_CLUSTERING[DATA_CLUSTERING.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION])
DATA_PAIR_CLASSIFICATION = DATA_PAIR_CLASSIFICATION[DATA_PAIR_CLASSIFICATION.iloc[:, 4:].ne("").any(axis=1)]
DATA_RERANKING = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING])
DATA_RERANKING = DATA_RERANKING[DATA_RERANKING.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL])
DATA_RETRIEVAL = DATA_RETRIEVAL[DATA_RETRIEVAL.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_EN = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS])
DATA_STS_EN = DATA_STS_EN[DATA_STS_EN.iloc[:, 4:].ne("").any(axis=1)]
DATA_SUMMARIZATION = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + 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 (Million Parameters)", "Memory Usage (GB, fp32)", "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", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + 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[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_ZH])
DATA_CLUSTERING_ZH = DATA_CLUSTERING_ZH[DATA_CLUSTERING_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_ZH])
DATA_PAIR_CLASSIFICATION_ZH = DATA_PAIR_CLASSIFICATION_ZH[DATA_PAIR_CLASSIFICATION_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_RERANKING_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING_ZH])
DATA_RERANKING_ZH = DATA_RERANKING_ZH[DATA_RERANKING_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_ZH])
DATA_RETRIEVAL_ZH = DATA_RETRIEVAL_ZH[DATA_RETRIEVAL_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_ZH])
DATA_STS_ZH = DATA_STS_ZH[DATA_STS_ZH.iloc[:, 4:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_ZH.fillna("", inplace=True)
DATA_OVERALL_ZH = DATA_OVERALL_ZH[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "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", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION_FR])
DATA_CLASSIFICATION_FR = DATA_CLASSIFICATION_FR[DATA_CLASSIFICATION_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_FR])
DATA_CLUSTERING_FR = DATA_CLUSTERING_FR[DATA_CLUSTERING_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_FR])
DATA_PAIR_CLASSIFICATION_FR = DATA_PAIR_CLASSIFICATION_FR[DATA_PAIR_CLASSIFICATION_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_RERANKING_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING_FR])
DATA_RERANKING_FR = DATA_RERANKING_FR[DATA_RERANKING_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_FR])
DATA_RETRIEVAL_FR = DATA_RETRIEVAL_FR[DATA_RETRIEVAL_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_FR])
DATA_STS_FR = DATA_STS_FR[DATA_STS_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_SUMMARIZATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + 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 (Million Parameters)", "Memory Usage (GB, fp32)", "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", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + 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[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_PL])
DATA_CLUSTERING_PL = DATA_CLUSTERING_PL[DATA_CLUSTERING_PL.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_PL])
DATA_PAIR_CLASSIFICATION_PL = DATA_PAIR_CLASSIFICATION_PL[DATA_PAIR_CLASSIFICATION_PL.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_PL])
DATA_RETRIEVAL_PL = DATA_RETRIEVAL_PL[DATA_RETRIEVAL_PL.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_PL])
DATA_STS_PL = DATA_STS_PL[DATA_STS_PL.iloc[:, 4:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_PL.fillna("", inplace=True)
DATA_OVERALL_PL = DATA_OVERALL_PL[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "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)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_BITEXT_MINING]
DATA_BITEXT_MINING_DA = get_mteb_data(["BitextMining"], [], TASK_LIST_BITEXT_MINING_DA)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_BITEXT_MINING_DA]
DATA_CLASSIFICATION_DA = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_DA)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_DA]
DATA_CLASSIFICATION_NB = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_NB)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_NB]
DATA_CLASSIFICATION_SV = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_SV)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_SV]
DATA_CLASSIFICATION_OTHER = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_OTHER)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_OTHER]
DATA_CLUSTERING_DE = get_mteb_data(["Clustering"], [], TASK_LIST_CLUSTERING_DE)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLUSTERING_DE]
