diff --git a/.gitignore b/.gitignore
index 470aca5314b4706dc53c5849fbb13ccf5820d9c2..dc8f8c2ac63ef18770df575874f0973a3599e01e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,24 @@ space
.venv
results
mteb
+**/.DS_Store
+
+# FastAPI
+**/__pycache__/
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Gradio
+flagged/
+gradio_cached_examples/
+tmp/
+logs/
+
+# IDE
+.idea/
+.vscode/
+
+# 环境相关
+.env
+.env.*
diff --git a/EXTERNAL_MODEL_RESULTS.json b/EXTERNAL_MODEL_RESULTS.json
deleted file mode 100644
index 10c57b4fa45929a7c76146dfd59c989886b94153..0000000000000000000000000000000000000000
--- a/EXTERNAL_MODEL_RESULTS.json
+++ /dev/null
@@ -1,25145 +0,0 @@
-{
- "Alibaba-NLP__gte-Qwen1.5-7B-instruct": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "AmazonCounterfactualClassification (en)": 83.16,
- "AmazonPolarityClassification": 96.7,
- "AmazonReviewsClassification (en)": 62.17,
- "AmazonReviewsClassification (zh)": 52.95,
- "Banking77Classification": 81.68,
- "EmotionClassification": 54.53,
- "IFlyTek": 53.77,
- "ImdbClassification": 95.58,
- "JDReview": 88.2,
- "MTOPDomainClassification (en)": 95.75,
- "MTOPIntentClassification (en)": 84.26,
- "MassiveIntentClassification (zh-CN)": 76.25,
- "MassiveIntentClassification (en)": 78.47,
- "MassiveScenarioClassification (en)": 78.19,
- "MassiveScenarioClassification (zh-CN)": 77.26,
- "MultilingualSentiment": 77.42,
- "OnlineShopping": 94.48,
- "TNews": 51.24,
- "ToxicConversationsClassification": 78.75,
- "TweetSentimentExtractionClassification": 66.0,
- "Waimai": 88.63
- }
- ]
- },
- "Clustering": {
- "v_measure": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "ArxivClusteringP2P": 56.4,
- "ArxivClusteringS2S": 51.45,
- "BiorxivClusteringP2P": 49.01,
- "BiorxivClusteringS2S": 45.06,
- "CLSClusteringP2P": 47.21,
- "CLSClusteringS2S": 45.79,
- "MedrxivClusteringP2P": 44.37,
- "MedrxivClusteringS2S": 42.0,
- "RedditClustering": 73.37,
- "RedditClusteringP2P": 72.51,
- "StackExchangeClustering": 79.07,
- "StackExchangeClusteringP2P": 49.57,
- "ThuNewsClusteringP2P": 87.43,
- "ThuNewsClusteringS2S": 87.9,
- "TwentyNewsgroupsClustering": 51.31
- }
- ]
- },
- "PairClassification": {
- "max_ap": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "Cmnli": 91.81,
- "Ocnli": 85.22,
- "SprintDuplicateQuestions": 95.99,
- "TwitterSemEval2015": 79.36,
- "TwitterURLCorpus": 86.79
- },
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "Cmnli": 91.85,
- "Ocnli": 85.28,
- "SprintDuplicateQuestions": 96.07,
- "TwitterSemEval2015": 79.36,
- "TwitterURLCorpus": 86.79
- }
- ]
- },
- "Reranking": {
- "map": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "AskUbuntuDupQuestions": 66.0,
- "CMedQAv1": 86.37,
- "CMedQAv2": 87.41,
- "MindSmallReranking": 32.71,
- "SciDocsRR": 87.89,
- "StackOverflowDupQuestions": 53.93,
- "T2Reranking": 68.11
- }
- ]
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "ArguAna": 62.65,
- "BrightRetrieval (stackoverflow)": 19.85,
- "BrightRetrieval (earth_science)": 36.22,
- "BrightRetrieval (leetcode)": 25.46,
- "BrightRetrieval (theoremqa_questions)": 26.97,
- "BrightRetrieval (economics)": 17.72,
- "BrightRetrieval (robotics)": 13.47,
- "BrightRetrieval (pony)": 9.79,
- "BrightRetrieval (aops)": 14.36,
- "BrightRetrieval (psychology)": 24.61,
- "BrightRetrieval (theoremqa_theorems)": 30.8,
- "BrightRetrieval (biology)": 30.92,
- "BrightRetrieval (sustainable_living)": 14.93,
- "CQADupstackRetrieval": 40.64,
- "ClimateFEVER": 44.0,
- "CmedqaRetrieval": 43.47,
- "CovidRetrieval": 80.87,
- "DBPedia": 48.04,
- "DuRetrieval": 86.01,
- "EcomRetrieval": 66.46,
- "FEVER": 93.35,
- "FiQA2018": 55.31,
- "HotpotQA": 72.25,
- "MMarcoRetrieval": 73.83,
- "MSMARCO": 41.68,
- "MedicalRetrieval": 61.33,
- "NFCorpus": 38.25,
- "NQ": 61.79,
- "QuoraRetrieval": 89.61,
- "SCIDOCS": 27.69,
- "SciFact": 75.31,
- "T2Retrieval": 83.58,
- "TRECCOVID": 72.72,
- "Touche2020": 20.3,
- "VideoRetrieval": 69.41
- }
- ],
- "recall_at_1": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "BrightRetrieval (economics)": 25.73,
- "BrightRetrieval (pony)": 1.32,
- "BrightRetrieval (robotics)": 21.29,
- "BrightRetrieval (biology)": 39.24,
- "BrightRetrieval (earth_science)": 36.13,
- "BrightRetrieval (stackoverflow)": 23.5,
- "BrightRetrieval (psychology)": 42.28,
- "BrightRetrieval (sustainable_living)": 33.1
- }
- ]
- },
- "STS": {
- "cosine_spearman": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "AFQMC": 58.47,
- "ATEC": 55.46,
- "BIOSSES": 81.12,
- "BQ": 77.59,
- "LCQMC": 76.29,
- "PAWSX": 50.22,
- "QBQTC": 31.82,
- "SICK-R": 79.15,
- "STS12": 76.52,
- "STS13": 88.63,
- "STS14": 83.32,
- "STS15": 87.5,
- "STS16": 86.39,
- "STS17 (en-en)": 87.79,
- "STS22 (en)": 66.4,
- "STS22 (zh)": 67.36,
- "STSB": 81.37,
- "STSBenchmark": 87.35
- }
- ]
- },
- "Summarization": {
- "cosine_spearman": [
- {
- "Model": "gte-Qwen1.5-7B-instruct",
- "SummEval": 31.46
- }
- ]
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "Alibaba-NLP__gte-Qwen2-7B-instruct": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": []
- },
- "Clustering": {
- "v_measure": []
- },
- "PairClassification": {
- "max_ap": []
- },
- "Reranking": {
- "map": []
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "gte-Qwen2-7B-instruct",
- "BrightRetrieval (earth_science)": 40.66,
- "BrightRetrieval (sustainable_living)": 20.82,
- "BrightRetrieval (theoremqa_theorems)": 34.22,
- "BrightRetrieval (aops)": 15.1,
- "BrightRetrieval (economics)": 16.18,
- "BrightRetrieval (pony)": 1.25,
- "BrightRetrieval (stackoverflow)": 13.95,
- "BrightRetrieval (leetcode)": 31.07,
- "BrightRetrieval (biology)": 32.09,
- "BrightRetrieval (theoremqa_questions)": 29.9,
- "BrightRetrieval (robotics)": 12.82,
- "BrightRetrieval (psychology)": 26.58
- }
- ],
- "recall_at_1": [
- {
- "Model": "gte-Qwen2-7B-instruct",
- "BrightRetrieval (psychology)": 46.73,
- "BrightRetrieval (biology)": 34.87,
- "BrightRetrieval (sustainable_living)": 31.28,
- "BrightRetrieval (robotics)": 10.89,
- "BrightRetrieval (pony)": 1.17,
- "BrightRetrieval (earth_science)": 38.36,
- "BrightRetrieval (stackoverflow)": 16.67,
- "BrightRetrieval (economics)": 27.67
- }
- ]
- },
- "STS": {
- "cosine_spearman": []
- },
- "Summarization": {
- "cosine_spearman": []
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "BAAI__bge-base-en-v1.5": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": []
- },
- "Clustering": {
- "v_measure": [
- {
- "Model": "bge-base-en-v1.5",
- "BiorxivClusteringP2P": 39.44,
- "BiorxivClusteringS2S": 36.62,
- "MedrxivClusteringP2P": 33.21,
- "MedrxivClusteringS2S": 31.68,
- "RedditClustering": 56.61,
- "RedditClusteringP2P": 62.66,
- "StackExchangeClustering": 66.11,
- "StackExchangeClusteringP2P": 35.24,
- "TwentyNewsgroupsClustering": 50.75
- }
- ]
- },
- "PairClassification": {
- "max_ap": []
- },
- "Reranking": {
- "map": []
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "bge-base-en-v1.5",
- "ARCChallenge": 9.66,
- "AlphaNLI": 10.99,
- "HellaSwag": 26.64,
- "PIQA": 25.69,
- "Quail": 1.42,
- "RARbCode": 46.47,
- "RARbMath": 46.86,
- "SIQA": 0.94,
- "SpartQA": 3.37,
- "TempReasonL1": 1.07,
- "TempReasonL2Fact": 17.23,
- "TempReasonL2Pure": 1.29,
- "TempReasonL3Fact": 13.36,
- "TempReasonL3Pure": 5.2,
- "WinoGrande": 13.76
- }
- ]
- },
- "STS": {
- "cosine_spearman": []
- },
- "Summarization": {
- "cosine_spearman": []
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "BAAI__bge-base-en-v1.5-instruct": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": []
- },
- "Clustering": {
- "v_measure": []
- },
- "PairClassification": {
- "max_ap": []
- },
- "Reranking": {
- "map": []
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "bge-base-en-v1.5-instruct",
- "ARCChallenge": 8.85,
- "AlphaNLI": 4.13,
- "HellaSwag": 24.03,
- "PIQA": 23.03,
- "Quail": 1.25,
- "RARbCode": 46.32,
- "RARbMath": 45.62,
- "SIQA": 0.24,
- "SpartQA": 2.67,
- "TempReasonL1": 0.8,
- "TempReasonL2Fact": 16.56,
- "TempReasonL2Pure": 1.33,
- "TempReasonL3Fact": 12.68,
- "TempReasonL3Pure": 5.08,
- "WinoGrande": 10.27
- }
- ]
- },
- "STS": {
- "cosine_spearman": []
- },
- "Summarization": {
- "cosine_spearman": []
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "BAAI__bge-base-zh-v1.5": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": [
- {
- "Model": "bge-base-zh-v1.5",
- "AmazonReviewsClassification (zh)": 40.15,
- "IFlyTek": 48.62,
- "JDReview": 83.62,
- "MassiveIntentClassification (zh-CN)": 67.93,
- "MassiveScenarioClassification (zh-CN)": 73.98,
- "MultilingualSentiment": 70.67,
- "OnlineShopping": 91.26,
- "TNews": 51.08,
- "Waimai": 85.36
- }
- ]
- },
- "Clustering": {
- "v_measure": [
- {
- "Model": "bge-base-zh-v1.5",
- "CLSClusteringP2P": 39.91,
- "CLSClusteringS2S": 37.63,
- "ThuNewsClusteringP2P": 58.45,
- "ThuNewsClusteringS2S": 54.12
- }
- ]
- },
- "PairClassification": {
- "max_ap": [
- {
- "Model": "bge-base-zh-v1.5",
- "Cmnli": 84.1,
- "Ocnli": 75.41
- },
- {
- "Model": "bge-base-zh-v1.5",
- "Cmnli": 84.11,
- "Ocnli": 75.43
- }
- ]
- },
- "Reranking": {
- "map": [
- {
- "Model": "bge-base-zh-v1.5",
- "CMedQAv1": 80.47,
- "CMedQAv2": 84.88,
- "MMarcoReranking": 29.74,
- "T2Reranking": 66.49
- }
- ]
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "bge-base-zh-v1.5",
- "CmedqaRetrieval": 41.61,
- "CovidRetrieval": 74.7,
- "DuRetrieval": 85.07,
- "EcomRetrieval": 64.25,
- "MMarcoRetrieval": 77.69,
- "MedicalRetrieval": 56.51,
- "T2Retrieval": 83.71,
- "VideoRetrieval": 72.35
- }
- ]
- },
- "STS": {
- "cosine_spearman": [
- {
- "Model": "bge-base-zh-v1.5",
- "AFQMC": 42.4,
- "ATEC": 48.17,
- "BQ": 61.78,
- "LCQMC": 74.45,
- "PAWSX": 20.4,
- "QBQTC": 36.22,
- "STS22 (zh)": 68.01,
- "STSB": 78.31
- }
- ]
- },
- "Summarization": {
- "cosine_spearman": []
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "BAAI__bge-large-en-v1.5": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": []
- },
- "Clustering": {
- "v_measure": []
- },
- "PairClassification": {
- "max_ap": []
- },
- "Reranking": {
- "map": []
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "bge-large-en-v1.5",
- "AILACasedocs": 25.15,
- "AILAStatutes": 20.74,
- "ARCChallenge": 9.99,
- "AlphaNLI": 13.13,
- "BrightRetrieval (stackoverflow)": 9.51,
- "BrightRetrieval (earth_science)": 24.15,
- "BrightRetrieval (aops)": 6.08,
- "BrightRetrieval (sustainable_living)": 13.27,
- "BrightRetrieval (psychology)": 17.44,
- "BrightRetrieval (robotics)": 12.21,
- "BrightRetrieval (theoremqa_theorems)": 6.72,
- "BrightRetrieval (pony)": 5.64,
- "BrightRetrieval (biology)": 11.96,
- "BrightRetrieval (theoremqa_questions)": 12.56,
- "BrightRetrieval (leetcode)": 26.68,
- "BrightRetrieval (economics)": 16.59,
- "GerDaLIRSmall": 3.96,
- "HellaSwag": 28.5,
- "LeCaRDv2": 22.68,
- "LegalBenchConsumerContractsQA": 73.52,
- "LegalBenchCorporateLobbying": 91.51,
- "LegalQuAD": 16.22,
- "LegalSummarization": 59.99,
- "PIQA": 27.99,
- "Quail": 1.83,
- "RARbCode": 48.12,
- "RARbMath": 57.36,
- "SIQA": 1.04,
- "SpartQA": 2.99,
- "TempReasonL1": 1.46,
- "TempReasonL2Fact": 24.25,
- "TempReasonL2Pure": 2.35,
- "TempReasonL3Fact": 20.64,
- "TempReasonL3Pure": 6.67,
- "WinoGrande": 19.18
- }
- ],
- "recall_at_1": [
- {
- "Model": "bge-large-en-v1.5",
- "BrightRetrieval (pony)": 0.36,
- "BrightRetrieval (psychology)": 11.58,
- "BrightRetrieval (stackoverflow)": 13.25,
- "BrightRetrieval (robotics)": 10.89,
- "BrightRetrieval (earth_science)": 27.73,
- "BrightRetrieval (biology)": 16.42,
- "BrightRetrieval (economics)": 20.87,
- "BrightRetrieval (sustainable_living)": 16.9
- }
- ]
- },
- "STS": {
- "cosine_spearman": []
- },
- "Summarization": {
- "cosine_spearman": []
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "BAAI__bge-large-en-v1.5-instruct": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": []
- },
- "Clustering": {
- "v_measure": []
- },
- "PairClassification": {
- "max_ap": []
- },
- "Reranking": {
- "map": []
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "bge-large-en-v1.5-instruct",
- "ARCChallenge": 8.86,
- "AlphaNLI": 0.86,
- "HellaSwag": 26.24,
- "PIQA": 23.26,
- "Quail": 2.72,
- "RARbCode": 45.25,
- "RARbMath": 49.82,
- "SIQA": 0.59,
- "SpartQA": 2.34,
- "TempReasonL1": 1.17,
- "TempReasonL2Fact": 21.19,
- "TempReasonL2Pure": 2.1,
- "TempReasonL3Fact": 17.59,
- "TempReasonL3Pure": 5.99,
- "WinoGrande": 10.31
- }
- ]
- },
- "STS": {
- "cosine_spearman": []
- },
- "Summarization": {
- "cosine_spearman": []
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "BAAI__bge-large-zh-noinstruct": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": [
- {
- "Model": "bge-large-zh-noinstruct",
- "AmazonReviewsClassification (zh)": 41.94,
- "IFlyTek": 45.32,
- "JDReview": 85.38,
- "MassiveIntentClassification (zh-CN)": 66.96,
- "MassiveScenarioClassification (zh-CN)": 73.39,
- "MultilingualSentiment": 73.7,
- "OnlineShopping": 91.66,
- "TNews": 52.05,
- "Waimai": 86.83
- }
- ]
- },
- "Clustering": {
- "v_measure": [
- {
- "Model": "bge-large-zh-noinstruct",
- "CLSClusteringP2P": 41.23,
- "CLSClusteringS2S": 40.04,
- "ThuNewsClusteringP2P": 62.03,
- "ThuNewsClusteringS2S": 56.75
- }
- ]
- },
- "PairClassification": {
- "max_ap": [
- {
- "Model": "bge-large-zh-noinstruct",
- "Cmnli": 82.17,
- "Ocnli": 71.37
- },
- {
- "Model": "bge-large-zh-noinstruct",
- "Cmnli": 82.18,
- "Ocnli": 71.37
- }
- ]
- },
- "Reranking": {
- "map": [
- {
- "Model": "bge-large-zh-noinstruct",
- "CMedQAv1": 81.72,
- "CMedQAv2": 84.64,
- "MMarcoReranking": 27.1,
- "T2Reranking": 66.16
- }
- ]
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "bge-large-zh-noinstruct",
- "CmedqaRetrieval": 41.03,
- "CovidRetrieval": 75.07,
- "DuRetrieval": 84.68,
- "EcomRetrieval": 65.6,
- "MMarcoRetrieval": 81.38,
- "MedicalRetrieval": 58.28,
- "T2Retrieval": 84.39,
- "VideoRetrieval": 73.93
- }
- ]
- },
- "STS": {
- "cosine_spearman": [
- {
- "Model": "bge-large-zh-noinstruct",
- "AFQMC": 43.06,
- "ATEC": 48.29,
- "BQ": 60.53,
- "LCQMC": 74.71,
- "PAWSX": 16.64,
- "QBQTC": 35.2,
- "STS22 (zh)": 67.19,
- "STSB": 78.41
- }
- ]
- },
- "Summarization": {
- "cosine_spearman": []
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "BAAI__bge-large-zh-v1.5": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": [
- {
- "Model": "bge-large-zh-v1.5",
- "AmazonReviewsClassification (zh)": 41.38,
- "IFlyTek": 48.74,
- "JDReview": 85.14,
- "MassiveIntentClassification (zh-CN)": 68.84,
- "MassiveScenarioClassification (zh-CN)": 74.7,
- "MultilingualSentiment": 72.97,
- "OnlineShopping": 91.43,
- "TNews": 52.1,
- "Waimai": 86.9
- }
- ]
- },
- "Clustering": {
- "v_measure": [
- {
- "Model": "bge-large-zh-v1.5",
- "CLSClusteringP2P": 41.44,
- "CLSClusteringS2S": 38.33,
- "ThuNewsClusteringP2P": 59.61,
- "ThuNewsClusteringS2S": 56.58
- }
- ]
- },
- "PairClassification": {
- "max_ap": [
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- "deepvk__USER-bge-m3": {
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- "STS17 (fr-en)": 80.18,
- "STS17 (en-tr)": 63.3,
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- "STS17 (en-ar)": 71.27,
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- "STS17 (nl-en)": 79.29,
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- "STS22 (zh-en)": 69.8,
- "STS22 (ar)": 57.82,
- "STS22 (es-it)": 66.43,
- "STS22 (it)": 77.76,
- "STS22 (fr-pl)": 73.25,
- "STS22 (de-en)": 54.89,
- "STS22 (pl-en)": 70.37,
- "STS22 (zh)": 65.63,
- "STS22 (de)": 55.95,
- "STS22 (pl)": 34.08,
- "STS22 (de-fr)": 59.68,
- "STS22 (es)": 66.67,
- "STS22 (ru)": 60.67,
- "STS22 (es-en)": 74.0,
- "STS22 (en)": 62.26,
- "STS22 (tr)": 63.71,
- "STS22 (de-pl)": 39.35,
- "STSB": 79.04,
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- "STSBenchmarkMultilingualSTS (zh)": 79.87,
- "STSBenchmarkMultilingualSTS (de)": 79.68,
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- "STSBenchmarkMultilingualSTS (en)": 85.64,
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- "STSBenchmarkMultilingualSTS (pl)": 74.93,
- "STSBenchmarkMultilingualSTS (pt)": 67.16,
- "STSBenchmarkMultilingualSTS (nl)": 75.96,
- "STSBenchmarkMultilingualSTS (it)": 78.09,
- "STSBenchmarkMultilingualSTS (ru)": 79.33
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- "Model": "multilingual-e5-base",
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- }
- ]
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- "Summarization": {
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- }
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- }
- ]
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- }
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- "Tatoeba (ido-eng)": 83.52,
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- "AmazonReviewsClassification (fr)": 41.91,
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- "MTOPDomainClassification (fr)": 86.41,
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- ]
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- "PawsXPairClassification (fr)": 58.68,
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- "PawsXPairClassification (ko)": 52.08,
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- "PawsXPairClassification (fr)": 58.61,
- "PawsXPairClassification (ja)": 50.84,
- "PawsXPairClassification (ko)": 52.22,
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- "MasakhaNEWSClusteringS2S (swa)": 42.09,
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- }
- ]
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- "CDSC-E": 76.17,
- "OpusparcusPC (de)": 97.56,
- "OpusparcusPC (en)": 98.91,
- "OpusparcusPC (fi)": 94.74,
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- "PSC": 99.31,
- "PawsXPairClassification (de)": 56.46,
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- "PawsXPairClassification (ja)": 50.82,
- "PawsXPairClassification (ko)": 51.63,
- "PawsXPairClassification (zh)": 57.34,
- "SICK-E-PL": 79.69,
- "SprintDuplicateQuestions": 92.19,
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- "OpusparcusPC (en)": 98.92,
- "OpusparcusPC (fi)": 94.74,
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- "PawsXPairClassification (es)": 57.28,
- "PawsXPairClassification (fr)": 59.76,
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- "PawsXPairClassification (ko)": 51.74,
- "PawsXPairClassification (zh)": 57.75,
- "SICK-E-PL": 79.69,
- "SprintDuplicateQuestions": 92.19,
- "TERRa": 63.89,
- "TwitterSemEval2015": 79.79,
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- }
- ]
- },
- "Reranking": {
- "map": [
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- "Model": "multilingual-e5-large-instruct",
- "AlloprofReranking": 74.68,
- "AskUbuntuDupQuestions": 64.41,
- "MMarcoReranking": 23.6,
- "MindSmallReranking": 33.07,
- "RuBQReranking": 75.84,
- "SciDocsRR": 85.75,
- "StackOverflowDupQuestions": 52.45,
- "SyntecReranking": 89.95,
- "T2Reranking": 67.12
- },
- {
- "Model": "multilingual-e5-large-instruct",
- "MIRACLReranking (ru)": 62.49
- }
- ]
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "multilingual-e5-large-instruct",
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- "AILAStatutes": 29.66,
- "ARCChallenge": 15.03,
- "AlloprofRetrieval": 52.12,
- "AlphaNLI": 24.87,
- "AppsRetrieval": 34.89,
- "ArguAna": 58.48,
- "ArguAna-PL": 44.6,
- "BSARDRetrieval": 24.61,
- "CmedqaRetrieval": 34.15,
- "CodeFeedbackMT": 39.87,
- "CodeFeedbackST": 75.89,
- "CodeSearchNetCCRetrieval (python)": 86.24,
- "CodeSearchNetCCRetrieval (javascript)": 79.2,
- "CodeSearchNetCCRetrieval (go)": 72.31,
- "CodeSearchNetCCRetrieval (ruby)": 81.9,
- "CodeSearchNetCCRetrieval (java)": 80.07,
- "CodeSearchNetCCRetrieval (php)": 72.49,
- "CodeSearchNetRetrieval (python)": 88.96,
- "CodeSearchNetRetrieval (javascript)": 75.37,
- "CodeSearchNetRetrieval (go)": 90.41,
- "CodeSearchNetRetrieval (ruby)": 79.56,
- "CodeSearchNetRetrieval (java)": 80.55,
- "CodeSearchNetRetrieval (php)": 83.7,
- "CodeTransOceanContest": 83.36,
- "CodeTransOceanDL": 29.01,
- "CosQA": 37.75,
- "CovidRetrieval": 75.8,
- "DuRetrieval": 85.16,
- "EcomRetrieval": 53.92,
- "FiQA-PL": 32.01,
- "FiQA2018": 48.42,
- "GerDaLIRSmall": 21.34,
- "HellaSwag": 32.02,
- "LEMBNarrativeQARetrieval": 26.71,
- "LEMBQMSumRetrieval": 26.08,
- "LEMBSummScreenFDRetrieval": 72.75,
- "LEMBWikimQARetrieval": 57.79,
- "LeCaRDv2": 64.22,
- "LegalBenchConsumerContractsQA": 76.8,
- "LegalBenchCorporateLobbying": 94.25,
- "LegalQuAD": 51.25,
- "LegalSummarization": 68.07,
- "MIRACLRetrieval (ru)": 66.08,
- "MMarcoRetrieval": 78.81,
- "MedicalRetrieval": 56.55,
- "MintakaRetrieval (ar)": 26.13,
- "MintakaRetrieval (de)": 35.13,
- "MintakaRetrieval (es)": 34.51,
- "MintakaRetrieval (fr)": 33.49,
- "MintakaRetrieval (hi)": 28.19,
- "MintakaRetrieval (it)": 35.01,
- "MintakaRetrieval (ja)": 26.31,
- "MintakaRetrieval (pt)": 35.11,
- "NFCorpus": 36.34,
- "NFCorpus-PL": 32.66,
- "PIQA": 32.26,
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- "RARbCode": 71.22,
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- "RiaNewsRetrieval": 83.26,
- "RuBQRetrieval": 73.9,
- "SCIDOCS": 19.24,
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- "SIQA": 7.33,
- "SciFact": 71.62,
- "SciFact-PL": 63.31,
- "SpartQA": 13.54,
- "StackOverflowQA": 85.82,
- "SyntecRetrieval": 87.8,
- "SyntheticText2SQL": 58.85,
- "T2Retrieval": 82.92,
- "TRECCOVID": 82.51,
- "TRECCOVID-PL": 62.03,
- "TempReasonL1": 1.2,
- "TempReasonL2Fact": 40.19,
- "TempReasonL2Pure": 3.64,
- "TempReasonL3Fact": 37.37,
- "TempReasonL3Pure": 9.82,
- "Touche2020": 27.4,
- "VideoRetrieval": 52.24,
- "WinoGrande": 54.27,
- "XPQARetrieval (ara-ara)": 48.56,
- "XPQARetrieval (eng-ara)": 34.01,
- "XPQARetrieval (ara-eng)": 45.13,
- "XPQARetrieval (deu-deu)": 79.18,
- "XPQARetrieval (eng-deu)": 50.55,
- "XPQARetrieval (deu-eng)": 75.23,
- "XPQARetrieval (spa-spa)": 65.07,
- "XPQARetrieval (eng-spa)": 41.75,
- "XPQARetrieval (spa-eng)": 61.02,
- "XPQARetrieval (fra-fra)": 72.72,
- "XPQARetrieval (eng-fra)": 46.17,
- "XPQARetrieval (fra-eng)": 69.33,
- "XPQARetrieval (hin-hin)": 76.62,
- "XPQARetrieval (eng-hin)": 18.12,
- "XPQARetrieval (hin-eng)": 71.95,
- "XPQARetrieval (ita-ita)": 77.03,
- "XPQARetrieval (eng-ita)": 41.42,
- "XPQARetrieval (ita-eng)": 70.6,
- "XPQARetrieval (jpn-jpn)": 74.36,
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- "XPQARetrieval (jpn-eng)": 70.5,
- "XPQARetrieval (kor-kor)": 39.74,
- "XPQARetrieval (eng-kor)": 34.9,
- "XPQARetrieval (kor-eng)": 37.39,
- "XPQARetrieval (pol-pol)": 52.53,
- "XPQARetrieval (eng-pol)": 34.22,
- "XPQARetrieval (pol-eng)": 48.53,
- "XPQARetrieval (por-por)": 51.17,
- "XPQARetrieval (eng-por)": 31.76,
- "XPQARetrieval (por-eng)": 48.79,
- "XPQARetrieval (tam-tam)": 49.69,
- "XPQARetrieval (eng-tam)": 6.8,
- "XPQARetrieval (tam-eng)": 40.83,
- "XPQARetrieval (cmn-cmn)": 66.42,
- "XPQARetrieval (eng-cmn)": 33.94,
- "XPQARetrieval (cmn-eng)": 59.78
- }
- ]
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- "cosine_spearman": [
- {
- "Model": "multilingual-e5-large-instruct",
- "AFQMC": 37.53,
- "ATEC": 43.27,
- "BIOSSES": 87.46,
- "BQ": 48.8,
- "CDSC-R": 92.35,
- "LCQMC": 76.06,
- "PAWSX": 15.06,
- "RUParaPhraserSTS": 75.4,
- "RuSTSBenchmarkSTS": 83.97,
- "SICK-R": 81.73,
- "SICK-R-PL": 77.62,
- "SICKFr": 80.2,
- "STS12": 82.53,
- "STS13": 88.05,
- "STS14": 84.83,
- "STS15": 91.02,
- "STS16": 87.32,
- "STS17 (ar-ar)": 82.71,
- "STS17 (nl-en)": 87.44,
- "STS17 (fr-en)": 86.28,
- "STS17 (ko-ko)": 84.31,
- "STS17 (es-es)": 89.4,
- "STS17 (en-ar)": 81.83,
- "STS17 (en-de)": 87.61,
- "STS17 (en-tr)": 80.47,
- "STS17 (es-en)": 87.03,
- "STS17 (en-en)": 90.33,
- "STS17 (it-en)": 88.25,
- "STS22 (es-en)": 78.65,
- "STS22 (de)": 61.53,
- "STS22 (zh)": 67.4,
- "STS22 (es)": 68.45,
- "STS22 (de-fr)": 65.52,
- "STS22 (pl)": 40.97,
- "STS22 (ru)": 65.17,
- "STS22 (en)": 68.67,
- "STS22 (fr)": 82.25,
- "STS22 (es-it)": 75.25,
- "STS22 (pl-en)": 74.86,
- "STS22 (fr-pl)": 61.98,
- "STS22 (ar)": 59.52,
- "STS22 (it)": 79.45,
- "STS22 (tr)": 67.2,
- "STS22 (zh-en)": 71.45,
- "STS22 (de-pl)": 54.6,
- "STS22 (de-en)": 58.22,
- "STSB": 81.68,
- "STSBenchmark": 88.39,
- "STSBenchmarkMultilingualSTS (de)": 85.38,
- "STSBenchmarkMultilingualSTS (nl)": 84.29,
- "STSBenchmarkMultilingualSTS (pl)": 83.42,
- "STSBenchmarkMultilingualSTS (fr)": 84.94,
- "STSBenchmarkMultilingualSTS (pt)": 84.9,
- "STSBenchmarkMultilingualSTS (it)": 84.38,
- "STSBenchmarkMultilingualSTS (es)": 86.15,
- "STSBenchmarkMultilingualSTS (zh)": 82.54,
- "STSBenchmarkMultilingualSTS (en)": 88.39,
- "STSBenchmarkMultilingualSTS (ru)": 83.86
- },
- {
- "Model": "multilingual-e5-large-instruct",
- "AFQMC": 37.53,
- "ATEC": 43.27,
- "BIOSSES": 87.46,
- "BQ": 48.8,
- "CDSC-R": 92.35,
- "LCQMC": 76.06,
- "PAWSX": 15.06,
- "RUParaPhraserSTS": 75.4,
- "RuSTSBenchmarkSTS": 83.97,
- "SICK-R": 81.73,
- "SICK-R-PL": 77.62,
- "SICKFr": 80.2,
- "STS12": 82.53,
- "STS13": 88.05,
- "STS14": 84.83,
- "STS15": 91.02,
- "STS16": 87.32,
- "STS17 (ar-ar)": 82.71,
- "STS17 (nl-en)": 87.44,
- "STS17 (fr-en)": 86.28,
- "STS17 (ko-ko)": 84.31,
- "STS17 (es-es)": 89.4,
- "STS17 (en-ar)": 81.83,
- "STS17 (en-de)": 87.61,
- "STS17 (en-tr)": 80.47,
- "STS17 (es-en)": 87.03,
- "STS17 (en-en)": 90.33,
- "STS17 (it-en)": 88.25,
- "STS22 (es-en)": 78.65,
- "STS22 (de)": 61.53,
- "STS22 (zh)": 67.4,
- "STS22 (es)": 68.45,
- "STS22 (de-fr)": 65.52,
- "STS22 (pl)": 40.97,
- "STS22 (ru)": 65.17,
- "STS22 (en)": 68.67,
- "STS22 (fr)": 82.25,
- "STS22 (es-it)": 75.25,
- "STS22 (pl-en)": 74.86,
- "STS22 (fr-pl)": 61.98,
- "STS22 (ar)": 59.52,
- "STS22 (it)": 79.45,
- "STS22 (tr)": 67.2,
- "STS22 (zh-en)": 71.45,
- "STS22 (de-pl)": 54.6,
- "STS22 (de-en)": 58.22,
- "STSB": 81.68,
- "STSBenchmark": 88.39,
- "STSBenchmarkMultilingualSTS (de)": 85.38,
- "STSBenchmarkMultilingualSTS (nl)": 84.29,
- "STSBenchmarkMultilingualSTS (pl)": 83.42,
- "STSBenchmarkMultilingualSTS (fr)": 84.94,
- "STSBenchmarkMultilingualSTS (pt)": 84.9,
- "STSBenchmarkMultilingualSTS (it)": 84.38,
- "STSBenchmarkMultilingualSTS (es)": 86.15,
- "STSBenchmarkMultilingualSTS (zh)": 82.54,
- "STSBenchmarkMultilingualSTS (en)": 88.39,
- "STSBenchmarkMultilingualSTS (ru)": 83.86
- }
- ]
- },
- "Summarization": {
- "cosine_spearman": [
- {
- "Model": "multilingual-e5-large-instruct",
- "SummEval": 30.46,
- "SummEvalFr": 31.72
- },
- {
- "Model": "multilingual-e5-large-instruct",
- "SummEval": 30.46,
- "SummEvalFr": 31.72
- }
- ]
- },
- "MultilabelClassification": {
- "accuracy": [
- {
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- }
- ]
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- "InstructionRetrieval": {
- "p-MRR": [
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- "Model": "multilingual-e5-large-instruct",
- "Core17InstructionRetrieval": 1.82,
- "News21InstructionRetrieval": 1.5,
- "Robust04InstructionRetrieval": -4.52
- }
- ]
- }
- },
- "intfloat__multilingual-e5-small": {
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- "f1": [
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- "Tatoeba (dan-eng)": 86.38,
- "Tatoeba (aze-eng)": 80.79,
- "Tatoeba (tur-eng)": 88.42,
- "Tatoeba (ces-eng)": 80.99,
- "Tatoeba (swh-eng)": 65.43,
- "Tatoeba (hrv-eng)": 84.42,
- "Tatoeba (est-eng)": 56.47,
- "Tatoeba (bel-eng)": 80.89,
- "Tatoeba (mhr-eng)": 5.58,
- "Tatoeba (cat-eng)": 79.3,
- "Tatoeba (ben-eng)": 81.4,
- "Tatoeba (tgl-eng)": 77.54,
- "Tatoeba (cym-eng)": 62.3,
- "Tatoeba (tha-eng)": 90.88,
- "Tatoeba (swe-eng)": 87.46,
- "Tatoeba (oci-eng)": 38.27,
- "Tatoeba (gsw-eng)": 40.13,
- "Tatoeba (ceb-eng)": 42.35,
- "Tatoeba (ind-eng)": 88.28,
- "Tatoeba (ron-eng)": 85.68,
- "Tatoeba (gla-eng)": 35.96,
- "Tatoeba (ile-eng)": 70.31,
- "Tatoeba (orv-eng)": 14.89,
- "Tatoeba (vie-eng)": 89.03,
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- "OpusparcusPC (fi)": 88.29,
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- "PawsXPairClassification (fr)": 52.69,
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- "PawsXPairClassification (ko)": 49.95,
- "PawsXPairClassification (zh)": 54.01,
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- "PawsXPairClassification (ko)": 49.98,
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- "MintakaRetrieval (ar)": 21.22,
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- "MintakaRetrieval (fr)": 22.53,
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- ]
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- "OpusparcusPC (fi)": 94.44,
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- "PawsXPairClassification (fr)": 54.63,
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- "PawsXPairClassification (zh)": 54.26,
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- "OpusparcusPC (fi)": 94.44,
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- "OpusparcusPC (sv)": 93.69,
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- "PawsXPairClassification (es)": 52.19,
- "PawsXPairClassification (fr)": 54.69,
- "PawsXPairClassification (ja)": 47.74,
- "PawsXPairClassification (ko)": 49.42,
- "PawsXPairClassification (zh)": 54.55,
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- "SprintDuplicateQuestions": 89.26,
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- "TwitterSemEval2015": 62.78,
- "TwitterURLCorpus": 84.58
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- "Model": "LaBSE",
- "CDSC-E": 68.91,
- "OpusparcusPC (fr)": 93.96,
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- "PSC": 97.42,
- "PawsXPairClassification (fr)": 54.63,
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- "SprintDuplicateQuestions": 89.26,
- "TwitterSemEval2015": 62.78,
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- }
- ]
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- "Reranking": {
- "map": [
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- "Model": "LaBSE",
- "AlloprofReranking": 49.51,
- "AskUbuntuDupQuestions": 52.75,
- "MMarcoReranking": 14.83,
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- "SciDocsRR": 68.72,
- "StackOverflowDupQuestions": 42.42,
- "SyntecReranking": 73.28,
- "T2Reranking": 63.29
- }
- ]
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- "Model": "LaBSE",
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- "AILAStatutes": 16.72,
- "ARCChallenge": 3.78,
- "AlloprofRetrieval": 19.77,
- "AlphaNLI": 13.11,
- "AppsRetrieval": 2.39,
- "ArguAna": 34.18,
- "ArguAna-PL": 38.52,
- "BSARDRetrieval": 0.0,
- "CQADupstackRetrieval": 18.75,
- "ClimateFEVER": 3.83,
- "CmedqaRetrieval": 5.49,
- "CodeFeedbackMT": 17.98,
- "CodeFeedbackST": 35.98,
- "CodeSearchNetCCRetrieval (python)": 40.41,
- "CodeSearchNetCCRetrieval (javascript)": 52.0,
- "CodeSearchNetCCRetrieval (go)": 41.73,
- "CodeSearchNetCCRetrieval (ruby)": 41.46,
- "CodeSearchNetCCRetrieval (java)": 45.83,
- "CodeSearchNetCCRetrieval (php)": 31.96,
- "CodeSearchNetRetrieval (python)": 60.52,
- "CodeSearchNetRetrieval (javascript)": 46.24,
- "CodeSearchNetRetrieval (go)": 48.56,
- "CodeSearchNetRetrieval (ruby)": 51.34,
- "CodeSearchNetRetrieval (java)": 35.05,
- "CodeSearchNetRetrieval (php)": 46.03,
- "CodeTransOceanContest": 28.1,
- "CodeTransOceanDL": 26.45,
- "CosQA": 8.77,
- "CovidRetrieval": 28.6,
- "DBPedia": 15.57,
- "DBPedia-PL": 16.1,
- "DuRetrieval": 26.34,
- "EcomRetrieval": 25.42,
- "FEVER": 12.18,
- "FiQA-PL": 7.63,
- "FiQA2018": 7.0,
- "GerDaLIRSmall": 4.59,
- "HellaSwag": 5.59,
- "HotpotQA": 18.75,
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- "LEMBNarrativeQARetrieval": 11.45,
- "LEMBQMSumRetrieval": 14.07,
- "LEMBSummScreenFDRetrieval": 40.52,
- "LEMBWikimQARetrieval": 28.1,
- "LeCaRDv2": 24.68,
- "LegalBenchConsumerContractsQA": 54.66,
- "LegalBenchCorporateLobbying": 69.39,
- "LegalQuAD": 16.64,
- "LegalSummarization": 53.89,
- "MMarcoRetrieval": 34.78,
- "MSMARCO": 7.6,
- "MSMARCO-PL": 7.22,
- "MedicalRetrieval": 6.68,
- "MintakaRetrieval (ar)": 14.06,
- "MintakaRetrieval (de)": 15.26,
- "MintakaRetrieval (es)": 15.65,
- "MintakaRetrieval (fr)": 15.53,
- "MintakaRetrieval (hi)": 13.67,
- "MintakaRetrieval (it)": 15.94,
- "MintakaRetrieval (ja)": 12.8,
- "MintakaRetrieval (pt)": 15.03,
- "NFCorpus": 16.54,
- "NFCorpus-PL": 17.45,
- "NQ": 8.42,
- "NQ-PL": 9.65,
- "PIQA": 6.53,
- "Quail": 1.91,
- "Quora-PL": 74.96,
- "QuoraRetrieval": 77.03,
- "RARbCode": 2.31,
- "RARbMath": 27.19,
- "RiaNewsRetrieval": 42.75,
- "RuBQRetrieval": 30.02,
- "SCIDOCS": 5.63,
- "SCIDOCS-PL": 7.48,
- "SIQA": 1.07,
- "SciFact": 38.2,
- "SciFact-PL": 39.79,
- "SpartQA": 1.56,
- "StackOverflowQA": 38.23,
- "SyntecRetrieval": 55.31,
- "SyntheticText2SQL": 43.28,
- "T2Retrieval": 25.32,
- "TRECCOVID": 16.34,
- "TRECCOVID-PL": 18.45,
- "TempReasonL1": 1.56,
- "TempReasonL2Fact": 7.06,
- "TempReasonL2Pure": 0.14,
