Add voyage-lite-01-instruct

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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.gitignore

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-*.pyc
-model_infos.json
-space
-.venv
-results
-mteb

DESCRIPTION.md

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+Massive Text Embedding Benchmark (MTEB) Leaderboard.

Dockerfile

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-FROM python:3.12-bookworm
-
-RUN apt update && apt install -y git make
-RUN useradd -m -u 1000 user
-ENV PATH="/home/user/.local/bin:$PATH"
-
-RUN git clone https://github.com/embeddings-benchmark/mteb.git
-RUN chown -R user:user /mteb
-
-USER user
-WORKDIR /mteb
-
-RUN pip install "pydantic<2.11"
-RUN pip install ".[leaderboard]"
-# ENV XDG_CACHE_HOME=/home/user/.cache
-ENV GRADIO_SERVER_NAME="0.0.0.0"
-EXPOSE 7860
-
-CMD ["make", "run-leaderboard"]

README.md

@@ -1,20 +1,10 @@
 ---
-title: MTEB Leaderboard
+title: MTEB Leaderboard 
 emoji: 🥇
 colorFrom: blue
 colorTo: indigo
-sdk: docker
-app_port: 7860
+sdk: gradio
+sdk_version: 4.0.2
 app_file: app.py
-pinned: true
-tags:
-- leaderboard
-startup_duration_timeout: 1h
-fullWidth: true
-license: mit
-short_description: Embedding Leaderboard
+pinned: false
 ---
-
-# MTEB Leaderboard
-
-Embedding Leaderboard

app.py

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

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+datasets
+pandas
+huggingface_hub