DATA_STS_OTHER = get_mteb_data(["STS"], [], TASK_LIST_STS_OTHER)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_STS_OTHER]
DATA_RETRIEVAL_LAW = get_mteb_data(["Retrieval"], [], TASK_LIST_RETRIEVAL_LAW)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + 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_DA,
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 = 4 if "Average" in d.columns else 3
# 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
# 4. Prevent checkbox groups from taking up too much space
css = """
table > thead {
white-space: normal
}
table {
--cell-width-1: 250px
}
table > tbody > tr > td:nth-child(2) > div {
overflow-x: auto
}
.filter-checkbox-group {
max-width: max-content;
}
"""
"""
Each inner tab can have the following keys:
- language: The language of the leaderboard
- language_long: [optional] The long form of the language
- description: The description of the leaderboard
- credits: [optional] The credits for the leaderboard
- data: The data for the leaderboard
- refresh: The function to refresh the leaderboard
"""
chinese_credits = "[FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)"
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)"
danish_credits = "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
norwegian_credits = "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
polish_credits = "[Rafaล Poลwiata](https://github.com/rafalposwiata)"
data = {
"Overall": {
"metric": "Various, refer to task tabs",
"data": [
{
"language": "English",
"description": "**Overall MTEB English leaderboard** ๐ฎ",
"data": DATA_OVERALL,
"refresh": get_mteb_average,
},
{
"language": "Chinese",
"data": DATA_OVERALL_ZH,
"description": "**Overall MTEB Chinese leaderboard (C-MTEB)** ๐ฎ๐จ๐ณ",
"credits": chinese_credits,
"refresh": get_mteb_average_zh,
},
{
"language": "French",
"data": DATA_OVERALL_FR,
"description": "**Overall MTEB French leaderboard (F-MTEB)** ๐ฎ๐ซ๐ท",
"credits": french_credits,
"refresh": get_mteb_average_fr,
},
{
"language": "Polish",
"data": DATA_OVERALL_PL,
"description": "**Overall MTEB Polish leaderboard** ๐ฎ๐ต๐ฑ",
"refresh": get_mteb_average_pl,
},
]
},
"Bitext Mining": {
"metric": "[F1](https://huggingface.co/spaces/evaluate-metric/f1)",
"data": [
{
"language": "English-X",
"language_long": "117 (Pairs of: English & other language)",
"description": "**Bitext Mining English-X Leaderboard** ๐",
"data": DATA_BITEXT_MINING,
"refresh": partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING),
},
{
"language": "Danish",
"language_long": "Danish & Bornholmsk (Danish Dialect)",
"description": "**Bitext Mining Danish Leaderboard** ๐๐ฉ๐ฐ",
"credits": danish_credits,
"data": DATA_BITEXT_MINING_DA,
"refresh": partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING_DA),
}
]
},
"Classification": {
"metric": "[Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)",
"data": [
{
"language": "English",
"description": "**Classification English Leaderboard** โค๏ธ",
"data": DATA_CLASSIFICATION_EN,
"refresh": partial(get_mteb_data, tasks=["Classification"], langs=["en"])
},
{
"language": "Chinese",
"description": "**Classification Chinese Leaderboard** ๐งก๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_CLASSIFICATION_ZH,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_ZH)
},
{
"language": "Danish",
"description": "**Classification Danish Leaderboard** ๐ค๐ฉ๐ฐ",
"credits": danish_credits,
"data": DATA_CLASSIFICATION_DA,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_DA)
},
{
"language": "French",
"description": "**Classification French Leaderboard** ๐๐ซ๐ท",
"credits": french_credits,
"data": DATA_CLASSIFICATION_FR,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_FR)
},
{
"language": "Norwegian",
"language_long": "Norwegian Bokmรฅl",
"description": "**Classification Norwegian Leaderboard** ๐๐ณ๐ด",
"credits": norwegian_credits,
"data": DATA_CLASSIFICATION_NB,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_NB)
},
{
"language": "Polish",
"description": "**Classification Polish Leaderboard** ๐ค๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_CLASSIFICATION_PL,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_PL)
},
{
"language": "Swedish",
"description": "**Classification Swedish Leaderboard** ๐๐ธ๐ช",
"credits": norwegian_credits,
"data": DATA_CLASSIFICATION_SV,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_SV)
},
{
"language": "Other",
"language_long": "47 (Only languages not included in the other tabs)",
"description": "**Classification Other Languages Leaderboard** ๐๐๐",
