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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 (nb)",
]
TASK_LIST_CLASSIFICATION_SV = [
"DalajClassification",
"MassiveIntentClassification (sv)",
"MassiveScenarioClassification (sv)",
"NordicLangClassification",
"ScalaNbClassification",
"ScalaSvClassification",
"SweRecClassification",
]
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 (pl)', '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-CN)', '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 (pl)', '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-CN)', '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_PAIR_CLASSIFICATION = [
"SprintDuplicateQuestions",
"TwitterSemEval2015",
"TwitterURLCorpus",
]
TASK_LIST_RERANKING = [
"AskUbuntuDupQuestions",
"MindSmallReranking",
"SciDocsRR",
"StackOverflowDupQuestions",
]
TASK_LIST_RETRIEVAL = [
"ArguAna",
"ClimateFEVER",
"CQADupstackRetrieval",
"DBPedia",
"FEVER",
"FiQA2018",
"HotpotQA",
"MSMARCO",
"NFCorpus",
"NQ",
"QuoraRetrieval",
"SCIDOCS",
"SciFact",
"Touche2020",
"TRECCOVID",
]
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_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_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",
"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",
"komninos",
"LASER2",
"LaBSE",
"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",
"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",
"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",
"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",
"komninos": "https://huggingface.co/sentence-transformers/average_word_embeddings_komninos",
"LASER2": "https://github.com/facebookresearch/LASER",
"LaBSE": "https://huggingface.co/sentence-transformers/LaBSE",
"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",
"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",
"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,
"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,
"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,
"komninos": 300,
"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,
"use-cmlm-multilingual": 768,
"unsup-simcse-bert-base-uncased": 768,
"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,
"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,
"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,
"komninos": "N/A",
"LASER2": "N/A",
"LaBSE": 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,
"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,
"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,
"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,
"komninos": 0.27,
"LASER2": 0.17,
"LaBSE": 1.88,
"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,
"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",
}
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_SV + TASK_LIST_CLASSIFICATION_NB:
examples["mteb_task"] = "Classification"
elif examples["mteb_dataset_name"] in TASK_LIST_CLUSTERING + TASK_LIST_CLUSTERING_DE:
examples["mteb_task"] = "Clustering"
elif examples["mteb_dataset_name"] in TASK_LIST_PAIR_CLASSIFICATION:
examples["mteb_task"] = "PairClassification"
elif examples["mteb_dataset_name"] in TASK_LIST_RERANKING:
examples["mteb_task"] = "Reranking"
elif examples["mteb_dataset_name"] in TASK_LIST_RETRIEVAL_NORM:
examples["mteb_task"] = "Retrieval"
elif examples["mteb_dataset_name"] in TASK_LIST_STS_NORM:
examples["mteb_task"] = "STS"
elif examples["mteb_dataset_name"] in TASK_LIST_SUMMARIZATION:
examples["mteb_task"] = "Summarization"
else:
examples["mteb_task"] = "BitextMining"
return examples
for model in EXTERNAL_MODELS:
ds = load_dataset("mteb/results", model)#, download_mode='force_redownload', verification_mode="no_checks")
# 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 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
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
# Save to csv
df.to_csv("mteb.csv", index=False)
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, NUM_SCORES
DATA_OVERALL = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
"Summarization",
],
langs=["en", "en-en"],
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])
DATA_CLUSTERING = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_CLUSTERING])
DATA_PAIR_CLASSIFICATION = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_PAIR_CLASSIFICATION])