- "TempReasonL3Fact": 8.74,
- "TempReasonL3Pure": 4.73,
- "Touche2020": 4.88,
- "VideoRetrieval": 22.04,
- "WinoGrande": 54.3,
- "XPQARetrieval (ara-ara)": 35.19,
- "XPQARetrieval (eng-ara)": 20.64,
- "XPQARetrieval (ara-eng)": 32.47,
- "XPQARetrieval (deu-deu)": 53.56,
- "XPQARetrieval (eng-deu)": 24.31,
- "XPQARetrieval (deu-eng)": 54.87,
- "XPQARetrieval (spa-spa)": 44.49,
- "XPQARetrieval (eng-spa)": 25.31,
- "XPQARetrieval (spa-eng)": 43.4,
- "XPQARetrieval (fra-fra)": 51.74,
- "XPQARetrieval (eng-fra)": 21.29,
- "XPQARetrieval (fra-eng)": 49.4,
- "XPQARetrieval (hin-hin)": 66.64,
- "XPQARetrieval (eng-hin)": 23.25,
- "XPQARetrieval (hin-eng)": 64.54,
- "XPQARetrieval (ita-ita)": 56.27,
- "XPQARetrieval (eng-ita)": 25.8,
- "XPQARetrieval (ita-eng)": 52.69,
- "XPQARetrieval (jpn-jpn)": 58.6,
- "XPQARetrieval (eng-jpn)": 21.49,
- "XPQARetrieval (jpn-eng)": 52.41,
- "XPQARetrieval (kor-kor)": 27.63,
- "XPQARetrieval (eng-kor)": 23.33,
- "XPQARetrieval (kor-eng)": 23.97,
- "XPQARetrieval (pol-pol)": 37.33,
- "XPQARetrieval (eng-pol)": 16.19,
- "XPQARetrieval (pol-eng)": 37.7,
- "XPQARetrieval (por-por)": 38.48,
- "XPQARetrieval (eng-por)": 19.41,
- "XPQARetrieval (por-eng)": 37.31,
- "XPQARetrieval (tam-tam)": 37.33,
- "XPQARetrieval (eng-tam)": 20.53,
- "XPQARetrieval (tam-eng)": 30.14,
- "XPQARetrieval (cmn-cmn)": 50.7,
- "XPQARetrieval (eng-cmn)": 20.59,
- "XPQARetrieval (cmn-eng)": 48.23,
- "XPQARetrieval (fr)": 51.74
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- ]
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- "cosine_spearman": [
- {
- "Model": "LaBSE",
- "AFQMC": 21.02,
- "ATEC": 26.61,
- "BIOSSES": 78.7,
- "BQ": 42.6,
- "CDSC-R": 85.53,
- "LCQMC": 52.19,
- "PAWSX": 10.23,
- "RUParaPhraserSTS": 65.74,
- "RuSTSBenchmarkSTS": 73.34,
- "SICK-R": 69.99,
- "SICK-R-PL": 65.9,
- "SICKFr": 69.94,
- "STS12": 65.08,
- "STS13": 67.98,
- "STS14": 64.03,
- "STS15": 76.59,
- "STS16": 72.98,
- "STS17 (es-en)": 65.71,
- "STS17 (en-de)": 73.85,
- "STS17 (fr-en)": 76.98,
- "STS17 (en-tr)": 72.07,
- "STS17 (ko-ko)": 71.32,
- "STS17 (es-es)": 80.83,
- "STS17 (it-en)": 76.99,
- "STS17 (ar-ar)": 69.07,
- "STS17 (en-en)": 79.45,
- "STS17 (nl-en)": 75.22,
- "STS17 (en-ar)": 74.51,
- "STS22 (pl-en)": 69.41,
- "STS22 (de-en)": 50.14,
- "STS22 (es)": 63.18,
- "STS22 (de-pl)": 58.69,
- "STS22 (fr)": 77.95,
- "STS22 (fr-pl)": 61.98,
- "STS22 (de)": 48.58,
- "STS22 (pl)": 39.3,
- "STS22 (en)": 61.63,
- "STS22 (es-it)": 69.69,
- "STS22 (zh-en)": 64.02,
- "STS22 (de-fr)": 53.28,
- "STS22 (tr)": 58.15,
- "STS22 (it)": 72.22,
- "STS22 (ru)": 57.49,
- "STS22 (es-en)": 71.86,
- "STS22 (ar)": 57.67,
- "STS22 (zh)": 63.02,
- "STSB": 68.38,
- "STSBenchmark": 72.25,
- "STSBenchmarkMultilingualSTS (nl)": 70.22,
- "STSBenchmarkMultilingualSTS (de)": 72.43,
- "STSBenchmarkMultilingualSTS (pt)": 71.65,
- "STSBenchmarkMultilingualSTS (fr)": 75.1,
- "STSBenchmarkMultilingualSTS (en)": 72.25,
- "STSBenchmarkMultilingualSTS (pl)": 72.58,
- "STSBenchmarkMultilingualSTS (es)": 72.92,
- "STSBenchmarkMultilingualSTS (zh)": 69.5,
- "STSBenchmarkMultilingualSTS (ru)": 73.06,
- "STSBenchmarkMultilingualSTS (it)": 72.97
- },
- {
- "Model": "LaBSE",
- "BIOSSES": 78.7,
- "CDSC-R": 85.53,
- "SICK-R": 69.99,
- "SICK-R-PL": 65.9,
- "SICKFr": 69.94,
- "STS12": 65.08,
- "STS13": 67.98,
- "STS14": 64.03,
- "STS15": 76.59,
- "STS16": 72.98,
- "STS17 (ar-ar)": 69.07,
- "STS17 (en-ar)": 74.51,
- "STS17 (en-de)": 73.85,
- "STS17 (en-en)": 79.45,
- "STS17 (en-tr)": 72.07,
- "STS17 (es-en)": 65.71,
- "STS17 (es-es)": 80.83,
- "STS17 (fr-en)": 76.98,
- "STS17 (it-en)": 76.99,
- "STS17 (ko-ko)": 71.32,
- "STS17 (nl-en)": 75.22,
- "STS22 (ar)": 57.67,
- "STS22 (de)": 48.58,
- "STS22 (de-en)": 50.14,
- "STS22 (de-fr)": 53.28,
- "STS22 (de-pl)": 58.69,
- "STS22 (en)": 60.97,
- "STS22 (es)": 63.18,
- "STS22 (es-en)": 71.86,
- "STS22 (es-it)": 69.69,
- "STS22 (fr)": 77.95,
- "STS22 (fr-pl)": 61.98,
- "STS22 (it)": 72.22,
- "STS22 (pl)": 39.28,
- "STS22 (pl-en)": 69.41,
- "STS22 (ru)": 57.49,
- "STS22 (tr)": 58.15,
- "STS22 (zh)": 63.02,
- "STS22 (zh-en)": 64.02,
- "STSBenchmark": 72.25,
- "STSBenchmarkMultilingualSTS (fr)": 75.1
- }
- ]
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- "Summarization": {
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- {
- "Model": "LaBSE",
- "SummEval": 31.05,
- "SummEvalFr": 30.16
- },
- {
- "Model": "LaBSE",
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- "SummEvalFr": 30.16
- },
- {
- "Model": "LaBSE",
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- }
- ]
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- "MultilabelClassification": {
- "accuracy": [
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- "STS22 (fr-pl)": 16.9,
- "STS22 (es)": 43.98,
- "STS22 (ru)": 11.19,
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- "STS22 (de-en)": 42.86,
- "STS22 (de)": 22.53,
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- "STS22 (de-pl)": 1.63,
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- "STS22 (zh)": 33.15,
- "STS22 (es-en)": 53.99,
- "STSB": 36.66,
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- "STSBenchmarkMultilingualSTS (en)": 83.09,
- "STSBenchmarkMultilingualSTS (fr)": 66.68,
- "STSBenchmarkMultilingualSTS (it)": 60.71,
- "STSBenchmarkMultilingualSTS (nl)": 60.03,
- "STSBenchmarkMultilingualSTS (pt)": 63.85,
- "STSBenchmarkMultilingualSTS (ru)": 56.09,
- "STSBenchmarkMultilingualSTS (de)": 63.28,
- "STSBenchmarkMultilingualSTS (pl)": 60.2
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- "Model": "all-MiniLM-L12-v2",
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- "STS22 (tr)": 21.6,
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- }
- ]
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- "Model": "all-MiniLM-L12-v2",
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- },
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- }
- ]
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- }
- ]
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- "Core17InstructionRetrieval": 1.69,
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- "Robust04InstructionRetrieval": -3.59
- }
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- "MTOPDomainClassification (th)": 15.66,
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- "MassiveScenarioClassification (ka)": 14.92,
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- "MultilingualSentiment": 41.28,
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- "NordicLangClassification": 54.7,
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- "MTOPIntentClassification (hi)": 45.54,
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- "MassiveIntentClassification (nl)": 58.29,
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- "MassiveIntentClassification (pt)": 58.63,
- "MassiveIntentClassification (ro)": 50.63,
- "MassiveIntentClassification (ru)": 57.96,
- "MassiveIntentClassification (sl)": 50.66,
- "MassiveIntentClassification (sq)": 50.25,
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- "MassiveIntentClassification (ta)": 3.79,
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- "MassiveIntentClassification (th)": 45.28,
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- "MassiveIntentClassification (ur)": 46.03,
- "MassiveIntentClassification (vi)": 45.25,
- "MassiveIntentClassification (zh-CN)": 59.22,
- "MassiveIntentClassification (zh-TW)": 54.96,
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- "MassiveScenarioClassification (ar)": 52.19,
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- "MassiveScenarioClassification (da)": 62.55,
- "MassiveScenarioClassification (de)": 61.4,
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- "MassiveScenarioClassification (en)": 74.0,
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- "MassiveScenarioClassification (fa)": 59.24,
- "MassiveScenarioClassification (fi)": 54.66,
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- "MassiveScenarioClassification (hi)": 55.99,
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- "MassiveScenarioClassification (id)": 65.25,
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- "MassiveScenarioClassification (ja)": 62.32,
- "MassiveScenarioClassification (jv)": 35.77,
- "MassiveScenarioClassification (ka)": 39.08,
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- "MassiveScenarioClassification (nb)": 54.98,
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- "MassiveScenarioClassification (pt)": 64.26,
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- "MassiveScenarioClassification (sl)": 59.36,
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- "MassiveScenarioClassification (sv)": 64.35,
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- "MassiveScenarioClassification (tl)": 36.09,
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- "MassiveScenarioClassification (vi)": 55.15,
- "MassiveScenarioClassification (zh-CN)": 66.44,
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- "PAC": 68.17,
- "PolEmo2.0-IN": 48.84,
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- "TweetSentimentExtractionClassification": 59.97
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- "MasakhaNEWSClusteringS2S (fra)": 32.76
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- "PSC": 96.35,
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- }
- ]
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- "map": [
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- "Model": "distiluse-base-multilingual-cased-v2",
- "AlloprofReranking": 51.77,
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- "StackOverflowDupQuestions": 41.92,
- "SyntecReranking": 74.78
- }
- ]
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- "ArguAna-PL": 36.7,
- "BSARDRetrieval": 0.0,
- "DBPedia-PL": 12.36,
- "FiQA-PL": 8.02,
- "HotpotQA-PL": 20.83,
- "MSMARCO-PL": 4.57,
- "MintakaRetrieval (fr)": 22.55,
- "NFCorpus-PL": 16.28,
- "NQ-PL": 5.85,
- "Quora-PL": 71.95,
- "SCIDOCS-PL": 6.5,
- "SciFact-PL": 33.03,
- "SyntecRetrieval": 65.34,
- "TRECCOVID-PL": 16.91,
- "XPQARetrieval (fr)": 51.2
- }
- ]
- },
- "STS": {
- "cosine_spearman": [
- {
- "Model": "distiluse-base-multilingual-cased-v2",
- "BIOSSES": 78.34,
- "CDSC-R": 87.67,
- "SICK-R": 75.25,
- "SICK-R-PL": 65.53,
- "SICKFr": 72.49,
- "STS12": 72.96,
- "STS13": 70.58,
- "STS14": 70.29,
- "STS15": 81.94,
- "STS16": 76.8,
- "STS17 (ar-ar)": 77.34,
- "STS17 (en-ar)": 77.46,
- "STS17 (en-de)": 80.24,
- "STS17 (en-en)": 86.19,
- "STS17 (en-tr)": 74.34,
- "STS17 (es-en)": 77.4,
- "STS17 (es-es)": 83.71,
- "STS17 (fr-en)": 79.28,
- "STS17 (it-en)": 80.82,
- "STS17 (ko-ko)": 76.4,
- "STS17 (nl-en)": 80.51,
- "STS22 (ar)": 49.04,
- "STS22 (de)": 35.73,
- "STS22 (de-en)": 47.51,
- "STS22 (de-fr)": 60.76,
- "STS22 (de-pl)": 36.09,
- "STS22 (en)": 62.88,
- "STS22 (es)": 59.34,
- "STS22 (es-en)": 68.96,
- "STS22 (es-it)": 63.28,
- "STS22 (fr)": 76.41,
- "STS22 (fr-pl)": 61.98,
- "STS22 (it)": 65.1,
- "STS22 (pl)": 34.58,
- "STS22 (pl-en)": 71.33,
- "STS22 (ru)": 52.4,
- "STS22 (tr)": 54.07,
- "STS22 (zh)": 54.32,
- "STS22 (zh-en)": 61.75,
- "STSBenchmark": 80.75,
- "STSBenchmarkMultilingualSTS (fr)": 77.49
- }
- ]
- },
- "Summarization": {
- "cosine_spearman": [
- {
- "Model": "distiluse-base-multilingual-cased-v2",
- "SummEvalFr": 28.12
- }
- ]
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
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- }
- },
- "sentence-transformers__gtr-t5-base": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": [
- {
- "Model": "gtr-t5-base",
- "AmazonCounterfactualClassification (en)": 69.33,
- "AmazonPolarityClassification": 67.82,
- "AmazonReviewsClassification (en)": 38.48,
- "Banking77Classification": 79.26,
- "EmotionClassification": 42.2,
- "ImdbClassification": 65.99,
- "MTOPDomainClassification (en)": 92.42,
- "MTOPIntentClassification (en)": 62.44,
- "MassiveIntentClassification (en)": 67.05,
- "MassiveScenarioClassification (en)": 75.4,
- "ToxicConversationsClassification": 66.6,
- "TweetSentimentExtractionClassification": 56.02
- }
- ]
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- "Clustering": {
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- {
- "Model": "gtr-t5-base",
- "ArxivClusteringP2P": 35.49,
- "ArxivClusteringS2S": 27.18,
- "BiorxivClusteringP2P": 27.66,
- "BiorxivClusteringS2S": 23.25,
- "MedrxivClusteringP2P": 27.57,
- "MedrxivClusteringS2S": 25.13,
- "RedditClustering": 56.13,
- "RedditClusteringP2P": 58.53,
- "StackExchangeClustering": 64.21,
- "StackExchangeClusteringP2P": 33.01,
- "TwentyNewsgroupsClustering": 46.72
- }
- ]
- },
- "PairClassification": {
- "max_ap": [
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- "Model": "gtr-t5-base",
- "SprintDuplicateQuestions": 94.55,
- "TwitterSemEval2015": 72.23,
- "TwitterURLCorpus": 84.77
- },
- {
- "Model": "gtr-t5-base",
- "SprintDuplicateQuestions": 94.55,
- "TwitterSemEval2015": 72.23,
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- }
- ]
- },
- "Reranking": {
- "map": [
- {
- "Model": "gtr-t5-base",
- "AskUbuntuDupQuestions": 60.86,
- "MindSmallReranking": 31.33,
- "SciDocsRR": 73.71,
- "StackOverflowDupQuestions": 51.01
- }
- ]
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- "Retrieval": {
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- "Model": "gtr-t5-base",
- "ArguAna": 50.83,
- "CQADupstackRetrieval": 34.55,
- "ClimateFEVER": 24.88,
- "DBPedia": 35.24,
- "FEVER": 68.93,
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- "NFCorpus": 30.22,
- "NQ": 50.47,
- "QuoraRetrieval": 87.98,
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- "STS": {
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- "AmazonReviewsClassification (de)": 33.06,
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- "Banking77Classification": 81.21,
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- "MTOPDomainClassification (fr)": 82.48,
- "MTOPDomainClassification (hi)": 22.11,
- "MTOPDomainClassification (th)": 16.36,
- "MTOPIntentClassification (de)": 52.13,
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- "MTOPIntentClassification (fr)": 46.39,
- "MTOPIntentClassification (hi)": 3.9,
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- "MassiveIntentClassification (af)": 41.02,
- "MassiveIntentClassification (am)": 2.34,
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- "STS17 (en-de)": 70.38,
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- "MintakaRetrieval (ar)": 12.61,
- "MintakaRetrieval (de)": 21.77,
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- "XPQARetrieval (fra-eng)": 43.08,
- "XPQARetrieval (hin-hin)": 52.09,
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- "XPQARetrieval (hin-eng)": 49.11,
- "XPQARetrieval (ita-ita)": 51.63,
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- "XPQARetrieval (ita-eng)": 46.53,
- "XPQARetrieval (jpn-jpn)": 51.57,
- "XPQARetrieval (eng-jpn)": 23.87,
- "XPQARetrieval (jpn-eng)": 44.93,
- "XPQARetrieval (kor-kor)": 21.36,
- "XPQARetrieval (eng-kor)": 21.51,
- "XPQARetrieval (kor-eng)": 22.59,
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- "XPQARetrieval (pol-eng)": 26.54,
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- "XPQARetrieval (tam-tam)": 6.37,
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- "XPQARetrieval (fr)": 42.51
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- ]
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- "STS17 (en-tr)": 76.74,
- "STS17 (en-de)": 84.22,
- "STS17 (fr-en)": 76.59,
- "STS17 (en-en)": 86.87,
- "STS17 (ar-ar)": 79.16,
- "STS17 (es-en)": 84.44,
- "STS22 (pl)": 33.74,
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- "STS22 (es-en)": 67.33,
- "STS22 (fr-pl)": 50.71,
- "STS22 (pl-en)": 69.02,
- "STS22 (de-fr)": 51.73,
- "STS22 (de)": 44.64,
- "STS22 (ar)": 46.2,
- "STS22 (de-pl)": 44.22,
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- "STS22 (it)": 55.22,
- "STS22 (tr)": 53.39,
- "STS22 (ru)": 57.08,
- "STS22 (zh)": 58.75,
- "STS22 (es-it)": 47.67,
- "STS22 (de-en)": 52.65,
- "STSB": 78.91,
- "STSBenchmark": 84.42,
- "STSBenchmarkMultilingualSTS (es)": 81.1,
- "STSBenchmarkMultilingualSTS (zh)": 80.47,
- "STSBenchmarkMultilingualSTS (en)": 84.42,
- "STSBenchmarkMultilingualSTS (de)": 78.87,
- "STSBenchmarkMultilingualSTS (nl)": 79.54,
- "STSBenchmarkMultilingualSTS (pl)": 78.29,
- "STSBenchmarkMultilingualSTS (fr)": 79.9,
- "STSBenchmarkMultilingualSTS (pt)": 80.16,
- "STSBenchmarkMultilingualSTS (it)": 80.39,
- "STSBenchmarkMultilingualSTS (ru)": 79.32
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- {
- "Model": "paraphrase-multilingual-MiniLM-L12-v2",
- "BIOSSES": 74.18,
- "CDSC-R": 88.98,
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- "STS17 (ar-ar)": 79.16,
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- "STS17 (en-de)": 84.22,
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- ]
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- ]
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- ]
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- "Tatoeba (aze-eng)": 76.36,
- "Tatoeba (bel-eng)": 79.94,
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- "Tatoeba (ber-eng)": 4.88,
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- "Tatoeba (cat-eng)": 96.05,
- "Tatoeba (cbk-eng)": 58.68,
- "Tatoeba (ceb-eng)": 7.39,
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- "Tatoeba (cha-eng)": 12.59,
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- "MintakaRetrieval (fr)": 24.45,
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- "FiQA2018": 43.55,
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- "MSMARCO": 23.96,
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- "TRECCOVID": 46.11,
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- }
- ]
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- "STS15": 88.28,
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- "STS17 (en-ar)": -4.71,
- "STS17 (en-de)": 73.62,
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- "STS17 (en-tr)": -0.42,
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- "STS22 (de)": 43.73,
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- "STS22 (de-fr)": 61.58,
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- ]
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- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- },
- "vprelovac__universal-sentence-encoder-multilingual-large-3": {
- "BitextMining": {
- "f1": []
- },
- "Classification": {
- "accuracy": [
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "AmazonReviewsClassification (fr)": 35.09,
- "MTOPDomainClassification (fr)": 88.19,
- "MTOPIntentClassification (fr)": 63.64,
- "MasakhaNEWSClassification (fra)": 72.04,
- "MassiveIntentClassification (fr)": 65.8,
- "MassiveScenarioClassification (fr)": 73.47
- }
- ]
- },
- "Clustering": {
- "v_measure": [
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "AlloProfClusteringP2P": 54.21,
- "AlloProfClusteringS2S": 37.95,
- "HALClusteringS2S": 18.94,
- "MLSUMClusteringP2P": 41.02,
- "MLSUMClusteringS2S": 37.97,
- "MasakhaNEWSClusteringP2P (fra)": 24.09,
- "MasakhaNEWSClusteringS2S (fra)": 40.24
- }
- ]
- },
- "PairClassification": {
- "max_ap": [
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "OpusparcusPC (fr)": 93.38,
- "PawsXPairClassification (fr)": 53.62
- },
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "OpusparcusPC (fr)": 93.38,
- "PawsXPairClassification (fr)": 53.66
- }
- ]
- },
- "Reranking": {
- "map": [
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "AlloprofReranking": 55.39,
- "SyntecReranking": 77.13
- }
- ]
- },
- "Retrieval": {
- "ndcg_at_10": [
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "AlloprofRetrieval": 33.78,
- "BSARDRetrieval": 0.0,
- "MintakaRetrieval (fr)": 26.21,
- "SyntecRetrieval": 63.69,
- "XPQARetrieval (fr)": 65.21
- }
- ]
- },
- "STS": {
- "cosine_spearman": [
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "SICKFr": 74.39,
- "STS22 (fr)": 71.11,
- "STSBenchmarkMultilingualSTS (fr)": 78.16
- }
- ]
- },
- "Summarization": {
- "cosine_spearman": [
- {
- "Model": "universal-sentence-encoder-multilingual-large-3",
- "SummEvalFr": 28.56
- }
- ]
- },
- "MultilabelClassification": {
- "accuracy": []
- },
- "InstructionRetrieval": {
- "p-MRR": []
- }
- }
-}
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..b0c71392e414d0905de9fbda3986cdfae836d0d8
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 6791872629847398b8dea5204ffeadca5ab983ed..94f5aa3fb0418102893259cac32c2a6f03f1446a 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
---
-title: MTEB Leaderboard
+title: Voyageai MTEB Leaderboard
emoji: 🥇
colorFrom: blue
colorTo: indigo
diff --git a/all_data_tasks/0/default.jsonl b/all_data_tasks/0/default.jsonl
deleted file mode 100644
index 10b873705d0debb9ec3722ab73548cfe89151621..0000000000000000000000000000000000000000
--- a/all_data_tasks/0/default.jsonl
+++ /dev/null
@@ -1,336 +0,0 @@
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diff --git a/all_data_tasks/1/default.jsonl b/all_data_tasks/1/default.jsonl
deleted file mode 100644
index ec7a43af3d6b8e6614c8a7b72a5ff0d215b8ff92..0000000000000000000000000000000000000000
--- a/all_data_tasks/1/default.jsonl
+++ /dev/null
@@ -1,330 +0,0 @@
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diff --git a/all_data_tasks/10/default.jsonl b/all_data_tasks/10/default.jsonl
deleted file mode 100644
index 40a2bc1174f3dcb37df56060fcc90b090375956f..0000000000000000000000000000000000000000
--- a/all_data_tasks/10/default.jsonl
+++ /dev/null
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diff --git a/all_data_tasks/11/default.jsonl b/all_data_tasks/11/default.jsonl
deleted file mode 100644
index b23b75eaef727266288f08db68d722b5c3428cc6..0000000000000000000000000000000000000000
--- a/all_data_tasks/11/default.jsonl
+++ /dev/null
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diff --git a/all_data_tasks/12/default.jsonl b/all_data_tasks/12/default.jsonl
deleted file mode 100644
index 3c1181b645cfdc8ea227ce032e12499a4c9ebf96..0000000000000000000000000000000000000000
--- a/all_data_tasks/12/default.jsonl
+++ /dev/null
@@ -1,318 +0,0 @@
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diff --git a/all_data_tasks/13/default.jsonl b/all_data_tasks/13/default.jsonl
deleted file mode 100644
index e760ab99a5227fe21e4ee0dfbcd883f2a4292771..0000000000000000000000000000000000000000
--- a/all_data_tasks/13/default.jsonl
+++ /dev/null
@@ -1,318 +0,0 @@
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diff --git a/all_data_tasks/14/default.jsonl b/all_data_tasks/14/default.jsonl
deleted file mode 100644
index 9191fa2d90c9ad3776c9bc8ff403ddb870da9291..0000000000000000000000000000000000000000
--- a/all_data_tasks/14/default.jsonl
+++ /dev/null
@@ -1,28 +0,0 @@
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diff --git a/all_data_tasks/15/default.jsonl b/all_data_tasks/15/default.jsonl
deleted file mode 100644
index 67cd6acade3e09c24b3a503b5902db96ec4fb31f..0000000000000000000000000000000000000000
--- a/all_data_tasks/15/default.jsonl
+++ /dev/null
@@ -1,56 +0,0 @@
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diff --git a/all_data_tasks/16/default.jsonl b/all_data_tasks/16/default.jsonl
deleted file mode 100644
index faf30db7dfc895e5f4d9fa911a96c5984d8de8ea..0000000000000000000000000000000000000000
--- a/all_data_tasks/16/default.jsonl
+++ /dev/null
@@ -1,104 +0,0 @@
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diff --git a/all_data_tasks/17/default.jsonl b/all_data_tasks/17/default.jsonl
deleted file mode 100644
index 047c1822f445012746a80881683d14d2db530229..0000000000000000000000000000000000000000
--- a/all_data_tasks/17/default.jsonl
+++ /dev/null
@@ -1,96 +0,0 @@
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-{"index":80,"Rank":45,"Model":"LaBSE<\/a>","Model Size (Million Parameters)":471,"Memory Usage (GB, fp32)":1.75,"Embedding Dimensions":768,"Max Tokens":512,"Average":38.32,"AlloProfClusteringP2P":54.78,"AlloProfClusteringS2S":31.6,"HALClusteringS2S":20.62,"MLSUMClusteringP2P (fr)":42.09,"MLSUMClusteringS2S (fr)":34.84,"MasakhaNEWSClusteringP2P (fra)":46.16,"MasakhaNEWSClusteringS2S (fra)":38.13}
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-{"index":67,"Rank":49,"Model":"udever-bloom-1b1<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1536,"Max Tokens":2048,"Average":35.04,"AlloProfClusteringP2P":62.22,"AlloProfClusteringS2S":27.06,"HALClusteringS2S":13.86,"MLSUMClusteringP2P (fr)":44.11,"MLSUMClusteringS2S (fr)":30.47,"MasakhaNEWSClusteringP2P (fra)":40.2,"MasakhaNEWSClusteringS2S (fra)":27.35}
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-{"index":6,"Rank":58,"Model":"LASER2<\/a>","Model Size (Million Parameters)":43,"Memory Usage (GB, fp32)":0.16,"Embedding Dimensions":1024,"Max Tokens":"N\/A","Average":29.92,"AlloProfClusteringP2P":48.45,"AlloProfClusteringS2S":25.81,"HALClusteringS2S":11.52,"MLSUMClusteringP2P (fr)":34.53,"MLSUMClusteringS2S (fr)":27.35,"MasakhaNEWSClusteringP2P (fra)":32.04,"MasakhaNEWSClusteringS2S (fra)":29.77}
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diff --git a/all_data_tasks/18/default.jsonl b/all_data_tasks/18/default.jsonl
deleted file mode 100644
index 78288e55da2d13e999f4318e2652809aa2c23e6a..0000000000000000000000000000000000000000
--- a/all_data_tasks/18/default.jsonl
+++ /dev/null
@@ -1,98 +0,0 @@
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-{"index":6,"Rank":8,"Model":"LASER2<\/a>","Model Size (Million Parameters)":43,"Memory Usage (GB, fp32)":0.16,"Embedding Dimensions":1024,"Max Tokens":"N\/A","Average":82.04,"OpusparcusPC (fr)":93.77,"PawsXPairClassification (fr)":70.31}
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-{"index":74,"Rank":10,"Model":"sentence_croissant_alpha_v0.2<\/a>","Model Size (Million Parameters)":1280,"Memory Usage (GB, fp32)":4.77,"Embedding Dimensions":2048,"Max Tokens":2048,"Average":80.23,"OpusparcusPC (fr)":93.48,"PawsXPairClassification (fr)":66.98}
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diff --git a/all_data_tasks/19/default.jsonl b/all_data_tasks/19/default.jsonl
deleted file mode 100644
index 09fa657895d1d97ed35fc940d1c0485eda5e9076..0000000000000000000000000000000000000000
--- a/all_data_tasks/19/default.jsonl
+++ /dev/null
@@ -1,96 +0,0 @@
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diff --git a/all_data_tasks/2/default.jsonl b/all_data_tasks/2/default.jsonl
deleted file mode 100644
index 837abce1c25a45e37d7ec761c0d831fda7fb5858..0000000000000000000000000000000000000000
--- a/all_data_tasks/2/default.jsonl
+++ /dev/null
@@ -1,336 +0,0 @@
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diff --git a/all_data_tasks/20/default.jsonl b/all_data_tasks/20/default.jsonl
deleted file mode 100644
index ab55260fa26cd4c295a016dd204a43d6dd6d458c..0000000000000000000000000000000000000000
--- a/all_data_tasks/20/default.jsonl
+++ /dev/null
@@ -1,97 +0,0 @@
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diff --git a/all_data_tasks/21/default.jsonl b/all_data_tasks/21/default.jsonl
deleted file mode 100644
index 13f24bf3a0eb773339b84e2632f94a641c6c5981..0000000000000000000000000000000000000000
--- a/all_data_tasks/21/default.jsonl
+++ /dev/null
@@ -1,102 +0,0 @@
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diff --git a/all_data_tasks/22/default.jsonl b/all_data_tasks/22/default.jsonl
deleted file mode 100644
index 8ba1924872f1c31ba759dc039482617e4ff8fabc..0000000000000000000000000000000000000000
--- a/all_data_tasks/22/default.jsonl
+++ /dev/null
@@ -1,96 +0,0 @@
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diff --git a/all_data_tasks/23/default.jsonl b/all_data_tasks/23/default.jsonl
deleted file mode 100644
index 1f5ea4327f95e6a06e392d2a9fb5923a00499556..0000000000000000000000000000000000000000
--- a/all_data_tasks/23/default.jsonl
+++ /dev/null
@@ -1,56 +0,0 @@
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diff --git a/all_data_tasks/24/default.jsonl b/all_data_tasks/24/default.jsonl
deleted file mode 100644
index 9289bf72179c99fd04d98f1cd405fcddfbc29e99..0000000000000000000000000000000000000000
--- a/all_data_tasks/24/default.jsonl
+++ /dev/null
@@ -1,31 +0,0 @@
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diff --git a/all_data_tasks/25/default.jsonl b/all_data_tasks/25/default.jsonl
deleted file mode 100644
index bda1c23ca8d89a7e290f8fda76516a73607b0450..0000000000000000000000000000000000000000
--- a/all_data_tasks/25/default.jsonl
+++ /dev/null
@@ -1,22 +0,0 @@
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-{"index":19,"Rank":3,"Model":"sentence-t5-xxl<\/a>","Model Size (Million Parameters)":4865,"Memory Usage (GB, fp32)":18.12,"Embedding Dimensions":768,"Max Tokens":512,"Average":29.74,"BlurbsClusteringP2P":39.91,"BlurbsClusteringS2S":15.94,"TenKGnadClusteringP2P":43.43,"TenKGnadClusteringS2S":19.69}
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-{"index":3,"Rank":22,"Model":"SGPT-125M-weightedmean-nli-bitfit<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":2048,"Average":"","BlurbsClusteringP2P":"","BlurbsClusteringS2S":8.0,"TenKGnadClusteringP2P":"","TenKGnadClusteringS2S":""}
diff --git a/all_data_tasks/26/default.jsonl b/all_data_tasks/26/default.jsonl
deleted file mode 100644
index e00b36217453ece7422d1863aa2c5f0d99af51fc..0000000000000000000000000000000000000000
--- a/all_data_tasks/26/default.jsonl
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/all_data_tasks/27/default.jsonl b/all_data_tasks/27/default.jsonl
deleted file mode 100644
index e1d5355896f4ba8630769a19c6fefacee15706df..0000000000000000000000000000000000000000
--- a/all_data_tasks/27/default.jsonl
+++ /dev/null
@@ -1,61 +0,0 @@
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diff --git a/all_data_tasks/28/default.jsonl b/all_data_tasks/28/default.jsonl
deleted file mode 100644
index a163258bdcfe444266e4bf55141b1f7d919ab76e..0000000000000000000000000000000000000000
--- a/all_data_tasks/28/default.jsonl
+++ /dev/null
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diff --git a/all_data_tasks/29/default.jsonl b/all_data_tasks/29/default.jsonl
deleted file mode 100644
index 56f5e9342c52c36c3db4f38d1c610f3e60a2eabf..0000000000000000000000000000000000000000
--- a/all_data_tasks/29/default.jsonl
+++ /dev/null
@@ -1,60 +0,0 @@
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diff --git a/all_data_tasks/3/default.jsonl b/all_data_tasks/3/default.jsonl
deleted file mode 100644
index 44a11c4bd1bd006f29b79358f5163a3b7cc4ad96..0000000000000000000000000000000000000000
--- a/all_data_tasks/3/default.jsonl
+++ /dev/null
@@ -1,329 +0,0 @@
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diff --git a/all_data_tasks/30/default.jsonl b/all_data_tasks/30/default.jsonl
deleted file mode 100644
index 31f3fb2ef52c8f34a5d46d017b853ae86ad1cd61..0000000000000000000000000000000000000000
--- a/all_data_tasks/30/default.jsonl
+++ /dev/null
@@ -1,70 +0,0 @@
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diff --git a/all_data_tasks/31/default.jsonl b/all_data_tasks/31/default.jsonl
deleted file mode 100644
index 039801c2ea255d438181a08cf60cd922576341c3..0000000000000000000000000000000000000000
--- a/all_data_tasks/31/default.jsonl
+++ /dev/null
@@ -1,64 +0,0 @@
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-{"index":64,"Rank":65,"Model":"llm_mse<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","GeoreviewClassification":"","HeadlineClassification":"","InappropriatenessClassification":"","KinopoiskClassification":"","RuReviewsClassification":"","RuSciBenchGRNTIClassification":"","RuSciBenchOECDClassification":"","MassiveIntentClassification (ru)":2.65,"MassiveScenarioClassification (ru)":8.14}
diff --git a/all_data_tasks/32/default.jsonl b/all_data_tasks/32/default.jsonl
deleted file mode 100644
index bb41e502201eaed78d8e4ea5762b96778222bb48..0000000000000000000000000000000000000000
--- a/all_data_tasks/32/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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-{"index":61,"Rank":12,"Model":"LaBSE-ru-turbo<\/a>","Model Size (Million Parameters)":128,"Memory Usage (GB, fp32)":0.48,"Embedding Dimensions":768,"Max Tokens":512,"Average":53.22,"GeoreviewClusteringP2P":64.55,"RuSciBenchGRNTIClusteringP2P":50.64,"RuSciBenchOECDClusteringP2P":44.48}
-{"index":4,"Rank":13,"Model":"bge-m3<\/a>","Model Size (Million Parameters)":567,"Memory Usage (GB, fp32)":2.11,"Embedding Dimensions":1024,"Max Tokens":8192,"Average":52.38,"GeoreviewClusteringP2P":63.09,"RuSciBenchGRNTIClusteringP2P":50.83,"RuSciBenchOECDClusteringP2P":43.21}