"data": DATA_CLASSIFICATION_OTHER,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_OTHER)
}
]
},
"Clustering": {
"metric": "Validity Measure (v_measure)",
"data": [
{
"language": "English",
"description": "**Clustering Leaderboard** โจ",
"data": DATA_CLUSTERING,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING)
},
{
"language": "Chinese",
"description": "**Clustering Chinese Leaderboard** โจ๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_CLUSTERING_ZH,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_ZH)
},
{
"language": "French",
"description": "**Clustering French Leaderboard** โจ๐ซ๐ท",
"credits": french_credits,
"data": DATA_CLUSTERING_FR,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_FR)
},
{
"language": "German",
"description": "**Clustering German Leaderboard** โจ๐ฉ๐ช",
"credits": "[Silvan](https://github.com/slvnwhrl)",
"data": DATA_CLUSTERING_DE,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_DE)
},
{
"language": "Polish",
"description": "**Clustering Polish Leaderboard** โจ๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_CLUSTERING_PL,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_PL)
},
]
},
"Pair Classification": {
"metric": "Average Precision based on Cosine Similarities (cos_sim_ap)",
"data": [
{
"language": "English",
"description": "**Pair Classification English Leaderboard** ๐ญ",
"data": DATA_PAIR_CLASSIFICATION,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION)
},
{
"language": "Chinese",
"description": "**Pair Classification Chinese Leaderboard** ๐ญ๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_PAIR_CLASSIFICATION_ZH,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_ZH)
},
{
"language": "French",
"description": "**Pair Classification French Leaderboard** ๐ญ๐ซ๐ท",
"credits": french_credits,
"data": DATA_PAIR_CLASSIFICATION_FR,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_FR)
},
{
"language": "Polish",
"description": "**Pair Classification Polish Leaderboard** ๐ญ๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_PAIR_CLASSIFICATION_PL,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_PL)
},
]
},
"Reranking": {
"metric": "Mean Average Precision (MAP)",
"data": [
{
"language": "English",
"description": "**Reranking English Leaderboard** ๐ฅ",
"data": DATA_RERANKING,
"refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING)
},
{
"language": "Chinese",
"description": "**Reranking Chinese Leaderboard** ๐ฅ๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_RERANKING_ZH,
"refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_ZH)
},
{
"language": "French",
"description": "**Reranking French Leaderboard** ๐ฅ๐ซ๐ท",
"credits": french_credits,
"data": DATA_RERANKING_FR,
"refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_FR)
}
]
},
"Retrieval": {
"metric": "Normalized Discounted Cumulative Gain @ k (ndcg_at_10)",
"data": [
{
"language": "English",
"description": "**Retrieval English Leaderboard** ๐",
"data": DATA_RETRIEVAL,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL)
},
{
"language": "Chinese",
"description": "**Retrieval Chinese Leaderboard** ๐๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_RETRIEVAL_ZH,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_ZH)
},
{
"language": "French",
"description": "**Retrieval French Leaderboard** ๐๐ซ๐ท",
"credits": french_credits,
"data": DATA_RETRIEVAL_FR,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_FR)
},
{
"language": "Law",
"language_long": "English, German, Chinese",
"description": "**Retrieval Law Leaderboard** ๐โ๏ธ",
"credits": "[Voyage AI](https://www.voyageai.com/)",
"data": DATA_RETRIEVAL_LAW,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_LAW)
},
{
"language": "Polish",
"description": "**Retrieval Polish Leaderboard** ๐๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_RETRIEVAL_PL,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_PL)
}
]
},
"STS": {
"metric": "Spearman correlation based on cosine similarity",
"data": [
{
"language": "English",
"description": "**STS English Leaderboard** ๐ค",
"data": DATA_STS_EN,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS)
},
{
"language": "Chinese",
"description": "**STS Chinese Leaderboard** ๐ค๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_STS_ZH,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_ZH)
},
{
"language": "French",
"description": "**STS French Leaderboard** ๐ค๐ซ๐ท",
"credits": french_credits,
"data": DATA_STS_FR,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_FR)
},
{
"language": "Polish",
