Add new CLF, BTM leaderboards

app.py

@@ -17,6 +17,9 @@ TASKS = [
     "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",
@@ -34,6 +37,38 @@ TASK_LIST_CLASSIFICATION = [
 
 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",
@@ -48,6 +83,7 @@ TASK_LIST_CLUSTERING = [
     "TwentyNewsgroupsClustering",
 ]
 
+
 TASK_LIST_CLUSTERING_DE = [
     "BlurbsClusteringP2P",
     "BlurbsClusteringS2S",
@@ -86,7 +122,8 @@ TASK_LIST_RETRIEVAL = [
     "TRECCOVID",
 ]
 
-TASK_LIST_RETRIEVAL_NORM = TASK_LIST_RETRIEVAL + ["CQADupstackAndroidRetrieval",
+TASK_LIST_RETRIEVAL_NORM = TASK_LIST_RETRIEVAL + [
+    "CQADupstackAndroidRetrieval",
     "CQADupstackEnglishRetrieval",
     "CQADupstackGamingRetrieval",
     "CQADupstackGisRetrieval",
@@ -124,7 +161,6 @@ TASK_LIST_EN = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_
 TASK_TO_METRIC = {
     "BitextMining": "f1",
     "Clustering": "v_measure",
-    "Clustering (DE)": "v_measure",    
     "Classification": "accuracy",
     "PairClassification": "cos_sim_ap",
     "Reranking": "map",
@@ -143,16 +179,23 @@ def make_clickable_model(model_name, link=None):
 
 # Models without metadata, thus we cannot fetch their results naturally
 EXTERNAL_MODELS = [
-    "LASER2",
-    "LaBSE",
     "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",
@@ -164,9 +207,19 @@ EXTERNAL_MODELS = [
     "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",
@@ -184,20 +237,58 @@ EXTERNAL_MODELS = [
     "text-search-davinci-001",
     "unsup-simcse-bert-base-uncased",
     "use-cmlm-multilingual",
+    "xlm-roberta-base",
     "xlm-roberta-large",  
 ]
 
 EXTERNAL_MODEL_TO_LINK = {
-    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large",
-    "use-cmlm-multilingual": "https://huggingface.co/sentence-transformers/use-cmlm-multilingual",
+    "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",
@@ -209,173 +300,192 @@ EXTERNAL_MODEL_TO_LINK = {
     "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",
-    "LaBSE": "https://huggingface.co/sentence-transformers/LaBSE",
-    "sentence-t5-xxl": "https://huggingface.co/sentence-transformers/sentence-t5-xxl",
-    "sentence-t5-xl": "https://huggingface.co/sentence-transformers/sentence-t5-xl",
-    "sentence-t5-large": "https://huggingface.co/sentence-transformers/sentence-t5-large",
-    "sentence-t5-base": "https://huggingface.co/sentence-transformers/sentence-t5-base",
-    "gtr-t5-xxl": "https://huggingface.co/sentence-transformers/gtr-t5-xxl",
-    "gtr-t5-xl": "https://huggingface.co/sentence-transformers/gtr-t5-xl",
-    "gtr-t5-large": "https://huggingface.co/sentence-transformers/gtr-t5-large",
-    "gtr-t5-base": "https://huggingface.co/sentence-transformers/gtr-t5-base",
-    "gtr-t5-xxl": "https://huggingface.co/sentence-transformers/gtr-t5-xxl",
-    "gtr-t5-xl": "https://huggingface.co/sentence-transformers/gtr-t5-xl",
-    "gtr-t5-large": "https://huggingface.co/sentence-transformers/gtr-t5-large",
-    "gtr-t5-base": "https://huggingface.co/sentence-transformers/gtr-t5-base",
-    "bert-base-uncased": "https://huggingface.co/bert-base-uncased",
-    "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter",
-    "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter",
     "unsup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased",
-    "sup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased",
-    "komninos": "https://huggingface.co/sentence-transformers/average_word_embeddings_komninos",
-    "glove.6B.300d": "https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d",
-    "msmarco-bert-co-condensor": "https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor",
-    "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",
-    "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",
-    "contriever-base-msmarco": "https://huggingface.co/nthakur/contriever-base-msmarco",
+    "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 = {
-    "xlm-roberta-large":  1024,
-    "use-cmlm-multilingual": 768,
-    "gottbert-base": 768,
-    "cross-en-de-roberta-sentence-transformer": 768,
-    "distiluse-base-multilingual-cased-v2": 512,    
-    "gbert-base": 768,
-    "gbert-large": 1024,
-    "gelectra-base": 768,
-    "gelectra-large": 1024,
-    "gottbert-base": 768,
-
-    "LASER2": 1024,
-    "LaBSE": 768,
     "all-MiniLM-L12-v2": 384,
     "all-MiniLM-L6-v2": 384,
     "all-mpnet-base-v2": 768,
-    "allenai-specter": 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,   
-
-    "unsup-simcse-bert-base-uncased": 768,
+    "text-search-davinci-001": 12288,
+    "xlm-roberta-base":  768,
+    "xlm-roberta-large":  1024,
 }
 