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-{"index":21,"Rank":17,"Model":"sbert_large_mt_nlu_ru<\/a>","Model Size (Million Parameters)":427,"Memory Usage (GB, fp32)":1.59,"Embedding Dimensions":1024,"Max Tokens":514,"Average":51.29,"GeoreviewClusteringP2P":57.07,"RuSciBenchGRNTIClusteringP2P":51.44,"RuSciBenchOECDClusteringP2P":45.36}
-{"index":22,"Rank":18,"Model":"sbert_large_nlu_ru<\/a>","Model Size (Million Parameters)":427,"Memory Usage (GB, fp32)":1.59,"Embedding Dimensions":1024,"Max Tokens":512,"Average":50.43,"GeoreviewClusteringP2P":57.12,"RuSciBenchGRNTIClusteringP2P":49.7,"RuSciBenchOECDClusteringP2P":44.48}
-{"index":35,"Rank":19,"Model":"multilingual-e5-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":50.27,"GeoreviewClusteringP2P":54.46,"RuSciBenchGRNTIClusteringP2P":51.56,"RuSciBenchOECDClusteringP2P":44.79}
-{"index":62,"Rank":20,"Model":"rubert-tiny-turbo<\/a>","Model Size (Million Parameters)":29,"Memory Usage (GB, fp32)":0.11,"Embedding Dimensions":312,"Max Tokens":2048,"Average":49.57,"GeoreviewClusteringP2P":59.71,"RuSciBenchGRNTIClusteringP2P":47.55,"RuSciBenchOECDClusteringP2P":41.44}
-{"index":57,"Rank":21,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":49.18,"GeoreviewClusteringP2P":56.18,"RuSciBenchGRNTIClusteringP2P":48.47,"RuSciBenchOECDClusteringP2P":42.9}
-{"index":56,"Rank":22,"Model":"paraphrase-multilingual-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":47.76,"GeoreviewClusteringP2P":53.37,"RuSciBenchGRNTIClusteringP2P":48.22,"RuSciBenchOECDClusteringP2P":41.68}
-{"index":47,"Rank":23,"Model":"LaBSE<\/a>","Model Size (Million Parameters)":471,"Memory Usage (GB, fp32)":1.75,"Embedding Dimensions":768,"Max Tokens":512,"Average":47.74,"GeoreviewClusteringP2P":52.19,"RuSciBenchGRNTIClusteringP2P":49.07,"RuSciBenchOECDClusteringP2P":41.97}
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-{"index":32,"Rank":26,"Model":"deberta-v1-base<\/a>","Model Size (Million Parameters)":124,"Memory Usage (GB, fp32)":0.46,"Embedding Dimensions":768,"Max Tokens":512,"Average":42.92,"GeoreviewClusteringP2P":58.79,"RuSciBenchGRNTIClusteringP2P":36.66,"RuSciBenchOECDClusteringP2P":33.31}
-{"index":29,"Rank":27,"Model":"rubert-tiny2<\/a>","Model Size (Million Parameters)":29,"Memory Usage (GB, fp32)":0.11,"Embedding Dimensions":2048,"Max Tokens":514,"Average":39.11,"GeoreviewClusteringP2P":41.58,"RuSciBenchGRNTIClusteringP2P":39.78,"RuSciBenchOECDClusteringP2P":35.98}
-{"index":9,"Rank":28,"Model":"distilrubert-small-cased-conversational<\/a>","Model Size (Million Parameters)":106,"Memory Usage (GB, fp32)":0.39,"Embedding Dimensions":768,"Max Tokens":512,"Average":38.41,"GeoreviewClusteringP2P":43.26,"RuSciBenchGRNTIClusteringP2P":37.84,"RuSciBenchOECDClusteringP2P":34.12}
-{"index":28,"Rank":29,"Model":"rubert-tiny<\/a>","Model Size (Million Parameters)":12,"Memory Usage (GB, fp32)":0.04,"Embedding Dimensions":512,"Max Tokens":514,"Average":30.76,"GeoreviewClusteringP2P":34.4,"RuSciBenchGRNTIClusteringP2P":29.89,"RuSciBenchOECDClusteringP2P":27.98}
-{"index":10,"Rank":30,"Model":"rubert-base-cased<\/a>","Model Size (Million Parameters)":180,"Memory Usage (GB, fp32)":0.67,"Embedding Dimensions":768,"Max Tokens":512,"Average":27.91,"GeoreviewClusteringP2P":28.77,"RuSciBenchGRNTIClusteringP2P":28.29,"RuSciBenchOECDClusteringP2P":26.67}
-{"index":50,"Rank":31,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":15.83,"GeoreviewClusteringP2P":20.33,"RuSciBenchGRNTIClusteringP2P":14.66,"RuSciBenchOECDClusteringP2P":12.49}
-{"index":48,"Rank":32,"Model":"all-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":13.87,"GeoreviewClusteringP2P":20.76,"RuSciBenchGRNTIClusteringP2P":10.65,"RuSciBenchOECDClusteringP2P":10.19}
-{"index":49,"Rank":33,"Model":"all-MiniLM-L6-v2<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Embedding Dimensions":384,"Max Tokens":512,"Average":13.3,"GeoreviewClusteringP2P":20.25,"RuSciBenchGRNTIClusteringP2P":10.21,"RuSciBenchOECDClusteringP2P":9.44}
-{"index":0,"Rank":34,"Model":"voyage-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":32000,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":1,"Rank":35,"Model":"LASER2<\/a>","Model Size (Million Parameters)":43,"Memory Usage (GB, fp32)":0.16,"Embedding Dimensions":1024,"Max Tokens":"N\/A","Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":2,"Rank":36,"Model":"gte-multilingual-base<\/a>","Model Size (Million Parameters)":305,"Memory Usage (GB, fp32)":1.14,"Embedding Dimensions":768,"Max Tokens":8192,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
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-{"index":16,"Rank":42,"Model":"MUG-B-1.6<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":18,"Rank":44,"Model":"SGPT-125M-weightedmean-msmarco-specb-bitfit<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":2048,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":19,"Rank":45,"Model":"SGPT-125M-weightedmean-nli-bitfit<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":2048,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":25,"Rank":47,"Model":"sgpt-bloom-7b1-msmarco<\/a>","Model Size (Million Parameters)":7068,"Memory Usage (GB, fp32)":26.33,"Embedding Dimensions":4096,"Max Tokens":2048,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":26,"Rank":48,"Model":"slx-v0.1<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.08,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":39,"Rank":50,"Model":"udever-bloom-1b1<\/a>","Model Size (Million Parameters)":1065,"Memory Usage (GB, fp32)":3.97,"Embedding Dimensions":1536,"Max Tokens":2048,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
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-{"index":43,"Rank":52,"Model":"ternary-weight-embedding<\/a>","Model Size (Million Parameters)":99,"Memory Usage (GB, fp32)":0.37,"Embedding Dimensions":1792,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":44,"Rank":53,"Model":"FAB-Ramy-v1<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":45,"Rank":54,"Model":"FAE-v1<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":46,"Rank":55,"Model":"FUE-v1<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":51,"Rank":56,"Model":"allenai-specter<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":52,"Rank":57,"Model":"average_word_embeddings_komninos<\/a>","Model Size (Million Parameters)":134,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":300,"Max Tokens":"N\/A","Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":53,"Rank":58,"Model":"distiluse-base-multilingual-cased-v2<\/a>","Model Size (Million Parameters)":135,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":512,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":54,"Rank":59,"Model":"gtr-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":55,"Rank":60,"Model":"gtr-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":58,"Rank":61,"Model":"sentence-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":59,"Rank":62,"Model":"sentence-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":60,"Rank":63,"Model":"sentence-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
-{"index":63,"Rank":64,"Model":"text2vec-base-multilingual<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","GeoreviewClusteringP2P":"","RuSciBenchGRNTIClusteringP2P":"","RuSciBenchOECDClusteringP2P":""}
diff --git a/all_data_tasks/33/default.jsonl b/all_data_tasks/33/default.jsonl
deleted file mode 100644
index 66636ef0954835f9a8f7afc02a854f2fe7580fac..0000000000000000000000000000000000000000
--- a/all_data_tasks/33/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
-{"index":31,"Rank":1,"Model":"USER-bge-m3<\/a>","Model Size (Million Parameters)":359,"Memory Usage (GB, fp32)":1.34,"Embedding Dimensions":1024,"Max Tokens":8192,"TERRa":65.07}
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-{"index":37,"Rank":3,"Model":"multilingual-e5-large-instruct<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"TERRa":63.89}
-{"index":34,"Rank":4,"Model":"e5-mistral-7b-instruct<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"TERRa":60.81}
-{"index":20,"Rank":5,"Model":"ru-en-RoSBERTa<\/a>","Model Size (Million Parameters)":404,"Memory Usage (GB, fp32)":1.5,"Embedding Dimensions":1024,"Max Tokens":514,"TERRa":60.79}
-{"index":4,"Rank":6,"Model":"bge-m3<\/a>","Model Size (Million Parameters)":567,"Memory Usage (GB, fp32)":2.11,"Embedding Dimensions":1024,"Max Tokens":8192,"TERRa":60.6}
-{"index":30,"Rank":7,"Model":"USER-base<\/a>","Model Size (Million Parameters)":124,"Memory Usage (GB, fp32)":0.46,"Embedding Dimensions":768,"Max Tokens":512,"TERRa":60.11}
-{"index":23,"Rank":8,"Model":"Giga-Embeddings-instruct<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","TERRa":59.98}
-{"index":11,"Rank":9,"Model":"rubert-base-cased-sentence<\/a>","Model Size (Million Parameters)":180,"Memory Usage (GB, fp32)":0.67,"Embedding Dimensions":768,"Max Tokens":512,"TERRa":59.12}
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diff --git a/all_data_tasks/34/default.jsonl b/all_data_tasks/34/default.jsonl
deleted file mode 100644
index 2a7fd4ae559765da31713cf79dd0d489b07f2b98..0000000000000000000000000000000000000000
--- a/all_data_tasks/34/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/all_data_tasks/35/default.jsonl b/all_data_tasks/35/default.jsonl
deleted file mode 100644
index 2bf2cef00bc5e17a119ff624995b2b8f52578476..0000000000000000000000000000000000000000
--- a/all_data_tasks/35/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/all_data_tasks/36/default.jsonl b/all_data_tasks/36/default.jsonl
deleted file mode 100644
index 0ac0d1bf95084399112b3af119c6e70a6db54ac5..0000000000000000000000000000000000000000
--- a/all_data_tasks/36/default.jsonl
+++ /dev/null
@@ -1,61 +0,0 @@
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diff --git a/all_data_tasks/37/default.jsonl b/all_data_tasks/37/default.jsonl
deleted file mode 100644
index a4201380173934f3abacd77cbec438f39d1b61bd..0000000000000000000000000000000000000000
--- a/all_data_tasks/37/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/all_data_tasks/38/default.jsonl b/all_data_tasks/38/default.jsonl
deleted file mode 100644
index 1f5ea4327f95e6a06e392d2a9fb5923a00499556..0000000000000000000000000000000000000000
--- a/all_data_tasks/38/default.jsonl
+++ /dev/null
@@ -1,56 +0,0 @@
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diff --git a/all_data_tasks/39/default.jsonl b/all_data_tasks/39/default.jsonl
deleted file mode 100644
index 05ffedd2e4a44e873d7555f875ad4d35af71786a..0000000000000000000000000000000000000000
--- a/all_data_tasks/39/default.jsonl
+++ /dev/null
@@ -1,174 +0,0 @@
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diff --git a/all_data_tasks/4/default.jsonl b/all_data_tasks/4/default.jsonl
deleted file mode 100644
index 51b688463abef61649bebd58447344e99745b0d2..0000000000000000000000000000000000000000
--- a/all_data_tasks/4/default.jsonl
+++ /dev/null
@@ -1,330 +0,0 @@
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diff --git a/all_data_tasks/40/default.jsonl b/all_data_tasks/40/default.jsonl
deleted file mode 100644
index 0358de0e3c46a10dbec8122012c023bf19d01182..0000000000000000000000000000000000000000
--- a/all_data_tasks/40/default.jsonl
+++ /dev/null
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-{"index":17,"Rank":54,"Model":"gte-Qwen2-1.5B-instruct<\/a>","Model Size (Million Parameters)":1776,"Memory Usage (GB, fp32)":6.62,"Embedding Dimensions":1536,"Max Tokens":131072,"Average":"","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)":81.84,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":40.83,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":86.38}
-{"index":18,"Rank":55,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Embedding Dimensions":3584,"Max Tokens":131072,"Average":"","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)":82.58,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":41.02,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":86.85}
-{"index":19,"Rank":56,"Model":"gte-base-en-v1.5<\/a>","Model Size (Million Parameters)":137,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":8192,"Average":"","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":86.04}
-{"index":20,"Rank":57,"Model":"gte-large-en-v1.5<\/a>","Model Size (Million Parameters)":434,"Memory Usage (GB, fp32)":1.62,"Embedding Dimensions":1024,"Max Tokens":8192,"Average":"","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":83.07}
-{"index":23,"Rank":58,"Model":"jina-embeddings-v2-base-es-Q2_K-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","STS17 (ar-ar)":"","STS17 (en-ar)":"","STS17 (en-de)":"","STS17 (en-tr)":"","STS17 (es-en)":86.49,"STS17 (es-es)":88.25,"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)":67.97,"STS22 (es-en)":78.8,"STS22 (es-it)":"","STS22 (fr)":"","STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":87.84}
-{"index":24,"Rank":59,"Model":"jina-embeddings-v2-base-es-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","STS17 (ar-ar)":"","STS17 (en-ar)":"","STS17 (en-de)":"","STS17 (en-tr)":"","STS17 (es-en)":86.49,"STS17 (es-es)":88.25,"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)":67.97,"STS22 (es-en)":78.8,"STS22 (es-it)":"","STS22 (fr)":"","STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":87.84}
-{"index":25,"Rank":60,"Model":"bge-base-en-v1.5<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","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":86.42}
-{"index":26,"Rank":61,"Model":"bge-en-icl<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":88.42}
-{"index":27,"Rank":62,"Model":"bge-large-en-v1.5<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","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":87.52}
-{"index":28,"Rank":63,"Model":"bge-m3<\/a>","Model Size (Million Parameters)":567,"Memory Usage (GB, fp32)":2.11,"Embedding Dimensions":1024,"Max Tokens":8192,"Average":"","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)":66.26,"STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":""}
-{"index":29,"Rank":64,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Embedding Dimensions":3584,"Max Tokens":8192,"Average":"","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)":83.28,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":42.79,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":87.25}
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-{"index":31,"Rank":66,"Model":"nomic-embed-text-v1.5-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":85.47}
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-{"index":46,"Rank":80,"Model":"gte-Qwen2-7B-instruct-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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)":82.58,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":41.02,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":86.85}
-{"index":47,"Rank":81,"Model":"STS-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","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":0.98}
-{"index":48,"Rank":82,"Model":"bert-base-10lang-cased<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","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)":40.31,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":""}
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-{"index":52,"Rank":86,"Model":"GritLM-8x7B<\/a>","Model Size (Million Parameters)":46703,"Memory Usage (GB, fp32)":173.98,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":87.43}
-{"index":53,"Rank":87,"Model":"KaLM-embedding-multilingual-max-instruct-v1<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","STS17 (ar-ar)":"","STS17 (en-ar)":75.04,"STS17 (en-de)":79.91,"STS17 (en-tr)":69.31,"STS17 (es-en)":81.5,"STS17 (es-es)":"","STS17 (fr-en)":80.85,"STS17 (it-en)":80.92,"STS17 (ko-ko)":"","STS17 (nl-en)":80.74,"STS22 (ar)":"","STS22 (de)":"","STS22 (de-en)":68.71,"STS22 (de-fr)":"","STS22 (de-pl)":60.26,"STS22 (es)":"","STS22 (es-en)":83.41,"STS22 (es-it)":"","STS22 (fr)":"","STS22 (fr-pl)":84.52,"STS22 (it)":"","STS22 (pl)":50.41,"STS22 (pl-en)":83.59,"STS22 (ru)":70.51,"STS22 (tr)":"","STS22 (zh-en)":75.38,"STSBenchmark":84.52}
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-{"index":98,"Rank":128,"Model":"Arabic-labse-Matryoshka<\/a>","Model Size (Million Parameters)":471,"Memory Usage (GB, fp32)":1.75,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","STS17 (ar-ar)":82.47,"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)":57.26,"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":82.05}
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-{"index":100,"Rank":130,"Model":"GATE-AraBert-v1<\/a>","Model Size (Million Parameters)":135,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","STS17 (ar-ar)":82.78,"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)":59.75,"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":""}
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-{"index":102,"Rank":132,"Model":"Solon-embeddings-large-0.1<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","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)":83.31,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":""}
-{"index":103,"Rank":133,"Model":"KartonBERT-USE-base-v1<\/a>","Model Size (Million Parameters)":104,"Memory Usage (GB, fp32)":0.39,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","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)":41.82,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":""}
-{"index":104,"Rank":134,"Model":"st-polish-kartonberta-base-alpha-v1<\/a>","Model Size (Million Parameters)":124,"Memory Usage (GB, fp32)":0.46,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","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)":36.78,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":""}
-{"index":105,"Rank":135,"Model":"nomic-embed-text-v1.5-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":85.47}
-{"index":106,"Rank":136,"Model":"bge_m3e_stella<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":61.83}
-{"index":107,"Rank":137,"Model":"SFR-Embedding-2_R<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":83.6}
-{"index":108,"Rank":138,"Model":"SFR-Embedding-Mistral<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":89.0}
-{"index":109,"Rank":139,"Model":"ember-v1-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":87.77}
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-{"index":125,"Rank":155,"Model":"German_Semantic_STS_V2<\/a>","Model Size (Million Parameters)":336,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","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":""}
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-{"index":133,"Rank":162,"Model":"bge-small-4096<\/a>","Model Size (Million Parameters)":35,"Memory Usage (GB, fp32)":0.13,"Embedding Dimensions":384,"Max Tokens":4096,"Average":"","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":81.94}
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-{"index":307,"Rank":308,"Model":"universal-sentence-encoder-large-5<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":79.93}
-{"index":308,"Rank":309,"Model":"universal-sentence-encoder-multilingual-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":512,"Max Tokens":512,"Average":"","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)":77.91,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":80.28}
-{"index":309,"Rank":310,"Model":"universal-sentence-encoder-multilingual-large-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":512,"Max Tokens":512,"Average":"","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)":71.11,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":81.24}
-{"index":310,"Rank":311,"Model":"b1ade-embed<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","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":88.77}
-{"index":311,"Rank":312,"Model":"b1ade-embed-kd<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","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":82.69}
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diff --git a/all_data_tasks/41/default.jsonl b/all_data_tasks/41/default.jsonl
deleted file mode 100644
index 78cecbf2fc63e3b1baca0042ef9fd40f8f8d5282..0000000000000000000000000000000000000000
--- a/all_data_tasks/41/default.jsonl
+++ /dev/null
@@ -1,21 +0,0 @@
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diff --git a/all_data_tasks/42/default.jsonl b/all_data_tasks/42/default.jsonl
deleted file mode 100644
index ddaba966966caa96fea538b1964922d2e6bb0843..0000000000000000000000000000000000000000
--- a/all_data_tasks/42/default.jsonl
+++ /dev/null
@@ -1,23 +0,0 @@
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diff --git a/all_data_tasks/43/default.jsonl b/all_data_tasks/43/default.jsonl
deleted file mode 100644
index a378ce9dcf50f327d80167ec41c981306c2edb0b..0000000000000000000000000000000000000000
--- a/all_data_tasks/43/default.jsonl
+++ /dev/null
@@ -1,37 +0,0 @@
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diff --git a/all_data_tasks/44/default.jsonl b/all_data_tasks/44/default.jsonl
deleted file mode 100644
index 0ce61322ddf399a1899ccddb95414a90d4177c0f..0000000000000000000000000000000000000000
--- a/all_data_tasks/44/default.jsonl
+++ /dev/null
@@ -1,14 +0,0 @@
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diff --git a/all_data_tasks/45/default.jsonl b/all_data_tasks/45/default.jsonl
deleted file mode 100644
index f6a6a211e9b2b1e248f807b20bfab07a3264668d..0000000000000000000000000000000000000000
--- a/all_data_tasks/45/default.jsonl
+++ /dev/null
@@ -1,14 +0,0 @@
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diff --git a/all_data_tasks/46/default.jsonl b/all_data_tasks/46/default.jsonl
deleted file mode 100644
index cd67480c2bcb8767bfb51db38ac9ca7bd43dbeb7..0000000000000000000000000000000000000000
--- a/all_data_tasks/46/default.jsonl
+++ /dev/null
@@ -1,13 +0,0 @@
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diff --git a/all_data_tasks/5/default.jsonl b/all_data_tasks/5/default.jsonl
deleted file mode 100644
index 51ad20626ee09c666b36cfff734333efec72df6c..0000000000000000000000000000000000000000
--- a/all_data_tasks/5/default.jsonl
+++ /dev/null
@@ -1,335 +0,0 @@
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diff --git a/all_data_tasks/6/default.jsonl b/all_data_tasks/6/default.jsonl
deleted file mode 100644
index d85b4112f84f1ba4fec7dedada1e78fa28346e7f..0000000000000000000000000000000000000000
--- a/all_data_tasks/6/default.jsonl
+++ /dev/null
@@ -1,322 +0,0 @@
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diff --git a/all_data_tasks/7/default.jsonl b/all_data_tasks/7/default.jsonl
deleted file mode 100644
index 29dbf97c6944fe1ed951b86f418a833e714b836a..0000000000000000000000000000000000000000
--- a/all_data_tasks/7/default.jsonl
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@@ -1,52 +0,0 @@
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diff --git a/all_data_tasks/8/default.jsonl b/all_data_tasks/8/default.jsonl
deleted file mode 100644
index 74cb5b242a8497f100adaff976903ba765c47a90..0000000000000000000000000000000000000000
--- a/all_data_tasks/8/default.jsonl
+++ /dev/null
@@ -1,318 +0,0 @@
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diff --git a/all_data_tasks/9/default.jsonl b/all_data_tasks/9/default.jsonl
deleted file mode 100644
index 028f26390cf96877282a885ebb67b877d1145a52..0000000000000000000000000000000000000000
--- a/all_data_tasks/9/default.jsonl
+++ /dev/null
@@ -1,314 +0,0 @@
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-{"index":318,"Rank":331,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":319,"Rank":332,"Model":"allenai-specter<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":320,"Rank":333,"Model":"average_word_embeddings_glove.6B.300d<\/a>","Model Size (Million Parameters)":120,"Memory Usage (GB, fp32)":0.45,"Embedding Dimensions":300,"Max Tokens":"N\/A","Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":321,"Rank":334,"Model":"average_word_embeddings_komninos<\/a>","Model Size (Million Parameters)":134,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":300,"Max Tokens":"N\/A","Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":322,"Rank":335,"Model":"distiluse-base-multilingual-cased-v2<\/a>","Model Size (Million Parameters)":135,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":512,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":323,"Rank":336,"Model":"gtr-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":324,"Rank":337,"Model":"gtr-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":325,"Rank":338,"Model":"gtr-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":326,"Rank":339,"Model":"gtr-t5-xxl<\/a>","Model Size (Million Parameters)":4865,"Memory Usage (GB, fp32)":18.12,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":327,"Rank":340,"Model":"msmarco-bert-co-condensor<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":328,"Rank":341,"Model":"multi-qa-MiniLM-L6-cos-v1<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":329,"Rank":342,"Model":"paraphrase-multilingual-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":330,"Rank":343,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":331,"Rank":344,"Model":"sentence-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":332,"Rank":345,"Model":"sentence-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":333,"Rank":346,"Model":"sentence-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":334,"Rank":347,"Model":"sentence-t5-xxl<\/a>","Model Size (Million Parameters)":4865,"Memory Usage (GB, fp32)":18.12,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":336,"Rank":348,"Model":"LaBSE-ru-turbo<\/a>","Model Size (Million Parameters)":128,"Memory Usage (GB, fp32)":0.48,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":337,"Rank":349,"Model":"rubert-tiny-turbo<\/a>","Model Size (Million Parameters)":29,"Memory Usage (GB, fp32)":0.11,"Embedding Dimensions":312,"Max Tokens":2048,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":340,"Rank":350,"Model":"text2vec-base-multilingual<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":256,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":343,"Rank":352,"Model":"BAAI-bge-reranker-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":344,"Rank":353,"Model":"bge-en-icl<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":349,"Rank":357,"Model":"gte-base<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":351,"Rank":358,"Model":"gte-large<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":353,"Rank":359,"Model":"gte-small<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":362,"Rank":365,"Model":"universal-sentence-encoder-multilingual-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":512,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":363,"Rank":366,"Model":"universal-sentence-encoder-multilingual-large-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":512,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":364,"Rank":367,"Model":"b1ade-embed<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":365,"Rank":368,"Model":"b1ade-embed-kd<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":366,"Rank":369,"Model":"b1ade-embed-kd_3<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":369,"Rank":372,"Model":"hktv-fine-tuned-cloudy-large-zh-metaphor14<\/a>","Model Size (Million Parameters)":326,"Memory Usage (GB, fp32)":1.21,"Embedding Dimensions":1024,"Max Tokens":1024,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":371,"Rank":374,"Model":"bilingual-embedding-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":376,"Rank":379,"Model":"Zeta-Alpha-E5-Mistral<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":377,"Rank":380,"Model":"bi-cse<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":379,"Rank":382,"Model":"text-similarity-ada-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":380,"Rank":383,"Model":"text-similarity-babbage-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":2048,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":381,"Rank":384,"Model":"text-similarity-curie-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":4096,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":382,"Rank":385,"Model":"text-similarity-davinci-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":12288,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":384,"Rank":386,"Model":"text-embedding-3-large-256<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":256,"Max Tokens":8191,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":385,"Rank":387,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":3072,"Max Tokens":8191,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":386,"Rank":388,"Model":"text-embedding-3-small<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1536,"Max Tokens":8191,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
-{"index":388,"Rank":389,"Model":"elser-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":30522,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""}
diff --git a/app.py b/app.py
index 17d4d2e0dae15360b97d28cf41efdac923cd8acc..b986e3130ceda886213410bdc1d1721bcc3c9292 100644
--- a/app.py
+++ b/app.py
@@ -1,417 +1,25 @@
-from functools import reduce
-import re
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 16:59
+# @Author : q275343119
+# @File : app.py
+# @Description: Main entry point for the application
+from app.ui.home import init_home
+from app.ui.about import init_about
import gradio as gr
-import pandas as pd
-from envs import REPO_ID
-from refresh import BOARDS_CONFIG, TASKS, TASKS_CONFIG, TASK_DESCRIPTIONS, PRETTY_NAMES, load_results, make_clickable_model
-from refresh import PROPRIETARY_MODELS, SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS, CROSS_ENCODERS, BI_ENCODERS, INSTRUCT_MODELS, NOINSTRUCT_MODELS, EXTERNAL_MODEL_TO_LINK
+def main():
+ home = init_home()
+ about = init_about()
-PROPRIETARY_MODELS = {
- make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
- for model in PROPRIETARY_MODELS
-}
-SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
- make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
- for model in SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS
-}
-INSTRUCT_MODELS = {
- make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
- for model in INSTRUCT_MODELS
-}
-NOINSTRUCT_MODELS = {
- make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
- for model in NOINSTRUCT_MODELS
-}
-CROSS_ENCODERS = {
- make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
- for model in CROSS_ENCODERS
-}
-BI_ENCODERS = {
- make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
- for model in BI_ENCODERS
-}
+ demo = gr.TabbedInterface(
+ [home, about],
+ ["Home", "About"],
+ css="footer {display: none} .sort-button.svelte-1bvc1p0.svelte-1bvc1p0.svelte-1bvc1p0 {display: none;}"
+ )
+ demo.launch()
-def make_datasets_clickable(df):
- """Does not work"""
- if "BornholmBitextMining" in df.columns:
- link = "https://huggingface.co/datasets/strombergnlp/bornholmsk_parallel"
- df = df.rename(
- columns={f'BornholmBitextMining': 'BornholmBitextMining',})
- return df
-
-
-# 1. Force headers to wrap
-# 2. Force model column (maximum) width
-# 3. Prevent model column from overflowing, scroll instead
-# 4. Prevent checkbox groups from taking up too much space
-css = """
-table > thead {
- white-space: normal
-}
-
-table {
- --cell-width-1: 250px
-}
-
-table > tbody > tr > td:nth-child(2) > div {
- overflow-x: auto
-}
-
-.filter-checkbox-group {
- max-width: max-content;
-}
-"""
-
-"""
-Each inner tab can have the following keys:
-- language: The language of the leaderboard
-- language_long: [optional] The long form of the language
-- description: The description of the leaderboard
-- credits: [optional] The credits for the leaderboard
-- desc: [optional] The description of the leaderboard
-- data: The data for the leaderboard
-"""
-
-# No more refreshing manually, happens daily
-# def get_refresh_function(task_category, task_list):
-# def _refresh():
-# data_task_category = get_mteb_data(tasks=[task_category], datasets=task_list)
-# data_task_category.drop(columns=["Embedding Dimensions", "Max Tokens"], inplace=True)
-# return data_task_category
-# return _refresh
-
-
-# def get_refresh_overall_function(tasks):
-# return lambda: get_mteb_average(tasks)[0]
-
-
-# load in the pre-calculated `all_data_tasks` and `boards_data`
-print(f"Loading pre-calculated data....")