"description": "**STS Polish Leaderboard** ๐ค๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_STS_PL,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_PL)
},
{
"language": "Other",
"language_long": "Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs)",
"description": "**STS Other Leaderboard** ๐ฝ",
"data": DATA_STS_OTHER,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_OTHER)
},
]
},
"Summarization": {
"metric": "Spearman correlation based on cosine similarity",
"data": [
{
"language": "English",
"description": "**Summarization Leaderboard** ๐",
"data": DATA_SUMMARIZATION,
"refresh": partial(get_mteb_data, tasks=TASK_LIST_SUMMARIZATION)
},
{
"language": "French",
"description": "**Summarization Leaderboard** ๐",
"credits": french_credits,
"data": DATA_SUMMARIZATION_FR,
"refresh": partial(get_mteb_data, tasks=TASK_LIST_SUMMARIZATION_FR)
}
]
}
}
dataframes = []
full_dataframes = []
tabs = []
# The following JavaScript function updates the URL parameters based on the selected task and language
# Additionally, `update_url_task` and `update_url_language` are used to update the current task and language
# The current task and language are stored in the `current_task_language` and `language_per_task` JSON objects
# This is all a bit hacky, but it might be the only way to pass options to a JavaScript function via Gradio
set_window_url_params = """
function(goalUrlObject) {
const params = new URLSearchParams(window.location.search);
for (const [key, value] of Object.entries(goalUrlObject)) {
params.set(key, value);
};
const queryString = '?' + params.toString();
console.log(queryString);
window.history.replaceState({}, '', queryString);
return [];
}
"""
def update_url_task(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
current_task_language["task"] = event.target.id
# Either use the cached language for this task or the 1st language
current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[0].children[0].id)
return current_task_language, language_per_task
def update_url_language(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
current_task_language["language"] = event.target.id
if "task" not in current_task_language:
current_task_language["task"] = "overall"
language_per_task[current_task_language["task"]] = event.target.id
return current_task_language, language_per_task
NUMERIC_INTERVALS = {
"<100M": pd.Interval(0, 100, closed="right"),
"100M to 250M": pd.Interval(100, 250, closed="right"),
"250M to 500M": pd.Interval(250, 500, closed="right"),
"500M to 1B": pd.Interval(500, 1000, closed="right"),
">1B": pd.Interval(1000, 1_000_000, closed="right"),
}
MODEL_TYPES = [
"Open",
"Proprietary",
"Sentence Transformers",
]
def filter_data(search_query, model_types, model_sizes, *full_dataframes):
output_dataframes = []
for df in full_dataframes:
# Apply the search query
if search_query:
names = df["Model"].map(lambda x: re.match("<a .+?>(.+)</a>", x).group(1))
masks = []
for query in search_query.split(";"):
masks.append(names.str.contains(query))
df = df[reduce(lambda a, b: a | b, masks)]
# Apply the model type filtering
if set(model_types) != set(MODEL_TYPES):
masks = []
for model_type in model_types:
if model_type == "Open":
masks.append(~df["Model"].isin(PROPRIETARY_MODELS))
elif model_type == "Proprietary":
masks.append(df["Model"].isin(PROPRIETARY_MODELS))
elif model_type == "Sentence Transformers":
masks.append(df["Model"].isin(SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS))
if masks:
df = df[reduce(lambda a, b: a | b, masks)]
else:
df = pd.DataFrame(columns=df.columns)
# Apply the model size filtering
if set(model_sizes) != set(NUMERIC_INTERVALS.keys()):
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[model_size] for model_size in model_sizes]))
sizes = df["Model Size (Million Parameters)"].replace('', 0)
mask = sizes.apply(lambda size: any(numeric_interval.contains(size)))
df = df[mask]
output_dataframes.append(df)
return output_dataframes
with gr.Blocks(css=css) as block:
# Store the current task and language for updating the URL. This is a bit hacky, but it works
# for passing the current task and language to the JavaScript function via Gradio
current_task_language = gr.JSON(value=dict(), visible=False)
language_per_task = gr.JSON(value=dict(), visible=False)
gr.Markdown(f"""
Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the <a href="https://github.com/embeddings-benchmark/mteb/blob/main/docs/adding_a_model.md" target="_blank" style="text-decoration: underline">MTEB GitHub repository</a> ๐ค Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models.