DATA_RERANKING = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_RERANKING])
DATA_RETRIEVAL = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_RETRIEVAL])
DATA_STS_EN = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_STS])
DATA_SUMMARIZATION = add_rank(DATA_OVERALL[["Model"] + TASK_LIST_SUMMARIZATION])
# 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)"]]
return DATA_OVERALL
get_mteb_average()
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_GERMAN = get_mteb_data(["Clustering"], [], TASK_LIST_CLUSTERING_DE)
DATA_STS = get_mteb_data(["STS"])
# Exact, add all non-nan integer values for every dataset
NUM_SCORES = 0
DATASETS = []
# LANGUAGES = []
for d in [DATA_BITEXT_MINING, DATA_BITEXT_MINING_OTHER, DATA_CLASSIFICATION_EN, DATA_CLASSIFICATION_DA, DATA_CLASSIFICATION_NB, DATA_CLASSIFICATION_SV, DATA_CLASSIFICATION_OTHER, DATA_CLUSTERING, DATA_CLUSTERING_GERMAN, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS, 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:]]
NUM_DATASETS = len(set(DATASETS))
# NUM_LANGUAGES = len(set(LANGUAGES))
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> ๐ค 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**: {len(DATA_OVERALL)}
""")
with gr.Tabs():
with gr.TabItem("Overall"):
with gr.Row():
gr.Markdown("""
**Overall MTEB English leaderboard ๐ฎ**
- **Metric:** Various, refer to task tabs
- **Languages:** English, refer to task tabs for others
""")
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 = gr.Button("Refresh")
data_run.click(get_mteb_average, inputs=None, outputs=data_overall)
with gr.TabItem("Bitext Mining"):
with gr.TabItem("English-X"):
with gr.Row():
gr.Markdown("""
**Bitext Mining Leaderboard ๐ด๓ ง๓ ข๓ ณ๓ ฃ๓ ด๓ ฟ**
- **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 = gr.Button("Refresh")
task_bitext_mining = gr.Variable(value=["BitextMining"])
lang_bitext_mining_other = gr.Variable(value=[])
datasets_bitext_mining_other = gr.Variable(value=TASK_LIST_BITEXT_MINING)
data_run.click(
get_mteb_data,
inputs=[task_bitext_mining, lang_bitext_mining_other, datasets_bitext_mining_other],
outputs=data_bitext_mining,
)
with gr.TabItem("Other"):
with gr.Row():
gr.Markdown("""
**Bitext Mining Other Leaderboard ๐**
- **Metric:** [F1](https://huggingface.co/spaces/evaluate-metric/f1)
- **Languages:** 2 (Pair of: Danish & Bornholmsk)
- **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen)
""")
with gr.Row():
data_bitext_mining_other = gr.components.Dataframe(
DATA_BITEXT_MINING_OTHER,
datatype=["number", "markdown"] + ["number"] * len(DATA_BITEXT_MINING_OTHER.columns),
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_bitext_mining_other = gr.Variable(value=["BitextMining"])
lang_bitext_mining_other = gr.Variable(value=[])
datasets_bitext_mining_other = gr.Variable(value=TASK_LIST_BITEXT_MINING_OTHER)
data_run.click(
get_mteb_data,
inputs=[
task_bitext_mining_other,
lang_bitext_mining_other,
datasets_bitext_mining_other,
],
outputs=data_bitext_mining_other,
)
with gr.TabItem("Classification"):
with gr.TabItem("English"):
with gr.Row():
gr.Markdown("""
**Classification Leaderboard โค๏ธ**
- **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
- **Languages:** English
""")
with gr.Row():
data_classification_en = gr.components.Dataframe(
DATA_CLASSIFICATION_EN,
datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_EN.columns),
type="pandas",
)
with gr.Row():
data_run_classification_en = gr.Button("Refresh")
task_classification_en = gr.Variable(value=["Classification"])
lang_classification_en = gr.Variable(value=["en"])
data_run_classification_en.click(
get_mteb_data,
inputs=[
task_classification_en,
lang_classification_en,
],
outputs=data_classification_en,
)
with gr.TabItem("Danish"):
with gr.Row():
gr.Markdown("""
**Classification Danish Leaderboard ๐ค๐ฉ๐ฐ**
- **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
- **Languages:** Danish
- **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen)
""")
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")
task_classification_da = gr.Variable(value=["Classification"])
lang_classification_da = gr.Variable(value=[])
datasets_classification_da = gr.Variable(value=TASK_LIST_CLASSIFICATION_DA)
data_run_classification_da.click(
get_mteb_data,
inputs=[
task_classification_da,
lang_classification_da,
datasets_classification_da,
],
outputs=data_classification_da,
)
with gr.TabItem("Norwegian"):
with gr.Row():
gr.Markdown("""