-all_data_tasks = load_results("all_data_tasks")
-boards_data = load_results("boards_data")
-
-#### Caclulate Metadata
-# Exact, add all non-nan integer values for every dataset
-NUM_SCORES = 0
-DATASETS = []
-MODELS = []
-# LANGUAGES = []
-for d in all_data_tasks:
- if isinstance(d, list) and len(d) == 0:
- continue
- # NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum()
- cols_to_ignore = 4 if "Average" in d.columns else 3
- # Count number of scores including only non-nan floats & excluding the rank column
- NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum()
- # Exclude rank & model name column (first two); Do not count different language versions as different datasets
- DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]]
- # LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]]
- MODELS += d["Model"].tolist()
-
-
-NUM_DATASETS = len(set(DATASETS))
-# NUM_LANGUAGES = len(set(LANGUAGES))
-NUM_MODELS = len(set(MODELS))
-
-data = {
- "Overall": {"metric": "Various, refer to task tabs", "data": []}
-}
-for task in TASKS:
- data[task] = {"metric": TASKS_CONFIG[task]["metric_description"], "data": []}
-
-for board, board_config in BOARDS_CONFIG.items():
- init_name = board_config["title"]
- if init_name in PRETTY_NAMES:
- init_name = PRETTY_NAMES[init_name]
- board_pretty_name = f"{init_name} leaderboard"
- acronym = board_config.get("acronym", None)
- board_icon = board_config.get("icon", None)
- if board_icon is None:
- 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
- if acronym is not None:
- overall_pretty_name += f" ({board_config['acronym']})"
- data["Overall"]["data"].append({
- "language": board_config["title"],
- "language_long": board_config["language_long"],
- "description": f"**Overall MTEB {overall_pretty_name}** 🔮{board_icon}",
- "data": boards_data[board]["data_overall"],
- # "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']
- if "special_icons" in board_config and isinstance(board_config["special_icons"], dict):
- task_icon = board_config["special_icons"].get(task_category, task_icon)
- data[task_category]["data"].append({
- "language": board_config["title"],
- "language_long": board_config["language_long"],
- "description": f"**{task_category} {board_pretty_name}** {task_icon}{board_icon}",
- "data": boards_data[board]["data_tasks"][task_category],
- # "refresh": get_refresh_function(task_category, task_category_list),
- "credits": credits,
- "metric": metric,
- "desc": desc,
- })
-
-dataframes = []
-full_dataframes = []
-tabs = []
-
-# The following JavaScript function updates the URL parameters based on the selected task and language
-# Additionally, `update_url_task` and `update_url_language` are used to update the current task and language
-# The current task and language are stored in the `current_task_language` and `language_per_task` JSON objects
-# This is all a bit hacky, but it might be the only way to pass options to a JavaScript function via Gradio
-set_window_url_params = """
-function(goalUrlObject) {
- const params = new URLSearchParams(window.location.search);
- for (const [key, value] of Object.entries(goalUrlObject)) {
- params.set(key, value);
- };
- const queryString = '?' + params.toString();
- console.log(queryString);
- window.history.replaceState({}, '', queryString);
- return [];
-}
-"""
-
-def update_url_task(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
- current_task_language["task"] = event.target.id
- # Either use the cached language for this task or the 1st language
- try:
- current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[1].children[0].id)
- except Exception as e: # is Overall tab, no description
- current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[0].children[0].id)
-
- return current_task_language, language_per_task
-
-def update_url_language(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
- current_task_language["language"] = event.target.id
- if "task" not in current_task_language:
- current_task_language["task"] = "overall"
- language_per_task[current_task_language["task"]] = event.target.id
- return current_task_language, language_per_task
-
-NUMERIC_INTERVALS = {
- "<100M": pd.Interval(0, 100, closed="right"),
- "100M to 250M": pd.Interval(100, 250, closed="right"),
- "250M to 500M": pd.Interval(250, 500, closed="right"),
- "500M to 1B": pd.Interval(500, 1000, closed="right"),
- ">1B": pd.Interval(1000, 1_000_000, closed="right"),
-}
-
-MODEL_TYPES = [
- "Open",
- "Proprietary",
- "Sentence Transformers",
- "Cross-Encoders",
- "Bi-Encoders",
- "Uses Instructions",
- "No Instructions",
-]
-
-def filter_data(search_query, model_types, model_sizes, *full_dataframes):
- output_dataframes = []
- for df in full_dataframes:
- # Apply the search query
- if search_query:
- names = df["Model"].map(lambda x: re.match("(.+)", x).group(1))
- masks = []
- for query in search_query.split(";"):
- masks.append(names.str.lower().str.contains(query.lower()))
- df = df[reduce(lambda a, b: a | b, masks)]
-
- # Apply the model type filtering
- if set(model_types) != set(MODEL_TYPES):
- masks = []
- for model_type in model_types:
- if model_type == "Open":
- masks.append(~df["Model"].isin(PROPRIETARY_MODELS))
- elif model_type == "Proprietary":
- masks.append(df["Model"].isin(PROPRIETARY_MODELS))
- elif model_type == "Sentence Transformers":
- masks.append(df["Model"].isin(SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS))
- elif model_type == "Cross-Encoders":
- masks.append(df["Model"].isin(CROSS_ENCODERS))
- elif model_type == "Bi-Encoders":
- masks.append(df["Model"].isin(BI_ENCODERS))
- elif model_type == "Uses Instructions":
- masks.append(df["Model"].isin(INSTRUCT_MODELS))
- elif model_type == "No Instructions":
- masks.append(df["Model"].isin(NOINSTRUCT_MODELS))
- if masks:
- df = df[reduce(lambda a, b: a | b, masks)]
- else:
- df = pd.DataFrame(columns=df.columns)
-
- # Apply the model size filtering
- if set(model_sizes) != set(NUMERIC_INTERVALS.keys()):
- numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[model_size] for model_size in model_sizes]))
- sizes = df["Model Size (Million Parameters)"].replace('', 0)
- mask = sizes.apply(lambda size: any(numeric_interval.contains(size)))
- df = df[mask]
- output_dataframes.append(df)
- return output_dataframes
-
-
-with gr.Blocks(css=css) as block:
-
- # Store the current task and language for updating the URL. This is a bit hacky, but it works
- # for passing the current task and language to the JavaScript function via Gradio
- current_task_language = gr.JSON(value=dict(), visible=False)
- language_per_task = gr.JSON(value=dict(), visible=False)
-
- gr.Markdown(f"""
- [Voyageai] Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the MTEB GitHub repository 🤗 Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models. Also check out [MTEB Arena](https://huggingface.co/spaces/mteb/arena) ⚔️
- """)
-
- with gr.Row():
- search_bar = gr.Textbox(
- label="Search Bar (separate multiple queries with `;`)",
- placeholder=" 🔍 Search for a model and press enter...",
- )
- filter_model_type = gr.CheckboxGroup(
- label="Model types",
- choices=MODEL_TYPES,
- value=MODEL_TYPES,
- interactive=True,
- elem_classes=["filter-checkbox-group"],
- scale=3,
- )
- filter_model_sizes = gr.CheckboxGroup(
- label="Model sizes (in number of parameters)",
- choices=list(NUMERIC_INTERVALS.keys()),
- value=list(NUMERIC_INTERVALS.keys()),
- interactive=True,
- elem_classes=["filter-checkbox-group"],
- scale=2,
- )
-
- with gr.Tabs() as outer_tabs:
- # Store the tabs for updating them on load based on URL parameters
- tabs.append(outer_tabs)
- for task, task_values in data.items():
- metric = task_values["metric"]
- task_tab_id = task.lower().replace(" ", "-")
-
- # Overall, Bitext Mining, Classification, etc.
- pretty_task_name = task if task not in PRETTY_NAMES.keys() else PRETTY_NAMES[task]
- with gr.Tab(pretty_task_name, id=task_tab_id) as task_tab:
- # For updating the 'task' in the URL
- task_tab.select(update_url_task, [current_task_language, language_per_task], [current_task_language, language_per_task]).then(None, [current_task_language], [], js=set_window_url_params)
- if "Overall" != task:
- gr.Markdown(TASK_DESCRIPTIONS[task])
- with gr.Tabs() as task_tabs:
- # Store the task tabs for updating them on load based on URL parameters
- tabs.append(task_tabs)
-
- for item in task_values["data"]:
- item_tab_id = item["language"].lower().replace(" ", "-")
-
- # English, Chinese, French, etc.
- with gr.Tab(item["language"], id=item_tab_id) as item_tab:
- # For updating the 'language' in the URL
- item_tab.select(update_url_language, [current_task_language, language_per_task], [current_task_language, language_per_task], trigger_mode="always_last").then(None, [current_task_language], [], js=set_window_url_params)
-
- specific_metric = metric
- if item.get("metric", None) is not None:
- specific_metric = item['metric']
-
- with gr.Row():
- gr.Markdown(f"""
- {item['description']}
-
- - **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():
- datatype = ["number", "markdown"] + ["number"] * len(item["data"])
- dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", height=500)
- dataframes.append(dataframe)
-
- full_dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", visible=False)
- full_dataframes.append(full_dataframe)
-
- # with gr.Row():
- # refresh_button = gr.Button("Refresh")
- # refresh_button.click(item["refresh"], inputs=None, outputs=dataframe, concurrency_limit=20)
-
- gr.Markdown(f"""
- - **Total Datasets**: {NUM_DATASETS}
- - **Total Languages**: 113
- - **Total Scores**: {NUM_SCORES}
- - **Total Models**: {NUM_MODELS}
- """ + r"""
- Made with ❤️ for NLP. If this work is useful to you, please consider citing:
-
- ```bibtex
- @article{muennighoff2022mteb,
- doi = {10.48550/ARXIV.2210.07316},
- url = {https://arxiv.org/abs/2210.07316},
- author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
- title = {MTEB: Massive Text Embedding Benchmark},
- publisher = {arXiv},
- journal={arXiv preprint arXiv:2210.07316},
- year = {2022}
- }
- ```
- """)
-
- def set_tabs_on_load(request: gr.Request):
- """Set the selected tab based on the URL parameters on load."""
- global tabs
- valid_task_keys = [child.id for child in tabs[0].children]
- return_tabs = [gr.Tabs()] * len(tabs)
-
- query_params = request.request.query_params
- task_key = query_params.get("task", "overall")
- if task_key not in valid_task_keys:
- task_key = "overall"
- return_tabs[0] = gr.Tabs(selected=task_key)
-
- tabs_idx = valid_task_keys.index(task_key) + 1
- language_key = query_params.get("language", "english")
- return_tabs[tabs_idx] = gr.Tabs(selected=language_key)
- current_task_language = {"task": task_key, "language": language_key}
- language_per_task = {task_key: language_key}
- return return_tabs + [current_task_language, language_per_task]
-
- block.load(set_tabs_on_load, inputs=[], outputs=tabs + [current_task_language, language_per_task])
-
- search_bar.submit(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
- filter_model_type.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
- filter_model_sizes.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
-
-block.queue(max_size=10)
-block.launch()
-
-# Add model names here so the mteb/leaderboard space shows up on their model page
-# from envs import MODEL_META
-# print("','".join(MODEL_META["models_to_skip"]))
-# print("','".join(list(MODEL_META['model_meta'].keys())))
-# print("','".join([x['link'].split("co/")[-1] for x in MODEL_META['model_meta'].values() if (x.get('link', None)) and ("huggingface.co" in x['link'])]))
-# from envs import API; print("','".join([x.modelId for x in list(API.list_models(filter="mteb")) if x.modelId not in UNUSED]))
-UNUSED = ['michaelfeil/ct2fast-e5-large-v2','McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse','newsrx/instructor-xl','sionic-ai/sionic-ai-v1','lsf1000/bge-evaluation','Intel/bge-small-en-v1.5-sst2','newsrx/instructor-xl-newsrx','McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse','McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse','davidpeer/gte-small','goldenrooster/multilingual-e5-large','kozistr/fused-large-en','mixamrepijey/instructor-small','McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised','DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext','Intel/bge-base-en-v1.5-sst2-int8-dynamic','morgendigital/multilingual-e5-large-quantized','BAAI/bge-small-en','ggrn/e5-small-v2','vectoriseai/gte-small','giulio98/placeholder','odunola/UAE-Large-VI','vectoriseai/e5-large-v2','gruber/e5-small-v2-ggml','Severian/nomic','arcdev/e5-mistral-7b-instruct','mlx-community/multilingual-e5-base-mlx','michaelfeil/ct2fast-bge-base-en-v1.5','Intel/bge-small-en-v1.5-sst2-int8-static','jncraton/stella-base-en-v2-ct2-int8','vectoriseai/multilingual-e5-large','rlsChapters/Chapters-SFR-Embedding-Mistral','arcdev/SFR-Embedding-Mistral','McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised','McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised','vectoriseai/gte-base','mixamrepijey/instructor-models','GovCompete/e5-large-v2','ef-zulla/e5-multi-sml-torch','khoa-klaytn/bge-small-en-v1.5-angle','krilecy/e5-mistral-7b-instruct','vectoriseai/bge-base-en-v1.5','vectoriseai/instructor-base','jingyeom/korean_embedding_model','rizki/bgr-tf','barisaydin/bge-base-en','jamesgpt1/zzz','Malmuk1/e5-large-v2_Sharded','vectoriseai/ember-v1','Consensus/instructor-base','barisaydin/bge-small-en','barisaydin/gte-base','woody72/multilingual-e5-base','Einas/einas_ashkar','michaelfeil/ct2fast-bge-large-en-v1.5','vectoriseai/bge-small-en-v1.5','iampanda/Test','cherubhao/yogamodel','ieasybooks/multilingual-e5-large-onnx','jncraton/e5-small-v2-ct2-int8','radames/e5-large','khoa-klaytn/bge-base-en-v1.5-angle','Intel/bge-base-en-v1.5-sst2-int8-static','vectoriseai/e5-large','TitanML/jina-v2-base-en-embed','Koat/gte-tiny','binqiangliu/EmbeddingModlebgelargeENv1.5','beademiguelperez/sentence-transformers-multilingual-e5-small','sionic-ai/sionic-ai-v2','jamesdborin/jina-v2-base-en-embed','maiyad/multilingual-e5-small','dmlls/all-mpnet-base-v2','odunola/e5-base-v2','vectoriseai/bge-large-en-v1.5','vectoriseai/bge-small-en','karrar-alwaili/UAE-Large-V1','t12e/instructor-base','Frazic/udever-bloom-3b-sentence','Geolumina/instructor-xl','hsikchi/dump','recipe/embeddings','michaelfeil/ct2fast-bge-small-en-v1.5','ildodeltaRule/multilingual-e5-large','shubham-bgi/UAE-Large','BAAI/bge-large-en','michaelfeil/ct2fast-e5-small-v2','cgldo/semanticClone','barisaydin/gte-small','aident-ai/bge-base-en-onnx','jamesgpt1/english-large-v1','michaelfeil/ct2fast-e5-small','baseplate/instructor-large-1','newsrx/instructor-large','Narsil/bge-base-en','michaelfeil/ct2fast-e5-large','mlx-community/multilingual-e5-small-mlx','lightbird-ai/nomic','MaziyarPanahi/GritLM-8x7B-GGUF','newsrx/instructor-large-newsrx','dhairya0907/thenlper-get-large','barisaydin/bge-large-en','jncraton/bge-small-en-ct2-int8','retrainai/instructor-xl','BAAI/bge-base-en','gentlebowl/instructor-large-safetensors','d0rj/e5-large-en-ru','atian-chapters/Chapters-SFR-Embedding-Mistral','Intel/bge-base-en-v1.5-sts-int8-static','Intel/bge-base-en-v1.5-sts-int8-dynamic','jncraton/GIST-small-Embedding-v0-ct2-int8','jncraton/gte-tiny-ct2-int8','d0rj/e5-small-en-ru','vectoriseai/e5-small-v2','SmartComponents/bge-micro-v2','michaelfeil/ct2fast-gte-base','vectoriseai/e5-base-v2','Intel/bge-base-en-v1.5-sst2','McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised','Research2NLP/electrical_stella','weakit-v/bge-base-en-v1.5-onnx','GovCompete/instructor-xl','barisaydin/text2vec-base-multilingual','Intel/bge-small-en-v1.5-sst2-int8-dynamic','jncraton/gte-small-ct2-int8','d0rj/e5-base-en-ru','barisaydin/gte-large','fresha/e5-large-v2-endpoint','vectoriseai/instructor-large','Severian/embed','vectoriseai/e5-base','mlx-community/multilingual-e5-large-mlx','vectoriseai/gte-large','anttip/ct2fast-e5-small-v2-hfie','michaelfeil/ct2fast-gte-large','gizmo-ai/Cohere-embed-multilingual-v3.0','McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse','Kenknight1999/tungdd7_ft_e5','joteqwork/new_gsev0','vantagediscovery/jina-embeddings-v2-base-en','vantagediscovery/nomic-embed-text-v1','vantagediscovery/nomic-embed-text-v1.5','srikanthmalla/hkunlp-instructor-xl','afrideva/GIST-all-MiniLM-L6-v2-GGUF','nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF','agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF','ekorman-strive/bge-large-en-v1.5','raghavlight/SE_v1','liddlefish/privacyembeddingv2_bge_small','ahmet1338/finetuned_embedder','radia/snowflake-arctic-embed-l-Q4_K_M-GGUF','GregorBiswanger/GritLM-7B-Q4_K_M-GGUF','powermove72/GritLM-7B-Q4_K_M-GGUF','sunzx0810/gte-Qwen2-7B-instruct-Q5_K_M-GGUF','nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF','nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF','fishbone64/gte-Qwen2-7B-instruct-Q8_0-GGUF','tobchef/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF','liddlefish/privacy_embedding_rag','liddlefish/privacy_embedding_rag_10k_tmp','liddlefish/privacy_embedding_bge_small_synthetic','mxs980/gte-Qwen2-1.5B-instruct-Q8_0-GGUF','leonn71/gte-Qwen2-1.5B-instruct-Q6_K-GGUF', 'Baichuan-text-embedding','Cohere-embed-english-v3.0','Cohere-embed-multilingual-light-v3.0','Cohere-embed-multilingual-v3.0','DanskBERT','FollowIR-7B','GritLM-7B','LASER2','LLM2Vec-Llama-2-supervised','LLM2Vec-Llama-2-unsupervised','LLM2Vec-Meta-Llama-3-supervised','LLM2Vec-Meta-Llama-3-unsupervised','LLM2Vec-Mistral-supervised','LLM2Vec-Mistral-unsupervised','LLM2Vec-Sheared-Llama-supervised','LLM2Vec-Sheared-Llama-unsupervised','LaBSE','OpenSearch-text-hybrid','SFR-Embedding-Mistral','all-MiniLM-L12-v2','all-MiniLM-L6-v2','all-mpnet-base-v2','allenai-specter','bert-base-10lang-cased','bert-base-15lang-cased','bert-base-25lang-cased','bert-base-multilingual-cased','bert-base-multilingual-uncased','bert-base-swedish-cased','bert-base-uncased','bge-base-zh-v1.5','bge-large-en-v1.5','bge-large-zh-noinstruct','bge-large-zh-v1.5','bge-m3','bge-small-zh-v1.5','bm25','camembert-base','camembert-large','contriever-base-msmarco','cross-en-de-roberta-sentence-transformer','dfm-encoder-large-v1','dfm-sentence-encoder-large-1','distilbert-base-25lang-cased','distilbert-base-en-fr-cased','distilbert-base-en-fr-es-pt-it-cased','distilbert-base-fr-cased','distilbert-base-uncased','distiluse-base-multilingual-cased-v2','e5-base-4k','e5-base-v2','e5-base','e5-large-v2','e5-large','e5-mistral-7b-instruct','e5-small','electra-small-nordic','electra-small-swedish-cased-discriminator','elser-v2','flan-t5-base','flan-t5-large','flaubert_base_cased','flaubert_base_uncased','flaubert_large_cased','gbert-base','gbert-large','gelectra-base','gelectra-large','glove.6B.300d','google-gecko-256.text-embedding-004','google-gecko.text-embedding-004','gottbert-base','gte-Qwen1.5-7B-instruct','gte-Qwen2-7B-instruct','gtr-t5-base','gtr-t5-large','gtr-t5-xl','gtr-t5-xxl','herbert-base-retrieval-v2','instructor-base','instructor-large','instructor-xl','jina-embeddings-v2-base-en','komninos','llama-2-7b-chat','luotuo-bert-medium','m3e-base','m3e-large','mistral-7b-instruct-v0.2','mistral-embed','monobert-large-msmarco','monot5-3b-msmarco-10k','monot5-base-msmarco-10k','msmarco-bert-co-condensor','multi-qa-MiniLM-L6-cos-v1','multilingual-e5-base','multilingual-e5-large','multilingual-e5-small','nb-bert-base','nb-bert-large','nomic-embed-text-v1','nomic-embed-text-v1.5-128','nomic-embed-text-v1.5-256','nomic-embed-text-v1.5-512','nomic-embed-text-v1.5-64','norbert3-base','norbert3-large','paraphrase-multilingual-MiniLM-L12-v2','paraphrase-multilingual-mpnet-base-v2','sentence-bert-swedish-cased','sentence-camembert-base','sentence-camembert-large','sentence-croissant-llm-base','sentence-t5-base','sentence-t5-large','sentence-t5-xl','sentence-t5-xxl','silver-retriever-base-v1','st-polish-paraphrase-from-distilroberta','st-polish-paraphrase-from-mpnet','sup-simcse-bert-base-uncased','text-embedding-3-large','text-embedding-3-large-256','text-embedding-3-small','text-embedding-ada-002','text-search-ada-001','text-search-ada-doc-001','text-search-ada-query-001','text-search-babbage-001','text-search-curie-001','text-search-davinci-001','text-similarity-ada-001','text-similarity-babbage-001','text-similarity-curie-001','text-similarity-davinci-001','tart-dual-contriever-msmarco','tart-full-flan-t5-xl','text2vec-base-chinese','text2vec-base-multilingual','text2vec-large-chinese','titan-embed-text-v1','udever-bloom-1b1','udever-bloom-560m','universal-sentence-encoder-multilingual-3','universal-sentence-encoder-multilingual-large-3','unsup-simcse-bert-base-uncased','use-cmlm-multilingual','voyage-2','voyage-code-2','voyage-large-2-instruct','voyage-law-2','voyage-lite-01-instruct','voyage-lite-02-instruct','voyage-multilingual-2','xlm-roberta-base','xlm-roberta-large','NV-Retriever-v1','NV-Embed-v1','Linq-Embed-Mistral','Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit','Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit','Muennighoff/SGPT-125M-weightedmean-nli-bitfit','Muennighoff/SGPT-2.7B-weightedmean-msmarco-specb-bitfit','Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit','Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit','DMetaSoul/sbert-chinese-general-v1','bigscience-data/sgpt-bloom-1b7-nli','bigscience/sgpt-bloom-7b1-msmarco','aari1995/German_Semantic_STS_V2','intfloat/e5-small','hkunlp/instructor-large','hkunlp/instructor-base','hkunlp/instructor-xl','intfloat/e5-base','intfloat/e5-large','Shimin/yiyouliao','vprelovac/universal-sentence-encoder-multilingual-large-3','vprelovac/universal-sentence-encoder-multilingual-3','vprelovac/universal-sentence-encoder-4','vprelovac/universal-sentence-encoder-large-5','ManiShankar-AlpesAi/paraphrase-multilingual-mpnet-base-v2-KE_Sieve','nickprock/mmarco-bert-base-italian-uncased','intfloat/e5-small-v2','intfloat/e5-base-v2','intfloat/e5-large-v2','intfloat/multilingual-e5-base','Shimin/LLaMA-embeeding','Forbu14/openai_clip_embeddings','shibing624/text2vec-base-multilingual','consciousAI/cai-lunaris-text-embeddings','consciousAI/cai-stellaris-text-embeddings','intfloat/multilingual-e5-small'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'Cohere/Cohere-embed-english-v3.0','Cohere/Cohere-embed-english-v3.0','Cohere/Cohere-embed-multilingual-light-v3.0','Cohere/Cohere-embed-multilingual-v3.0','vesteinn/DanskBERT','jhu-clsp/FollowIR-7B','GritLM/GritLM-7B','GritLM/GritLM-7B','McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised','McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse','McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised','McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse','McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised','McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse','McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised','McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse','sentence-transformers/LaBSE','Linq-AI-Research/Linq-Embed-Mistral','nvidia/NV-Embed-v1','nvidia/NV-Retriever-v1','Salesforce/SFR-Embedding-Mistral','sentence-transformers/all-MiniLM-L12-v2','sentence-transformers/all-MiniLM-L12-v2','sentence-transformers/all-MiniLM-L6-v2','sentence-transformers/all-MiniLM-L6-v2','sentence-transformers/all-mpnet-base-v2','sentence-transformers/all-mpnet-base-v2','sentence-transformers/allenai-specter','Geotrend/bert-base-10lang-cased','Geotrend/bert-base-15lang-cased','Geotrend/bert-base-25lang-cased','google-bert/bert-base-multilingual-cased','google-bert/bert-base-multilingual-uncased','KB/bert-base-swedish-cased','bert-base-uncased','BAAI/bge-base-en-v1.5','BAAI/bge-base-en-v1.5','BAAI/bge-base-zh-v1.5','BAAI/bge-large-en-v1.5','BAAI/bge-large-en-v1.5','BAAI/bge-large-zh-noinstruct','BAAI/bge-large-zh-v1.5','BAAI/bge-m3','BAAI/bge-m3','BAAI/bge-small-en-v1.5','BAAI/bge-small-en-v1.5','BAAI/bge-small-zh-v1.5','almanach/camembert-base','almanach/camembert-large','nthakur/contriever-base-msmarco','facebook/contriever','facebook/contriever','T-Systems-onsite/cross-en-de-roberta-sentence-transformer','chcaa/dfm-encoder-large-v1','chcaa/dfm-encoder-large-v1','Geotrend/distilbert-base-25lang-cased','Geotrend/distilbert-base-en-fr-cased','Geotrend/distilbert-base-en-fr-es-pt-it-cased','Geotrend/distilbert-base-fr-cased','distilbert-base-uncased','sentence-transformers/distiluse-base-multilingual-cased-v2','dwzhu/e5-base-4k','intfloat/e5-base-v2','intfloat/e5-base','intfloat/e5-large-v2','intfloat/e5-large','intfloat/e5-mistral-7b-instruct','intfloat/e5-mistral-7b-instruct-noinstruct','intfloat/e5-small','jonfd/electra-small-nordic','KBLab/electra-small-swedish-cased-discriminator','google/flan-t5-base','google/flan-t5-large','flaubert/flaubert_base_cased','flaubert/flaubert_base_uncased','flaubert/flaubert_large_cased','deepset/gbert-base','deepset/gbert-large','deepset/gelectra-base','deepset/gelectra-large','sentence-transformers/average_word_embeddings_glove.6B.300d','uklfr/gottbert-base','Alibaba-NLP/gte-Qwen1.5-7B-instruct','Alibaba-NLP/gte-Qwen2-7B-instruct','sentence-transformers/gtr-t5-base','sentence-transformers/gtr-t5-large','sentence-transformers/gtr-t5-xl','sentence-transformers/gtr-t5-xxl','ipipan/herbert-base-retrieval-v2','hkunlp/instructor-base','hkunlp/instructor-large','hkunlp/instructor-xl','jinaai/jina-embeddings-v2-base-en','sentence-transformers/average_word_embeddings_komninos','meta-llama/Llama-2-7b-chat-hf','silk-road/luotuo-bert-medium','moka-ai/m3e-base','moka-ai/m3e-large','mistralai/Mistral-7B-Instruct-v0.2','castorini/monobert-large-msmarco','castorini/monot5-3b-msmarco-10k','castorini/monot5-base-msmarco-10k','sentence-transformers/msmarco-bert-co-condensor','sentence-transformers/multi-qa-MiniLM-L6-cos-v1','intfloat/multilingual-e5-base','intfloat/multilingual-e5-large','intfloat/multilingual-e5-small','NbAiLab/nb-bert-base','NbAiLab/nb-bert-large','nomic-ai/nomic-embed-text-v1','nomic-ai/nomic-embed-text-v1.5','nomic-ai/nomic-embed-text-v1.5','nomic-ai/nomic-embed-text-v1.5','nomic-ai/nomic-embed-text-v1.5','ltg/norbert3-base','ltg/norbert3-large','sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2','sentence-transformers/paraphrase-multilingual-mpnet-base-v2','KBLab/sentence-bert-swedish-cased','dangvantuan/sentence-camembert-base','dangvantuan/sentence-camembert-large','Wissam42/sentence-croissant-llm-base','sentence-transformers/sentence-t5-base','sentence-transformers/sentence-t5-large','sentence-transformers/sentence-t5-xl','sentence-transformers/sentence-t5-xxl','ipipan/silver-retriever-base-v1','sdadas/st-polish-paraphrase-from-distilroberta','sdadas/st-polish-paraphrase-from-mpnet','princeton-nlp/sup-simcse-bert-base-uncased','orionweller/tart-dual-contriever-msmarco','facebook/tart-full-flan-t5-xl','shibing624/text2vec-base-chinese','GanymedeNil/text2vec-large-chinese','izhx/udever-bloom-1b1','izhx/udever-bloom-560m','vprelovac/universal-sentence-encoder-multilingual-3','vprelovac/universal-sentence-encoder-multilingual-large-3','princeton-nlp/unsup-simcse-bert-base-uncased','sentence-transformers/use-cmlm-multilingual','xlm-roberta-base','xlm-roberta-large']
-
-# Possible changes:
-# Could add graphs / other visual content
-# Could add verification marks
-
-# Sources:
-# https://huggingface.co/spaces/gradio/leaderboard
-# https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
-# https://getemoji.com/
+if __name__ == '__main__':
+ main()
diff --git a/app/__init__.py b/app/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..72cde7353cdb0a7722864065b3955d7395edd37a
--- /dev/null
+++ b/app/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:13
+# @Author : q275343119
+# @File : __init__.py.py
+# @Description:
diff --git a/app/backend/__init__.py b/app/backend/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1be8b93e2e86a2a5f433d8a72ae6d1b1c31b49fc
--- /dev/null
+++ b/app/backend/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:16
+# @Author : q275343119
+# @File : __init__.py.py
+# @Description: Backend module initialization
diff --git a/app/backend/constant.py b/app/backend/constant.py
new file mode 100644
index 0000000000000000000000000000000000000000..1320b686985bca69e732af1767c38970ff3c1d40
--- /dev/null
+++ b/app/backend/constant.py
@@ -0,0 +1,46 @@
+from enum import Enum
+
+
+class Navigation(Enum):
+ TEXT_LEADERBOARD = "Text Leaderboard"
+ MULTIMODAL_LEADERBOARD = "Multimodal Leaderboard"
+
+
+class TaskCategory(Enum):
+ LAW = "Law"
+ CODE = "Code"
+ CONVERSATIONAL = "Conversational"
+ TECH = "Tech"
+ LONG_CONTEXT = "Long-context"
+ MULTILINGUAL = "Multilingual"
+
+
+class ModelProvider(Enum):
+ OPENAI = "OpenAI"
+ VOYAGEAI = "VoyageAI"
+ COHERE = "Cohere"
+ OTHERS = "Others"
+
+
+class EvaluationMetric(Enum):
+ NDCG_1 = "NDCG@1"
+ NDCG_3 = "NDCG@3"
+ NDCG_5 = "NDCG@5"
+ NDCG_10 = "NDCG@10"
+ NDCG_20 = "NDCG@20"
+ NDCG_50 = "NDCG@50"
+ NDCG_100 = "NDCG@100"
+ RECALL_1 = "RECALL@1"
+ RECALL_3 = "RECALL@3"
+ RECALL_5 = "RECALL@5"
+ RECALL_10 = "RECALL@10"
+ RECALL_20 = "RECALL@20"
+ RECALL_50 = "RECALL@50"
+ RECALL_100 = "RECALL@100"
+ PRECISION_1 = "PRECISION@1"
+ PRECISION_3 = "PRECISION@3"
+ PRECISION_5 = "PRECISION@5"
+ PRECISION_10 = "PRECISION@10"
+ PRECISION_20 = "PRECISION@20"
+ PRECISION_50 = "PRECISION@50"
+ PRECISION_100 = "PRECISION@100"
diff --git a/app/backend/data_engine.py b/app/backend/data_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..589c9733d138039c0cc756748266075702e62339
--- /dev/null
+++ b/app/backend/data_engine.py
@@ -0,0 +1,184 @@
+"""
+Data service provider
+"""
+import json
+from typing import List
+
+import pandas as pd
+
+from app.backend.constant import ModelProvider
+
+
+class DataEngine:
+
+ def __init__(self):
+ self.df = self.init_dataframe()
+
+ @property
+ def leaderboards(self):
+ """
+ Get leaderboard data
+ """
+ with open('./mock_data/leaderboard.json', 'r') as f:
+ return json.load(f)
+
+ @property
+ def models(self):
+ """
+ Get models data
+ """
+ with open('./mock_data/models.json', 'r') as f:
+ return json.load(f)
+
+ @property
+ def tasks(self):
+ """
+ Get tasks data
+ """
+ with open('./mock_data/tasks.json', 'r') as f:
+ return json.load(f)
+
+ @property
+ def results(self):
+ """
+ Get results data
+ """
+ with open('./mock_data/results.json', 'r') as f:
+ return json.load(f)
+
+ def init_dataframe(self):
+ """
+ Initialize DataFrame
+ """
+ d = {"hello": [123], "world": [456]}
+ return pd.DataFrame(d)
+
+ def _check_providers(self, organization: str, providers: List):
+ if not providers:
+ return True
+ if "Others" in providers:
+ if organization not in (
+ ModelProvider.OPENAI.value, ModelProvider.COHERE.value, ModelProvider.VOYAGEAI.value):
+ return True
+ return organization in providers
+
+ def filter_df(self, leaderboard: str, task: str, providers: List, sort_key: str):
+
+ tasks = []
+ for lb in self.leaderboards:
+ if lb["name"] == leaderboard:
+ tasks = lb["tasks"]
+ break
+ df_list = []
+ for t in (filter(lambda x: x.upper() == task.upper(), tasks)):
+ datasets = []
+ for ta in self.tasks:
+ if ta["slug"].upper() == t.upper():
+ datasets = ta["datasets"]
+ break
+ for model in self.models:
+ if t in model["tasks"] and self._check_providers(model["organization"], providers):
+
+ for dataset in datasets:
+ results = self.results[dataset]
+ for result in results:
+ if result['model_name'] == model["model_name"]:
+ d = result["results"]
+ d["class"] = result["class"]
+ d["organization"] = result["organization"]
+ d["model_name"] = result["model_name"]
+ df = pd.DataFrame([d])
+ df = df[["class", "organization", "model_name", "ndcg_at_1", "ndcg_at_3", "ndcg_at_5",
+ "ndcg_at_10",
+ "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+ "recall_at_5", "recall_at_10",
+ "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+ "precision_at_3", "precision_at_5",
+ "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]]
+ df_list.append(df)
+ if df_list:
+ return pd.concat(df_list).sort_values(by=sort_key.replace("@", '_at_').lower())
+ return pd.DataFrame(columns=["class", "organization", "model_name", "ndcg_at_1", "ndcg_at_3", "ndcg_at_5",
+ "ndcg_at_10",
+ "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+ "recall_at_5", "recall_at_10",
+ "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+ "precision_at_3", "precision_at_5",
+ "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"])
+
+ def get_model_result(self, model: dict, task_datasets_map: dict, results: dict):
+ """
+ get_model_result
+ """
+ model_class = model["class"]
+ model_organization = model["organization"]
+ model_model_name = model["model_name"]
+ for leaderboard in model["leaderboards"]:
+ for task in model["tasks"]:
+ for dateset in task_datasets_map.get(task, []):
+ for result in results[dateset]:
+ if result["model_name"] == model_model_name:
+ d_result = result["results"]
+ d_result["class"] = model_class
+ d_result["organization"] = model_organization
+ d_result["model_name"] = model_model_name
+ d_result["leaderboard"] = leaderboard
+ d_result["dateset"] = dateset
+ d_result["task"] = task
+ yield d_result
+
+ def jsons_to_df(self):
+
+ # change leaderboards to task_leaderboard_map
+ task_leaderboard_map = {}
+ leaderboards = self.leaderboards
+ for leaderboard in leaderboards:
+ for task in leaderboard["tasks"]:
+ task_leaderboard_map[task] = leaderboard["name"]
+
+ # change tasks to task_datasets_map
+ task_datasets_map = {}
+ for task in self.tasks:
+ task_datasets_map[task["slug"]] = task["datasets"]
+
+ df_results_list = []
+ results = self.results
+ for model in self.models:
+ for d_result in self.get_model_result(model, task_datasets_map, results):
+ if d_result:
+ df_results_list.append(pd.DataFrame([d_result]))
+
+ if df_results_list:
+ df_result = pd.concat(df_results_list)
+ return df_result[
+ ["leaderboard", "task", "class", "organization", "model_name", "dateset", "ndcg_at_1", "ndcg_at_3",
+ "ndcg_at_5",
+ "ndcg_at_10",
+ "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+ "recall_at_5", "recall_at_10",
+ "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+ "precision_at_3", "precision_at_5",
+ "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]], leaderboards
+ return pd.DataFrame(
+ columns=["leaderboard", "task", "class", "organization", "model_name", "dateset", "ndcg_at_1", "ndcg_at_3",
+ "ndcg_at_5",
+ "ndcg_at_10",
+ "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
+ "recall_at_5", "recall_at_10",
+ "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
+ "precision_at_3", "precision_at_5",
+ "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]), leaderboards
+
+ def filter_by_providers(self, df_result: pd.DataFrame, providers: List):
+ """
+ filter_by_providers
+ """
+ if not providers:
+ # providers are empty, return empty
+ return df_result[0:0]
+ return df_result[df_result['organization'].apply(lambda x: self._check_providers(x, providers))]
+
+ def summarize_dataframe(self):
+ """
+ Summarize data statistics
+ """
diff --git a/app/ui/__init__.py b/app/ui/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6453fd31038e27b3413221ca653c1b9d27d8719
--- /dev/null
+++ b/app/ui/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:16
+# @Author : q275343119
+# @File : __init__.py.py
+# @Description:
diff --git a/app/ui/about.py b/app/ui/about.py
new file mode 100644
index 0000000000000000000000000000000000000000..06ef3a0935bb662512af8c41ba06e30e9964766f
--- /dev/null
+++ b/app/ui/about.py
@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:20
+# @Author : q275343119
+# @File : about.py
+# @Description: About page implementation
+
+import gradio as gr
+
+
+def init_about():
+ """Initialize the about page"""
+ with gr.Blocks() as block:
+ with gr.Column():
+ # Motivation section
+ gr.Markdown("## Motivation")
+ gr.Markdown("""
+ [Placeholder] This section explains why we created this embedding benchmark:
+ - To standardize embedding evaluation
+ - To promote transparency in model performance
+ - To accelerate progress in embedding research
+ """)
+
+ # Organizers section
+ gr.Markdown("## Organizers")
+ gr.Markdown("""
+ [Placeholder] The MTEB benchmark is organized by:
+ - Person A - Organization X
+ - Person B - Organization Y
+ - Person C - Organization Z
+ """)
+
+ # FAQ section
+ gr.Markdown("## Frequently Asked Questions")
+ gr.Markdown("""
+ [Placeholder] Common questions about MTEB:
+
+ **Q: How can I submit my model?**
+ A: Follow the submission guidelines in our GitHub repository.