""")
with gr.Row():
search_bar = gr.Textbox(
label="Search Bar (separate multiple queries with `;`)",
placeholder=" ๐ Search for a model and press enter...",
)
filter_model_type = gr.CheckboxGroup(
label="Model types",
choices=MODEL_TYPES,
value=MODEL_TYPES,
interactive=True,
elem_classes=["filter-checkbox-group"]
)
filter_model_sizes = gr.CheckboxGroup(
label="Model sizes (in number of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_classes=["filter-checkbox-group"],
scale=2,
)
with gr.Tabs() as outer_tabs:
# Store the tabs for updating them on load based on URL parameters
tabs.append(outer_tabs)
for task, task_values in data.items():
metric = task_values["metric"]
task_tab_id = task.lower().replace(" ", "-")
# Overall, Bitext Mining, Classification, etc.
with gr.Tab(task, id=task_tab_id) as task_tab:
# For updating the 'task' in the URL
task_tab.select(update_url_task, [current_task_language, language_per_task], [current_task_language, language_per_task]).then(None, [current_task_language], [], js=set_window_url_params)
with gr.Tabs() as task_tabs:
# Store the task tabs for updating them on load based on URL parameters
tabs.append(task_tabs)
for item in task_values["data"]:
item_tab_id = item["language"].lower().replace(" ", "-")
# English, Chinese, French, etc.
with gr.Tab(item["language"], id=item_tab_id) as item_tab:
# For updating the 'language' in the URL
item_tab.select(update_url_language, [current_task_language, language_per_task], [current_task_language, language_per_task], trigger_mode="always_last").then(None, [current_task_language], [], js=set_window_url_params)
with gr.Row():
gr.Markdown(f"""
{item['description']}
- **Metric:** {metric}
- **Languages:** {item['language_long'] if 'language_long' in item else item['language']}
{"- **Credits:** " + item['credits'] if "credits" in item else ''}
""")
with gr.Row():
datatype = ["number", "markdown"] + ["number"] * len(item["data"])
dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", height=500)
dataframes.append(dataframe)
full_dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", visible=False)
full_dataframes.append(full_dataframe)
with gr.Row():
refresh_button = gr.Button("Refresh")
refresh_button.click(item["refresh"], inputs=None, outputs=dataframe)
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}
}
```
""")
def set_tabs_on_load(request: gr.Request):
"""Set the selected tab based on the URL parameters on load."""
global tabs
valid_task_keys = [child.id for child in tabs[0].children]
return_tabs = [gr.Tabs()] * len(tabs)
query_params = request.request.query_params
task_key = query_params.get("task", "overall")
if task_key not in valid_task_keys:
task_key = "overall"
return_tabs[0] = gr.Tabs(selected=task_key)
tabs_idx = valid_task_keys.index(task_key) + 1
language_key = query_params.get("language", "english")
return_tabs[tabs_idx] = gr.Tabs(selected=language_key)
current_task_language = {"task": task_key, "language": language_key}
language_per_task = {task_key: language_key}
return return_tabs + [current_task_language, language_per_task]
block.load(set_tabs_on_load, inputs=[], outputs=tabs + [current_task_language, language_per_task])
search_bar.submit(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
filter_model_type.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
filter_model_sizes.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
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/
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