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b/all_data_tasks/17/default.jsonl index d25adf0cdbd81de1df0ac8fb367dd9e9a0e8c734..23bc4a9e5fdeafa2c65e0f14f0ffc203bfc3d8cb 100644 --- a/all_data_tasks/17/default.jsonl +++ b/all_data_tasks/17/default.jsonl @@ -1,63 +1,60 @@ {"index":11,"Rank":1,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Average":56.48,"AlloProfClusteringP2P":71.2,"AlloProfClusteringS2S":59.64,"HALClusteringS2S":28.19,"MLSUMClusteringP2P (fr)":47.75,"MLSUMClusteringS2S (fr)":47.46,"MasakhaNEWSClusteringP2P (fra)":73.86,"MasakhaNEWSClusteringS2S (fra)":67.24} {"index":9,"Rank":2,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Average":55.56,"AlloProfClusteringP2P":76.06,"AlloProfClusteringS2S":64.52,"HALClusteringS2S":30.83,"MLSUMClusteringP2P (fr)":50.03,"MLSUMClusteringS2S (fr)":43.7,"MasakhaNEWSClusteringP2P (fra)":60.19,"MasakhaNEWSClusteringS2S (fra)":63.62} {"index":8,"Rank":3,"Model":"gte-Qwen2-1.5B-instruct<\/a>","Model Size (Million Parameters)":1776,"Memory Usage (GB, fp32)":6.62,"Average":55.01,"AlloProfClusteringP2P":70.55,"AlloProfClusteringS2S":55.42,"HALClusteringS2S":28.3,"MLSUMClusteringP2P (fr)":45.27,"MLSUMClusteringS2S (fr)":42.77,"MasakhaNEWSClusteringP2P (fra)":71.04,"MasakhaNEWSClusteringS2S (fra)":71.71} -{"index":29,"Rank":4,"Model":"bilingual-embedding-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Average":51.26,"AlloProfClusteringP2P":65.3,"AlloProfClusteringS2S":55.37,"HALClusteringS2S":26.27,"MLSUMClusteringP2P (fr)":42.6,"MLSUMClusteringS2S (fr)":42.92,"MasakhaNEWSClusteringP2P (fra)":71.29,"MasakhaNEWSClusteringS2S (fra)":55.09} -{"index":94,"Rank":5,"Model":"text-embedding-ada-002<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":49.67,"AlloProfClusteringP2P":64.83,"AlloProfClusteringS2S":53.52,"HALClusteringS2S":26.18,"MLSUMClusteringP2P 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-{"index":3,"Rank":11,"Model":"voyage-law-2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":44.23,"AlloProfClusteringP2P":62.5,"AlloProfClusteringS2S":44.28,"HALClusteringS2S":26.36,"MLSUMClusteringP2P (fr)":44.03,"MLSUMClusteringS2S (fr)":42.95,"MasakhaNEWSClusteringP2P (fra)":50.68,"MasakhaNEWSClusteringS2S (fra)":38.79} -{"index":83,"Rank":12,"Model":"sentence-t5-xxl<\/a>","Model Size (Million Parameters)":4865,"Memory Usage (GB, fp32)":18.12,"Average":43.25,"AlloProfClusteringP2P":60.98,"AlloProfClusteringS2S":43.5,"HALClusteringS2S":21.4,"MLSUMClusteringP2P (fr)":42.24,"MLSUMClusteringS2S (fr)":35.25,"MasakhaNEWSClusteringP2P (fra)":61.15,"MasakhaNEWSClusteringS2S (fra)":38.24} -{"index":46,"Rank":13,"Model":"sentence-camembert-large<\/a>","Model Size (Million Parameters)":337,"Memory Usage (GB, fp32)":1.26,"Average":43.17,"AlloProfClusteringP2P":62.69,"AlloProfClusteringS2S":42.06,"HALClusteringS2S":23.9,"MLSUMClusteringP2P 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+{"index":59,"Rank":55,"Model":"udever-bloom-560m<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":29.03,"AlloProfClusteringP2P":53.57,"AlloProfClusteringS2S":22.13,"HALClusteringS2S":7.68,"MLSUMClusteringP2P (fr)":36.43,"MLSUMClusteringS2S (fr)":25.26,"MasakhaNEWSClusteringP2P (fra)":37.57,"MasakhaNEWSClusteringS2S (fra)":20.58} +{"index":91,"Rank":56,"Model":"xlm-roberta-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Average":28.62,"AlloProfClusteringP2P":56.54,"AlloProfClusteringS2S":21.18,"HALClusteringS2S":5.94,"MLSUMClusteringP2P (fr)":42.67,"MLSUMClusteringS2S (fr)":18.5,"MasakhaNEWSClusteringP2P (fra)":34.02,"MasakhaNEWSClusteringS2S (fra)":21.52} +{"index":90,"Rank":57,"Model":"xlm-roberta-base<\/a>","Model Size (Million Parameters)":279,"Memory Usage (GB, fp32)":1.04,"Average":28.42,"AlloProfClusteringP2P":52.24,"AlloProfClusteringS2S":20.37,"HALClusteringS2S":8.68,"MLSUMClusteringP2P 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(Million Parameters)":"","Memory Usage (GB, fp32)":"","SummEvalFr":28.56} +{"index":2,"Rank":53,"Model":"voyage-code-2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","SummEvalFr":28.34} +{"index":69,"Rank":54,"Model":"all-MiniLM-L6-v2<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"SummEvalFr":28.28} +{"index":88,"Rank":55,"Model":"universal-sentence-encoder-multilingual-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","SummEvalFr":28.21} +{"index":73,"Rank":56,"Model":"distiluse-base-multilingual-cased-v2<\/a>","Model Size (Million Parameters)":135,"Memory Usage (GB, fp32)":0.5,"SummEvalFr":28.12} +{"index":76,"Rank":57,"Model":"multi-qa-MiniLM-L6-cos-v1<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"SummEvalFr":27.59} +{"index":59,"Rank":58,"Model":"udever-bloom-560m<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","SummEvalFr":23.63} diff --git a/all_data_tasks/23/default.jsonl b/all_data_tasks/23/default.jsonl index 8cce9fa388b252e2822a8f84bba8321d20dfdbbc..d6bf53bf1cb1d989846a49a818f87666149b21c0 100644 --- a/all_data_tasks/23/default.jsonl +++ b/all_data_tasks/23/default.jsonl @@ -1,21 +1,21 @@ {"index":15,"Rank":1,"Model":"nb-bert-large<\/a>","Model Size (Million Parameters)":355,"Memory Usage (GB, fp32)":1.32,"Average":66.73,"MassiveIntentClassification (nb)":62.68,"MassiveScenarioClassification (nb)":67.44,"NoRecClassification":55.46,"NordicLangClassification":85.27,"NorwegianParliament":62.58,"ScalaNbClassification":66.97} {"index":14,"Rank":2,"Model":"nb-bert-base<\/a>","Model Size (Million Parameters)":179,"Memory Usage (GB, fp32)":0.67,"Average":63.94,"MassiveIntentClassification (nb)":60.67,"MassiveScenarioClassification (nb)":67.31,"NoRecClassification":51.32,"NordicLangClassification":84.69,"NorwegianParliament":57.41,"ScalaNbClassification":62.25} -{"index":26,"Rank":3,"Model":"multilingual-e5-large<\/a>","Model 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-{"index":33,"Rank":6,"Model":"norbert3-large<\/a>","Model Size (Million Parameters)":368,"Memory Usage (GB, fp32)":1.37,"Average":60.34,"MassiveIntentClassification (nb)":47.42,"MassiveScenarioClassification (nb)":54.25,"NoRecClassification":50.46,"NordicLangClassification":84.25,"NorwegianParliament":58.85,"ScalaNbClassification":66.79} -{"index":28,"Rank":7,"Model":"multilingual-e5-small<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Average":58.86,"MassiveIntentClassification (nb)":53.96,"MassiveScenarioClassification (nb)":59.9,"NoRecClassification":53.96,"NordicLangClassification":75.15,"NorwegianParliament":60.15,"ScalaNbClassification":50.06} +{"index":27,"Rank":3,"Model":"multilingual-e5-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Average":63.64,"MassiveIntentClassification (nb)":59.83,"MassiveScenarioClassification 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(nb)":47.42,"MassiveScenarioClassification (nb)":54.25,"NoRecClassification":50.46,"NordicLangClassification":84.25,"NorwegianParliament":58.85,"ScalaNbClassification":66.79} +{"index":29,"Rank":7,"Model":"multilingual-e5-small<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Average":58.86,"MassiveIntentClassification (nb)":53.96,"MassiveScenarioClassification (nb)":59.9,"NoRecClassification":53.96,"NordicLangClassification":75.15,"NorwegianParliament":60.15,"ScalaNbClassification":50.06} {"index":19,"Rank":8,"Model":"dfm-encoder-large-v1<\/a>","Model Size (Million Parameters)":355,"Memory Usage (GB, fp32)":1.32,"Average":58.46,"MassiveIntentClassification (nb)":52.49,"MassiveScenarioClassification (nb)":54.59,"NoRecClassification":48.3,"NordicLangClassification":77.68,"NorwegianParliament":58.78,"ScalaNbClassification":58.95} -{"index":50,"Rank":9,"Model":"DanskBERT<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, 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+{"index":25,"Rank":15,"Model":"e5-small<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Average":48.46,"MassiveIntentClassification (nb)":40.25,"MassiveScenarioClassification (nb)":48.58,"NoRecClassification":41.84,"NordicLangClassification":53.47,"NorwegianParliament":56.57,"ScalaNbClassification":50.03} {"index":6,"Rank":16,"Model":"bert-base-swedish-cased<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Average":48.18,"MassiveIntentClassification (nb)":35.75,"MassiveScenarioClassification (nb)":35.76,"NoRecClassification":43.91,"NordicLangClassification":62.45,"NorwegianParliament":57.56,"ScalaNbClassification":53.63} -{"index":36,"Rank":17,"Model":"all-MiniLM-L6-v2<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Average":47.28,"MassiveIntentClassification (nb)":39.34,"MassiveScenarioClassification 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(nb)":24.6,"MassiveScenarioClassification (nb)":27.3,"NoRecClassification":45.44,"NordicLangClassification":57.82,"NorwegianParliament":53.25,"ScalaNbClassification":75.28} {"index":7,"Rank":19,"Model":"electra-small-swedish-cased-discriminator<\/a>","Model Size (Million Parameters)":16,"Memory Usage (GB, fp32)":0.06,"Average":34.34,"MassiveIntentClassification (nb)":5.66,"MassiveScenarioClassification (nb)":11.26,"NoRecClassification":39.72,"NordicLangClassification":44.53,"NorwegianParliament":52.44,"ScalaNbClassification":52.41} {"index":0,"Rank":20,"Model":"LASER2<\/a>","Model Size (Million Parameters)":43,"Memory Usage (GB, fp32)":0.16,"Average":"","MassiveIntentClassification (nb)":37.74,"MassiveScenarioClassification (nb)":43.9,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""} {"index":1,"Rank":21,"Model":"gte-multilingual-base<\/a>","Model Size (Million Parameters)":305,"Memory Usage (GB, 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{"index":1,"Rank":13,"Model":"bm25<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":-2.09,"Core17InstructionRetrieval":-1.06,"News21InstructionRetrieval":-2.15,"Robust04InstructionRetrieval":-3.06} -{"index":20,"Rank":14,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":-2.68,"Core17InstructionRetrieval":-0.2,"News21InstructionRetrieval":-2.03,"Robust04InstructionRetrieval":-5.81} +{"index":21,"Rank":14,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":-2.68,"Core17InstructionRetrieval":-0.2,"News21InstructionRetrieval":-2.03,"Robust04InstructionRetrieval":-5.81} {"index":11,"Rank":15,"Model":"instructor-xl<\/a>","Model Size (Million Parameters)":1241,"Memory Usage (GB, fp32)":4.62,"Average":-2.76,"Core17InstructionRetrieval":0.69,"News21InstructionRetrieval":-0.9,"Robust04InstructionRetrieval":-8.08} 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(GB, fp32)":0.41,"Average":-4.43,"Core17InstructionRetrieval":-1.09,"News21InstructionRetrieval":-1.78,"Robust04InstructionRetrieval":-10.42} +{"index":19,"Rank":22,"Model":"tart-dual-contriever-msmarco<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Average":-5.0,"Core17InstructionRetrieval":-3.04,"News21InstructionRetrieval":-2.98,"Robust04InstructionRetrieval":-8.98} diff --git a/all_data_tasks/25/default.jsonl b/all_data_tasks/25/default.jsonl index c0cd00e3b95bee0f706916ed5f8508d6e02ea18a..102730a91c9371386bb8fd8ccd5044c633abaa82 100644 --- a/all_data_tasks/25/default.jsonl +++ b/all_data_tasks/25/default.jsonl @@ -1,16 +1,21 @@ -{"index":1,"Rank":1,"Model":"voyage-law-2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, 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(rus-Cyrl)":37.84,"RuSciBenchOECDClusteringP2P (rus-Cyrl)":34.12} +{"index":8,"Rank":20,"Model":"rubert-tiny<\/a>","Model Size (Million Parameters)":12,"Memory Usage (GB, fp32)":0.04,"Average":30.76,"GeoreviewClusteringP2P (rus-Cyrl)":34.4,"RuSciBenchGRNTIClusteringP2P (rus-Cyrl)":29.89,"RuSciBenchOECDClusteringP2P (rus-Cyrl)":27.98} +{"index":2,"Rank":21,"Model":"rubert-base-cased<\/a>","Model Size (Million Parameters)":180,"Memory Usage (GB, fp32)":0.67,"Average":27.91,"GeoreviewClusteringP2P (rus-Cyrl)":28.77,"RuSciBenchGRNTIClusteringP2P (rus-Cyrl)":28.29,"RuSciBenchOECDClusteringP2P (rus-Cyrl)":26.67} +{"index":19,"Rank":22,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Average":15.83,"GeoreviewClusteringP2P (rus-Cyrl)":20.33,"RuSciBenchGRNTIClusteringP2P (rus-Cyrl)":14.66,"RuSciBenchOECDClusteringP2P (rus-Cyrl)":12.49} +{"index":17,"Rank":23,"Model":"all-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Average":13.87,"GeoreviewClusteringP2P (rus-Cyrl)":20.76,"RuSciBenchGRNTIClusteringP2P (rus-Cyrl)":10.65,"RuSciBenchOECDClusteringP2P (rus-Cyrl)":10.19} +{"index":18,"Rank":24,"Model":"all-MiniLM-L6-v2<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Average":13.3,"GeoreviewClusteringP2P (rus-Cyrl)":20.25,"RuSciBenchGRNTIClusteringP2P (rus-Cyrl)":10.21,"RuSciBenchOECDClusteringP2P (rus-Cyrl)":9.43} diff --git a/all_data_tasks/35/default.jsonl b/all_data_tasks/35/default.jsonl index 8581d0aaf57b112f68fe8a4930f37d306bc349af..e0e82ee2434ba4ea50feedb45c86eac31a9d26f1 100644 --- a/all_data_tasks/35/default.jsonl +++ b/all_data_tasks/35/default.jsonl @@ -1,25 +1,24 @@ -{"index":11,"Rank":1,"Model":"USER-bge-m3<\/a>","Model Size (Million Parameters)":359,"Memory Usage (GB, fp32)":1.34,"TERRa (rus-Cyrl)":64.99} -{"index":22,"Rank":2,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, 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+1,230 @@ -{"index":22,"Rank":1,"Model":"bge-en-icl<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Average":62.16,"ArguAna":83.08,"ClimateFEVER":45.43,"CQADupstackRetrieval":47.31,"DBPedia":51.63,"FEVER":92.83,"FiQA2018":59.67,"HotpotQA":85.14,"MSMARCO":46.79,"NFCorpus":41.85,"NQ":73.88,"QuoraRetrieval":90.95,"SCIDOCS":25.26,"SciFact":79.09,"Touche2020":30.48,"TRECCOVID":79.08} -{"index":156,"Rank":2,"Model":"stella_en_1.5B_v5<\/a>","Model Size (Million Parameters)":1543,"Memory Usage (GB, fp32)":5.75,"Average":61.01,"ArguAna":65.27,"ClimateFEVER":46.11,"CQADupstackRetrieval":47.75,"DBPedia":52.28,"FEVER":94.83,"FiQA2018":60.48,"HotpotQA":76.67,"MSMARCO":45.22,"NFCorpus":42.0,"NQ":71.8,"QuoraRetrieval":90.03,"SCIDOCS":26.64,"SciFact":80.09,"Touche2020":29.94,"TRECCOVID":85.98} -{"index":229,"Rank":3,"Model":"NV-Retriever-v1<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, 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Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Average":45.26,"CLSClusteringP2P":39.14,"CLSClusteringS2S":37.79,"ThuNewsClusteringP2P":55.18,"ThuNewsClusteringS2S":48.93} +{"index":298,"Rank":55,"Model":"luotuo-bert-medium<\/a>","Model Size (Million Parameters)":328,"Memory Usage (GB, fp32)":1.22,"Average":44.39,"CLSClusteringP2P":37.01,"CLSClusteringS2S":33.46,"ThuNewsClusteringP2P":58.83,"ThuNewsClusteringS2S":48.26} +{"index":32,"Rank":56,"Model":"bge-small-zh-v1.5<\/a>","Model Size (Million Parameters)":24,"Memory Usage (GB, fp32)":0.09,"Average":44.18,"CLSClusteringP2P":38.14,"CLSClusteringS2S":35.14,"ThuNewsClusteringP2P":54.22,"ThuNewsClusteringS2S":49.22} +{"index":201,"Rank":57,"Model":"udever-bloom-3b<\/a>","Model Size (Million Parameters)":3003,"Memory Usage (GB, fp32)":11.19,"Average":43.24,"CLSClusteringP2P":36.73,"CLSClusteringS2S":36.45,"ThuNewsClusteringP2P":50.24,"ThuNewsClusteringS2S":49.54} +{"index":202,"Rank":58,"Model":"udever-bloom-560m<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":42.42,"CLSClusteringP2P":35.21,"CLSClusteringS2S":35.12,"ThuNewsClusteringP2P":51.03,"ThuNewsClusteringS2S":48.32} +{"index":200,"Rank":59,"Model":"udever-bloom-1b1<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":42.42,"CLSClusteringP2P":35.36,"CLSClusteringS2S":37.07,"ThuNewsClusteringP2P":48.91,"ThuNewsClusteringS2S":48.34} +{"index":203,"Rank":60,"Model":"udever-bloom-7b1<\/a>","Model Size (Million Parameters)":7069,"Memory Usage (GB, fp32)":26.33,"Average":42.32,"CLSClusteringP2P":36.0,"CLSClusteringS2S":36.35,"ThuNewsClusteringP2P":49.19,"ThuNewsClusteringS2S":47.76} +{"index":196,"Rank":61,"Model":"multilingual-e5-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Average":40.68,"CLSClusteringP2P":32.41,"CLSClusteringS2S":36.99,"ThuNewsClusteringP2P":40.98,"ThuNewsClusteringS2S":52.36} +{"index":51,"Rank":62,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Average":39.67,"CLSClusteringP2P":39.24,"CLSClusteringS2S":35.36,"ThuNewsClusteringP2P":47.3,"ThuNewsClusteringS2S":36.77} +{"index":296,"Rank":63,"Model":"text2vec-base-chinese<\/a>","Model Size (Million Parameters)":102,"Memory Usage (GB, fp32)":0.38,"Average":37.66,"CLSClusteringP2P":35.27,"CLSClusteringS2S":32.42,"ThuNewsClusteringP2P":42.92,"ThuNewsClusteringS2S":40.01} +{"index":171,"Rank":64,"Model":"SONAR<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":34.44,"CLSClusteringP2P":27.43,"CLSClusteringS2S":28.32,"ThuNewsClusteringP2P":38.54,"ThuNewsClusteringS2S":43.45} +{"index":47,"Rank":65,"Model":"sbert-chinese-general-v1<\/a>","Model Size (Million Parameters)":102,"Memory Usage (GB, fp32)":0.38,"Average":31.8,"CLSClusteringP2P":34.98,"CLSClusteringS2S":27.82,"ThuNewsClusteringP2P":40.17,"ThuNewsClusteringS2S":24.23} +{"index":55,"Rank":66,"Model":"text2vec-large-chinese<\/a>","Model Size (Million Parameters)":326,"Memory Usage (GB, fp32)":1.21,"Average":30.02,"CLSClusteringP2P":30.13,"CLSClusteringS2S":28.77,"ThuNewsClusteringP2P":35.05,"ThuNewsClusteringS2S":26.14} +{"index":309,"Rank":67,"Model":"ALL_862873<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Average":18.34,"CLSClusteringP2P":18.66,"CLSClusteringS2S":16.82,"ThuNewsClusteringP2P":20.69,"ThuNewsClusteringS2S":17.19} diff --git a/app.py b/app.py index cab09fda88fef88e01863d90ebf8a5862fdab026..11f170511cb36f96af5dde3193265c89372b93be 100644 --- a/app.py +++ b/app.py @@ -72,6 +72,7 @@ Each inner tab can have the following keys: - language_long: [optional] The long form of the language - description: The description of the leaderboard - credits: [optional] The credits for the leaderboard +- desc: [optional] The description of the leaderboard - data: The data for the leaderboard """ @@ -133,6 +134,7 @@ for board, board_config in BOARDS_CONFIG.items(): board_icon = "" credits = board_config.get("credits", None) metric = board_config.get("metric", None) + desc = board_config.get("desc", None) if board_config["has_overall"]: overall_pretty_name = board_pretty_name @@ -146,6 +148,7 @@ for board, board_config in BOARDS_CONFIG.items(): # "refresh": get_refresh_overall_function(board_config["tasks"]), "credits": credits, "metric": metric, + "desc": desc, }) for task_category, task_category_list in board_config["tasks"].items(): task_icon = TASKS_CONFIG[task_category]['icon'] @@ -159,6 +162,7 @@ for board, board_config in BOARDS_CONFIG.items(): # "refresh": get_refresh_function(task_category, task_category_list), "credits": credits, "metric": metric, + "desc": desc, }) dataframes = [] @@ -331,6 +335,7 @@ with gr.Blocks(css=css) as block: - **Metric:** {specific_metric} - **Languages:** {item['language_long'] if 'language_long' in item else item['language']} {"- **Credits:** " + item['credits'] if ("credits" in item and item["credits"] is not None) else ''} + {"- **Description:** " + item['desc'] if ("desc" in item and item["desc"] is not None) else ''} """) with gr.Row(): diff --git a/boards_data/bright_long/data_overall/default.txt b/boards_data/bright_long/data_overall/default.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/boards_data/bright_long/data_tasks/Retrieval/default.jsonl b/boards_data/bright_long/data_tasks/Retrieval/default.