**Classification Norwegian Leaderboard ๐๐ณ๐ด**
- **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
- **Languages:** Norwegian Bokmรฅl
- **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen)
""")
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")
task_classification_nb = gr.Variable(value=["Classification"])
lang_classification_nb = gr.Variable(value=[])
datasets_classification_nb = gr.Variable(value=TASK_LIST_CLASSIFICATION_NB)
data_run_classification_nb.click(
get_mteb_data,
inputs=[
task_classification_nb,
lang_classification_nb,
datasets_classification_nb,
],
outputs=data_classification_nb,
)
with gr.TabItem("Swedish"):
with gr.Row():
gr.Markdown("""
**Classification Swedish Leaderboard ๐๐ธ๐ช**
- **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
- **Languages:** Swedish
- **Credits:** [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen)
""")
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")
task_classification_sv = gr.Variable(value=["Classification"])
lang_classification_sv = gr.Variable(value=[])
datasets_classification_sv = gr.Variable(value=TASK_LIST_CLASSIFICATION_SV)
data_run_classification_sv.click(
get_mteb_data,
inputs=[
task_classification_sv,
lang_classification_sv,
datasets_classification_sv,
],
outputs=data_classification_sv,
)
with gr.TabItem("Other"):
with gr.Row():
gr.Markdown("""
**Classification Other Languages Leaderboard ๐๐๐**
- **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
- **Languages:** 47 (Only languages not included in the other tabs)
""")
with gr.Row():
data_classification = gr.components.Dataframe(
DATA_CLASSIFICATION_OTHER,
datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION_OTHER) * 10,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_classification = gr.Variable(value=["Classification"])
lang_classification = gr.Variable(value=[])
datasets_classification = gr.Variable(value=TASK_LIST_CLASSIFICATION_OTHER)
data_run.click(
get_mteb_data,
inputs=[
task_classification,
lang_classification,
datasets_classification,
],
outputs=data_classification,
)
with gr.TabItem("Clustering"):
with gr.TabItem("English"):
with gr.Row():
gr.Markdown("""
**Clustering Leaderboard โจ**
- **Metric:** Validity Measure (v_measure)
- **Languages:** English
""")
with gr.Row():
data_clustering = gr.components.Dataframe(
DATA_CLUSTERING,
datatype=["number", "markdown"] + ["number"] * len(DATA_CLUSTERING.columns),
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_clustering = gr.Variable(value=["Clustering"])
empty = gr.Variable(value=[])
datasets_clustering = gr.Variable(value=TASK_LIST_CLUSTERING)
data_run.click(
get_mteb_data,
inputs=[task_clustering, empty, datasets_clustering],
outputs=data_clustering,
)
with gr.TabItem("German"):
with gr.Row():
gr.Markdown("""
**Clustering German Leaderboard โจ๐ฉ๐ช**
- **Metric:** Validity Measure (v_measure)
- **Languages:** German
- **Credits:** [Silvan](https://github.com/slvnwhrl)
""")
with gr.Row():
data_clustering_de = gr.components.Dataframe(
DATA_CLUSTERING_GERMAN,
datatype=["number", "markdown"] + ["number"] * len(DATA_CLUSTERING_GERMAN.columns) * 2,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_clustering_de = gr.Variable(value=["Clustering"])
empty_de = gr.Variable(value=[])
datasets_clustering_de = gr.Variable(value=TASK_LIST_CLUSTERING_DE)
data_run.click(
get_mteb_data,
inputs=[task_clustering_de, empty_de, datasets_clustering_de],
outputs=data_clustering_de,
)
with gr.TabItem("Pair Classification"):
with gr.Row():
gr.Markdown("""
**Pair Classification Leaderboard ๐ญ**
- **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap)
- **Languages:** English
""")
with gr.Row():
data_pair_classification = gr.components.Dataframe(
DATA_PAIR_CLASSIFICATION,
datatype=["number", "markdown"] + ["number"] * len(DATA_PAIR_CLASSIFICATION.columns),
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_pair_classification = gr.Variable(value=["PairClassification"])
data_run.click(
get_mteb_data,
inputs=[task_pair_classification],
outputs=data_pair_classification,
)
with gr.TabItem("Reranking"):
with gr.Row():
gr.Markdown("""
**Reranking Leaderboard ๐ฅ**
- **Metric:** Mean Average Precision (MAP)
- **Languages:** English
""")
with gr.Row():
data_reranking = gr.components.Dataframe(
DATA_RERANKING,
datatype=["number", "markdown"] + ["number"] * len(DATA_RERANKING.columns),
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_reranking = gr.Variable(value=["Reranking"])