+
+ **Q: What metrics are used?**
+ A: We use various metrics depending on the task type.
+
+ **Q: How often is the leaderboard updated?**
+ A: The leaderboard is updated daily.
+ """)
+
+ block.queue(max_size=10)
+ return block
diff --git a/app/ui/component/__init__.py b/app/ui/component/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..394a95e013bed6ac77dad2be4154ae529ea4381f
--- /dev/null
+++ b/app/ui/component/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:25
+# @Author : q275343119
+# @File : __init__.py.py
+# @Description:
diff --git a/app/ui/component/df_component.py b/app/ui/component/df_component.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac916c7af30e10bc78a27f732f137e759a17e393
--- /dev/null
+++ b/app/ui/component/df_component.py
@@ -0,0 +1,20 @@
+import pandas as pd
+import gradio as gr
+
+from app.backend.data_engine import DataEngine
+
+
+class DataFrameComponent:
+
+ def __init__(self, data_engine: DataEngine):
+ self.data_engine = data_engine
+
+ def show(self, df=None):
+ with gr.Row():
+ # Create DataFrame display area
+ if df is None:
+ df = self.data_engine.init_dataframe()
+ print(df)
+ df_display = gr.DataFrame(df, col_count=(len(df.columns), 'fixed'))
+
+ return df_display
diff --git a/app/ui/component/filter_component.py b/app/ui/component/filter_component.py
new file mode 100644
index 0000000000000000000000000000000000000000..b25114e8a7766815ca53f64378225f75e66e6dbe
--- /dev/null
+++ b/app/ui/component/filter_component.py
@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:26
+# @Author : q275343119
+# @File : filter_component.py
+# @Description: Filter component implementation
+"""
+Filter component for data filtering
+"""
+from typing import List
+import gradio as gr
+
+from app.backend.data_engine import DataEngine
+
+
+class FilterComponent:
+
+ def __init__(self, data_engine: DataEngine, navigations: List, model_provides: List,
+ evaluation_metrics: List):
+ self.data_engine = data_engine
+ self.navigations = navigations
+ self.task_categories = []
+ self.model_provides = model_provides
+ self.evaluation_metrics = evaluation_metrics
+
+ def show(self):
+ with gr.Row():
+ navigations = gr.Radio(
+ label="Navigation",
+ choices=self.navigations,
+ value=self.navigations[0],
+ interactive=True,
+ elem_classes=["filter-checkbox-group"],
+ scale=2,
+ )
+
+ model_provides = gr.CheckboxGroup(
+ label="Model Provides",
+ choices=self.model_provides,
+ value=self.model_provides,
+ interactive=True,
+ elem_classes=["filter-checkbox-group"],
+ scale=2,
+ )
+ evaluation_metrics = gr.Dropdown(
+ label="Evaluation Metrics",
+ choices=self.evaluation_metrics,
+ value="NDCG@10",
+ interactive=True,
+ elem_classes=["filter-checkbox-group"],
+ scale=2,
+ )
+
+ return navigations, model_provides, evaluation_metrics
diff --git a/app/ui/component/subtabs_component.py b/app/ui/component/subtabs_component.py
new file mode 100644
index 0000000000000000000000000000000000000000..f461522d1d711bdfa257e731596d6b5d2a5b519d
--- /dev/null
+++ b/app/ui/component/subtabs_component.py
@@ -0,0 +1,48 @@
+from typing import List
+import gradio as gr
+import pandas as pd
+
+from app.backend.data_engine import DataEngine
+
+
+class SubtabsComponent:
+
+ def __init__(self, data_engine: DataEngine):
+ self.data_engine = data_engine
+
+ def show(self, navigations: str = None, model_provides: List = None, evaluation_metrics: str = None):
+ df_result, leaderboards = self.data_engine.jsons_to_df()
+
+ navigations = "Text" if navigations is None else navigations.split(" ", maxsplit=1)[0]
+ model_provides = [] if model_provides is None else model_provides
+ evaluation_metrics = "NDCG@10" if evaluation_metrics is None else evaluation_metrics
+
+ df_result = self.data_engine.filter_by_providers(df_result, model_provides)
+
+ sort_col = evaluation_metrics.replace("@", '_at_').lower()
+
+ df_result = df_result.sort_values(by=sort_col, ascending=False)
+
+ items = []
+ for leaderboard in leaderboards:
+ with gr.Column(visible=leaderboard["name"] == navigations) as column:
+ with gr.Tabs():
+ with gr.TabItem("overall"):
+ df_leaderboard = df_result[df_result["leaderboard"] == leaderboard["slug"]]
+ gr_df = gr.Dataframe(
+ df_leaderboard,
+ interactive=False
+ )
+
+ items.append(gr_df)
+
+ for task in leaderboard["tasks"]:
+ with gr.TabItem(task):
+ df = df_leaderboard[df_leaderboard["task"] == task]
+ gr_df = gr.Dataframe(
+ df,
+ interactive=False
+ )
+ items.append(gr_df)
+ items.append(column)
+ return items
diff --git a/app/ui/home.py b/app/ui/home.py
new file mode 100644
index 0000000000000000000000000000000000000000..36dd562b5477342757f63c17316a2750e7f5848c
--- /dev/null
+++ b/app/ui/home.py
@@ -0,0 +1,83 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:20
+# @Author : q275343119
+# @File : home.py
+# @Description: Home page implementation and UI components
+import pandas as pd
+
+from app.backend.constant import Navigation, TaskCategory, ModelProvider, EvaluationMetric
+from app.backend.data_engine import DataEngine
+from app.ui.component.df_component import DataFrameComponent
+from app.ui.component.filter_component import FilterComponent
+from app.ui.component.subtabs_component import SubtabsComponent
+from app.ui.static import HOME_CSS
+
+import gradio as gr
+
+NUM_DATASETS = 1
+NUM_SCORES = 2
+NUM_MODELS = 3
+
+
+def init_home():
+ """
+ Initialize the home page
+ """
+ data_engine = DataEngine()
+
+ with gr.Blocks(css=HOME_CSS) as block:
+ gr.Markdown(f"""
+ [Voyageai] Massive Text Embedding Benchmark (MTEB) Leaderboard.
+ """)
+
+ filter_area = FilterComponent(
+ data_engine,
+ [element.value for element in Navigation],
+ [element.value for element in ModelProvider],
+ [element.value for element in EvaluationMetric],
+ )
+
+ navigations, model_provides, evaluation_metrics = filter_area.show()
+
+ sub_tabs = SubtabsComponent(data_engine)
+ columns = sub_tabs.show()
+
+ # df_area = DataFrameComponent(data_engine)
+ # df_display = df_area.show(pd.DataFrame(columns=[element.value for element in Navigation]))
+
+
+ gr.Markdown(f"""
+ - **Total Datasets**: {NUM_DATASETS}
+ - **Total Languages**: 113
+ - **Total Scores**: {NUM_SCORES}
+ - **Total Models**: {NUM_MODELS}
+ """ + r"""
+ Made with ❤️ for NLP. If this work is useful to you, please consider citing:
+
+ ```bibtex
+ @article{muennighoff2022mteb,
+ doi = {10.48550/ARXIV.2210.07316},
+ url = {https://arxiv.org/abs/2210.07316},
+ author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+ title = {MTEB: Massive Text Embedding Benchmark},
+ publisher = {arXiv},
+ journal={arXiv preprint arXiv:2210.07316},
+ year = {2022}
+ }
+ ```
+ """)
+ # all_outputs = list[sub_tab_map.values()]
+ block.load(sub_tabs.show,
+ inputs=[navigations, model_provides, evaluation_metrics], outputs=columns)
+
+ gr.on(triggers=[navigations.change,
+ model_provides.change,
+ evaluation_metrics.change],
+ inputs=[navigations, model_provides, evaluation_metrics],
+ fn=sub_tabs.show,
+ outputs=columns
+ )
+
+
+ block.queue(max_size=10)
+ return block
diff --git a/app/ui/static.py b/app/ui/static.py
new file mode 100644
index 0000000000000000000000000000000000000000..bddfba6e31a4d5a5bbba9efbfab5548b2c5a354a
--- /dev/null
+++ b/app/ui/static.py
@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+# @Date : 2024/12/24 17:16
+# @Author : q275343119
+# @File : static.py
+# @Description:
+
+HOME_CSS = """
+table > thead {
+ white-space: normal
+}
+
+table {
+ --cell-width-1: 250px
+}
+
+table > tbody > tr > td:nth-child(2) > div {
+ overflow-x: auto
+}
+
+.filter-checkbox-group {
+ max-width: max-content;
+}
+"""
diff --git a/boards_data/bright/data_overall/default.txt b/boards_data/bright/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/bright/data_tasks/Retrieval/default.jsonl b/boards_data/bright/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index 0ce61322ddf399a1899ccddb95414a90d4177c0f..0000000000000000000000000000000000000000
--- a/boards_data/bright/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,14 +0,0 @@
-{"index":4,"Rank":1,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Embedding Dimensions":3584,"Max Tokens":32768,"Average":22.89,"BrightRetrieval (aops)":15.1,"BrightRetrieval (biology)":32.09,"BrightRetrieval (earth_science)":40.66,"BrightRetrieval (economics)":16.18,"BrightRetrieval (leetcode)":31.07,"BrightRetrieval (pony)":1.25,"BrightRetrieval (psychology)":26.58,"BrightRetrieval (robotics)":12.82,"BrightRetrieval (stackoverflow)":13.95,"BrightRetrieval (sustainable_living)":20.82,"BrightRetrieval (theoremqa_questions)":29.9,"BrightRetrieval (theoremqa_theorems)":34.22}
-{"index":3,"Rank":2,"Model":"gte-Qwen1.5-7B-instruct<\/a>","Model Size (Million Parameters)":7099,"Memory Usage (GB, fp32)":26.45,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":22.09,"BrightRetrieval (aops)":14.36,"BrightRetrieval (biology)":30.92,"BrightRetrieval (earth_science)":36.22,"BrightRetrieval (economics)":17.72,"BrightRetrieval (leetcode)":25.46,"BrightRetrieval (pony)":9.79,"BrightRetrieval (psychology)":24.61,"BrightRetrieval (robotics)":13.47,"BrightRetrieval (stackoverflow)":19.85,"BrightRetrieval (sustainable_living)":14.93,"BrightRetrieval (theoremqa_questions)":26.97,"BrightRetrieval (theoremqa_theorems)":30.8}
-{"index":7,"Rank":3,"Model":"GritLM-7B<\/a>","Model Size (Million Parameters)":7240,"Memory Usage (GB, fp32)":26.97,"Embedding Dimensions":"","Max Tokens":4096,"Average":20.63,"BrightRetrieval (aops)":8.91,"BrightRetrieval (biology)":25.04,"BrightRetrieval (earth_science)":32.77,"BrightRetrieval (economics)":19.0,"BrightRetrieval (leetcode)":29.85,"BrightRetrieval (pony)":21.98,"BrightRetrieval (psychology)":19.92,"BrightRetrieval (robotics)":17.31,"BrightRetrieval (stackoverflow)":11.62,"BrightRetrieval (sustainable_living)":18.04,"BrightRetrieval (theoremqa_questions)":23.34,"BrightRetrieval (theoremqa_theorems)":19.75}
-{"index":0,"Rank":4,"Model":"text-embedding-004<\/a>","Model Size (Million Parameters)":1200,"Memory Usage (GB, fp32)":4.47,"Embedding Dimensions":768,"Max Tokens":2048,"Average":19.52,"BrightRetrieval (aops)":9.33,"BrightRetrieval (biology)":22.98,"BrightRetrieval (earth_science)":34.38,"BrightRetrieval (economics)":19.5,"BrightRetrieval (leetcode)":29.64,"BrightRetrieval (pony)":3.59,"BrightRetrieval (psychology)":27.86,"BrightRetrieval (robotics)":15.98,"BrightRetrieval (stackoverflow)":17.93,"BrightRetrieval (sustainable_living)":17.25,"BrightRetrieval (theoremqa_questions)":21.51,"BrightRetrieval (theoremqa_theorems)":14.31}
-{"index":10,"Rank":5,"Model":"instructor-xl<\/a>","Model Size (Million Parameters)":1241,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":18.61,"BrightRetrieval (aops)":8.26,"BrightRetrieval (biology)":21.91,"BrightRetrieval (earth_science)":34.35,"BrightRetrieval (economics)":22.81,"BrightRetrieval (leetcode)":27.5,"BrightRetrieval (pony)":5.02,"BrightRetrieval (psychology)":27.43,"BrightRetrieval (robotics)":17.39,"BrightRetrieval (stackoverflow)":19.06,"BrightRetrieval (sustainable_living)":18.82,"BrightRetrieval (theoremqa_questions)":14.59,"BrightRetrieval (theoremqa_theorems)":6.22}
-{"index":8,"Rank":6,"Model":"SFR-Embedding-Mistral<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":18.02,"BrightRetrieval (aops)":7.43,"BrightRetrieval (biology)":19.49,"BrightRetrieval (earth_science)":26.63,"BrightRetrieval (economics)":17.84,"BrightRetrieval (leetcode)":27.35,"BrightRetrieval (pony)":1.97,"BrightRetrieval (psychology)":18.97,"BrightRetrieval (robotics)":16.7,"BrightRetrieval (stackoverflow)":12.72,"BrightRetrieval (sustainable_living)":19.79,"BrightRetrieval (theoremqa_questions)":23.05,"BrightRetrieval (theoremqa_theorems)":24.32}
-{"index":1,"Rank":7,"Model":"voyage-large-2-instruct<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":16000,"Average":17.65,"BrightRetrieval (aops)":7.45,"BrightRetrieval (biology)":23.55,"BrightRetrieval (earth_science)":25.09,"BrightRetrieval (economics)":19.85,"BrightRetrieval (leetcode)":30.6,"BrightRetrieval (pony)":1.48,"BrightRetrieval (psychology)":24.79,"BrightRetrieval (robotics)":11.21,"BrightRetrieval (stackoverflow)":15.03,"BrightRetrieval (sustainable_living)":15.58,"BrightRetrieval (theoremqa_questions)":26.06,"BrightRetrieval (theoremqa_theorems)":11.1}
-{"index":13,"Rank":8,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":3072,"Max Tokens":8191,"Average":17.56,"BrightRetrieval (aops)":8.45,"BrightRetrieval (biology)":23.67,"BrightRetrieval (earth_science)":26.27,"BrightRetrieval (economics)":19.98,"BrightRetrieval (leetcode)":23.65,"BrightRetrieval (pony)":2.45,"BrightRetrieval (psychology)":27.52,"BrightRetrieval (robotics)":12.93,"BrightRetrieval (stackoverflow)":12.49,"BrightRetrieval (sustainable_living)":20.32,"BrightRetrieval (theoremqa_questions)":22.22,"BrightRetrieval (theoremqa_theorems)":10.82}
-{"index":11,"Rank":9,"Model":"e5-mistral-7b-instruct<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":17.54,"BrightRetrieval (aops)":7.1,"BrightRetrieval (biology)":18.84,"BrightRetrieval (earth_science)":25.96,"BrightRetrieval (economics)":15.49,"BrightRetrieval (leetcode)":28.72,"BrightRetrieval (pony)":4.81,"BrightRetrieval (psychology)":15.79,"BrightRetrieval (robotics)":16.37,"BrightRetrieval (stackoverflow)":9.83,"BrightRetrieval (sustainable_living)":18.51,"BrightRetrieval (theoremqa_questions)":23.94,"BrightRetrieval (theoremqa_theorems)":25.09}
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diff --git a/boards_data/bright_long/data_overall/default.txt b/boards_data/bright_long/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/bright_long/data_tasks/Retrieval/default.jsonl b/boards_data/bright_long/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index f6a6a211e9b2b1e248f807b20bfab07a3264668d..0000000000000000000000000000000000000000
--- a/boards_data/bright_long/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,14 +0,0 @@
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diff --git a/boards_data/coir/data_overall/default.txt b/boards_data/coir/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/coir/data_tasks/Retrieval/default.jsonl b/boards_data/coir/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index cd67480c2bcb8767bfb51db38ac9ca7bd43dbeb7..0000000000000000000000000000000000000000
--- a/boards_data/coir/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,13 +0,0 @@
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diff --git a/boards_data/da/data_overall/default.txt b/boards_data/da/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/da/data_tasks/BitextMining/default.jsonl b/boards_data/da/data_tasks/BitextMining/default.jsonl
deleted file mode 100644
index 9191fa2d90c9ad3776c9bc8ff403ddb870da9291..0000000000000000000000000000000000000000
--- a/boards_data/da/data_tasks/BitextMining/default.jsonl
+++ /dev/null
@@ -1,28 +0,0 @@
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diff --git a/boards_data/da/data_tasks/Classification/default.jsonl b/boards_data/da/data_tasks/Classification/default.jsonl
deleted file mode 100644
index 67cd6acade3e09c24b3a503b5902db96ec4fb31f..0000000000000000000000000000000000000000
--- a/boards_data/da/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,56 +0,0 @@
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-{"index":8,"Rank":27,"Model":"GritLM-7B<\/a>","Model Size (Million Parameters)":7240,"Memory Usage (GB, fp32)":26.97,"Embedding Dimensions":"","Max Tokens":4096,"Average":"","AngryTweetsClassification":65.35,"DKHateClassification":"","DanishPoliticalCommentsClassification":41.28,"LccSentimentClassification":70.13,"MassiveIntentClassification (da)":73.46,"MassiveScenarioClassification (da)":76.98,"NordicLangClassification":69.36,"ScalaDaClassification":""}
-{"index":9,"Rank":28,"Model":"e5-dansk-test-0.1<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","AngryTweetsClassification":56.08,"DKHateClassification":"","DanishPoliticalCommentsClassification":40.88,"LccSentimentClassification":59.6,"MassiveIntentClassification (da)":"","MassiveScenarioClassification (da)":"","NordicLangClassification":61.0,"ScalaDaClassification":50.43}
-{"index":13,"Rank":29,"Model":"MUG-B-1.6<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":41.0,"MassiveScenarioClassification (da)":51.92,"NordicLangClassification":"","ScalaDaClassification":""}
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-{"index":18,"Rank":32,"Model":"Titan-text-embeddings-v2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":55.06,"MassiveScenarioClassification (da)":64.26,"NordicLangClassification":"","ScalaDaClassification":""}
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-{"index":20,"Rank":34,"Model":"slx-v0.1<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.08,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":39.67,"MassiveScenarioClassification (da)":44.68,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":22,"Rank":35,"Model":"LaBSE-en-ru<\/a>","Model Size (Million Parameters)":129,"Memory Usage (GB, fp32)":0.48,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":38.66,"MassiveScenarioClassification (da)":44.69,"NordicLangClassification":"","ScalaDaClassification":""}
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-{"index":30,"Rank":37,"Model":"multilingual-e5-large-instruct<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","AngryTweetsClassification":59.53,"DKHateClassification":"","DanishPoliticalCommentsClassification":33.07,"LccSentimentClassification":60.27,"MassiveIntentClassification (da)":65.08,"MassiveScenarioClassification (da)":71.88,"NordicLangClassification":76.57,"ScalaDaClassification":""}
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-{"index":33,"Rank":39,"Model":"udever-bloom-560m<\/a>","Model Size (Million Parameters)":559,"Memory Usage (GB, fp32)":2.08,"Embedding Dimensions":1024,"Max Tokens":2048,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":46.93,"MassiveScenarioClassification (da)":49.11,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":34,"Rank":40,"Model":"multilingual-e5-small-ct2-int8<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":62.97,"MassiveScenarioClassification (da)":68.93,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":38,"Rank":41,"Model":"ternary-weight-embedding<\/a>","Model Size (Million Parameters)":99,"Memory Usage (GB, fp32)":0.37,"Embedding Dimensions":1792,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":43.94,"MassiveScenarioClassification (da)":44.0,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":39,"Rank":42,"Model":"LaBSE<\/a>","Model Size (Million Parameters)":471,"Memory Usage (GB, fp32)":1.75,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AngryTweetsClassification":51.11,"DKHateClassification":"","DanishPoliticalCommentsClassification":38.34,"LccSentimentClassification":50.07,"MassiveIntentClassification (da)":58.25,"MassiveScenarioClassification (da)":65.24,"NordicLangClassification":35.39,"ScalaDaClassification":""}
-{"index":40,"Rank":43,"Model":"all-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AngryTweetsClassification":42.87,"DKHateClassification":"","DanishPoliticalCommentsClassification":27.07,"LccSentimentClassification":41.93,"MassiveIntentClassification (da)":44.43,"MassiveScenarioClassification (da)":49.47,"NordicLangClassification":54.17,"ScalaDaClassification":""}
-{"index":42,"Rank":44,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","AngryTweetsClassification":44.13,"DKHateClassification":"","DanishPoliticalCommentsClassification":28.31,"LccSentimentClassification":39.27,"MassiveIntentClassification (da)":42.36,"MassiveScenarioClassification (da)":49.45,"NordicLangClassification":50.15,"ScalaDaClassification":""}
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-{"index":44,"Rank":46,"Model":"distiluse-base-multilingual-cased-v2<\/a>","Model Size (Million Parameters)":135,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":512,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":52.33,"MassiveScenarioClassification (da)":62.55,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":45,"Rank":47,"Model":"gtr-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":45.3,"MassiveScenarioClassification (da)":54.88,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":46,"Rank":48,"Model":"gtr-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":48.42,"MassiveScenarioClassification (da)":57.28,"NordicLangClassification":"","ScalaDaClassification":""}
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-{"index":48,"Rank":50,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","AngryTweetsClassification":54.84,"DKHateClassification":"","DanishPoliticalCommentsClassification":40.96,"LccSentimentClassification":58.4,"MassiveIntentClassification (da)":62.8,"MassiveScenarioClassification (da)":71.04,"NordicLangClassification":41.57,"ScalaDaClassification":""}
-{"index":49,"Rank":51,"Model":"sentence-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":38.82,"MassiveScenarioClassification (da)":48.36,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":50,"Rank":52,"Model":"sentence-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":41.76,"MassiveScenarioClassification (da)":51.44,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":51,"Rank":53,"Model":"sentence-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":47.69,"MassiveScenarioClassification (da)":55.79,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":52,"Rank":54,"Model":"rubert-tiny-turbo<\/a>","Model Size (Million Parameters)":29,"Memory Usage (GB, fp32)":0.11,"Embedding Dimensions":312,"Max Tokens":2048,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":33.95,"MassiveScenarioClassification (da)":43.69,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":53,"Rank":55,"Model":"text2vec-base-multilingual<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":56.74,"MassiveScenarioClassification (da)":63.07,"NordicLangClassification":"","ScalaDaClassification":""}
-{"index":54,"Rank":56,"Model":"llm_mse<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","AngryTweetsClassification":"","DKHateClassification":"","DanishPoliticalCommentsClassification":"","LccSentimentClassification":"","MassiveIntentClassification (da)":34.98,"MassiveScenarioClassification (da)":42.82,"NordicLangClassification":"","ScalaDaClassification":""}
diff --git a/boards_data/de/data_overall/default.txt b/boards_data/de/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/de/data_tasks/Clustering/default.jsonl b/boards_data/de/data_tasks/Clustering/default.jsonl
deleted file mode 100644
index bda1c23ca8d89a7e290f8fda76516a73607b0450..0000000000000000000000000000000000000000
--- a/boards_data/de/data_tasks/Clustering/default.jsonl
+++ /dev/null
@@ -1,22 +0,0 @@
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-{"index":8,"Rank":2,"Model":"gbert-large<\/a>","Model Size (Million Parameters)":337,"Memory Usage (GB, fp32)":1.26,"Embedding Dimensions":1024,"Max Tokens":512,"Average":32.34,"BlurbsClusteringP2P":39.3,"BlurbsClusteringS2S":13.38,"TenKGnadClusteringP2P":41.69,"TenKGnadClusteringS2S":34.97}
-{"index":19,"Rank":3,"Model":"sentence-t5-xxl<\/a>","Model Size (Million Parameters)":4865,"Memory Usage (GB, fp32)":18.12,"Embedding Dimensions":768,"Max Tokens":512,"Average":29.74,"BlurbsClusteringP2P":39.91,"BlurbsClusteringS2S":15.94,"TenKGnadClusteringP2P":43.43,"TenKGnadClusteringS2S":19.69}
-{"index":12,"Rank":4,"Model":"jina-embeddings-v2-base-de<\/a>","Model Size (Million Parameters)":161,"Memory Usage (GB, fp32)":0.6,"Embedding Dimensions":768,"Max Tokens":8192,"Average":29.55,"BlurbsClusteringP2P":35.49,"BlurbsClusteringS2S":16.17,"TenKGnadClusteringP2P":42.84,"TenKGnadClusteringS2S":23.69}
-{"index":2,"Rank":5,"Model":"jina-embeddings-v2-base-de-Q5_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":29.55,"BlurbsClusteringP2P":35.49,"BlurbsClusteringS2S":16.17,"TenKGnadClusteringP2P":42.84,"TenKGnadClusteringS2S":23.69}
-{"index":6,"Rank":6,"Model":"jina-embeddings-v2-base-de-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":29.55,"BlurbsClusteringP2P":35.49,"BlurbsClusteringS2S":16.17,"TenKGnadClusteringP2P":42.84,"TenKGnadClusteringS2S":23.69}
-{"index":5,"Rank":7,"Model":"German_Semantic_STS_V2<\/a>","Model Size (Million Parameters)":336,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":29.5,"BlurbsClusteringP2P":38.68,"BlurbsClusteringS2S":17.62,"TenKGnadClusteringP2P":38.0,"TenKGnadClusteringS2S":23.71}
-{"index":18,"Rank":8,"Model":"sentence-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":27.49,"BlurbsClusteringP2P":35.33,"BlurbsClusteringS2S":13.27,"TenKGnadClusteringP2P":44.11,"TenKGnadClusteringS2S":17.26}
-{"index":16,"Rank":9,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":27.04,"BlurbsClusteringP2P":34.38,"BlurbsClusteringS2S":15.81,"TenKGnadClusteringP2P":35.96,"TenKGnadClusteringS2S":22.0}
-{"index":7,"Rank":10,"Model":"gbert-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":27.0,"BlurbsClusteringP2P":35.36,"BlurbsClusteringS2S":11.27,"TenKGnadClusteringP2P":37.16,"TenKGnadClusteringS2S":24.23}
-{"index":20,"Rank":11,"Model":"use-cmlm-multilingual<\/a>","Model Size (Million Parameters)":472,"Memory Usage (GB, fp32)":1.76,"Embedding Dimensions":768,"Max Tokens":512,"Average":26.9,"BlurbsClusteringP2P":29.63,"BlurbsClusteringS2S":15.24,"TenKGnadClusteringP2P":37.1,"TenKGnadClusteringS2S":25.64}
-{"index":15,"Rank":12,"Model":"paraphrase-multilingual-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":26.3,"BlurbsClusteringP2P":32.46,"BlurbsClusteringS2S":14.33,"TenKGnadClusteringP2P":36.13,"TenKGnadClusteringS2S":22.26}
-{"index":17,"Rank":13,"Model":"sentence-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":26.29,"BlurbsClusteringP2P":30.59,"BlurbsClusteringS2S":11.57,"TenKGnadClusteringP2P":44.88,"TenKGnadClusteringS2S":18.11}
-{"index":11,"Rank":14,"Model":"SONAR<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":22.71,"BlurbsClusteringP2P":27.0,"BlurbsClusteringS2S":14.85,"TenKGnadClusteringP2P":21.83,"TenKGnadClusteringS2S":27.16}
-{"index":21,"Rank":15,"Model":"gottbert-base<\/a>","Model Size (Million Parameters)":127,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":512,"Average":21.46,"BlurbsClusteringP2P":34.49,"BlurbsClusteringS2S":8.37,"TenKGnadClusteringP2P":33.66,"TenKGnadClusteringS2S":9.34}
-{"index":4,"Rank":16,"Model":"cross-en-de-roberta-sentence-transformer<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":19.49,"BlurbsClusteringP2P":30.82,"BlurbsClusteringS2S":12.69,"TenKGnadClusteringP2P":23.5,"TenKGnadClusteringS2S":10.94}
-{"index":1,"Rank":17,"Model":"xlm-roberta-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":18.94,"BlurbsClusteringP2P":29.84,"BlurbsClusteringS2S":7.29,"TenKGnadClusteringP2P":32.46,"TenKGnadClusteringS2S":6.16}
-{"index":0,"Rank":18,"Model":"winberta-base<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":16.62,"BlurbsClusteringP2P":17.47,"BlurbsClusteringS2S":7.96,"TenKGnadClusteringP2P":29.79,"TenKGnadClusteringS2S":11.27}
-{"index":14,"Rank":19,"Model":"average_word_embeddings_komninos<\/a>","Model Size (Million Parameters)":134,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":300,"Max Tokens":"N\/A","Average":10.03,"BlurbsClusteringP2P":11.37,"BlurbsClusteringS2S":8.01,"TenKGnadClusteringP2P":15.89,"TenKGnadClusteringS2S":4.84}
-{"index":10,"Rank":20,"Model":"gelectra-large<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":9.23,"BlurbsClusteringP2P":13.96,"BlurbsClusteringS2S":7.57,"TenKGnadClusteringP2P":11.49,"TenKGnadClusteringS2S":3.91}
-{"index":9,"Rank":21,"Model":"gelectra-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":7.73,"BlurbsClusteringP2P":10.06,"BlurbsClusteringS2S":7.74,"TenKGnadClusteringP2P":9.02,"TenKGnadClusteringS2S":4.11}
-{"index":3,"Rank":22,"Model":"SGPT-125M-weightedmean-nli-bitfit<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":2048,"Average":"","BlurbsClusteringP2P":"","BlurbsClusteringS2S":8.0,"TenKGnadClusteringP2P":"","TenKGnadClusteringS2S":""}
diff --git a/boards_data/en-x/data_overall/default.txt b/boards_data/en-x/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/en-x/data_tasks/BitextMining/default.jsonl b/boards_data/en-x/data_tasks/BitextMining/default.jsonl
deleted file mode 100644
index 29dbf97c6944fe1ed951b86f418a833e714b836a..0000000000000000000000000000000000000000
--- a/boards_data/en-x/data_tasks/BitextMining/default.jsonl
+++ /dev/null
@@ -1,52 +0,0 @@
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diff --git a/boards_data/en/data_overall/default.jsonl b/boards_data/en/data_overall/default.jsonl
deleted file mode 100644
index 1146690c6caea2cf4a311ce0b7b38efc8dd135fa..0000000000000000000000000000000000000000
--- a/boards_data/en/data_overall/default.jsonl
+++ /dev/null
@@ -1,342 +0,0 @@
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-{"index":34,"Rank":195,"Model":"winberta-base<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":31.67,"PairClassification Average (3 datasets)":76.96,"Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
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-{"index":44,"Rank":201,"Model":"jina-embeddings-v2-small-en-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":68.82,"Clustering Average (11 datasets)":40.08,"PairClassification Average (3 datasets)":84.44,"Reranking Average (4 datasets)":55.09,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":80.0,"Summarization Average (1 datasets)":30.56}
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-{"index":68,"Rank":216,"Model":"e5-large-v2-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":75.24,"Clustering Average (11 datasets)":44.49,"PairClassification Average (3 datasets)":86.03,"Reranking Average (4 datasets)":56.61,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":82.05,"Summarization Average (1 datasets)":30.19}
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-{"index":75,"Rank":221,"Model":"jina-embeddings-v2-base-de-Q5_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":67.98,"Clustering Average (11 datasets)":38.99,"PairClassification Average (3 datasets)":83.65,"Reranking Average (4 datasets)":54.64,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":82.61,"Summarization Average (1 datasets)":31.55}
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-{"index":92,"Rank":225,"Model":"gte-micro-v2<\/a>","Model Size (Million Parameters)":17,"Memory Usage (GB, fp32)":0.06,"Embedding Dimensions":384,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":67.47,"Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
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-{"index":94,"Rank":227,"Model":"gte-micro-v4<\/a>","Model Size (Million Parameters)":19,"Memory Usage (GB, fp32)":0.07,"Embedding Dimensions":384,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":68.13,"Clustering Average (11 datasets)":39.54,"PairClassification Average (3 datasets)":82.59,"Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":95,"Rank":228,"Model":"test24<\/a>","Model Size (Million Parameters)":17,"Memory Usage (GB, fp32)":0.06,"Embedding Dimensions":384,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":63.54,"Clustering Average (11 datasets)":35.78,"PairClassification Average (3 datasets)":80.4,"Reranking Average (4 datasets)":51.68,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":76.56,"Summarization Average (1 datasets)":30.52}
-{"index":96,"Rank":229,"Model":"test25<\/a>","Model Size (Million Parameters)":17,"Memory Usage (GB, fp32)":0.06,"Embedding Dimensions":384,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":64.56,"Clustering Average (11 datasets)":33.15,"PairClassification Average (3 datasets)":79.7,"Reranking Average (4 datasets)":50.55,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":75.37,"Summarization Average (1 datasets)":29.33}
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-{"index":104,"Rank":231,"Model":"Arabic-MiniLM-L12-v2-all-nli-triplet<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":30.69}
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-{"index":141,"Rank":253,"Model":"Giga-Embeddings-instruct<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":78.64,"Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":48.03,"STS Average (10 datasets)":"","Summarization Average (1 datasets)":27.86}
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-{"index":159,"Rank":260,"Model":"rubert-tiny2<\/a>","Model Size (Million Parameters)":29,"Memory Usage (GB, fp32)":0.11,"Embedding Dimensions":2048,"Max Tokens":514,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
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-{"index":161,"Rank":262,"Model":"cai-stellaris-text-embeddings<\/a>","Model Size (Million Parameters)":53,"Memory Usage (GB, fp32)":0.2,"Embedding Dimensions":768,"Max Tokens":514,"Average (56 datasets)":"","Classification Average (12 datasets)":61.44,"Clustering Average (11 datasets)":37.92,"PairClassification Average (3 datasets)":82.11,"Reranking Average (4 datasets)":55.24,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
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-{"index":166,"Rank":267,"Model":"USER-base<\/a>","Model Size (Million Parameters)":124,"Memory Usage (GB, fp32)":0.46,"Embedding Dimensions":768,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":167,"Rank":268,"Model":"USER-bge-m3<\/a>","Model Size (Million Parameters)":359,"Memory Usage (GB, fp32)":1.34,"Embedding Dimensions":1024,"Max Tokens":8192,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":168,"Rank":269,"Model":"deberta-v1-base<\/a>","Model Size (Million Parameters)":124,"Memory Usage (GB, fp32)":0.46,"Embedding Dimensions":768,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":170,"Rank":270,"Model":"sft-bge-small<\/a>","Model Size (Million Parameters)":24,"Memory Usage (GB, fp32)":0.09,"Embedding Dimensions":512,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":174,"Rank":271,"Model":"mxbai-embed-large-v1-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":75.64,"Clustering Average (11 datasets)":46.71,"PairClassification Average (3 datasets)":87.2,"Reranking Average (4 datasets)":60.11,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":85.0,"Summarization Average (1 datasets)":32.71}
-{"index":176,"Rank":272,"Model":"flaubert_base_cased<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":177,"Rank":273,"Model":"flaubert_base_uncased<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":178,"Rank":274,"Model":"flaubert_large_cased<\/a>","Model Size (Million Parameters)":372,"Memory Usage (GB, fp32)":1.39,"Embedding Dimensions":1024,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":180,"Rank":275,"Model":"jina-embeddings-v2-base-en-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":73.45,"Clustering Average (11 datasets)":41.73,"PairClassification Average (3 datasets)":85.38,"Reranking Average (4 datasets)":56.98,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":80.7,"Summarization Average (1 datasets)":31.6}
-{"index":181,"Rank":276,"Model":"bert-base-multilingual-cased<\/a>","Model Size (Million Parameters)":179,"Memory Usage (GB, fp32)":0.67,"Embedding Dimensions":768,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":182,"Rank":277,"Model":"bert-base-multilingual-uncased<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":184,"Rank":278,"Model":"Gemma-Embeddings-v0.8<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":63.8,"STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":189,"Rank":279,"Model":"bge-large-en-v1.5-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":75.97,"Clustering Average (11 datasets)":46.08,"PairClassification Average (3 datasets)":87.12,"Reranking Average (4 datasets)":60.03,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":83.11,"Summarization Average (1 datasets)":31.61}
-{"index":203,"Rank":280,"Model":"herbert-base-retrieval-v2<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":514,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":204,"Rank":281,"Model":"silver-retriever-base-v1<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":514,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":213,"Rank":282,"Model":"jina-embeddings-v2-base-de<\/a>","Model Size (Million Parameters)":161,"Memory Usage (GB, fp32)":0.6,"Embedding Dimensions":768,"Max Tokens":8192,"Average (56 datasets)":"","Classification Average (12 datasets)":67.98,"Clustering Average (11 datasets)":38.99,"PairClassification Average (3 datasets)":83.65,"Reranking Average (4 datasets)":54.64,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":82.61,"Summarization Average (1 datasets)":31.55}
-{"index":215,"Rank":283,"Model":"jina-embeddings-v2-base-es<\/a>","Model Size (Million Parameters)":161,"Memory Usage (GB, fp32)":0.6,"Embedding Dimensions":768,"Max Tokens":8192,"Average (56 datasets)":"","Classification Average (12 datasets)":69.02,"Clustering Average (11 datasets)":39.29,"PairClassification Average (3 datasets)":84.56,"Reranking Average (4 datasets)":55.32,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":83.49,"Summarization Average (1 datasets)":29.87}