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..385c477888d962b30456f44667add1c329d37982 --- /dev/null +++ b/boards_data/bright_long/data_tasks/Retrieval/default.jsonl @@ -0,0 +1,13 @@ +{"index":3,"Rank":1,"Model":"gte-Qwen1.5-7B-instruct<\/a>","Model Size (Million Parameters)":7099,"Memory Usage (GB, fp32)":26.45,"Average":27.82,"BrightRetrieval (biology)":39.24,"BrightRetrieval (earth_science)":36.13,"BrightRetrieval (economics)":25.73,"BrightRetrieval (pony)":1.32,"BrightRetrieval (psychology)":42.28,"BrightRetrieval (robotics)":21.29,"BrightRetrieval (stackoverflow)":23.5,"BrightRetrieval (sustainable_living)":33.1} +{"index":7,"Rank":2,"Model":"GritLM-7B<\/a>","Model Size (Million Parameters)":7240,"Memory Usage (GB, fp32)":26.97,"Average":26.03,"BrightRetrieval (biology)":37.46,"BrightRetrieval (earth_science)":39.44,"BrightRetrieval (economics)":25.73,"BrightRetrieval (pony)":0.0,"BrightRetrieval (psychology)":35.35,"BrightRetrieval (robotics)":17.82,"BrightRetrieval (stackoverflow)":20.08,"BrightRetrieval (sustainable_living)":32.36} +{"index":8,"Rank":3,"Model":"SFR-Embedding-Mistral<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Average":26.01,"BrightRetrieval (biology)":30.26,"BrightRetrieval (earth_science)":37.0,"BrightRetrieval (economics)":24.27,"BrightRetrieval (pony)":2.0,"BrightRetrieval (psychology)":47.72,"BrightRetrieval (robotics)":17.33,"BrightRetrieval (stackoverflow)":14.53,"BrightRetrieval (sustainable_living)":34.99} +{"index":4,"Rank":4,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Average":25.96,"BrightRetrieval (biology)":34.87,"BrightRetrieval (earth_science)":38.36,"BrightRetrieval (economics)":27.67,"BrightRetrieval (pony)":1.17,"BrightRetrieval (psychology)":46.73,"BrightRetrieval (robotics)":10.89,"BrightRetrieval (stackoverflow)":16.67,"BrightRetrieval (sustainable_living)":31.28} +{"index":11,"Rank":5,"Model":"e5-mistral-7b-instruct<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Average":25.54,"BrightRetrieval (biology)":29.93,"BrightRetrieval (earth_science)":36.28,"BrightRetrieval (economics)":26.21,"BrightRetrieval (pony)":1.14,"BrightRetrieval (psychology)":46.73,"BrightRetrieval (robotics)":17.33,"BrightRetrieval (stackoverflow)":14.53,"BrightRetrieval (sustainable_living)":32.21} +{"index":1,"Rank":6,"Model":"voyage-large-2-instruct<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":24.62,"BrightRetrieval (biology)":34.38,"BrightRetrieval (earth_science)":35.35,"BrightRetrieval (economics)":26.7,"BrightRetrieval (pony)":1.28,"BrightRetrieval (psychology)":41.58,"BrightRetrieval (robotics)":12.87,"BrightRetrieval (stackoverflow)":13.68,"BrightRetrieval (sustainable_living)":31.1} +{"index":0,"Rank":7,"Model":"google-gecko.text-embedding-preview-0409<\/a>","Model Size (Million Parameters)":1200,"Memory Usage (GB, fp32)":4.47,"Average":22.44,"BrightRetrieval (biology)":30.91,"BrightRetrieval (earth_science)":38.0,"BrightRetrieval (economics)":21.84,"BrightRetrieval (pony)":0.29,"BrightRetrieval (psychology)":30.69,"BrightRetrieval (robotics)":12.87,"BrightRetrieval (stackoverflow)":19.23,"BrightRetrieval (sustainable_living)":25.65} +{"index":12,"Rank":8,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":21.88,"BrightRetrieval (biology)":33.09,"BrightRetrieval (earth_science)":32.26,"BrightRetrieval (economics)":24.76,"BrightRetrieval (pony)":0.0,"BrightRetrieval (psychology)":35.15,"BrightRetrieval (robotics)":11.88,"BrightRetrieval (stackoverflow)":11.54,"BrightRetrieval (sustainable_living)":26.34} +{"index":6,"Rank":9,"Model":"Cohere-embed-english-v3.0<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":18.4,"BrightRetrieval (biology)":31.47,"BrightRetrieval (earth_science)":35.49,"BrightRetrieval (economics)":17.96,"BrightRetrieval (pony)":0.84,"BrightRetrieval (psychology)":20.5,"BrightRetrieval (robotics)":9.9,"BrightRetrieval (stackoverflow)":15.81,"BrightRetrieval (sustainable_living)":15.23} +{"index":9,"Rank":10,"Model":"instructor-large<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Average":18.21,"BrightRetrieval (biology)":24.11,"BrightRetrieval (earth_science)":29.45,"BrightRetrieval (economics)":14.08,"BrightRetrieval (pony)":3.94,"BrightRetrieval (psychology)":21.29,"BrightRetrieval (robotics)":12.87,"BrightRetrieval (stackoverflow)":14.53,"BrightRetrieval (sustainable_living)":25.42} +{"index":10,"Rank":11,"Model":"instructor-xl<\/a>","Model Size (Million Parameters)":1241,"Memory Usage (GB, fp32)":4.62,"Average":17.82,"BrightRetrieval (biology)":22.01,"BrightRetrieval (earth_science)":32.04,"BrightRetrieval (economics)":14.08,"BrightRetrieval (pony)":5.93,"BrightRetrieval (psychology)":20.5,"BrightRetrieval (robotics)":12.87,"BrightRetrieval (stackoverflow)":14.96,"BrightRetrieval (sustainable_living)":20.14} +{"index":5,"Rank":12,"Model":"bge-large-en-v1.5<\/a>","Model Size (Million Parameters)":1340,"Memory Usage (GB, fp32)":4.99,"Average":14.75,"BrightRetrieval (biology)":16.42,"BrightRetrieval (earth_science)":27.73,"BrightRetrieval (economics)":20.87,"BrightRetrieval (pony)":0.36,"BrightRetrieval (psychology)":11.58,"BrightRetrieval (robotics)":10.89,"BrightRetrieval (stackoverflow)":13.25,"BrightRetrieval (sustainable_living)":16.9} +{"index":2,"Rank":13,"Model":"bm25<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":11.35,"BrightRetrieval (biology)":10.68,"BrightRetrieval (earth_science)":15.37,"BrightRetrieval (economics)":10.68,"BrightRetrieval (pony)":5.35,"BrightRetrieval (psychology)":8.42,"BrightRetrieval (robotics)":7.43,"BrightRetrieval (stackoverflow)":22.22,"BrightRetrieval (sustainable_living)":10.68} diff --git a/boards_data/coir/data_overall/default.txt b/boards_data/coir/data_overall/default.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/boards_data/coir/data_tasks/Retrieval/default.jsonl b/boards_data/coir/data_tasks/Retrieval/default.