metric_reranking = gr.Variable(value="map")
data_run.click(
get_mteb_data, inputs=[task_reranking], outputs=data_reranking
)
with gr.TabItem("Retrieval"):
with gr.Row():
gr.Markdown("""
**Retrieval Leaderboard ๐**
- **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10)
- **Languages:** English
""")
with gr.Row():
data_retrieval = gr.components.Dataframe(
DATA_RETRIEVAL,
# Add support for more columns than existing as a buffer for CQADupstack & other Retrieval tasks (e.g. MSMARCOv2)
datatype=["number", "markdown"] + ["number"] * len(DATA_RETRIEVAL.columns) * 2,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_retrieval = gr.Variable(value=["Retrieval"])
data_run.click(
get_mteb_data, inputs=[task_retrieval], outputs=data_retrieval
)
with gr.TabItem("STS"):
with gr.TabItem("English"):
with gr.Row():
gr.Markdown("""
**STS Leaderboard ๐ค**
- **Metric:** Spearman correlation based on cosine similarity
- **Languages:** English
""")
with gr.Row():
data_sts_en = gr.components.Dataframe(
DATA_STS_EN,
datatype=["number", "markdown"] + ["number"] * len(DATA_STS_EN.columns),
type="pandas",
)
with gr.Row():
data_run_sts_en = gr.Button("Refresh")
task_sts_en = gr.Variable(value=["STS"])
lang_sts_en = gr.Variable(value=["en", "en-en"])
data_run_sts_en.click(
get_mteb_data,
inputs=[task_sts_en, lang_sts_en],
outputs=data_sts_en,
)
with gr.TabItem("Multilingual"):
with gr.Row():
gr.Markdown("""
**STS Multilingual Leaderboard ๐ฝ**
- **Metric:** Spearman correlation based on cosine similarity
- **Languages:** Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish
""")
with gr.Row():
data_sts = gr.components.Dataframe(
DATA_STS,
datatype=["number", "markdown"] + ["number"] * len(DATA_STS.columns) * 2,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_sts = gr.Variable(value=["STS"])
data_run.click(get_mteb_data, inputs=[task_sts], outputs=data_sts)
with gr.TabItem("Summarization"):
with gr.Row():
gr.Markdown("""
**Summarization Leaderboard ๐**
- **Metric:** Spearman correlation based on cosine similarity
- **Languages:** English
""")
with gr.Row():
data_summarization = gr.components.Dataframe(
DATA_SUMMARIZATION,
datatype=["number", "markdown"] + ["number"] * 2,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_summarization = gr.Variable(value=["Summarization"])
data_run.click(
get_mteb_data,
inputs=[task_summarization],
outputs=data_summarization,
)
gr.Markdown(r"""
Made with โค๏ธ for NLP. If this work is useful to you, please consider citing:
```bibtex
@article{muennighoff2022mteb,
doi = {10.48550/ARXIV.2210.07316},
url = {https://arxiv.org/abs/2210.07316},
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
}
```
""")
# Running the functions on page load in addition to when the button is clicked
# This is optional - If deactivated the data loaded at "Build time" is shown like for Overall tab
"""
block.load(get_mteb_data, inputs=[task_bitext_mining], outputs=data_bitext_mining)
block.load(get_mteb_data, inputs=[task_classification_en, lang_classification_en], outputs=data_classification_en)
block.load(get_mteb_data, inputs=[task_classification], outputs=data_classification)
block.load(get_mteb_data, inputs=[task_clustering, empty, datasets_clustering], outputs=data_clustering)
block.load(get_mteb_data, inputs=[task_clustering_de, empty_de, datasets_clustering_de], outputs=data_clustering_de)
block.load(get_mteb_data, inputs=[task_pair_classification], outputs=data_pair_classification)
block.load(get_mteb_data, inputs=[task_retrieval], outputs=data_retrieval)
block.load(get_mteb_data, inputs=[task_reranking], outputs=data_reranking)
block.load(get_mteb_data, inputs=[task_sts_en, lang_sts_en], outputs=data_sts_en)
block.load(get_mteb_data, inputs=[task_sts], outputs=data_sts)
block.load(get_mteb_data, inputs=[task_summarization], outputs=data_summarization)
"""
block.queue(concurrency_count=40, max_size=10)
block.launch()
# Possible changes:
# Could check if tasks are valid (Currently users could just invent new tasks - similar for languages)
# Could make it load in the background without the Gradio logo closer to the Deep RL space
# Could add graphs / other visual content
# Could add verification marks
# Sources:
# https://huggingface.co/spaces/gradio/leaderboard
# https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
# https://getemoji.com/
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