-{"index":226,"Rank":284,"Model":"ternary-weight-embedding<\/a>","Model Size (Million Parameters)":99,"Memory Usage (GB, fp32)":0.37,"Embedding Dimensions":1792,"Max Tokens":512,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":227,"Rank":285,"Model":"bge-m3-custom-fr<\/a>","Model Size (Million Parameters)":568,"Memory Usage (GB, fp32)":2.12,"Embedding Dimensions":1024,"Max Tokens":8194,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":228,"Rank":286,"Model":"sentence_croissant_alpha_v0.1<\/a>","Model Size (Million Parameters)":1280,"Memory Usage (GB, fp32)":4.77,"Embedding Dimensions":2048,"Max Tokens":2048,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
-{"index":229,"Rank":287,"Model":"sentence_croissant_alpha_v0.2<\/a>","Model Size (Million Parameters)":1280,"Memory Usage (GB, fp32)":4.77,"Embedding Dimensions":2048,"Max Tokens":2048,"Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":"","PairClassification Average (3 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":"","Summarization Average (1 datasets)":""}
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-{"index":324,"Rank":333,"Model":"universal-sentence-encoder-4<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":64.45,"Clustering Average (11 datasets)":35.71,"PairClassification Average (3 datasets)":76.23,"Reranking Average (4 datasets)":"","Retrieval Average (15 datasets)":"","STS Average (10 datasets)":72.04,"Summarization Average (1 datasets)":29.42}
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-{"index":331,"Rank":339,"Model":"jina-embeddings-v2-base-en-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":73.45,"Clustering Average (11 datasets)":41.73,"PairClassification Average (3 datasets)":85.38,"Reranking Average (4 datasets)":56.98,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":80.7,"Summarization Average (1 datasets)":31.6}
-{"index":332,"Rank":340,"Model":"bge-small-en-v1.5-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":74.14,"Clustering Average (11 datasets)":43.82,"PairClassification Average (3 datasets)":84.92,"Reranking Average (4 datasets)":58.36,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":81.59,"Summarization Average (1 datasets)":30.12}
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-{"index":334,"Rank":342,"Model":"new_model<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (56 datasets)":"","Classification Average (12 datasets)":"","Clustering Average (11 datasets)":40.54,"PairClassification Average (3 datasets)":85.41,"Reranking Average (4 datasets)":54.46,"Retrieval Average (15 datasets)":"","STS Average (10 datasets)":80.59,"Summarization Average (1 datasets)":29.78}
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diff --git a/boards_data/en/data_tasks/Classification/default.jsonl b/boards_data/en/data_tasks/Classification/default.jsonl
deleted file mode 100644
index 10b873705d0debb9ec3722ab73548cfe89151621..0000000000000000000000000000000000000000
--- a/boards_data/en/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,336 +0,0 @@
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diff --git a/boards_data/en/data_tasks/Clustering/default.jsonl b/boards_data/en/data_tasks/Clustering/default.jsonl
deleted file mode 100644
index ec7a43af3d6b8e6614c8a7b72a5ff0d215b8ff92..0000000000000000000000000000000000000000
--- a/boards_data/en/data_tasks/Clustering/default.jsonl
+++ /dev/null
@@ -1,330 +0,0 @@
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diff --git a/boards_data/en/data_tasks/PairClassification/default.jsonl b/boards_data/en/data_tasks/PairClassification/default.jsonl
deleted file mode 100644
index 837abce1c25a45e37d7ec761c0d831fda7fb5858..0000000000000000000000000000000000000000
--- a/boards_data/en/data_tasks/PairClassification/default.jsonl
+++ /dev/null
@@ -1,336 +0,0 @@
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diff --git a/boards_data/en/data_tasks/Reranking/default.jsonl b/boards_data/en/data_tasks/Reranking/default.jsonl
deleted file mode 100644
index 44a11c4bd1bd006f29b79358f5163a3b7cc4ad96..0000000000000000000000000000000000000000
--- a/boards_data/en/data_tasks/Reranking/default.jsonl
+++ /dev/null
@@ -1,329 +0,0 @@
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diff --git a/boards_data/en/data_tasks/Retrieval/default.jsonl b/boards_data/en/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index 51b688463abef61649bebd58447344e99745b0d2..0000000000000000000000000000000000000000
--- a/boards_data/en/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,330 +0,0 @@
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diff --git a/boards_data/en/data_tasks/STS/default.jsonl b/boards_data/en/data_tasks/STS/default.jsonl
deleted file mode 100644
index 51ad20626ee09c666b36cfff734333efec72df6c..0000000000000000000000000000000000000000
--- a/boards_data/en/data_tasks/STS/default.jsonl
+++ /dev/null
@@ -1,335 +0,0 @@
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diff --git a/boards_data/en/data_tasks/Summarization/default.jsonl b/boards_data/en/data_tasks/Summarization/default.jsonl
deleted file mode 100644
index d85b4112f84f1ba4fec7dedada1e78fa28346e7f..0000000000000000000000000000000000000000
--- a/boards_data/en/data_tasks/Summarization/default.jsonl
+++ /dev/null
@@ -1,322 +0,0 @@
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diff --git a/boards_data/fr/data_overall/default.jsonl b/boards_data/fr/data_overall/default.jsonl
deleted file mode 100644
index 794631ab92b9bf2a6b05ff8fe56a7ed53d04a332..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_overall/default.jsonl
+++ /dev/null
@@ -1,96 +0,0 @@
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-{"index":8,"Rank":48,"Model":"llm2vec-occiglot-mntp<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (26 datasets)":45.65,"Classification Average (6 datasets)":60.98,"Clustering Average (7 datasets)":41.21,"PairClassification Average (2 datasets)":80.54,"Reranking Average (2 datasets)":49.98,"Retrieval Average (5 datasets)":12.94,"STS Average (3 datasets)":59.33,"Summarization Average (1 datasets)":28.72}
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-{"index":68,"Rank":57,"Model":"udever-bloom-560m<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":2048,"Average (26 datasets)":31.89,"Classification Average (6 datasets)":30.38,"Clustering Average (7 datasets)":29.03,"PairClassification Average (2 datasets)":73.93,"Reranking Average (2 datasets)":39.82,"Retrieval Average (5 datasets)":7.98,"STS Average (3 datasets)":50.89,"Summarization Average (1 datasets)":23.63}
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-{"index":48,"Rank":85,"Model":"slx-v0.1<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.08,"Embedding Dimensions":384,"Max Tokens":512,"Average (26 datasets)":"","Classification Average (6 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)":""}
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-{"index":71,"Rank":92,"Model":"ternary-weight-embedding<\/a>","Model Size (Million Parameters)":99,"Memory Usage (GB, fp32)":0.37,"Embedding Dimensions":1792,"Max Tokens":512,"Average (26 datasets)":"","Classification Average (6 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":73,"Rank":93,"Model":"sentence_croissant_alpha_v0.1<\/a>","Model Size (Million Parameters)":1280,"Memory Usage (GB, fp32)":4.77,"Embedding Dimensions":2048,"Max Tokens":2048,"Average (26 datasets)":"","Classification Average (6 datasets)":68.48,"Clustering Average (7 datasets)":41.33,"PairClassification Average (2 datasets)":"","Reranking Average (2 datasets)":"","Retrieval Average (5 datasets)":41.15,"STS Average (3 datasets)":72.28,"Summarization Average (1 datasets)":30.28}
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-{"index":83,"Rank":97,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average (26 datasets)":"","Classification Average (6 datasets)":52.87,"Clustering Average (7 datasets)":"","PairClassification Average (2 datasets)":71.52,"Reranking Average (2 datasets)":67.88,"Retrieval Average (5 datasets)":"","STS Average (3 datasets)":69.77,"Summarization Average (1 datasets)":28.11}
-{"index":84,"Rank":98,"Model":"allenai-specter<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average (26 datasets)":"","Classification Average (6 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":85,"Rank":99,"Model":"average_word_embeddings_komninos<\/a>","Model Size (Million Parameters)":134,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":300,"Max Tokens":"N\/A","Average (26 datasets)":"","Classification Average (6 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":87,"Rank":100,"Model":"gtr-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average (26 datasets)":"","Classification Average (6 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":88,"Rank":101,"Model":"gtr-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average (26 datasets)":"","Classification Average (6 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":96,"Rank":102,"Model":"LaBSE-ru-turbo<\/a>","Model Size (Million Parameters)":128,"Memory Usage (GB, fp32)":0.48,"Embedding Dimensions":768,"Max Tokens":512,"Average (26 datasets)":"","Classification Average (6 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)":""}
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diff --git a/boards_data/fr/data_tasks/Classification/default.jsonl b/boards_data/fr/data_tasks/Classification/default.jsonl
deleted file mode 100644
index faf30db7dfc895e5f4d9fa911a96c5984d8de8ea..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,104 +0,0 @@
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-{"index":26,"Rank":4,"Model":"gte-Qwen2-7B-instruct-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":81.76,"AmazonReviewsClassification (fr)":55.53,"MasakhaNEWSClassification (fra)":82.61,"MassiveIntentClassification (fr)":81.65,"MassiveScenarioClassification (fr)":86.64,"MTOPDomainClassification (fr)":96.69,"MTOPIntentClassification (fr)":87.47}
-{"index":13,"Rank":5,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Embedding Dimensions":3584,"Max Tokens":8192,"Average":81.62,"AmazonReviewsClassification (fr)":55.19,"MasakhaNEWSClassification (fra)":82.49,"MassiveIntentClassification (fr)":79.6,"MassiveScenarioClassification (fr)":82.18,"MTOPDomainClassification (fr)":97.2,"MTOPIntentClassification (fr)":93.07}
-{"index":9,"Rank":6,"Model":"gte-Qwen2-1.5B-instruct<\/a>","Model Size (Million Parameters)":1776,"Memory Usage (GB, fp32)":6.62,"Embedding Dimensions":1536,"Max Tokens":131072,"Average":78.02,"AmazonReviewsClassification (fr)":53.47,"MasakhaNEWSClassification (fra)":85.19,"MassiveIntentClassification (fr)":76.65,"MassiveScenarioClassification (fr)":79.1,"MTOPDomainClassification (fr)":93.48,"MTOPIntentClassification (fr)":80.23}
-{"index":25,"Rank":7,"Model":"gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":78.02,"AmazonReviewsClassification (fr)":53.47,"MasakhaNEWSClassification (fra)":85.19,"MassiveIntentClassification (fr)":76.65,"MassiveScenarioClassification (fr)":79.1,"MTOPDomainClassification (fr)":93.48,"MTOPIntentClassification (fr)":80.23}
-{"index":32,"Rank":8,"Model":"KaLM-embedding-multilingual-mini-instruct-v1<\/a>","Model Size (Million Parameters)":494,"Memory Usage (GB, fp32)":1.84,"Embedding Dimensions":896,"Max Tokens":131072,"Average":76.83,"AmazonReviewsClassification (fr)":52.62,"MasakhaNEWSClassification (fra)":83.63,"MassiveIntentClassification (fr)":69.54,"MassiveScenarioClassification (fr)":78.53,"MTOPDomainClassification (fr)":95.79,"MTOPIntentClassification (fr)":80.88}
-{"index":69,"Rank":9,"Model":"jina-embeddings-v3<\/a>","Model Size (Million Parameters)":572,"Memory Usage (GB, fp32)":2.13,"Embedding Dimensions":1024,"Max Tokens":8194,"Average":76.54,"AmazonReviewsClassification (fr)":47.31,"MasakhaNEWSClassification (fra)":74.03,"MassiveIntentClassification (fr)":76.32,"MassiveScenarioClassification (fr)":83.09,"MTOPDomainClassification (fr)":96.3,"MTOPIntentClassification (fr)":82.21}
-{"index":30,"Rank":10,"Model":"GritLM-7B<\/a>","Model Size (Million Parameters)":7240,"Memory Usage (GB, fp32)":26.97,"Embedding Dimensions":"","Max Tokens":4096,"Average":76.28,"AmazonReviewsClassification (fr)":54.43,"MasakhaNEWSClassification (fra)":81.35,"MassiveIntentClassification (fr)":75.51,"MassiveScenarioClassification (fr)":77.29,"MTOPDomainClassification (fr)":92.25,"MTOPIntentClassification (fr)":76.87}
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-{"index":65,"Rank":12,"Model":"multilingual-e5-large-instruct<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":69.32,"AmazonReviewsClassification (fr)":49.78,"MasakhaNEWSClassification (fra)":78.93,"MassiveIntentClassification (fr)":66.88,"MassiveScenarioClassification (fr)":71.16,"MTOPDomainClassification (fr)":85.89,"MTOPIntentClassification (fr)":63.26}
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diff --git a/boards_data/fr/data_tasks/Clustering/default.jsonl b/boards_data/fr/data_tasks/Clustering/default.jsonl
deleted file mode 100644
index 047c1822f445012746a80881683d14d2db530229..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_tasks/Clustering/default.jsonl
+++ /dev/null
@@ -1,96 +0,0 @@
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diff --git a/boards_data/fr/data_tasks/PairClassification/default.jsonl b/boards_data/fr/data_tasks/PairClassification/default.jsonl
deleted file mode 100644
index 78288e55da2d13e999f4318e2652809aa2c23e6a..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_tasks/PairClassification/default.jsonl
+++ /dev/null
@@ -1,98 +0,0 @@
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diff --git a/boards_data/fr/data_tasks/Reranking/default.jsonl b/boards_data/fr/data_tasks/Reranking/default.jsonl
deleted file mode 100644
index 09fa657895d1d97ed35fc940d1c0485eda5e9076..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_tasks/Reranking/default.jsonl
+++ /dev/null
@@ -1,96 +0,0 @@
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diff --git a/boards_data/fr/data_tasks/Retrieval/default.jsonl b/boards_data/fr/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index ab55260fa26cd4c295a016dd204a43d6dd6d458c..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,97 +0,0 @@
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diff --git a/boards_data/fr/data_tasks/STS/default.jsonl b/boards_data/fr/data_tasks/STS/default.jsonl
deleted file mode 100644
index 13f24bf3a0eb773339b84e2632f94a641c6c5981..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_tasks/STS/default.jsonl
+++ /dev/null
@@ -1,102 +0,0 @@
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diff --git a/boards_data/fr/data_tasks/Summarization/default.jsonl b/boards_data/fr/data_tasks/Summarization/default.jsonl
deleted file mode 100644
index 8ba1924872f1c31ba759dc039482617e4ff8fabc..0000000000000000000000000000000000000000
--- a/boards_data/fr/data_tasks/Summarization/default.jsonl
+++ /dev/null
@@ -1,96 +0,0 @@
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diff --git a/boards_data/instructions/data_overall/default.txt b/boards_data/instructions/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/instructions/data_tasks/InstructionRetrieval/default.jsonl b/boards_data/instructions/data_tasks/InstructionRetrieval/default.jsonl
deleted file mode 100644
index 9289bf72179c99fd04d98f1cd405fcddfbc29e99..0000000000000000000000000000000000000000
--- a/boards_data/instructions/data_tasks/InstructionRetrieval/default.jsonl
+++ /dev/null
@@ -1,31 +0,0 @@
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diff --git a/boards_data/law/data_overall/default.txt b/boards_data/law/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/law/data_tasks/Retrieval/default.jsonl b/boards_data/law/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index 78cecbf2fc63e3b1baca0042ef9fd40f8f8d5282..0000000000000000000000000000000000000000
--- a/boards_data/law/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,21 +0,0 @@
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diff --git a/boards_data/longembed/data_overall/default.txt b/boards_data/longembed/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/longembed/data_tasks/Retrieval/default.jsonl b/boards_data/longembed/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index ddaba966966caa96fea538b1964922d2e6bb0843..0000000000000000000000000000000000000000
--- a/boards_data/longembed/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,23 +0,0 @@
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-{"index":22,"Rank":23,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":3072,"Max Tokens":8191,"Average":"","LEMBNarrativeQARetrieval":44.09,"LEMBNeedleRetrieval":"","LEMBPasskeyRetrieval":"","LEMBQMSumRetrieval":32.49,"LEMBSummScreenFDRetrieval":84.8,"LEMBWikimQARetrieval":54.16}
diff --git a/boards_data/no/data_overall/default.txt b/boards_data/no/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/no/data_tasks/Classification/default.jsonl b/boards_data/no/data_tasks/Classification/default.jsonl
deleted file mode 100644
index 1f5ea4327f95e6a06e392d2a9fb5923a00499556..0000000000000000000000000000000000000000
--- a/boards_data/no/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,56 +0,0 @@
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-{"index":36,"Rank":4,"Model":"norbert3-base<\/a>","Model Size (Million Parameters)":131,"Memory Usage (GB, fp32)":0.49,"Embedding Dimensions":768,"Max Tokens":512,"Average":61.75,"MassiveIntentClassification (nb)":54.2,"MassiveScenarioClassification (nb)":60.69,"NoRecClassification":53.4,"NordicLangClassification":82.67,"NorwegianParliament":59.33,"ScalaNbClassification":60.19}
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-{"index":37,"Rank":6,"Model":"norbert3-large<\/a>","Model Size (Million Parameters)":368,"Memory Usage (GB, fp32)":1.37,"Embedding Dimensions":1024,"Max Tokens":512,"Average":60.34,"MassiveIntentClassification (nb)":47.42,"MassiveScenarioClassification (nb)":54.25,"NoRecClassification":50.46,"NordicLangClassification":84.25,"NorwegianParliament":58.85,"ScalaNbClassification":66.79}
-{"index":31,"Rank":7,"Model":"multilingual-e5-small<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":58.86,"MassiveIntentClassification (nb)":53.96,"MassiveScenarioClassification (nb)":59.9,"NoRecClassification":53.96,"NordicLangClassification":75.15,"NorwegianParliament":60.15,"ScalaNbClassification":50.06}
-{"index":21,"Rank":8,"Model":"dfm-encoder-large-v1<\/a>","Model Size (Million Parameters)":355,"Memory Usage (GB, fp32)":1.32,"Embedding Dimensions":1024,"Max Tokens":512,"Average":58.46,"MassiveIntentClassification (nb)":52.49,"MassiveScenarioClassification (nb)":54.59,"NoRecClassification":48.3,"NordicLangClassification":77.68,"NorwegianParliament":58.78,"ScalaNbClassification":58.95}
-{"index":55,"Rank":9,"Model":"DanskBERT<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":514,"Average":55.0,"MassiveIntentClassification (nb)":45.38,"MassiveScenarioClassification (nb)":47.55,"NoRecClassification":46.06,"NordicLangClassification":74.25,"NorwegianParliament":56.79,"ScalaNbClassification":59.99}
-{"index":23,"Rank":10,"Model":"SONAR<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":54.92,"MassiveIntentClassification (nb)":59.9,"MassiveScenarioClassification (nb)":65.81,"NoRecClassification":48.25,"NordicLangClassification":48.4,"NorwegianParliament":55.99,"ScalaNbClassification":51.18}
-{"index":7,"Rank":11,"Model":"xlm-roberta-base<\/a>","Model Size (Million Parameters)":279,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":54.34,"MassiveIntentClassification (nb)":40.46,"MassiveScenarioClassification (nb)":44.83,"NoRecClassification":46.28,"NordicLangClassification":79.39,"NorwegianParliament":56.75,"ScalaNbClassification":58.33}
-{"index":24,"Rank":12,"Model":"e5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":50.14,"MassiveIntentClassification (nb)":41.57,"MassiveScenarioClassification (nb)":50.33,"NoRecClassification":42.0,"NordicLangClassification":59.34,"NorwegianParliament":57.42,"ScalaNbClassification":50.18}
-{"index":25,"Rank":13,"Model":"e5-large<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":50.01,"MassiveIntentClassification (nb)":40.63,"MassiveScenarioClassification (nb)":51.91,"NoRecClassification":41.83,"NordicLangClassification":58.3,"NorwegianParliament":57.26,"ScalaNbClassification":50.13}
-{"index":12,"Rank":14,"Model":"sentence-bert-swedish-cased<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":512,"Average":48.88,"MassiveIntentClassification (nb)":42.74,"MassiveScenarioClassification (nb)":49.49,"NoRecClassification":43.53,"NordicLangClassification":51.45,"NorwegianParliament":55.74,"ScalaNbClassification":50.34}
-{"index":27,"Rank":15,"Model":"e5-small<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":48.46,"MassiveIntentClassification (nb)":40.25,"MassiveScenarioClassification (nb)":48.58,"NoRecClassification":41.84,"NordicLangClassification":53.47,"NorwegianParliament":56.57,"ScalaNbClassification":50.03}
-{"index":10,"Rank":16,"Model":"bert-base-swedish-cased<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":512,"Average":48.18,"MassiveIntentClassification (nb)":35.75,"MassiveScenarioClassification (nb)":35.76,"NoRecClassification":43.91,"NordicLangClassification":62.45,"NorwegianParliament":57.56,"ScalaNbClassification":53.63}
-{"index":35,"Rank":17,"Model":"electra-small-nordic<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Embedding Dimensions":256,"Max Tokens":512,"Average":47.28,"MassiveIntentClassification (nb)":24.6,"MassiveScenarioClassification (nb)":27.3,"NoRecClassification":45.44,"NordicLangClassification":57.82,"NorwegianParliament":53.25,"ScalaNbClassification":75.28}
-{"index":41,"Rank":18,"Model":"all-MiniLM-L6-v2<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Embedding Dimensions":384,"Max Tokens":512,"Average":46.94,"MassiveIntentClassification (nb)":39.36,"MassiveScenarioClassification (nb)":44.67,"NoRecClassification":37.93,"NordicLangClassification":54.7,"NorwegianParliament":54.8,"ScalaNbClassification":50.17}
-{"index":11,"Rank":19,"Model":"electra-small-swedish-cased-discriminator<\/a>","Model Size (Million Parameters)":16,"Memory Usage (GB, fp32)":0.06,"Embedding Dimensions":256,"Max Tokens":512,"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,"Embedding Dimensions":1024,"Max Tokens":"N\/A","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, fp32)":1.14,"Embedding Dimensions":768,"Max Tokens":8192,"Average":"","MassiveIntentClassification (nb)":63.74,"MassiveScenarioClassification (nb)":71.5,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":2,"Rank":22,"Model":"e5-mistral-7b-instruct<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","MassiveIntentClassification (nb)":70.93,"MassiveScenarioClassification (nb)":75.7,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":3,"Rank":23,"Model":"multilingual-e5-base-similarity-v1-onnx-quantized<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":66.18,"MassiveScenarioClassification (nb)":71.63,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":4,"Rank":24,"Model":"winberta-base<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":36.89,"MassiveScenarioClassification (nb)":44.27,"NoRecClassification":43.53,"NordicLangClassification":"","NorwegianParliament":54.9,"ScalaNbClassification":""}
-{"index":5,"Rank":25,"Model":"winberta-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":28.65,"MassiveScenarioClassification (nb)":35.24,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":6,"Rank":26,"Model":"windberta-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":31.07,"MassiveScenarioClassification (nb)":38.02,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":8,"Rank":27,"Model":"GritLM-7B<\/a>","Model Size (Million Parameters)":7240,"Memory Usage (GB, fp32)":26.97,"Embedding Dimensions":"","Max Tokens":4096,"Average":"","MassiveIntentClassification (nb)":70.92,"MassiveScenarioClassification (nb)":75.62,"NoRecClassification":56.05,"NordicLangClassification":69.36,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":9,"Rank":28,"Model":"e5-dansk-test-0.1<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","MassiveIntentClassification (nb)":"","MassiveScenarioClassification (nb)":"","NoRecClassification":"","NordicLangClassification":61.0,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":13,"Rank":29,"Model":"MUG-B-1.6<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":39.67,"MassiveScenarioClassification (nb)":50.89,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":14,"Rank":30,"Model":"SGPT-125M-weightedmean-msmarco-specb-bitfit<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":2048,"Average":"","MassiveIntentClassification (nb)":39.82,"MassiveScenarioClassification (nb)":39.02,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":15,"Rank":31,"Model":"SGPT-125M-weightedmean-nli-bitfit<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":2048,"Average":"","MassiveIntentClassification (nb)":39.48,"MassiveScenarioClassification (nb)":40.47,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":18,"Rank":32,"Model":"Titan-text-embeddings-v2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":54.41,"MassiveScenarioClassification (nb)":64.64,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":19,"Rank":33,"Model":"sgpt-bloom-7b1-msmarco<\/a>","Model Size (Million Parameters)":7068,"Memory Usage (GB, fp32)":26.33,"Embedding Dimensions":4096,"Max Tokens":2048,"Average":"","MassiveIntentClassification (nb)":49.41,"MassiveScenarioClassification (nb)":51.8,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":20,"Rank":34,"Model":"slx-v0.1<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.08,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":38.18,"MassiveScenarioClassification (nb)":43.39,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":22,"Rank":35,"Model":"LaBSE-en-ru<\/a>","Model Size (Million Parameters)":129,"Memory Usage (GB, fp32)":0.48,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":35.78,"MassiveScenarioClassification (nb)":42.43,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":26,"Rank":36,"Model":"e5-mistral-7b-instruct<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","MassiveIntentClassification (nb)":65.35,"MassiveScenarioClassification (nb)":72.48,"NoRecClassification":55.0,"NordicLangClassification":69.13,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":30,"Rank":37,"Model":"multilingual-e5-large-instruct<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","MassiveIntentClassification (nb)":65.85,"MassiveScenarioClassification (nb)":71.28,"NoRecClassification":57.06,"NordicLangClassification":76.57,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":32,"Rank":38,"Model":"udever-bloom-1b1<\/a>","Model Size (Million Parameters)":1065,"Memory Usage (GB, fp32)":3.97,"Embedding Dimensions":1536,"Max Tokens":2048,"Average":"","MassiveIntentClassification (nb)":46.18,"MassiveScenarioClassification (nb)":50.32,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
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-{"index":34,"Rank":40,"Model":"multilingual-e5-small-ct2-int8<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":61.96,"MassiveScenarioClassification (nb)":67.37,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":38,"Rank":41,"Model":"ternary-weight-embedding<\/a>","Model Size (Million Parameters)":99,"Memory Usage (GB, fp32)":0.37,"Embedding Dimensions":1792,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":40.4,"MassiveScenarioClassification (nb)":39.53,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":39,"Rank":42,"Model":"LaBSE<\/a>","Model Size (Million Parameters)":471,"Memory Usage (GB, fp32)":1.75,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":57.91,"MassiveScenarioClassification (nb)":64.29,"NoRecClassification":45.45,"NordicLangClassification":35.39,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":40,"Rank":43,"Model":"all-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":41.91,"MassiveScenarioClassification (nb)":47.36,"NoRecClassification":37.73,"NordicLangClassification":54.17,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":42,"Rank":44,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","MassiveIntentClassification (nb)":39.3,"MassiveScenarioClassification (nb)":45.75,"NoRecClassification":38.34,"NordicLangClassification":50.15,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":43,"Rank":45,"Model":"allenai-specter<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":34.75,"MassiveScenarioClassification (nb)":35.71,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":44,"Rank":46,"Model":"distiluse-base-multilingual-cased-v2<\/a>","Model Size (Million Parameters)":135,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":512,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":46.01,"MassiveScenarioClassification (nb)":54.98,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":45,"Rank":47,"Model":"gtr-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":43.78,"MassiveScenarioClassification (nb)":52.71,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":46,"Rank":48,"Model":"gtr-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":45.87,"MassiveScenarioClassification (nb)":54.44,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":47,"Rank":49,"Model":"paraphrase-multilingual-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":55.52,"MassiveScenarioClassification (nb)":64.25,"NoRecClassification":46.7,"NordicLangClassification":42.52,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":48,"Rank":50,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","MassiveIntentClassification (nb)":62.62,"MassiveScenarioClassification (nb)":70.23,"NoRecClassification":50.32,"NordicLangClassification":41.57,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":49,"Rank":51,"Model":"sentence-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":38.53,"MassiveScenarioClassification (nb)":46.6,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":50,"Rank":52,"Model":"sentence-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":41.29,"MassiveScenarioClassification (nb)":49.92,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":51,"Rank":53,"Model":"sentence-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":45.91,"MassiveScenarioClassification (nb)":53.43,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":52,"Rank":54,"Model":"rubert-tiny-turbo<\/a>","Model Size (Million Parameters)":29,"Memory Usage (GB, fp32)":0.11,"Embedding Dimensions":312,"Max Tokens":2048,"Average":"","MassiveIntentClassification (nb)":32.3,"MassiveScenarioClassification (nb)":39.05,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":53,"Rank":55,"Model":"text2vec-base-multilingual<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":54.64,"MassiveScenarioClassification (nb)":60.26,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
-{"index":54,"Rank":56,"Model":"llm_mse<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":32.13,"MassiveScenarioClassification (nb)":38.99,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
diff --git a/boards_data/other-cls/data_overall/default.txt b/boards_data/other-cls/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/other-cls/data_tasks/Classification/default.jsonl b/boards_data/other-cls/data_tasks/Classification/default.jsonl
deleted file mode 100644
index 05ffedd2e4a44e873d7555f875ad4d35af71786a..0000000000000000000000000000000000000000
--- a/boards_data/other-cls/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,174 +0,0 @@
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diff --git a/boards_data/other-sts/data_overall/default.txt b/boards_data/other-sts/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/other-sts/data_tasks/STS/default.jsonl b/boards_data/other-sts/data_tasks/STS/default.jsonl
deleted file mode 100644
index 0358de0e3c46a10dbec8122012c023bf19d01182..0000000000000000000000000000000000000000
--- a/boards_data/other-sts/data_tasks/STS/default.jsonl
+++ /dev/null
@@ -1,332 +0,0 @@
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-{"index":15,"Rank":52,"Model":"llm2vec-occiglot-mntp<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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)":67.83,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":""}
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-{"index":17,"Rank":54,"Model":"gte-Qwen2-1.5B-instruct<\/a>","Model Size (Million Parameters)":1776,"Memory Usage (GB, fp32)":6.62,"Embedding Dimensions":1536,"Max Tokens":131072,"Average":"","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)":81.84,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":40.83,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":86.38}
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-{"index":19,"Rank":56,"Model":"gte-base-en-v1.5<\/a>","Model Size (Million Parameters)":137,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":8192,"Average":"","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":86.04}
-{"index":20,"Rank":57,"Model":"gte-large-en-v1.5<\/a>","Model Size (Million Parameters)":434,"Memory Usage (GB, fp32)":1.62,"Embedding Dimensions":1024,"Max Tokens":8192,"Average":"","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":83.07}
-{"index":23,"Rank":58,"Model":"jina-embeddings-v2-base-es-Q2_K-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","STS17 (ar-ar)":"","STS17 (en-ar)":"","STS17 (en-de)":"","STS17 (en-tr)":"","STS17 (es-en)":86.49,"STS17 (es-es)":88.25,"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)":67.97,"STS22 (es-en)":78.8,"STS22 (es-it)":"","STS22 (fr)":"","STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":87.84}
-{"index":24,"Rank":59,"Model":"jina-embeddings-v2-base-es-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","STS17 (ar-ar)":"","STS17 (en-ar)":"","STS17 (en-de)":"","STS17 (en-tr)":"","STS17 (es-en)":86.49,"STS17 (es-es)":88.25,"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)":67.97,"STS22 (es-en)":78.8,"STS22 (es-it)":"","STS22 (fr)":"","STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":87.84}
-{"index":25,"Rank":60,"Model":"bge-base-en-v1.5<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","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":86.42}
-{"index":26,"Rank":61,"Model":"bge-en-icl<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":88.42}
-{"index":27,"Rank":62,"Model":"bge-large-en-v1.5<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","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":87.52}
-{"index":28,"Rank":63,"Model":"bge-m3<\/a>","Model Size (Million Parameters)":567,"Memory Usage (GB, fp32)":2.11,"Embedding Dimensions":1024,"Max Tokens":8192,"Average":"","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)":66.26,"STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":""}
-{"index":29,"Rank":64,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Embedding Dimensions":3584,"Max Tokens":8192,"Average":"","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)":83.28,"STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":42.79,"STS22 (pl-en)":"","STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":"","STSBenchmark":87.25}
-{"index":30,"Rank":65,"Model":"bge-small-en-v1.5<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","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":85.86}
-{"index":31,"Rank":66,"Model":"nomic-embed-text-v1.5-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":85.47}
-{"index":33,"Rank":67,"Model":"GritLM-7B-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":85.64}
-{"index":34,"Rank":68,"Model":"Cohere-embed-english-light-v3.0<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":86.58}
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-{"index":52,"Rank":86,"Model":"GritLM-8x7B<\/a>","Model Size (Million Parameters)":46703,"Memory Usage (GB, fp32)":173.98,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":87.43}
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-{"index":56,"Rank":90,"Model":"speed-embedding-7b-instruct<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":89.21}
-{"index":57,"Rank":91,"Model":"lodestone-base-4096-v1<\/a>","Model Size (Million Parameters)":69,"Memory Usage (GB, fp32)":0.26,"Embedding Dimensions":768,"Max Tokens":4096,"Average":"","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":75.57}
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-{"index":79,"Rank":111,"Model":"LLM2Vec-Sheared-Llama-supervised<\/a>","Model Size (Million Parameters)":1280,"Memory Usage (GB, fp32)":4.77,"Embedding Dimensions":2048,"Max Tokens":4096,"Average":"","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":87.16}
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-{"index":106,"Rank":136,"Model":"bge_m3e_stella<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":61.83}
-{"index":107,"Rank":137,"Model":"SFR-Embedding-2_R<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":83.6}
-{"index":108,"Rank":138,"Model":"SFR-Embedding-Mistral<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","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":89.0}