 
 EXTERNAL_MODEL_TO_SEQLEN = {
-    "xlm-roberta-large":  514,
-    "use-cmlm-multilingual": 512,
-    "gottbert-base": 512,
-    "cross-en-de-roberta-sentence-transformer": 514,
-    "distiluse-base-multilingual-cased-v2": 512,    
-    "gbert-base": 512,
-    "gbert-large": 512,
-    "gelectra-base": 512,
-    "gelectra-large": 512,
-    "gottbert-base": 512,
-
-    "LASER2": "N/A",
-    "LaBSE": 512,
     "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 = {
-    "gtr-t5-xxl": 9.73,
-    "gtr-t5-xl": 2.48,
-    "gtr-t5-large": 0.67,
-    "gtr-t5-base": 0.22,
-    "sentence-t5-xxl": 9.73,
-    "sentence-t5-xl": 2.48,
-    "sentence-t5-large": 0.67,
-    "sentence-t5-base": 0.22,
-    "all-mpnet-base-v2": 0.44,
+    "allenai-specter": 0.44,
     "all-MiniLM-L12-v2": 0.13,
     "all-MiniLM-L6-v2": 0.09,
-    "contriever-base-msmarco": 0.44,
-    "paraphrase-multilingual-mpnet-base-v2": 1.11,
-    "paraphrase-multilingual-MiniLM-L12-v2": 0.47,
-    "msmarco-bert-co-condensor": 0.44,
-    "sup-simcse-bert-base-uncased": 0.44,
-    "unsup-simcse-bert-base-uncased": 0.44,
-    "LaBSE": 1.88,
-    "komninos": 0.27,
-    "glove.6B.300d": 0.48,
-    "allenai-specter": 0.44,
-    "bert-base-uncased": 0.44,
-    "LASER2": 0.17,
+    "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,
-    "use-cmlm-multilingual": 1.89,
+    "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,
-    "gottbert-base": 0.51
 }
 
 MODELS_TO_SKIP = {
@@ -413,7 +523,7 @@ def add_lang(examples):
 
 def add_task(examples):
     # Could be added to the dataset loading script instead
-    if examples["mteb_dataset_name"] in TASK_LIST_CLASSIFICATION_NORM:
+    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"
@@ -547,6 +657,11 @@ def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_
                 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")))
@@ -607,8 +722,12 @@ def get_mteb_average():
     return DATA_OVERALL
 
 get_mteb_average()
-DATA_BITEXT_MINING = get_mteb_data(["BitextMining"])
-DATA_CLASSIFICATION = get_mteb_data(["Classification"])
+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"])
 