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5b8e1165f7ca5957335b991110540b7a0044f124 --- /dev/null +++ b/boards_data/coir/data_tasks/Retrieval/default.jsonl @@ -0,0 +1 @@ +{"index":0,"Rank":1,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Average":59.88,"CodeTransOceanDL":29.45,"StackOverflowQA":90.32} diff --git a/boards_data/da/data_tasks/Classification/default.jsonl b/boards_data/da/data_tasks/Classification/default.jsonl index d4530731ce2ee850f1f2f2dbf4efbe6321e52612..231e37d5ffd9104fa78cd9c53c3e41a28c5f5ab0 100644 --- a/boards_data/da/data_tasks/Classification/default.jsonl +++ b/boards_data/da/data_tasks/Classification/default.jsonl @@ -1,21 +1,21 @@ -{"index":26,"Rank":1,"Model":"multilingual-e5-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Average":59.94,"AngryTweetsClassification":54.95,"DKHateClassification":66.02,"DanishPoliticalCommentsClassification":38.27,"LccSentimentClassification":59.6,"MassiveIntentClassification (da)":60.16,"MassiveScenarioClassification (da)":67.46,"NordicLangClassification":82.29,"ScalaDaClassification":50.77} +{"index":27,"Rank":1,"Model":"multilingual-e5-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Average":59.94,"AngryTweetsClassification":54.95,"DKHateClassification":66.02,"DanishPoliticalCommentsClassification":38.27,"LccSentimentClassification":59.6,"MassiveIntentClassification (da)":60.16,"MassiveScenarioClassification (da)":67.46,"NordicLangClassification":82.29,"ScalaDaClassification":50.77} {"index":19,"Rank":2,"Model":"dfm-encoder-large-v1<\/a>","Model Size (Million Parameters)":355,"Memory Usage (GB, fp32)":1.32,"Average":59.16,"AngryTweetsClassification":53.8,"DKHateClassification":60.09,"DanishPoliticalCommentsClassification":36.6,"LccSentimentClassification":57.33,"MassiveIntentClassification (da)":60.55,"MassiveScenarioClassification (da)":64.16,"NordicLangClassification":77.68,"ScalaDaClassification":63.08} {"index":15,"Rank":3,"Model":"nb-bert-large<\/a>","Model Size (Million Parameters)":355,"Memory Usage (GB, fp32)":1.32,"Average":58.9,"AngryTweetsClassification":52.14,"DKHateClassification":62.13,"DanishPoliticalCommentsClassification":35.04,"LccSentimentClassification":56.27,"MassiveIntentClassification (da)":57.03,"MassiveScenarioClassification (da)":60.43,"NordicLangClassification":85.27,"ScalaDaClassification":62.85} -{"index":25,"Rank":4,"Model":"multilingual-e5-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Average":58.61,"AngryTweetsClassification":54.65,"DKHateClassification":63.53,"DanishPoliticalCommentsClassification":36.69,"LccSentimentClassification":59.67,"MassiveIntentClassification (da)":60.16,"MassiveScenarioClassification (da)":67.46,"NordicLangClassification":75.94,"ScalaDaClassification":50.79} -{"index":50,"Rank":5,"Model":"DanskBERT<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Average":58.31,"AngryTweetsClassification":54.28,"DKHateClassification":59.3,"DanishPoliticalCommentsClassification":39.81,"LccSentimentClassification":58.0,"MassiveIntentClassification (da)":54.68,"MassiveScenarioClassification (da)":59.56,"NordicLangClassification":74.25,"ScalaDaClassification":66.59} +{"index":26,"Rank":4,"Model":"multilingual-e5-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Average":58.61,"AngryTweetsClassification":54.65,"DKHateClassification":63.53,"DanishPoliticalCommentsClassification":36.69,"LccSentimentClassification":59.67,"MassiveIntentClassification (da)":60.16,"MassiveScenarioClassification (da)":67.46,"NordicLangClassification":75.94,"ScalaDaClassification":50.79} +{"index":53,"Rank":5,"Model":"DanskBERT<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Average":58.31,"AngryTweetsClassification":54.28,"DKHateClassification":59.3,"DanishPoliticalCommentsClassification":39.81,"LccSentimentClassification":58.0,"MassiveIntentClassification (da)":54.68,"MassiveScenarioClassification (da)":59.56,"NordicLangClassification":74.25,"ScalaDaClassification":66.59} {"index":14,"Rank":6,"Model":"nb-bert-base<\/a>","Model Size (Million Parameters)":179,"Memory Usage (GB, fp32)":0.67,"Average":57.68,"AngryTweetsClassification":52.14,"DKHateClassification":61.73,"DanishPoliticalCommentsClassification":34.84,"LccSentimentClassification":51.4,"MassiveIntentClassification (da)":56.69,"MassiveScenarioClassification (da)":61.93,"NordicLangClassification":84.69,"ScalaDaClassification":57.99} -{"index":32,"Rank":7,"Model":"norbert3-base<\/a>","Model Size (Million Parameters)":131,"Memory Usage (GB, fp32)":0.49,"Average":56.34,"AngryTweetsClassification":52.48,"DKHateClassification":58.78,"DanishPoliticalCommentsClassification":34.14,"LccSentimentClassification":54.07,"MassiveIntentClassification (da)":53.16,"MassiveScenarioClassification (da)":57.17,"NordicLangClassification":82.67,"ScalaDaClassification":58.25} -{"index":28,"Rank":8,"Model":"multilingual-e5-small<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Average":56.12,"AngryTweetsClassification":53.57,"DKHateClassification":60.73,"DanishPoliticalCommentsClassification":34.38,"LccSentimentClassification":57.87,"MassiveIntentClassification (da)":54.63,"MassiveScenarioClassification (da)":62.34,"NordicLangClassification":75.15,"ScalaDaClassification":50.3} -{"index":20,"Rank":9,"Model":"SONAR<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":55.03,"AngryTweetsClassification":49.62,"DKHateClassification":69.97,"DanishPoliticalCommentsClassification":37.59,"LccSentimentClassification":54.27,"MassiveIntentClassification (da)":62.03,"MassiveScenarioClassification (da)":67.76,"NordicLangClassification":48.4,"ScalaDaClassification":50.63} -{"index":33,"Rank":10,"Model":"norbert3-large<\/a>","Model Size (Million Parameters)":368,"Memory Usage (GB, fp32)":1.37,"Average":54.21,"AngryTweetsClassification":49.04,"DKHateClassification":62.71,"DanishPoliticalCommentsClassification":33.53,"LccSentimentClassification":46.93,"MassiveIntentClassification (da)":45.98,"MassiveScenarioClassification (da)":50.51,"NordicLangClassification":84.25,"ScalaDaClassification":60.72} -{"index":51,"Rank":11,"Model":"xlm-roberta-base<\/a>","Model Size (Million Parameters)":279,"Memory Usage (GB, fp32)":1.04,"Average":52.14,"AngryTweetsClassification":52.41,"DKHateClassification":56.78,"DanishPoliticalCommentsClassification":34.03,"LccSentimentClassification":52.27,"MassiveIntentClassification (da)":41.06,"MassiveScenarioClassification (da)":43.91,"NordicLangClassification":79.39,"ScalaDaClassification":57.3} -{"index":22,"Rank":12,"Model":"e5-large<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, 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fp32)":0.49,"Average":56.34,"AngryTweetsClassification":52.48,"DKHateClassification":58.78,"DanishPoliticalCommentsClassification":34.14,"LccSentimentClassification":54.07,"MassiveIntentClassification (da)":53.16,"MassiveScenarioClassification (da)":57.17,"NordicLangClassification":82.67,"ScalaDaClassification":58.25} +{"index":29,"Rank":8,"Model":"multilingual-e5-small<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Average":56.12,"AngryTweetsClassification":53.57,"DKHateClassification":60.73,"DanishPoliticalCommentsClassification":34.38,"LccSentimentClassification":57.87,"MassiveIntentClassification (da)":54.63,"MassiveScenarioClassification (da)":62.34,"NordicLangClassification":75.15,"ScalaDaClassification":50.3} +{"index":21,"Rank":9,"Model":"SONAR<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, 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b/boards_data/fr/data_overall/default.jsonl index dfe61a9e05e016af8e6feb890c7584b4951e37ef..0d227a5a0a30113ae29d9d33b64d9e697920f993 100644 --- a/boards_data/fr/data_overall/default.jsonl +++ b/boards_data/fr/data_overall/default.jsonl @@ -1,87 +1,85 @@ {"index":11,"Rank":1,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Embedding Dimensions":3584,"Max Tokens":8192,"Average (26 datasets)":70.08,"Classification Average (6 datasets)":81.62,"Clustering Average (7 datasets)":56.48,"PairClassification Average (2 datasets)":85.07,"Reranking Average (2 datasets)":85.22,"Retrieval Average (5 datasets)":63.47,"STS Average (3 datasets)":82.59,"Summarization Average (1 datasets)":31.26} {"index":9,"Rank":2,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Embedding Dimensions":3584,"Max Tokens":131072,"Average (26 datasets)":68.25,"Classification Average (6 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datasets)":"","Clustering Average (7 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (2 datasets)":"","Retrieval Average (5 datasets)":"","STS Average (3 datasets)":"","Summarization Average (1 datasets)":""} +{"index":93,"Rank":94,"Model":"text-embedding-ada-002<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1536,"Max Tokens":8191,"Average (26 datasets)":"","Classification Average (6 datasets)":69.27,"Clustering Average (7 datasets)":49.67,"PairClassification Average (2 datasets)":77.14,"Reranking Average (2 datasets)":"","Retrieval Average (5 datasets)":48.23,"STS Average (3 datasets)":78.31,"Summarization Average (1 datasets)":30.5} +{"index":94,"Rank":95,"Model":"text-embedding-3-small<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1536,"Max Tokens":8191,"Average (26 datasets)":"","Classification Average (6 datasets)":"","Clustering Average (7 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(fra)":"","MassiveIntentClassification (fr)":30.84,"MassiveScenarioClassification (fr)":42.42,"MTOPDomainClassification (fr)":"","MTOPIntentClassification (fr)":""} +{"index":85,"Rank":93,"Model":"text2vec-base-multilingual<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Average":"","AmazonReviewsClassification (fr)":33.45,"MasakhaNEWSClassification (fra)":"","MassiveIntentClassification (fr)":58.8,"MassiveScenarioClassification (fr)":63.39,"MTOPDomainClassification (fr)":76.17,"MTOPIntentClassification (fr)":53.26} +{"index":86,"Rank":94,"Model":"multilingual-e5-large-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":"","AmazonReviewsClassification (fr)":41.89,"MasakhaNEWSClassification (fra)":"","MassiveIntentClassification (fr)":69.34,"MassiveScenarioClassification (fr)":73.87,"MTOPDomainClassification (fr)":89.0,"MTOPIntentClassification (fr)":70.56} diff --git a/boards_data/fr/data_tasks/Clustering/default.jsonl b/boards_data/fr/data_tasks/Clustering/default.jsonl index d25adf0cdbd81de1df0ac8fb367dd9e9a0e8c734..23bc4a9e5fdeafa2c65e0f14f0ffc203bfc3d8cb 100644 --- a/boards_data/fr/data_tasks/Clustering/default.jsonl +++ b/boards_data/fr/data_tasks/Clustering/default.jsonl @@ -1,63 +1,60 @@ {"index":11,"Rank":1,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Average":56.48,"AlloProfClusteringP2P":71.2,"AlloProfClusteringS2S":59.64,"HALClusteringS2S":28.19,"MLSUMClusteringP2P (fr)":47.75,"MLSUMClusteringS2S (fr)":47.46,"MasakhaNEWSClusteringP2P (fra)":73.86,"MasakhaNEWSClusteringS2S (fra)":67.24} {"index":9,"Rank":2,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Average":55.56,"AlloProfClusteringP2P":76.06,"AlloProfClusteringS2S":64.52,"HALClusteringS2S":30.83,"MLSUMClusteringP2P (fr)":50.03,"MLSUMClusteringS2S (fr)":43.7,"MasakhaNEWSClusteringP2P 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--git a/boards_data/fr/data_tasks/PairClassification/default.jsonl b/boards_data/fr/data_tasks/PairClassification/default.jsonl index 76f12fe2b807a601770344c6b0d55fd14fecba29..888b5a1aa9fc08a339ee662e8d9b2fc993bd8eb0 100644 --- a/boards_data/fr/data_tasks/PairClassification/default.jsonl +++ b/boards_data/fr/data_tasks/PairClassification/default.jsonl @@ -1,64 +1,60 @@ {"index":9,"Rank":1,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Average":90.43,"OpusparcusPC (fr)":100.0,"PawsXPairClassification (fr)":80.86} -{"index":23,"Rank":2,"Model":"gte-Qwen2-7B-instruct-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":90.43,"OpusparcusPC (fr)":100.0,"PawsXPairClassification (fr)":80.86} -{"index":24,"Rank":3,"Model":"gte-Qwen2-7B-instruct-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Average":90.43,"OpusparcusPC (fr)":100.0,"PawsXPairClassification 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b/boards_data/no/data_tasks/Classification/default.jsonl index 8cce9fa388b252e2822a8f84bba8321d20dfdbbc..d6bf53bf1cb1d989846a49a818f87666149b21c0 100644 --- a/boards_data/no/data_tasks/Classification/default.jsonl +++ b/boards_data/no/data_tasks/Classification/default.jsonl @@ -1,21 +1,21 @@ {"index":15,"Rank":1,"Model":"nb-bert-large<\/a>","Model Size (Million Parameters)":355,"Memory Usage (GB, fp32)":1.32,"Average":66.73,"MassiveIntentClassification (nb)":62.68,"MassiveScenarioClassification (nb)":67.44,"NoRecClassification":55.46,"NordicLangClassification":85.27,"NorwegianParliament":62.58,"ScalaNbClassification":66.97} {"index":14,"Rank":2,"Model":"nb-bert-base<\/a>","Model Size (Million Parameters)":179,"Memory Usage (GB, fp32)":0.67,"Average":63.94,"MassiveIntentClassification (nb)":60.67,"MassiveScenarioClassification (nb)":67.31,"NoRecClassification":51.32,"NordicLangClassification":84.69,"NorwegianParliament":57.41,"ScalaNbClassification":62.25} 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(de-en)":"","STS22 (de-fr)":"","STS22 (de-pl)":"","STS22 (es)":"","STS22 (es-en)":"","STS22 (es-it)":"","STS22 (fr)":"","STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":79.54} diff --git