-{"index":109,"Rank":139,"Model":"ember-v1-Q8_0-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":87.77}
-{"index":110,"Rank":140,"Model":"LLaMA-embeeding<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":52.67}
-{"index":111,"Rank":141,"Model":"snowflake-arctic-embed-l<\/a>","Model Size (Million Parameters)":334,"Memory Usage (GB, fp32)":1.24,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","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":75.68}
-{"index":112,"Rank":142,"Model":"snowflake-arctic-embed-l-v2.0<\/a>","Model Size (Million Parameters)":568,"Memory Usage (GB, fp32)":2.12,"Embedding Dimensions":1024,"Max Tokens":8194,"Average":"","STS17 (ar-ar)":"","STS17 (en-ar)":61.05,"STS17 (en-de)":79.31,"STS17 (en-tr)":63.63,"STS17 (es-en)":71.05,"STS17 (es-es)":"","STS17 (fr-en)":76.27,"STS17 (it-en)":76.88,"STS17 (ko-ko)":"","STS17 (nl-en)":78.27,"STS22 (ar)":"","STS22 (de)":"","STS22 (de-en)":55.11,"STS22 (de-fr)":"","STS22 (de-pl)":"","STS22 (es)":"","STS22 (es-en)":78.59,"STS22 (es-it)":"","STS22 (fr)":"","STS22 (fr-pl)":"","STS22 (it)":"","STS22 (pl)":"","STS22 (pl-en)":76.85,"STS22 (ru)":"","STS22 (tr)":"","STS22 (zh-en)":70.79,"STSBenchmark":79.19}
-{"index":113,"Rank":143,"Model":"snowflake-arctic-embed-m<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","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":74.1}
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-{"index":139,"Rank":168,"Model":"NoInstruct-small-Embedding-v0<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","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":86.98}
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-{"index":241,"Rank":260,"Model":"nomic-embed-text-v1.5-512<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":512,"Max Tokens":8192,"Average":"","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":85.14}
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-{"index":316,"Rank":317,"Model":"new_model<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","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":84.92}
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diff --git a/boards_data/pl/data_overall/default.jsonl b/boards_data/pl/data_overall/default.jsonl
deleted file mode 100644
index b60da62e5b4746c5d94a2047828c740e76efe09a..0000000000000000000000000000000000000000
--- a/boards_data/pl/data_overall/default.jsonl
+++ /dev/null
@@ -1,62 +0,0 @@
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-{"index":13,"Rank":2,"Model":"KaLM-embedding-multilingual-max-instruct-v1<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (26 datasets)":68.75,"Classification Average (7 datasets)":79.0,"Clustering Average (1 datasets)":51.5,"PairClassification Average (4 datasets)":86.9,"Retrieval Average (11 datasets)":55.96,"STS Average (3 datasets)":73.25}
-{"index":11,"Rank":3,"Model":"gte-Qwen2-7B-instruct-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (26 datasets)":67.86,"Classification Average (7 datasets)":77.84,"Clustering Average (1 datasets)":51.36,"PairClassification Average (4 datasets)":88.48,"Retrieval Average (11 datasets)":54.69,"STS Average (3 datasets)":70.86}
-{"index":2,"Rank":4,"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)":67.86,"Classification Average (7 datasets)":77.84,"Clustering Average (1 datasets)":51.36,"PairClassification Average (4 datasets)":88.48,"Retrieval Average (11 datasets)":54.69,"STS Average (3 datasets)":70.86}
-{"index":34,"Rank":5,"Model":"gte-Qwen2-7B-instruct-fp16<\/a>","Model Size (Million Parameters)":7069,"Memory Usage (GB, fp32)":26.33,"Embedding Dimensions":3584,"Max Tokens":131072,"Average (26 datasets)":67.86,"Classification Average (7 datasets)":77.84,"Clustering Average (1 datasets)":51.36,"PairClassification Average (4 datasets)":88.48,"Retrieval Average (11 datasets)":54.69,"STS Average (3 datasets)":70.86}
-{"index":73,"Rank":6,"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)":67.86,"Classification Average (7 datasets)":77.84,"Clustering Average (1 datasets)":51.36,"PairClassification Average (4 datasets)":88.48,"Retrieval Average (11 datasets)":54.69,"STS Average (3 datasets)":70.86}
-{"index":10,"Rank":7,"Model":"gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (26 datasets)":64.04,"Classification Average (7 datasets)":72.29,"Clustering Average (1 datasets)":44.59,"PairClassification Average (4 datasets)":84.87,"Retrieval Average (11 datasets)":51.88,"STS Average (3 datasets)":68.12}
-{"index":1,"Rank":8,"Model":"gte-Qwen2-1.5B-instruct<\/a>","Model Size (Million Parameters)":1776,"Memory Usage (GB, fp32)":6.62,"Embedding Dimensions":1536,"Max Tokens":131072,"Average (26 datasets)":64.04,"Classification Average (7 datasets)":72.29,"Clustering Average (1 datasets)":44.59,"PairClassification Average (4 datasets)":84.87,"Retrieval Average (11 datasets)":51.88,"STS Average (3 datasets)":68.12}
-{"index":44,"Rank":9,"Model":"jina-embeddings-v3<\/a>","Model Size (Million Parameters)":572,"Memory Usage (GB, fp32)":2.13,"Embedding Dimensions":1024,"Max Tokens":8194,"Average (26 datasets)":63.97,"Classification Average (7 datasets)":70.81,"Clustering Average (1 datasets)":43.66,"PairClassification Average (4 datasets)":83.7,"Retrieval Average (11 datasets)":51.89,"STS Average (3 datasets)":72.77}
-{"index":54,"Rank":10,"Model":"mmlw-roberta-large<\/a>","Model Size (Million Parameters)":435,"Memory Usage (GB, fp32)":1.62,"Embedding Dimensions":1024,"Max Tokens":514,"Average (26 datasets)":63.23,"Classification Average (7 datasets)":66.39,"Clustering Average (1 datasets)":31.16,"PairClassification Average (4 datasets)":89.13,"Retrieval Average (11 datasets)":52.71,"STS Average (3 datasets)":70.59}
-{"index":27,"Rank":11,"Model":"KartonBERT-USE-base-v1<\/a>","Model Size (Million Parameters)":104,"Memory Usage (GB, fp32)":0.39,"Embedding Dimensions":768,"Max Tokens":512,"Average (26 datasets)":61.67,"Classification Average (7 datasets)":67.57,"Clustering Average (1 datasets)":29.88,"PairClassification Average (4 datasets)":87.04,"Retrieval Average (11 datasets)":49.14,"STS Average (3 datasets)":70.65}
-{"index":51,"Rank":12,"Model":"mmlw-e5-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average (26 datasets)":61.17,"Classification Average (7 datasets)":61.07,"Clustering Average (1 datasets)":30.62,"PairClassification Average (4 datasets)":85.9,"Retrieval Average (11 datasets)":52.63,"STS Average (3 datasets)":69.98}
-{"index":53,"Rank":13,"Model":"mmlw-roberta-base<\/a>","Model Size (Million Parameters)":124,"Memory Usage (GB, fp32)":0.46,"Embedding Dimensions":768,"Max Tokens":514,"Average (26 datasets)":61.05,"Classification Average (7 datasets)":62.92,"Clustering Average (1 datasets)":33.08,"PairClassification Average (4 datasets)":88.14,"Retrieval Average (11 datasets)":49.92,"STS Average (3 datasets)":70.7}
-{"index":37,"Rank":14,"Model":"multilingual-e5-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average (26 datasets)":60.08,"Classification Average (7 datasets)":63.82,"Clustering Average (1 datasets)":33.88,"PairClassification Average (4 datasets)":85.5,"Retrieval Average (11 datasets)":48.98,"STS Average (3 datasets)":66.91}
-{"index":50,"Rank":15,"Model":"mmlw-e5-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average (26 datasets)":59.71,"Classification Average (7 datasets)":59.52,"Clustering Average (1 datasets)":30.25,"PairClassification Average (4 datasets)":86.16,"Retrieval Average (11 datasets)":50.06,"STS Average (3 datasets)":70.13}
-{"index":3,"Rank":16,"Model":"gte-multilingual-base<\/a>","Model Size (Million Parameters)":305,"Memory Usage (GB, fp32)":1.14,"Embedding Dimensions":768,"Max Tokens":8192,"Average (26 datasets)":58.22,"Classification Average (7 datasets)":60.15,"Clustering Average (1 datasets)":33.67,"PairClassification Average (4 datasets)":85.45,"Retrieval Average (11 datasets)":46.4,"STS Average (3 datasets)":68.92}
-{"index":14,"Rank":17,"Model":"KaLM-embedding-multilingual-mini-instruct-v1<\/a>","Model Size (Million Parameters)":494,"Memory Usage (GB, fp32)":1.84,"Embedding Dimensions":896,"Max Tokens":131072,"Average (26 datasets)":58.16,"Classification Average (7 datasets)":65.63,"Clustering Average (1 datasets)":37.57,"PairClassification Average (4 datasets)":80.52,"Retrieval Average (11 datasets)":44.66,"STS Average (3 datasets)":67.32}
-{"index":28,"Rank":18,"Model":"st-polish-kartonberta-base-alpha-v1<\/a>","Model Size (Million Parameters)":124,"Memory Usage (GB, fp32)":0.46,"Embedding Dimensions":768,"Max Tokens":514,"Average (26 datasets)":56.92,"Classification Average (7 datasets)":60.44,"Clustering Average (1 datasets)":32.85,"PairClassification Average (4 datasets)":87.92,"Retrieval Average (11 datasets)":42.19,"STS Average (3 datasets)":69.47}
-{"index":52,"Rank":19,"Model":"mmlw-e5-small<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average (26 datasets)":55.84,"Classification Average (7 datasets)":57.73,"Clustering Average (1 datasets)":31.77,"PairClassification Average (4 datasets)":84.61,"Retrieval Average (11 datasets)":42.83,"STS Average (3 datasets)":68.77}
-{"index":36,"Rank":20,"Model":"multilingual-e5-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average (26 datasets)":55.62,"Classification Average (7 datasets)":59.01,"Clustering Average (1 datasets)":24.97,"PairClassification Average (4 datasets)":82.15,"Retrieval Average (11 datasets)":44.01,"STS Average (3 datasets)":65.13}
-{"index":15,"Rank":21,"Model":"KaLM-embedding-multilingual-mini-v1<\/a>","Model Size (Million Parameters)":494,"Memory Usage (GB, fp32)":1.84,"Embedding Dimensions":896,"Max Tokens":131072,"Average (26 datasets)":54.79,"Classification Average (7 datasets)":59.88,"Clustering Average (1 datasets)":37.96,"PairClassification Average (4 datasets)":79.79,"Retrieval Average (11 datasets)":40.61,"STS Average (3 datasets)":67.21}
-{"index":39,"Rank":22,"Model":"multilingual-e5-small<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average (26 datasets)":54.14,"Classification Average (7 datasets)":57.05,"Clustering Average (1 datasets)":23.92,"PairClassification Average (4 datasets)":80.5,"Retrieval Average (11 datasets)":42.43,"STS Average (3 datasets)":65.18}
-{"index":41,"Rank":23,"Model":"silver-retriever-base-v1<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":514,"Average (26 datasets)":52.8,"Classification Average (7 datasets)":58.99,"Clustering Average (1 datasets)":31.49,"PairClassification Average (4 datasets)":77.67,"Retrieval Average (11 datasets)":38.43,"STS Average (3 datasets)":65.01}
-{"index":40,"Rank":24,"Model":"herbert-base-retrieval-v2<\/a>","Model Size (Million Parameters)":125,"Memory Usage (GB, fp32)":0.47,"Embedding Dimensions":768,"Max Tokens":514,"Average (26 datasets)":52.55,"Classification Average (7 datasets)":59.26,"Clustering Average (1 datasets)":28.15,"PairClassification Average (4 datasets)":75.48,"Retrieval Average (11 datasets)":39.16,"STS Average (3 datasets)":63.53}
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diff --git a/boards_data/pl/data_tasks/Classification/default.jsonl b/boards_data/pl/data_tasks/Classification/default.jsonl
deleted file mode 100644
index e00b36217453ece7422d1863aa2c5f0d99af51fc..0000000000000000000000000000000000000000
--- a/boards_data/pl/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/boards_data/pl/data_tasks/Clustering/default.jsonl b/boards_data/pl/data_tasks/Clustering/default.jsonl
deleted file mode 100644
index e1d5355896f4ba8630769a19c6fefacee15706df..0000000000000000000000000000000000000000
--- a/boards_data/pl/data_tasks/Clustering/default.jsonl
+++ /dev/null
@@ -1,61 +0,0 @@
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diff --git a/boards_data/pl/data_tasks/PairClassification/default.jsonl b/boards_data/pl/data_tasks/PairClassification/default.jsonl
deleted file mode 100644
index a163258bdcfe444266e4bf55141b1f7d919ab76e..0000000000000000000000000000000000000000
--- a/boards_data/pl/data_tasks/PairClassification/default.jsonl
+++ /dev/null
@@ -1,67 +0,0 @@
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diff --git a/boards_data/pl/data_tasks/Retrieval/default.jsonl b/boards_data/pl/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index 56f5e9342c52c36c3db4f38d1c610f3e60a2eabf..0000000000000000000000000000000000000000
--- a/boards_data/pl/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,60 +0,0 @@
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diff --git a/boards_data/pl/data_tasks/STS/default.jsonl b/boards_data/pl/data_tasks/STS/default.jsonl
deleted file mode 100644
index 31f3fb2ef52c8f34a5d46d017b853ae86ad1cd61..0000000000000000000000000000000000000000
--- a/boards_data/pl/data_tasks/STS/default.jsonl
+++ /dev/null
@@ -1,70 +0,0 @@
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diff --git a/boards_data/rar-b/data_overall/default.txt b/boards_data/rar-b/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/rar-b/data_tasks/Retrieval/default.jsonl b/boards_data/rar-b/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index a378ce9dcf50f327d80167ec41c981306c2edb0b..0000000000000000000000000000000000000000
--- a/boards_data/rar-b/data_tasks/Retrieval/default.jsonl
+++ /dev/null
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diff --git a/boards_data/ru/data_overall/default.jsonl b/boards_data/ru/data_overall/default.jsonl
deleted file mode 100644
index 454f94a1cb9adb213dc684b5f82fb665b1ff8b55..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_overall/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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-{"index":44,"Rank":47,"Model":"FAB-Ramy-v1<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average (23 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (3 datasets)":"","PairClassification Average (1 datasets)":"","Reranking Average (2 datasets)":"","Retrieval Average (3 datasets)":"","STS Average (3 datasets)":"","MultilabelClassification Average (2 datasets)":""}
-{"index":45,"Rank":48,"Model":"FAE-v1<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average (23 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (3 datasets)":"","PairClassification Average (1 datasets)":"","Reranking Average (2 datasets)":"","Retrieval Average (3 datasets)":"","STS Average (3 datasets)":"","MultilabelClassification Average (2 datasets)":""}
-{"index":46,"Rank":49,"Model":"FUE-v1<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average (23 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (3 datasets)":"","PairClassification Average (1 datasets)":"","Reranking Average (2 datasets)":"","Retrieval Average (3 datasets)":"","STS Average (3 datasets)":"","MultilabelClassification Average (2 datasets)":""}
-{"index":47,"Rank":50,"Model":"LaBSE<\/a>","Model Size (Million Parameters)":471,"Memory Usage (GB, fp32)":1.75,"Embedding Dimensions":768,"Max Tokens":512,"Average (23 datasets)":"","Classification Average (9 datasets)":54.71,"Clustering Average (3 datasets)":47.74,"PairClassification Average (1 datasets)":55.71,"Reranking Average (2 datasets)":"","Retrieval Average (3 datasets)":"","STS Average (3 datasets)":65.52,"MultilabelClassification Average (2 datasets)":31.42}
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-{"index":54,"Rank":57,"Model":"gtr-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average (23 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (3 datasets)":"","PairClassification Average (1 datasets)":"","Reranking Average (2 datasets)":"","Retrieval Average (3 datasets)":"","STS Average (3 datasets)":"","MultilabelClassification Average (2 datasets)":""}
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diff --git a/boards_data/ru/data_tasks/Classification/default.jsonl b/boards_data/ru/data_tasks/Classification/default.jsonl
deleted file mode 100644
index 039801c2ea255d438181a08cf60cd922576341c3..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,64 +0,0 @@
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diff --git a/boards_data/ru/data_tasks/Clustering/default.jsonl b/boards_data/ru/data_tasks/Clustering/default.jsonl
deleted file mode 100644
index bb41e502201eaed78d8e4ea5762b96778222bb48..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_tasks/Clustering/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/boards_data/ru/data_tasks/MultilabelClassification/default.jsonl b/boards_data/ru/data_tasks/MultilabelClassification/default.jsonl
deleted file mode 100644
index a4201380173934f3abacd77cbec438f39d1b61bd..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_tasks/MultilabelClassification/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/boards_data/ru/data_tasks/PairClassification/default.jsonl b/boards_data/ru/data_tasks/PairClassification/default.jsonl
deleted file mode 100644
index 66636ef0954835f9a8f7afc02a854f2fe7580fac..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_tasks/PairClassification/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/boards_data/ru/data_tasks/Reranking/default.jsonl b/boards_data/ru/data_tasks/Reranking/default.jsonl
deleted file mode 100644
index 2a7fd4ae559765da31713cf79dd0d489b07f2b98..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_tasks/Reranking/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/boards_data/ru/data_tasks/Retrieval/default.jsonl b/boards_data/ru/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index 2bf2cef00bc5e17a119ff624995b2b8f52578476..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,57 +0,0 @@
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diff --git a/boards_data/ru/data_tasks/STS/default.jsonl b/boards_data/ru/data_tasks/STS/default.jsonl
deleted file mode 100644
index 0ac0d1bf95084399112b3af119c6e70a6db54ac5..0000000000000000000000000000000000000000
--- a/boards_data/ru/data_tasks/STS/default.jsonl
+++ /dev/null
@@ -1,61 +0,0 @@
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diff --git a/boards_data/se/data_overall/default.txt b/boards_data/se/data_overall/default.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/boards_data/se/data_tasks/Classification/default.jsonl b/boards_data/se/data_tasks/Classification/default.jsonl
deleted file mode 100644
index 1f5ea4327f95e6a06e392d2a9fb5923a00499556..0000000000000000000000000000000000000000
--- a/boards_data/se/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,56 +0,0 @@
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-{"index":3,"Rank":23,"Model":"multilingual-e5-base-similarity-v1-onnx-quantized<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":66.18,"MassiveScenarioClassification (nb)":71.63,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
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-{"index":47,"Rank":49,"Model":"paraphrase-multilingual-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":55.52,"MassiveScenarioClassification (nb)":64.25,"NoRecClassification":46.7,"NordicLangClassification":42.52,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":48,"Rank":50,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","MassiveIntentClassification (nb)":62.62,"MassiveScenarioClassification (nb)":70.23,"NoRecClassification":50.32,"NordicLangClassification":41.57,"NorwegianParliament":"","ScalaNbClassification":""}
-{"index":49,"Rank":51,"Model":"sentence-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","MassiveIntentClassification (nb)":38.53,"MassiveScenarioClassification (nb)":46.6,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
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-{"index":54,"Rank":56,"Model":"llm_mse<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":"","MassiveIntentClassification (nb)":32.13,"MassiveScenarioClassification (nb)":38.99,"NoRecClassification":"","NordicLangClassification":"","NorwegianParliament":"","ScalaNbClassification":""}
diff --git a/boards_data/zh/data_overall/default.jsonl b/boards_data/zh/data_overall/default.jsonl
deleted file mode 100644
index f55590ac70e53766c66c327115827ca8133ff1b2..0000000000000000000000000000000000000000
--- a/boards_data/zh/data_overall/default.jsonl
+++ /dev/null
@@ -1,321 +0,0 @@
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-{"index":7,"Rank":81,"Model":"voyage-code-2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1536,"Max Tokens":16000,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":8,"Rank":82,"Model":"voyage-large-2-instruct<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":16000,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":9,"Rank":83,"Model":"voyage-law-2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":16000,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":10,"Rank":84,"Model":"voyage-lite-01-instruct<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":4000,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
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-{"index":12,"Rank":86,"Model":"voyage-multilingual-2<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":32000,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":13,"Rank":87,"Model":"LASER2<\/a>","Model Size (Million Parameters)":43,"Memory Usage (GB, fp32)":0.16,"Embedding Dimensions":1024,"Max Tokens":"N\/A","Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
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-{"index":34,"Rank":97,"Model":"bge-m3<\/a>","Model Size (Million Parameters)":567,"Memory Usage (GB, fp32)":2.11,"Embedding Dimensions":1024,"Max Tokens":8192,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":36,"Rank":98,"Model":"bge-reranker-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":67.03,"Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
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-{"index":43,"Rank":104,"Model":"ListConRanker<\/a>","Model Size (Million Parameters)":326,"Memory Usage (GB, fp32)":1.21,"Embedding Dimensions":1024,"Max Tokens":512,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":73.24,"Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":50,"Rank":107,"Model":"Cohere-embed-multilingual-light-v3.0<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":384,"Max Tokens":512,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":51,"Rank":108,"Model":"Cohere-embed-multilingual-v3.0<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":512,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
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-{"index":58,"Rank":112,"Model":"bge-reranker-large-Q4_K_M-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":67.03,"Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":63,"Rank":114,"Model":"xlm-roberta-base<\/a>","Model Size (Million Parameters)":279,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":64,"Rank":115,"Model":"xlm-roberta-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":67,"Rank":116,"Model":"STS-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":69,"Rank":117,"Model":"bert-base-10lang-cased<\/a>","Model Size (Million Parameters)":138,"Memory Usage (GB, fp32)":0.51,"Embedding Dimensions":768,"Max Tokens":512,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
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-{"index":72,"Rank":120,"Model":"GritLM-7B<\/a>","Model Size (Million Parameters)":7240,"Memory Usage (GB, fp32)":26.97,"Embedding Dimensions":"","Max Tokens":4096,"Average (35 datasets)":"","Classification Average (9 datasets)":72.67,"Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":65.08,"STS Average (8 datasets)":""}
-{"index":73,"Rank":121,"Model":"GritLM-8x7B<\/a>","Model Size (Million Parameters)":46703,"Memory Usage (GB, fp32)":173.98,"Embedding Dimensions":4096,"Max Tokens":32768,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
-{"index":77,"Rank":122,"Model":"speed-embedding-7b-instruct<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
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-{"index":145,"Rank":189,"Model":"gte-tiny<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.08,"Embedding Dimensions":384,"Max Tokens":512,"Average (35 datasets)":"","Classification Average (9 datasets)":"","Clustering Average (4 datasets)":"","PairClassification Average (2 datasets)":"","Reranking Average (4 datasets)":"","Retrieval Average (8 datasets)":"","STS Average (8 datasets)":""}
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diff --git a/boards_data/zh/data_tasks/Classification/default.jsonl b/boards_data/zh/data_tasks/Classification/default.jsonl
deleted file mode 100644
index 74cb5b242a8497f100adaff976903ba765c47a90..0000000000000000000000000000000000000000
--- a/boards_data/zh/data_tasks/Classification/default.jsonl
+++ /dev/null
@@ -1,318 +0,0 @@
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diff --git a/boards_data/zh/data_tasks/Clustering/default.jsonl b/boards_data/zh/data_tasks/Clustering/default.jsonl
deleted file mode 100644
index 028f26390cf96877282a885ebb67b877d1145a52..0000000000000000000000000000000000000000
--- a/boards_data/zh/data_tasks/Clustering/default.jsonl
+++ /dev/null
@@ -1,314 +0,0 @@
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diff --git a/boards_data/zh/data_tasks/PairClassification/default.jsonl b/boards_data/zh/data_tasks/PairClassification/default.jsonl
deleted file mode 100644
index 40a2bc1174f3dcb37df56060fcc90b090375956f..0000000000000000000000000000000000000000
--- a/boards_data/zh/data_tasks/PairClassification/default.jsonl
+++ /dev/null
@@ -1,314 +0,0 @@
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diff --git a/boards_data/zh/data_tasks/Reranking/default.jsonl b/boards_data/zh/data_tasks/Reranking/default.jsonl
deleted file mode 100644
index b23b75eaef727266288f08db68d722b5c3428cc6..0000000000000000000000000000000000000000
--- a/boards_data/zh/data_tasks/Reranking/default.jsonl
+++ /dev/null
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diff --git a/boards_data/zh/data_tasks/Retrieval/default.jsonl b/boards_data/zh/data_tasks/Retrieval/default.jsonl
deleted file mode 100644
index 3c1181b645cfdc8ea227ce032e12499a4c9ebf96..0000000000000000000000000000000000000000
--- a/boards_data/zh/data_tasks/Retrieval/default.jsonl
+++ /dev/null
@@ -1,318 +0,0 @@
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diff --git a/boards_data/zh/data_tasks/STS/default.jsonl b/boards_data/zh/data_tasks/STS/default.jsonl
deleted file mode 100644
index e760ab99a5227fe21e4ee0dfbcd883f2a4292771..0000000000000000000000000000000000000000
--- a/boards_data/zh/data_tasks/STS/default.jsonl
+++ /dev/null
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-{"index":304,"Rank":321,"Model":"360Zhinao-1.8B-Reranking<\/a>","Model Size (Million Parameters)":941,"Memory Usage (GB, fp32)":3.51,"Embedding Dimensions":2048,"Max Tokens":4096,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":305,"Rank":322,"Model":"360Zhinao-search<\/a>","Model Size (Million Parameters)":163,"Memory Usage (GB, fp32)":0.61,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":306,"Rank":323,"Model":"FAB-Ramy-v1<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":42.94,"STSB":""}
-{"index":307,"Rank":324,"Model":"FAE-v1<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":46.31,"STSB":""}
-{"index":308,"Rank":325,"Model":"FUE-v1<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":26.92,"STSB":""}
-{"index":315,"Rank":329,"Model":"LaBSE<\/a>","Model Size (Million Parameters)":471,"Memory Usage (GB, fp32)":1.75,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":21.02,"ATEC":26.61,"BQ":42.6,"LCQMC":52.19,"PAWSX":10.23,"QBQTC":"","STS22 (zh)":63.02,"STSB":68.38}
-{"index":316,"Rank":330,"Model":"all-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AFQMC":7.94,"ATEC":12.97,"BQ":23.31,"LCQMC":21.04,"PAWSX":7.31,"QBQTC":"","STS22 (zh)":33.15,"STSB":36.66}
-{"index":317,"Rank":331,"Model":"all-MiniLM-L6-v2<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AFQMC":8.59,"ATEC":13.52,"BQ":23.84,"LCQMC":23.85,"PAWSX":7.21,"QBQTC":"","STS22 (zh)":44.93,"STSB":37.8}
-{"index":318,"Rank":332,"Model":"all-mpnet-base-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","AFQMC":8.01,"ATEC":14.03,"BQ":21.39,"LCQMC":22.84,"PAWSX":6.44,"QBQTC":"","STS22 (zh)":42.24,"STSB":37.7}
-{"index":319,"Rank":333,"Model":"allenai-specter<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":16.35,"STSB":""}
-{"index":320,"Rank":334,"Model":"average_word_embeddings_glove.6B.300d<\/a>","Model Size (Million Parameters)":120,"Memory Usage (GB, fp32)":0.45,"Embedding Dimensions":300,"Max Tokens":"N\/A","Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":321,"Rank":335,"Model":"average_word_embeddings_komninos<\/a>","Model Size (Million Parameters)":134,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":300,"Max Tokens":"N\/A","Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":4.78,"STSB":""}
-{"index":322,"Rank":336,"Model":"distiluse-base-multilingual-cased-v2<\/a>","Model Size (Million Parameters)":135,"Memory Usage (GB, fp32)":0.5,"Embedding Dimensions":512,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":54.32,"STSB":""}
-{"index":323,"Rank":337,"Model":"gtr-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":324,"Rank":338,"Model":"gtr-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":27.32,"STSB":""}
-{"index":325,"Rank":339,"Model":"gtr-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":31.16,"STSB":""}
-{"index":326,"Rank":340,"Model":"gtr-t5-xxl<\/a>","Model Size (Million Parameters)":4865,"Memory Usage (GB, fp32)":18.12,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":327,"Rank":341,"Model":"msmarco-bert-co-condensor<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":328,"Rank":342,"Model":"multi-qa-MiniLM-L6-cos-v1<\/a>","Model Size (Million Parameters)":23,"Memory Usage (GB, fp32)":0.09,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":329,"Rank":343,"Model":"paraphrase-multilingual-MiniLM-L12-v2<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AFQMC":14.3,"ATEC":18.42,"BQ":38.53,"LCQMC":63.96,"PAWSX":10.13,"QBQTC":"","STS22 (zh)":58.75,"STSB":78.91}
-{"index":330,"Rank":344,"Model":"paraphrase-multilingual-mpnet-base-v2<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","AFQMC":15.69,"ATEC":20.27,"BQ":36.33,"LCQMC":63.3,"PAWSX":12.16,"QBQTC":"","STS22 (zh)":61.75,"STSB":80.84}
-{"index":331,"Rank":345,"Model":"sentence-t5-base<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":32.9,"STSB":""}
-{"index":332,"Rank":346,"Model":"sentence-t5-large<\/a>","Model Size (Million Parameters)":168,"Memory Usage (GB, fp32)":0.63,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":30.47,"STSB":""}
-{"index":333,"Rank":347,"Model":"sentence-t5-xl<\/a>","Model Size (Million Parameters)":1240,"Memory Usage (GB, fp32)":4.62,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":33.55,"STSB":""}
-{"index":334,"Rank":348,"Model":"sentence-t5-xxl<\/a>","Model Size (Million Parameters)":4865,"Memory Usage (GB, fp32)":18.12,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":336,"Rank":349,"Model":"LaBSE-ru-turbo<\/a>","Model Size (Million Parameters)":128,"Memory Usage (GB, fp32)":0.48,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":337,"Rank":350,"Model":"rubert-tiny-turbo<\/a>","Model Size (Million Parameters)":29,"Memory Usage (GB, fp32)":0.11,"Embedding Dimensions":312,"Max Tokens":2048,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":32.83,"STSB":""}
-{"index":340,"Rank":351,"Model":"text2vec-base-multilingual<\/a>","Model Size (Million Parameters)":118,"Memory Usage (GB, fp32)":0.44,"Embedding Dimensions":384,"Max Tokens":256,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":63.24,"STSB":""}
-{"index":343,"Rank":353,"Model":"BAAI-bge-reranker-large<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":344,"Rank":354,"Model":"bge-en-icl<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":349,"Rank":358,"Model":"gte-base<\/a>","Model Size (Million Parameters)":109,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":768,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":351,"Rank":359,"Model":"gte-large<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":353,"Rank":360,"Model":"gte-small<\/a>","Model Size (Million Parameters)":33,"Memory Usage (GB, fp32)":0.12,"Embedding Dimensions":384,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":362,"Rank":366,"Model":"universal-sentence-encoder-multilingual-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":512,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":363,"Rank":367,"Model":"universal-sentence-encoder-multilingual-large-3<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":512,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":364,"Rank":368,"Model":"b1ade-embed<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":365,"Rank":369,"Model":"b1ade-embed-kd<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":366,"Rank":370,"Model":"b1ade-embed-kd_3<\/a>","Model Size (Million Parameters)":335,"Memory Usage (GB, fp32)":1.25,"Embedding Dimensions":1024,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":369,"Rank":373,"Model":"hktv-fine-tuned-cloudy-large-zh-metaphor14<\/a>","Model Size (Million Parameters)":326,"Memory Usage (GB, fp32)":1.21,"Embedding Dimensions":1024,"Max Tokens":1024,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":371,"Rank":375,"Model":"bilingual-embedding-base<\/a>","Model Size (Million Parameters)":278,"Memory Usage (GB, fp32)":1.04,"Embedding Dimensions":768,"Max Tokens":514,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":376,"Rank":380,"Model":"Zeta-Alpha-E5-Mistral<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":379,"Rank":382,"Model":"text-similarity-ada-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":2046,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":380,"Rank":383,"Model":"text-similarity-babbage-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":2048,"Max Tokens":2046,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":381,"Rank":384,"Model":"text-similarity-curie-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":4096,"Max Tokens":2046,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":382,"Rank":385,"Model":"text-similarity-davinci-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":12288,"Max Tokens":2046,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":384,"Rank":386,"Model":"text-embedding-3-large-256<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":256,"Max Tokens":8191,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":385,"Rank":387,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":3072,"Max Tokens":8191,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":386,"Rank":388,"Model":"text-embedding-3-small<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1536,"Max Tokens":8191,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
-{"index":388,"Rank":389,"Model":"elser-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":30522,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""}
diff --git a/config.yaml b/config.yaml
deleted file mode 100644
index be1e9b4d6be1d231659288ece458746b32d77b1a..0000000000000000000000000000000000000000
--- a/config.yaml
+++ /dev/null
@@ -1,552 +0,0 @@
-config:
- REPO_ID: "mteb/leaderboard"
- RESULTS_REPO: mteb/results
- LEADERBOARD_NAME: "MTEB Leaderboard"
-tasks:
- BitextMining:
- icon: "🎌"
- metric: f1
- metric_description: "[F1](https://huggingface.co/spaces/evaluate-metric/f1)"
- task_description: "Bitext mining is the task of finding parallel sentences in two languages."
- Classification:
- icon: "❤️"
- metric: accuracy
- metric_description: "[Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)"
- task_description: "Classification is the task of assigning a label to a text."
- Clustering:
- icon: "✨"
- metric: v_measure
- metric_description: "Validity Measure (V-measure)"
- task_description: "Clustering is the task of grouping similar documents together."
- PairClassification:
- icon: "🎭"
- metric: max_ap
- metric_description: "Average Precision (AP) based on the models similarity metric (usually cosine)"
- task_description: "Pair classification is the task of determining whether two texts are similar."
- Reranking:
- icon: "🥈"
- metric: map
- metric_description: "Mean Average Precision (MAP)"
- task_description: "Reranking is the task of reordering a list of documents to improve relevance."
- Retrieval:
- icon: "🔎"
- metric: ndcg_at_10
- metric_description: "Normalized Discounted Cumulative Gain @ 10 (nDCG@10)"
- task_description: "Retrieval is the task of finding relevant documents for a query."
- STS:
- icon: "☘️"
- metric: cosine_spearman
- metric_description: "Spearman correlation based on the model's similarity metric (usually cosine)"
- task_description: "Semantic Textual Similarity is the task of determining how similar two texts are."
- Summarization:
- icon: "📜"
- metric: cosine_spearman
- metric_description: "Spearman correlation based on the model's similarity metric (usually cosine)"
- task_description: "Summarization is the task of generating a summary of a text."
- MultilabelClassification:
- icon: "🏷️"
- metric: accuracy
- metric_description: "Accuracy"
- task_description: "Multilabel classification is the task of assigning multiple labels to a text."