@@ -616,7 +735,7 @@ DATA_STS = get_mteb_data(["STS"])
 NUM_SCORES = 0
 DATASETS = []
 # LANGUAGES = []
-for d in [DATA_BITEXT_MINING, DATA_CLASSIFICATION, DATA_CLUSTERING, DATA_CLUSTERING_GERMAN, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS, DATA_SUMMARIZATION]:
+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
@@ -634,7 +753,7 @@ with block:
     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**: 112
+    - **Total Languages**: 113
     - **Total Scores**: {NUM_SCORES}
     - **Total Models**: {len(DATA_OVERALL)}
     """)
@@ -656,29 +775,61 @@ with block:
                 )
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                data_run.click(get_mteb_average, inputs=None, outputs=data_overall)                
+                data_run.click(get_mteb_average, inputs=None, outputs=data_overall)
         with gr.TabItem("Bitext Mining"):
-            with gr.Row():
-                    gr.Markdown("""
-                    **Bitext Mining Leaderboard 🎌**
-                    
-                    - **Metric:** [F1](https://huggingface.co/spaces/evaluate-metric/f1)
-                    - **Languages:** 117
-                    """)
-            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"])
-                data_run.click(
-                    get_mteb_data,
-                    inputs=[task_bitext_mining],
-                    outputs=data_bitext_mining,
-                )
+            with gr.TabItem("English-X"):
+                with gr.Row():
+                        gr.Markdown("""
+                        **Bitext Mining 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 = 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 🎌**
+                        
+                        - **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():
@@ -706,28 +857,121 @@ with block:
                         ],
                         outputs=data_classification_en,
                     )
-            with gr.TabItem("Multilingual"):
+            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)
+                    """)
+                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 💙🇳🇴**
+                    
+                    - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
+                    - **Languages:** Norwegian Bokmål
+                    - **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 💛🇸🇪**
+                    
+                    - **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 Multilingual Leaderboard 💜💚💙**
+                    **Classification Other Languages Leaderboard 💜💚💙**
                     
                     - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)
-                    - **Languages:** 51
+                    - **Languages:** 47 (Only languages not included in the other tabs)
                     """)
                 with gr.Row():
                     data_classification = gr.components.Dataframe(
-                        DATA_CLASSIFICATION,
-                        datatype=["number", "markdown"] + ["number"] * len(DATA_CLASSIFICATION) * 10,
+                        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],
+                        inputs=[
+                            task_classification,
+                            lang_classification,
+                            datasets_classification,
+                        ],
                         outputs=data_classification,
-                    )
+                    )                                      
         with gr.TabItem("Clustering"):
             with gr.TabItem("English"):
                 with gr.Row():
@@ -756,7 +1000,7 @@ with block:
             with gr.TabItem("German"):
                 with gr.Row():
                     gr.Markdown("""
-                    **Clustering Leaderboard ✨🇩🇪**
+                    **Clustering German Leaderboard ✨🇩🇪**
                     
                     - **Metric:** Validity Measure (v_measure)
                     - **Languages:** German
@@ -800,48 +1044,48 @@ with block:
                     inputs=[task_pair_classification],
                     outputs=data_pair_classification,
                 )
-        with gr.TabItem("Retrieval"):
+        with gr.TabItem("Reranking"):
             with gr.Row():
                 gr.Markdown("""
-                **Retrieval Leaderboard  🔎**
+                **Reranking Leaderboard 🥈**
                 
-                - **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10)
+                - **Metric:** Mean Average Precision (MAP)
                 - **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,
+                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_retrieval = gr.Variable(value=["Retrieval"])
+                task_reranking = gr.Variable(value=["Reranking"])
+                metric_reranking = gr.Variable(value="map")
                 data_run.click(
-                    get_mteb_data, inputs=[task_retrieval], outputs=data_retrieval
+                    get_mteb_data, inputs=[task_reranking], outputs=data_reranking
                 )
-        with gr.TabItem("Reranking"):
+        with gr.TabItem("Retrieval"):
             with gr.Row():
                 gr.Markdown("""
-                **Reranking Leaderboard 🥈**
+                **Retrieval Leaderboard  🔎**
                 
-                - **Metric:** Mean Average Precision (MAP)
+                - **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10)
                 - **Languages:** English
                 """)
             with gr.Row():
-                data_reranking = gr.components.Dataframe(
-                    DATA_RERANKING,
-                    datatype=["number", "markdown"] + ["number"] * len(DATA_RERANKING.columns),
+                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_reranking = gr.Variable(value=["Reranking"])
-                metric_reranking = gr.Variable(value="map")
+                task_retrieval = gr.Variable(value=["Retrieval"])
                 data_run.click(
-                    get_mteb_data, inputs=[task_reranking], outputs=data_reranking
-                )
+                    get_mteb_data, inputs=[task_retrieval], outputs=data_retrieval
+                )                
         with gr.TabItem("STS"):
             with gr.TabItem("English"):
                 with gr.Row():