a/boards_data/pl/data_overall/default.jsonl b/boards_data/pl/data_overall/default.jsonl index 77f55f5acfec6011d9cc69612d652148520e1ea8..d733cedc4004c579b8eff51cc5cdce0ca7a445f1 100644 --- a/boards_data/pl/data_overall/default.jsonl +++ b/boards_data/pl/data_overall/default.jsonl @@ -1,50 +1,47 @@ {"index":4,"Rank":1,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Embedding Dimensions":3584,"Max Tokens":8192,"Average (26 datasets)":70.0,"Classification Average (7 datasets)":77.99,"Clustering Average (1 datasets)":50.29,"PairClassification Average (4 datasets)":89.62,"Retrieval Average (11 datasets)":59.41,"STS Average (3 datasets)":70.64} 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"⚖️" - special_icons: null - credits: "[Voyage AI](https://www.voyageai.com/)" - tasks: - Retrieval: - - AILACasedocs - - AILAStatutes - - GerDaLIRSmall - - LeCaRDv2 - - LegalBenchConsumerContractsQA - - LegalBenchCorporateLobbying - - LegalQuAD - - LegalSummarization - longembed: - title: LongEmbed - language_long: "English" - has_overall: false - acronym: null - icon: "📚" - special_icons: null - credits: "[LongEmbed](https://arxiv.org/abs/2404.12096v2)" - metric: nDCG@10 (for NarrativeQA, QMSum, SummScreenFD, WikimQA) & nDCG@1 (for passkey and needle) - tasks: - Retrieval: - - LEMBNarrativeQARetrieval - - LEMBNeedleRetrieval - - LEMBPasskeyRetrieval - - LEMBQMSumRetrieval - - LEMBSummScreenFDRetrieval - - LEMBWikimQARetrieval de: title: German language_long: "German" @@ -447,6 +412,41 @@ boards: credits: null tasks: STS: ["STS17 (ar-ar)", "STS17 (en-ar)", "STS17 (en-de)", "STS17 (en-tr)", "STS17 (es-en)", "STS17 (es-es)", "STS17 (fr-en)", "STS17 (it-en)", "STS17 (ko-ko)", "STS17 (nl-en)", "STS22 (ar)", "STS22 (de)", "STS22 (de-en)", "STS22 (de-fr)", "STS22 (de-pl)", "STS22 (es)", "STS22 (es-en)", "STS22 (es-it)", "STS22 (fr)", "STS22 (fr-pl)", "STS22 (it)", "STS22 (pl)", "STS22 (pl-en)", "STS22 (ru)", "STS22 (tr)", "STS22 (zh-en)", "STSBenchmark"] + law: + title: Law + language_long: "English, German, Chinese" + has_overall: false + acronym: null + icon: "⚖️" + special_icons: null + credits: "[Voyage AI](https://www.voyageai.com/)" + tasks: + Retrieval: + - AILACasedocs + - AILAStatutes + - GerDaLIRSmall + - LeCaRDv2 + - LegalBenchConsumerContractsQA + - LegalBenchCorporateLobbying + - LegalQuAD + - LegalSummarization + longembed: + title: LongEmbed + language_long: "English" + has_overall: false + acronym: null + icon: "📚" + special_icons: null + credits: "[LongEmbed (Dawei Zhu et al.)](https://arxiv.org/abs/2404.12096v2)" + metric: nDCG@10 (for NarrativeQA, QMSum, SummScreenFD, WikimQA) & nDCG@1 (for passkey and needle) + tasks: + Retrieval: + - LEMBNarrativeQARetrieval + - LEMBNeedleRetrieval + - LEMBPasskeyRetrieval + - LEMBQMSumRetrieval + - LEMBSummScreenFDRetrieval + - LEMBWikimQARetrieval rar-b: title: RAR-b language_long: "English" @@ -454,7 +454,7 @@ boards: acronym: null icon: "📚" special_icons: null - credits: "[RAR-b (Xiao et al.)](https://arxiv.org/abs/2404.06347/)" + credits: "[RAR-b (Chenghao Xiao et al.)](https://arxiv.org/abs/2404.06347/)" metric: nDCG@10 tasks: Retrieval: @@ -482,6 +482,8 @@ boards: special_icons: null credits: "[BRIGHT (Hongjin Su, Howard Yen, Mengzhou Xia et al.)](https://brightbenchmark.github.io/)" metric: nDCG@10 + split: standard + desc: This tab only allows submissions with the original queries; not results from LLM rewritten queries or using reranking. tasks: Retrieval: - BrightRetrieval (biology) @@ -496,3 +498,45 @@ boards: - BrightRetrieval (aops) - BrightRetrieval (theoremqa_theorems) - BrightRetrieval (theoremqa_questions) + bright_long: + title: BRIGHT Long + language_long: "English" + has_overall: false + acronym: null + icon: "🌟" + special_icons: null + credits: "[BRIGHT (Hongjin Su, Howard Yen, Mengzhou Xia et al.)](https://brightbenchmark.github.io/)" + metric: Recall@1 + split: long + desc: This tab is for the long document setting of BRIGHT. + tasks: + Retrieval: + - BrightRetrieval (biology) + - BrightRetrieval (earth_science) + - BrightRetrieval (economics) + - BrightRetrieval (psychology) + - BrightRetrieval (robotics) + - BrightRetrieval (stackoverflow) + - BrightRetrieval (sustainable_living) + - BrightRetrieval (pony) + coir: + title: CoIR + language_long: "Code" + has_overall: false + acronym: null + icon: "💻" + special_icons: null + credits: "[Samoed](https://github.com/Samoed) and [monikernemo](https://github.com/monikernemo) and [CoIR (Xiangyang Li, Kuicai Dong, Yi Quan Lee et al.)](https://arxiv.org/abs/2407.02883)" + metric: nDCG@10 + tasks: + Retrieval: + - AppsRetrieval + - CodeFeedbackMT + - CodeFeedbackST + - CodeSearchNetCCRetrieval + - CodeSearchNetRetrieval + - CodeTransOceanContest + - CodeTransOceanDL + - CosQA + - StackOverflowQA + - SyntheticText2SQL \ No newline at end of file diff --git a/model_meta.yaml b/model_meta.yaml index d3e19de6c827912bee12fd4c7c824912e7805571..ade181121a2feacdb08c052957a4b5561e734422 100644 --- a/model_meta.yaml +++ b/model_meta.yaml @@ -185,6 +185,15 @@ model_meta: is_proprietary: false is_sentence_transformers_compatible: false uses_instruct: true + NV-Embed-v2: + link: https://huggingface.co/nvidia/NV-Embed-v2 + seq_len: 32768 + size: 7851 + dim: 4096 + is_external: false + is_proprietary: false + is_sentence_transformers_compatible: true + uses_instruct: true NV-Retriever-v1: link: https://huggingface.co/nvidia/NV-Retriever-v1 seq_len: 512 @@ -1943,6 +1952,19 @@ models_to_skip: - VenkatNDivi77/gte-Qwen2-7B-instruct-Q4_K_M-GGUF - barnowak/gte-Qwen2-7B-instruct-Q4_K_M-GGUF - arisylafeta/gte-Qwen2-1.5B-instruct-Q4_0-GGUF +- KeyurRamoliya/gte-Qwen2-7B-instruct-Q4_K_M-GGUF +- cedarwud/gte-Qwen2-7B-instruct-Q4_K_M-GGUF +- KeyurRamoliya/gte-Qwen2-7B-instruct-GGUF +- agier9/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF +- dopeba/gte-Qwen2-1.5B-instruct-Q8_0-GGUF +- Yingda/gte-Qwen2-7B-instruct-Q5_K_M-GGUF +- soichisumi/gte-Qwen2-7B-instruct-Q8_0-GGUF +- Bearsaerker/gte-Qwen2-7B-instruct-Q5_K_M-GGUF +- redshiva/gte-Qwen2-7B-instruct-Q8_0-GGUF +- qaz-t/gte-Qwen2-7B-instruct-Q8_0-GGUF +- biggunnyso4/stella_en_400M_v5_cpu +- PotatoOff/mxbai-embed-large-safetensors +- KeyurRamoliya/multilingual-e5-large-instruct-GGUF cross_encoders: - FollowIR-7B - flan-t5-base diff --git a/refresh.py b/refresh.py index e0ed17f3d9cc966bbe16ccec1887f25b2f6a528a..26175ff8ba599953cad226d40abd6f3c9ede6924 100644 --- a/refresh.py +++ b/refresh.py @@ -78,9 +78,12 @@ NOINSTRUCT_MODELS = { TASK_TO_TASK_TYPE = {task_category: [] for task_category in TASKS} -for board_config in BOARDS_CONFIG.values(): +TASK_TO_SPLIT = {} +for k, board_config in BOARDS_CONFIG.items(): for task_category, task_list in board_config["tasks"].items(): TASK_TO_TASK_TYPE[task_category].extend(task_list) + if "split" in board_config: + TASK_TO_SPLIT[k] = board_config["split"] ## Don't cache this because we want to re-compute every time @@ -161,19 +164,22 @@ def filter_metric_external(x, task, metrics) -> bool: # This is a hack for the passkey and needle retrieval test, which reports ndcg_at_1 (i.e. accuracy), rather than the ndcg_at_10 that is commonly used for retrieval tasks. if x["mteb_dataset_name"] in ["LEMBNeedleRetrieval", "LEMBPasskeyRetrieval"]: return bool(x["mteb_task"] == task and x["metric"] == "ndcg_at_1") + elif (x["mteb_dataset_name"].startswith("BrightRetrieval") and (x["split"] == "long")): + return bool(x["mteb_task"] == task and x["metric"] in ["recall_at_1"]) elif x["mteb_dataset_name"] == "MIRACLReranking": return bool(x["mteb_task"] == task and x["metric"] in ["NDCG@10(MIRACL)"]) else: return bool(x["mteb_task"] == task and x["metric"] in metrics) -def filter_metric_fetched(name: str, metric: str, expected_metrics) -> bool: +def filter_metric_fetched(name: str, metric: str, expected_metrics, split: str) -> bool: # This is a hack for the passkey and needle retrieval test, which reports ndcg_at_1 (i.e. accuracy), rather than the ndcg_at_10 that is commonly used for retrieval tasks. - return bool( - metric == "ndcg_at_1" - if name in ["LEMBNeedleRetrieval", "LEMBPasskeyRetrieval"] - else metric in expected_metrics - ) + if name in ["LEMBNeedleRetrieval", "LEMBPasskeyRetrieval"]: + return bool(metric == "ndcg_at_1") + elif (name.startswith("BrightRetrieval") and (split == "long")): + return bool(metric in ["recall_at_1"]) + else: + return bool(metric in expected_metrics) def get_dim_seq_size(model): @@ -237,13 +243,23 @@ def get_external_model_results(): pbar = tqdm(models_to_run, desc="Fetching external model results") for model in pbar: pbar.set_description(f"Fetching external model results for {model!r}") - ds = load_dataset( - RESULTS_REPO, - model, - trust_remote_code=True, - download_mode="force_redownload", - verification_mode="no_checks", - ) + try: + ds = load_dataset( + RESULTS_REPO, + model, + trust_remote_code=True, + download_mode="force_redownload", + verification_mode="no_checks", + ) + except KeyError as e: + model_tmp = "__".join(MODEL_META["model_meta"][model]["link"].split("/")[-2:]) + ds = load_dataset( + RESULTS_REPO, + model_tmp, + trust_remote_code=True, + download_mode="force_redownload", + verification_mode="no_checks", + ) ds = ds.map(add_lang) ds = ds.map(add_task) base_dict = { @@ -256,19 +272,39 @@ def get_external_model_results(): } for task, metrics in TASK_TO_METRIC.items(): - ds_dict = ds.filter(lambda x: filter_metric_external(x, task, metrics))[ + ds_sub = ds.filter(lambda x: filter_metric_external(x, task, metrics))[ "test" - ].to_dict() - ds_dict = { - k: round(v, 2) - for k, v in zip( - ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"] + ] + metrics = ds_sub.unique("metric") + for metric in metrics: + ds_dict = ds_sub.filter(lambda x: x["metric"] == metric).to_dict() + ds_dict = { + k: round(v, 2) + for k, v in zip( + ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"] + ) + } + # metrics[0] is the main name for this metric; other names in the list are legacy for backward-compat + # except for recall_at_1, which is the main name for BrightRetrieval (Long) + metric = metrics[0] if metric != "recall_at_1" else metric + if metric not in EXTERNAL_MODEL_RESULTS[model][task]: + EXTERNAL_MODEL_RESULTS[model][task][metric] = [] + EXTERNAL_MODEL_RESULTS[model][task][metric].append( + {**base_dict, **ds_dict} ) - } - # metrics[0] is the main name for this metric; other names in the list are legacy for backward-compat - EXTERNAL_MODEL_RESULTS[model][task][metrics[0]].append( - {**base_dict, **ds_dict} - ) + #ds_dict = ds.filter(lambda x: filter_metric_external(x, task, metrics))[ + # "test" + #].to_dict() + #ds_dict = { + # k: round(v, 2) + # for k, v in zip( + # ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"] + # ) + #} + ## metrics[0] is the main name for this metric; other names in the list are legacy for backward-compat + #EXTERNAL_MODEL_RESULTS[model][task][metrics[0]].append( + # {**base_dict, **ds_dict} + #) # Save & cache EXTERNAL_MODEL_RESULTS with open("EXTERNAL_MODEL_RESULTS.json", "w") as f: @@ -320,8 +356,8 @@ def get_mteb_data( results_list = [] for task in tasks: # Not all models have InstructionRetrieval, other new tasks - if task not in external_model_results[model]: - continue + if task not in external_model_results[model]: continue + if task_to_metric[task][0] not in external_model_results[model][task]: continue results_list += external_model_results[model][task][task_to_metric[task][0]] if len(datasets) > 0: @@ -416,6 +452,7 @@ def get_mteb_data( res["dataset"]["name"].replace("MTEB ", ""), score["type"], task_to_metric.get(res["task"]["type"]), + res["dataset"]["split"], ) ][0] } @@ -536,7 +573,7 @@ def get_mteb_average(task_dict: dict) -> tuple[Any, dict]: rank=False, ) # Debugging: - DATA_OVERALL.to_csv("overall.csv") + # DATA_OVERALL.to_csv("overall.csv") DATA_OVERALL.insert( 1, f"Average ({len(all_tasks)} datasets)", @@ -610,8 +647,11 @@ def refresh_leaderboard() -> tuple[list, dict]: leave=True, ) for board, board_config in pbar_tasks: - if board == "longembed": - pass + # Optional fetch only for a specific board + # if board != "bright_long": continue + # Very hacky - should fix this as soon as possible + if board == "bright_long": + TASK_TO_METRIC["Retrieval"] = ["recall_at_1"] boards_data[board] = {"data_overall": None, "data_tasks": {}} pbar_tasks.set_description(f"Fetching leaderboard results for {board!r}") pbar_tasks.refresh() @@ -630,10 +670,10 @@ def refresh_leaderboard() -> tuple[list, dict]: ) boards_data[board]["data_tasks"][task_category] = data_task_category all_data_tasks.append(data_task_category) - + if board == "bright_long": + TASK_TO_METRIC["Retrieval"] = ["ndcg_at_10"] return all_data_tasks, boards_data - def write_out_results(item: dict, item_name: str) -> None: """ Due to their complex structure, let's recursively create subfolders until we reach the end