- InstructionRetrieval:
- icon: "🔎📋"
- metric: "p-MRR"
- metric_description: "paired mean reciprocal rank (p-MRR)"
- task_description: "Retrieval w/Instructions is the task of finding relevant documents for a query that has detailed instructions."
-boards:
- en:
- title: English
- language_long: "English"
- has_overall: true
- acronym: null
- icon: null
- special_icons: null
- credits: null
- tasks:
- Classification:
- - AmazonCounterfactualClassification (en)
- - AmazonPolarityClassification
- - AmazonReviewsClassification (en)
- - Banking77Classification
- - EmotionClassification
- - ImdbClassification
- - MassiveIntentClassification (en)
- - MassiveScenarioClassification (en)
- - MTOPDomainClassification (en)
- - MTOPIntentClassification (en)
- - ToxicConversationsClassification
- - TweetSentimentExtractionClassification
- Clustering:
- - ArxivClusteringP2P
- - ArxivClusteringS2S
- - BiorxivClusteringP2P
- - BiorxivClusteringS2S
- - MedrxivClusteringP2P
- - MedrxivClusteringS2S
- - RedditClustering
- - RedditClusteringP2P
- - StackExchangeClustering
- - StackExchangeClusteringP2P
- - TwentyNewsgroupsClustering
- PairClassification:
- - SprintDuplicateQuestions
- - TwitterSemEval2015
- - TwitterURLCorpus
- Reranking:
- - AskUbuntuDupQuestions
- - MindSmallReranking
- - SciDocsRR
- - StackOverflowDupQuestions
- Retrieval:
- - ArguAna
- - ClimateFEVER
- - CQADupstackRetrieval
- - DBPedia
- - FEVER
- - FiQA2018
- - HotpotQA
- - MSMARCO
- - NFCorpus
- - NQ
- - QuoraRetrieval
- - SCIDOCS
- - SciFact
- - Touche2020
- - TRECCOVID
- STS:
- - BIOSSES
- - SICK-R
- - STS12
- - STS13
- - STS14
- - STS15
- - STS16
- - STS17 (en-en)
- - STS22 (en)
- - STSBenchmark
- Summarization:
- - SummEval
- en-x:
- title: "English-X"
- language_long: "117 (Pairs of: English & other language)"
- has_overall: false
- acronym: null
- icon: null
- special_icons: null
- credits: null
- tasks:
- BitextMining: ['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)']
- zh:
- title: Chinese
- language_long: Chinese
- has_overall: true
- acronym: C-MTEB
- icon: "🇨🇳"
- special_icons:
- Classification: "🧡"
- credits: "[FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)"
- tasks:
- Classification:
- - AmazonReviewsClassification (zh)
- - IFlyTek
- - JDReview
- - MassiveIntentClassification (zh-CN)
- - MassiveScenarioClassification (zh-CN)
- - MultilingualSentiment
- - OnlineShopping
- - TNews
- - Waimai
- Clustering:
- - CLSClusteringP2P
- - CLSClusteringS2S
- - ThuNewsClusteringP2P
- - ThuNewsClusteringS2S
- PairClassification:
- - Cmnli
- - Ocnli
- Reranking:
- - CMedQAv1
- - CMedQAv2
- - MMarcoReranking
- - T2Reranking
- Retrieval:
- - CmedqaRetrieval
- - CovidRetrieval
- - DuRetrieval
- - EcomRetrieval
- - MedicalRetrieval
- - MMarcoRetrieval
- - T2Retrieval
- - VideoRetrieval
- STS:
- - AFQMC
- - ATEC
- - BQ
- - LCQMC
- - PAWSX
- - QBQTC
- - STS22 (zh)
- - STSB
- da:
- title: Danish
- language_long: Danish
- has_overall: false
- acronym: null
- icon: "🇩🇰"
- special_icons:
- Classification: "🤍"
- credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
- tasks:
- BitextMining:
- - BornholmBitextMining
- Classification:
- - AngryTweetsClassification
- - DanishPoliticalCommentsClassification
- - DKHateClassification
- - LccSentimentClassification
- - MassiveIntentClassification (da)
- - MassiveScenarioClassification (da)
- - NordicLangClassification
- - ScalaDaClassification
- fr:
- title: French
- language_long: "French"
- has_overall: true
- acronym: "F-MTEB"
- icon: "🇫🇷"
- special_icons:
- Classification: "💙"
- credits: "[Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [Wissam Siblini](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)"
- tasks:
- Classification:
- - AmazonReviewsClassification (fr)
- - MasakhaNEWSClassification (fra)
- - MassiveIntentClassification (fr)
- - MassiveScenarioClassification (fr)
- - MTOPDomainClassification (fr)
- - MTOPIntentClassification (fr)
- Clustering:
- - AlloProfClusteringP2P
- - AlloProfClusteringS2S
- - HALClusteringS2S
- - MLSUMClusteringP2P (fr)
- - MLSUMClusteringS2S (fr)
- - MasakhaNEWSClusteringP2P (fra)
- - MasakhaNEWSClusteringS2S (fra)
- PairClassification:
- - OpusparcusPC (fr)
- - PawsXPairClassification (fr)
- Reranking:
- - AlloprofReranking
- - SyntecReranking
- Retrieval:
- - AlloprofRetrieval
- - BSARDRetrieval
- - MintakaRetrieval (fr)
- - SyntecRetrieval
- - XPQARetrieval (fr)
- STS:
- - STS22 (fr)
- - STSBenchmarkMultilingualSTS (fr)
- - SICKFr
- Summarization:
- - SummEvalFr
- 'no':
- title: Norwegian
- language_long: "Norwegian Bokmål"
- has_overall: false
- acronym: null
- icon: "🇳🇴"
- special_icons:
- Classification: "💙"
- credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
- tasks:
- Classification: &id001
- - NoRecClassification
- - NordicLangClassification
- - NorwegianParliament
- - MassiveIntentClassification (nb)
- - MassiveScenarioClassification (nb)
- - ScalaNbClassification
- instructions:
- title: English
- language_long: "English"
- has_overall: false
- acronym: null
- icon: null
- credits: "[Orion Weller, FollowIR](https://arxiv.org/abs/2403.15246)"
- tasks:
- InstructionRetrieval:
- - Robust04InstructionRetrieval
- - News21InstructionRetrieval
- - Core17InstructionRetrieval
- de:
- title: German
- language_long: "German"
- has_overall: false
- acronym: null
- icon: "🇩🇪"
- special_icons: null
- credits: "[Silvan](https://github.com/slvnwhrl)"
- tasks:
- Clustering:
- - BlurbsClusteringP2P
- - BlurbsClusteringS2S
- - TenKGnadClusteringP2P
- - TenKGnadClusteringS2S
- pl:
- title: Polish
- language_long: Polish
- has_overall: true
- acronym: null
- icon: "🇵🇱"
- special_icons:
- Classification: "🤍"
- credits: "[Rafał Poświata](https://github.com/rafalposwiata)"
- tasks:
- Classification:
- - AllegroReviews
- - CBD
- - MassiveIntentClassification (pl)
- - MassiveScenarioClassification (pl)
- - PAC
- - PolEmo2.0-IN
- - PolEmo2.0-OUT
- Clustering:
- - 8TagsClustering
- PairClassification:
- - CDSC-E
- - PPC
- - PSC
- - SICK-E-PL
- Retrieval:
- - ArguAna-PL
- - DBPedia-PL
- - FiQA-PL
- - HotpotQA-PL
- - MSMARCO-PL
- - NFCorpus-PL
- - NQ-PL
- - Quora-PL
- - SCIDOCS-PL
- - SciFact-PL
- - TRECCOVID-PL
- STS:
- - CDSC-R
- - SICK-R-PL
- - STS22 (pl)
- ru:
- title: Russian
- language_long: "Russian"
- has_overall: true
- acronym: null
- icon: "🇷🇺"
- special_icons: null
- credits: "[Roman Solomatin](https://github.com/Samoed) and SaluteDevices: [Alena Fenogenova](https://github.com/Alenush), [Aleksandr Abramov](https://github.com/Ab1992ao), [Artem Snegirev](https://github.com/artemsnegirev), [Anna Maksimova](https://github.com/anpalmak2003), [Maria Tikhonova](https://github.com/MariyaTikhonova)"
- tasks:
- Classification:
- - GeoreviewClassification
- - HeadlineClassification
- - InappropriatenessClassification
- - KinopoiskClassification
- - RuReviewsClassification
- - RuSciBenchGRNTIClassification
- - RuSciBenchOECDClassification
- - MassiveIntentClassification (ru)
- - MassiveScenarioClassification (ru)
- Clustering:
- - GeoreviewClusteringP2P
- - RuSciBenchGRNTIClusteringP2P
- - RuSciBenchOECDClusteringP2P
- PairClassification:
- - TERRa
- Reranking:
- - RuBQReranking
- - MIRACLReranking (ru)
- Retrieval:
- - RiaNewsRetrieval
- - RuBQRetrieval
- - MIRACLRetrieval (ru)
- STS:
- - RUParaPhraserSTS
- - RuSTSBenchmarkSTS
- - STS22 (ru)
- MultilabelClassification:
- - CEDRClassification
- - SensitiveTopicsClassification
- se:
- title: Swedish
- language_long: Swedish
- has_overall: false
- acronym: null
- icon: "🇸🇪"
- special_icons:
- Classification: "💛"
- credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
- tasks:
- Classification:
- - NoRecClassification
- - NordicLangClassification
- - NorwegianParliament
- - MassiveIntentClassification (nb)
- - MassiveScenarioClassification (nb)
- - ScalaNbClassification
- other-cls:
- title: "Other Languages"
- language_long: "47 (Only languages not included in the other tabs)"
- has_overall: false
- acronym: null
- icon: null
- special_icons:
- Classification: "💜💚💙"
- credits: null
- tasks:
- Classification: ['AmazonCounterfactualClassification (de)', 'AmazonCounterfactualClassification (ja)', 'AmazonReviewsClassification (de)', 'AmazonReviewsClassification (es)', 'AmazonReviewsClassification (fr)', 'AmazonReviewsClassification (ja)', 'AmazonReviewsClassification (zh)', 'MTOPDomainClassification (de)', 'MTOPDomainClassification (es)', 'MTOPDomainClassification (fr)', 'MTOPDomainClassification (hi)', 'MTOPDomainClassification (th)', 'MTOPIntentClassification (de)', 'MTOPIntentClassification (es)', 'MTOPIntentClassification (fr)', 'MTOPIntentClassification (hi)', 'MTOPIntentClassification (th)', 'MassiveIntentClassification (af)', 'MassiveIntentClassification (am)', 'MassiveIntentClassification (ar)', 'MassiveIntentClassification (az)', 'MassiveIntentClassification (bn)', 'MassiveIntentClassification (cy)', 'MassiveIntentClassification (de)', 'MassiveIntentClassification (el)', 'MassiveIntentClassification (es)', 'MassiveIntentClassification (fa)', 'MassiveIntentClassification (fi)', 'MassiveIntentClassification (fr)', 'MassiveIntentClassification (he)', 'MassiveIntentClassification (hi)', 'MassiveIntentClassification (hu)', 'MassiveIntentClassification (hy)', 'MassiveIntentClassification (id)', 'MassiveIntentClassification (is)', 'MassiveIntentClassification (it)', 'MassiveIntentClassification (ja)', 'MassiveIntentClassification (jv)', 'MassiveIntentClassification (ka)', 'MassiveIntentClassification (km)', 'MassiveIntentClassification (kn)', 'MassiveIntentClassification (ko)', 'MassiveIntentClassification (lv)', 'MassiveIntentClassification (ml)', 'MassiveIntentClassification (mn)', 'MassiveIntentClassification (ms)', 'MassiveIntentClassification (my)', 'MassiveIntentClassification (nl)', 'MassiveIntentClassification (pt)', 'MassiveIntentClassification (ro)', 'MassiveIntentClassification (ru)', 'MassiveIntentClassification (sl)', 'MassiveIntentClassification (sq)', 'MassiveIntentClassification (sw)', 'MassiveIntentClassification (ta)', 'MassiveIntentClassification (te)', 'MassiveIntentClassification (th)', 'MassiveIntentClassification (tl)', 'MassiveIntentClassification (tr)', 'MassiveIntentClassification (ur)', 'MassiveIntentClassification (vi)', 'MassiveIntentClassification (zh-TW)', 'MassiveScenarioClassification (af)', 'MassiveScenarioClassification (am)', 'MassiveScenarioClassification (ar)', 'MassiveScenarioClassification (az)', 'MassiveScenarioClassification (bn)', 'MassiveScenarioClassification (cy)', 'MassiveScenarioClassification (de)', 'MassiveScenarioClassification (el)', 'MassiveScenarioClassification (es)', 'MassiveScenarioClassification (fa)', 'MassiveScenarioClassification (fi)', 'MassiveScenarioClassification (fr)', 'MassiveScenarioClassification (he)', 'MassiveScenarioClassification (hi)', 'MassiveScenarioClassification (hu)', 'MassiveScenarioClassification (hy)', 'MassiveScenarioClassification (id)', 'MassiveScenarioClassification (is)', 'MassiveScenarioClassification (it)', 'MassiveScenarioClassification (ja)', 'MassiveScenarioClassification (jv)', 'MassiveScenarioClassification (ka)', 'MassiveScenarioClassification (km)', 'MassiveScenarioClassification (kn)', 'MassiveScenarioClassification (ko)', 'MassiveScenarioClassification (lv)', 'MassiveScenarioClassification (ml)', 'MassiveScenarioClassification (mn)', 'MassiveScenarioClassification (ms)', 'MassiveScenarioClassification (my)', 'MassiveScenarioClassification (nl)', 'MassiveScenarioClassification (pt)', 'MassiveScenarioClassification (ro)', 'MassiveScenarioClassification (ru)', 'MassiveScenarioClassification (sl)', 'MassiveScenarioClassification (sq)', 'MassiveScenarioClassification (sw)', 'MassiveScenarioClassification (ta)', 'MassiveScenarioClassification (te)', 'MassiveScenarioClassification (th)', 'MassiveScenarioClassification (tl)', 'MassiveScenarioClassification (tr)', 'MassiveScenarioClassification (ur)', 'MassiveScenarioClassification (vi)', 'MassiveScenarioClassification (zh-TW)']
- other-sts:
- title: Other
- language_long: "Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs)"
- has_overall: false
- acronym: null
- icon: null
- special_icons: null
- 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"
- has_overall: false
- acronym: null
- icon: "📚"
- special_icons: null
- credits: "[RAR-b (Chenghao Xiao et al.)](https://arxiv.org/abs/2404.06347/)"
- metric: nDCG@10
- tasks:
- Retrieval:
- - ARCChallenge
- - AlphaNLI
- - HellaSwag
- - PIQA
- - Quail
- - RARbCode
- - RARbMath
- - SIQA
- - SpartQA
- - TempReasonL1
- - TempReasonL2Fact
- - TempReasonL2Pure
- - TempReasonL3Fact
- - TempReasonL3Pure
- - WinoGrande
- bright:
- title: BRIGHT
- 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: 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)
- - BrightRetrieval (earth_science)
- - BrightRetrieval (economics)
- - BrightRetrieval (psychology)
- - BrightRetrieval (robotics)
- - BrightRetrieval (stackoverflow)
- - BrightRetrieval (sustainable_living)
- - BrightRetrieval (pony)
- - BrightRetrieval (leetcode)
- - 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 (python)
- - CodeSearchNetCCRetrieval (javascript)
- - CodeSearchNetCCRetrieval (go)
- - CodeSearchNetCCRetrieval (ruby)
- - CodeSearchNetCCRetrieval (java)
- - CodeSearchNetCCRetrieval (php)
- - CodeSearchNetRetrieval (python)
- - CodeSearchNetRetrieval (javascript)
- - CodeSearchNetRetrieval (go)
- - CodeSearchNetRetrieval (ruby)
- - CodeSearchNetRetrieval (java)
- - CodeSearchNetRetrieval (php)
- - CodeTransOceanContest
- - CodeTransOceanDL
- - CosQA
- - StackOverflowQA
- - SyntheticText2SQL
diff --git a/envs.py b/envs.py
deleted file mode 100644
index 0ff0be03cf8d4ed4fdcb8f2fb665eac760238b99..0000000000000000000000000000000000000000
--- a/envs.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import os
-from yaml import safe_load
-
-from huggingface_hub import HfApi
-
-LEADERBOARD_CONFIG_PATH = "config.yaml"
-with open(LEADERBOARD_CONFIG_PATH, 'r', encoding='utf-8') as f:
- LEADERBOARD_CONFIG = safe_load(f)
-MODEL_META_PATH = "model_meta.yaml"
-with open(MODEL_META_PATH, 'r', encoding='utf-8') as f:
- MODEL_META = safe_load(f)
-
-# Try first to get the config from the environment variables, then from the config.yaml file
-def get_config(name, default):
- res = None
-
- if name in os.environ:
- res = os.environ[name]
- elif 'config' in LEADERBOARD_CONFIG:
- res = LEADERBOARD_CONFIG['config'].get(name, None)
-
- if res is None:
- return default
- return res
-
-
-def str2bool(v):
- return str(v).lower() in ("yes", "true", "t", "1")
-
-
-# clone / pull the lmeh eval data
-HF_TOKEN = get_config("HF_TOKEN", None)
-
-LEADERBOARD_NAME = get_config("LEADERBOARD_NAME", "MTEB Leaderboard")
-
-REPO_ID = get_config("REPO_ID", "mteb/leaderboard")
-RESULTS_REPO = get_config("RESULTS_REPO", "mteb/results")
-
-CACHE_PATH = get_config("HF_HOME", ".")
-os.environ["HF_HOME"] = CACHE_PATH
-
-# Check if it is using persistent storage
-if not os.access(CACHE_PATH, os.W_OK):
- print(f"No write access to HF_HOME: {CACHE_PATH}. Resetting to current directory.")
- CACHE_PATH = "."
- os.environ["HF_HOME"] = CACHE_PATH
-else:
- print(f"Write access confirmed for HF_HOME")
-
-API = HfApi(token=HF_TOKEN)
diff --git a/mock_data/leaderboard.json b/mock_data/leaderboard.json
new file mode 100644
index 0000000000000000000000000000000000000000..15259155927931b3740a5c1ce22a411ad7e49bec
--- /dev/null
+++ b/mock_data/leaderboard.json
@@ -0,0 +1,26 @@
+[
+ {
+ "slug": "text",
+ "name": "Text",
+ "tasks": [
+ "law",
+ "long-context",
+ "finance",
+ "conversational",
+ "tech",
+ "multilingual",
+ "code",
+ "healthcare"
+ ]
+ },
+ {
+ "slug": "multimodal",
+ "name": "Multimodal",
+ "tasks": [
+ "text-to-photo",
+ "document-screenshot",
+ "figures-and-tables",
+ "text-to-text"
+ ]
+ }
+]
diff --git a/mock_data/models.json b/mock_data/models.json
new file mode 100644
index 0000000000000000000000000000000000000000..b98bb050ed7082fc062a3a26e80efbec361f442c
--- /dev/null
+++ b/mock_data/models.json
@@ -0,0 +1,400 @@
+[
+
+ {
+ "class": "sentence-transformers",
+ "organization": "BAAI",
+ "model_name": "bge-law-transformer",
+ "leaderboards": [
+ "text"
+ ],
+ "tasks": [
+ "law"
+ ]
+ },
+ {
+ "class": "custom",
+ "organization": "OpenAI",
+ "model_name": "openai-law-embed-v2",
+ "leaderboards": [
+ "text"
+ ],
+ "tasks": [
+ "law"
+ ]
+ },
+ {
+ "class": "proprietary",
+ "organization": "Google",
+ "model_name": "google-law-model-v1",
+ "leaderboards": [
+ "text"
+ ],
+ "tasks": [
+ "law"
+ ]
+ },
+ {
+ "class": "sentence-transformers",
+ "organization": "HuggingFace",
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diff --git a/mock_data/results.json b/mock_data/results.json
new file mode 100644
index 0000000000000000000000000000000000000000..3dc69c7800c94220516867fe2a3eb17c7d13ff79
--- /dev/null
+++ b/mock_data/results.json
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+ "recall_at_100": 0.93102,
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+ "precision_at_10": 0.17645,
+ "precision_at_20": 0.11864,
+ "precision_at_50": 0.05695,
+ "precision_at_100": 0.03949
+ }
+ },
+ {
+ "class": "proprietary",
+ "organization": "Apple",
+ "model_name": "apple-figures-tables",
+ "results": {
+ "ndcg_at_1": 0.39147,
+ "ndcg_at_3": 0.42671,
+ "ndcg_at_5": 0.42096,
+ "ndcg_at_10": 0.43618,
+ "ndcg_at_20": 0.48445,
+ "ndcg_at_50": 0.51566,
+ "ndcg_at_100": 0.5336,
+ "recall_at_1": 0.13291,
+ "recall_at_3": 0.26486,
+ "recall_at_5": 0.35456,
+ "recall_at_10": 0.4442,
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+ "recall_at_50": 0.78357,
+ "recall_at_100": 0.94492,
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+ "precision_at_10": 0.17791,
+ "precision_at_20": 0.11699,
+ "precision_at_50": 0.05242,
+ "precision_at_100": 0.03666
+ }
+ }
+ ],
+ "ParaphraseDatasets": [
+ {
+ "class": "sentence-transformers",
+ "organization": "OpenAI",
+ "model_name": "openai-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.37017,
+ "ndcg_at_3": 0.41477,
+ "ndcg_at_5": 0.40753,
+ "ndcg_at_10": 0.43993,
+ "ndcg_at_20": 0.54746,
+ "ndcg_at_50": 0.50086,
+ "ndcg_at_100": 0.54416,
+ "recall_at_1": 0.14535,
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+ "recall_at_5": 0.36108,
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+ "precision_at_10": 0.17798,
+ "precision_at_20": 0.11749,
+ "precision_at_50": 0.05015,
+ "precision_at_100": 0.03747
+ }
+ },
+ {
+ "class": "proprietary",
+ "organization": "Facebook",
+ "model_name": "fb-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.34583,
+ "ndcg_at_3": 0.38838,
+ "ndcg_at_5": 0.41596,
+ "ndcg_at_10": 0.40132,
+ "ndcg_at_20": 0.46592,
+ "ndcg_at_50": 0.52785,
+ "ndcg_at_100": 0.54444,
+ "recall_at_1": 0.12692,
+ "recall_at_3": 0.27724,
+ "recall_at_5": 0.35507,
+ "recall_at_10": 0.44902,
+ "recall_at_20": 0.60721,
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+ "recall_at_100": 0.94368,
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+ "precision_at_10": 0.17087,
+ "precision_at_20": 0.1152,
+ "precision_at_50": 0.0552,
+ "precision_at_100": 0.03908
+ }
+ },
+ {
+ "class": "custom",
+ "organization": "Apple",
+ "model_name": "apple-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.43867,
+ "ndcg_at_3": 0.42089,
+ "ndcg_at_5": 0.43117,
+ "ndcg_at_10": 0.42119,
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+ "precision_at_10": 0.17913,
+ "precision_at_20": 0.11763,
+ "precision_at_50": 0.05776,
+ "precision_at_100": 0.03729
+ }
+ }
+ ],
+ "SummarizationDatasets": [
+ {
+ "class": "sentence-transformers",
+ "organization": "OpenAI",
+ "model_name": "openai-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.44164,
+ "ndcg_at_3": 0.39176,
+ "ndcg_at_5": 0.42126,
+ "ndcg_at_10": 0.40666,
+ "ndcg_at_20": 0.47097,
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+ "ndcg_at_100": 0.53449,
+ "recall_at_1": 0.13735,
+ "recall_at_3": 0.26976,
+ "recall_at_5": 0.35961,
+ "recall_at_10": 0.44875,
+ "recall_at_20": 0.60002,
+ "recall_at_50": 0.77108,
+ "recall_at_100": 0.94887,
+ "precision_at_1": 0.36923,
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+ "precision_at_10": 0.1757,
+ "precision_at_20": 0.11329,
+ "precision_at_50": 0.05302,
+ "precision_at_100": 0.03504
+ }
+ },
+ {
+ "class": "proprietary",
+ "organization": "Facebook",
+ "model_name": "fb-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.39128,
+ "ndcg_at_3": 0.37037,
+ "ndcg_at_5": 0.41629,
+ "ndcg_at_10": 0.42365,
+ "ndcg_at_20": 0.48047,
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+ "ndcg_at_100": 0.54065,
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+ "recall_at_3": 0.27352,
+ "recall_at_5": 0.3577,
+ "recall_at_10": 0.44456,
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+ "recall_at_100": 0.94875,
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+ "precision_at_5": 0.25081,
+ "precision_at_10": 0.17885,
+ "precision_at_20": 0.11355,
+ "precision_at_50": 0.05582,
+ "precision_at_100": 0.03625
+ }
+ },
+ {
+ "class": "custom",
+ "organization": "Apple",
+ "model_name": "apple-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.41353,
+ "ndcg_at_3": 0.38203,
+ "ndcg_at_5": 0.40762,
+ "ndcg_at_10": 0.42419,
+ "ndcg_at_20": 0.54232,
+ "ndcg_at_50": 0.50979,
+ "ndcg_at_100": 0.53747,
+ "recall_at_1": 0.13057,
+ "recall_at_3": 0.27323,
+ "recall_at_5": 0.35986,
+ "recall_at_10": 0.44274,
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+ "precision_at_10": 0.17365,
+ "precision_at_20": 0.11018,
+ "precision_at_50": 0.05929,
+ "precision_at_100": 0.03584
+ }
+ }
+ ],
+ "TextRewriting": [
+ {
+ "class": "sentence-transformers",
+ "organization": "OpenAI",
+ "model_name": "openai-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.48474,
+ "ndcg_at_3": 0.38171,
+ "ndcg_at_5": 0.35397,
+ "ndcg_at_10": 0.4255,
+ "ndcg_at_20": 0.48694,
+ "ndcg_at_50": 0.50416,
+ "ndcg_at_100": 0.53381,
+ "recall_at_1": 0.12291,
+ "recall_at_3": 0.26987,
+ "recall_at_5": 0.35696,
+ "recall_at_10": 0.44125,
+ "recall_at_20": 0.62412,
+ "recall_at_50": 0.76108,
+ "recall_at_100": 0.94192,
+ "precision_at_1": 0.38738,
+ "precision_at_3": 0.32594,
+ "precision_at_5": 0.25406,
+ "precision_at_10": 0.17381,
+ "precision_at_20": 0.11778,
+ "precision_at_50": 0.05507,
+ "precision_at_100": 0.03618
+ }
+ },
+ {
+ "class": "proprietary",
+ "organization": "Facebook",
+ "model_name": "fb-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.36275,
+ "ndcg_at_3": 0.44189,
+ "ndcg_at_5": 0.36213,
+ "ndcg_at_10": 0.40005,
+ "ndcg_at_20": 0.46742,
+ "ndcg_at_50": 0.53436,
+ "ndcg_at_100": 0.53368,
+ "recall_at_1": 0.13381,
+ "recall_at_3": 0.26192,
+ "recall_at_5": 0.36402,
+ "recall_at_10": 0.44473,
+ "recall_at_20": 0.62827,
+ "recall_at_50": 0.78524,
+ "recall_at_100": 0.94364,
+ "precision_at_1": 0.37658,
+ "precision_at_3": 0.31831,
+ "precision_at_5": 0.25089,
+ "precision_at_10": 0.17347,
+ "precision_at_20": 0.11446,
+ "precision_at_50": 0.05213,
+ "precision_at_100": 0.037
+ }
+ },
+ {
+ "class": "custom",
+ "organization": "Apple",
+ "model_name": "apple-text-to-text",
+ "results": {
+ "ndcg_at_1": 0.4582,
+ "ndcg_at_3": 0.38506,
+ "ndcg_at_5": 0.41886,
+ "ndcg_at_10": 0.40925,
+ "ndcg_at_20": 0.52724,
+ "ndcg_at_50": 0.52855,
+ "ndcg_at_100": 0.54976,
+ "recall_at_1": 0.13668,
+ "recall_at_3": 0.26086,
+ "recall_at_5": 0.35085,
+ "recall_at_10": 0.44351,
+ "recall_at_20": 0.62795,
+ "recall_at_50": 0.78031,
+ "recall_at_100": 0.93716,
+ "precision_at_1": 0.35769,
+ "precision_at_3": 0.32803,
+ "precision_at_5": 0.26491,
+ "precision_at_10": 0.17186,
+ "precision_at_20": 0.11379,
+ "precision_at_50": 0.05455,
+ "precision_at_100": 0.03843
+ }
+ }
+ ]
+}
\ No newline at end of file
diff --git a/mock_data/tasks.json b/mock_data/tasks.json
new file mode 100644
index 0000000000000000000000000000000000000000..7549beb66504af2d7e543b87629babe341eeba1d
--- /dev/null
+++ b/mock_data/tasks.json
@@ -0,0 +1,110 @@
+[
+ {
+ "slug": "law",
+ "name": "Law",
+ "datasets": [
+ "ALIA_casedocs",
+ "LegalTexts",
+ "CaseLaw"
+ ]
+ },
+ {
+ "slug": "finance",
+ "name": "Finance",
+ "datasets": [
+ "FinancialStatements",
+ "StockMarketData",
+ "BankingRecords"
+ ]
+ },
+ {
+ "slug": "healthcare",
+ "name": "Healthcare",
+ "datasets": [
+ "PatientRecords",
+ "MedicalImages",
+ "ClinicalTrials"
+ ]
+ },
+ {
+ "slug": "tech",
+ "name": "Technology",
+ "datasets": [
+ "TechBlogs",
+ "DeviceSpecifications",
+ "SoftwareDocumentation"
+ ]
+ },
+ {
+ "slug": "conversational",
+ "name": "Conversational",
+ "datasets": [
+ "ChatLogs",
+ "CustomerServiceInteractions",
+ "ForumPosts"
+ ]
+ },
+ {
+ "slug": "code",
+ "name": "Code",
+ "datasets": [
+ "GitHubRepositories",
+ "CodeSnippets",
+ "ProgrammingQuestions"
+ ]
+ },
+ {
+ "slug": "multilingual",
+ "name": "Multilingual",
+ "datasets": [
+ "ParallelCorpora",
+ "TranslationDatasets",
+ "MultilingualText"
+ ]
+ },
+ {
+ "slug": "long-context",
+ "name": "Long Context",
+ "datasets": [
+ "ResearchPapers",
+ "Books",
+ "LegalDocuments"
+ ]
+ },
+ {
+ "slug": "text-to-photo",
+ "name": "Text to Photo",
+ "datasets": [
+ "TextPhotoPairs",
+ "ImageCaptions",
+ "VisualDescriptions"
+ ]
+ },
+ {
+ "slug": "document-screenshot",
+ "name": "Document Screenshot",
+ "datasets": [
+ "ScannedDocuments",
+ "PDFScreenshots",
+ "DigitalForms"
+ ]
+ },
+ {
+ "slug": "figures-and-tables",
+ "name": "Figures and Tables",
+ "datasets": [
+ "ScientificFigures",
+ "DataTables",
+ "ChartsAndGraphs"
+ ]
+ },
+ {
+ "slug": "text-to-text",
+ "name": "Text to Text",
+ "datasets": [
+ "ParaphraseDatasets",
+ "SummarizationDatasets",
+ "TextRewriting"
+ ]
+ }
+]
diff --git a/model_meta.yaml b/model_meta.yaml
deleted file mode 100644
index 948538a7c90ebab9bbd86f1e2ff3e01329b87c92..0000000000000000000000000000000000000000
--- a/model_meta.yaml
+++ /dev/null
@@ -1,2144 +0,0 @@
-model_meta:
- baichuan-ai__text-embedding:
- link: https://platform.baichuan-ai.com/docs/text-Embedding
- seq_len: 512
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- Cohere__Cohere-embed-english-v3.0:
- link: https://huggingface.co/Cohere/Cohere-embed-english-v3.0
- seq_len: 512
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- Cohere__Cohere-embed-english-v3.0-instruct:
- link: https://huggingface.co/Cohere/Cohere-embed-english-v3.0
- seq_len: 512
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: true
- Cohere__Cohere-embed-multilingual-light-v3.0:
- link: https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0
- seq_len: 512
- size: null
- dim: 384
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- Cohere__Cohere-embed-multilingual-v3.0:
- link: https://huggingface.co/Cohere/Cohere-embed-multilingual-v3.0
- seq_len: 512
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- vesteinn__DanskBERT:
- link: https://huggingface.co/vesteinn/DanskBERT
- seq_len: 514
- size: 125
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- jhu-clsp__FollowIR-7B:
- link: https://huggingface.co/jhu-clsp/FollowIR-7B
- seq_len: 4096
- size: 7240
- is_external: true
- is_propietary: false
- is_sentence_transformer_compatible: false
- uses_instruct: true
- GritLM__GritLM-7B:
- link: https://huggingface.co/GritLM/GritLM-7B
- seq_len: 4096
- size: 7240
- is_external: true
- is_propietary: false
- is_sentence_transformer_compatible: false
- uses_instruct: true
- GritLM__GritLM-7B-noinstruct:
- link: https://huggingface.co/GritLM/GritLM-7B
- seq_len: 4096
- size: 7240
- is_external: true
- is_propietary: false
- is_sentence_transformer_compatible: false
- uses_instruct: false
- facebookresearch__LASER2:
- link: https://github.com/facebookresearch/LASER
- seq_len: N/A
- size: 43
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- McGill-NLP__LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised
- seq_len: 4096
- size: 6607
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- McGill-NLP__LLM2Vec-Llama-2-unsupervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse
- seq_len: 4096
- size: 6607
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- McGill-NLP__LLM2Vec-Meta-Llama-3-supervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised
- seq_len: 8192
- size: 7505
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- McGill-NLP__LLM2Vec-Meta-Llama-3-unsupervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse
- seq_len: 8192
- size: 7505
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- McGill-NLP__LLM2Vec-Mistral-supervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised
- seq_len: 32768
- size: 7111
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- McGill-NLP__LLM2Vec-Mistral-unsupervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse
- seq_len: 32768
- size: 7111
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- McGill-NLP__LLM2Vec-Sheared-Llama-supervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised
- seq_len: 4096
- size: 1280
- dim: 2048
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- McGill-NLP__LLM2Vec-Sheared-Llama-unsupervised:
- link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse
- seq_len: 4096
- size: 1280
- dim: 2048
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- sentence-transformers__LaBSE:
- link: https://huggingface.co/sentence-transformers/LaBSE
- seq_len: 512
- size: 471
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- Linq-Embed-Mistral:
- link: https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral
- seq_len: 32768
- size: 7111
- dim: 4096
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- NV-Embed-v1:
- link: https://huggingface.co/nvidia/NV-Embed-v1
- seq_len: 32768
- size: 7851
- dim: 4096
- is_external: false
- 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
- size: 7111
- dim: 4096
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- aliyun__OpenSearch-text-hybrid:
- link: https://help.aliyun.com/zh/open-search/vector-search-edition/hybrid-retrieval
- seq_len: 512
- size: null
- dim: 1792
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- Salesforce__SFR-Embedding-Mistral:
- link: https://huggingface.co/Salesforce/SFR-Embedding-Mistral
- seq_len: 32768
- size: 7111
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- SFR-Embedding-2_R:
- link: https://huggingface.co/Salesforce/SFR-Embedding-2_R
- seq_len: 32768
- size: 7111
- dim: 4096
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- sentence-transformers__all-MiniLM-L12-v2:
- link: https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2
- seq_len: 512
- size: 33
- dim: 384
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__all-MiniLM-L6-v2:
- link: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
- seq_len: 512
- size: 23
- dim: 384
- is_external: true
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- link: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
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- size: 23
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- link: https://huggingface.co/sentence-transformers/all-mpnet-base-v2
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- link: https://huggingface.co/sentence-transformers/all-mpnet-base-v2
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- link: https://huggingface.co/sentence-transformers/allenai-specter
- seq_len: 512
- size: 110
- dim: 768
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- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
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- link: https://huggingface.co/Geotrend/bert-base-10lang-cased
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- size: 138
- dim: 768
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- is_sentence_transformers_compatible: true
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- link: https://huggingface.co/Geotrend/bert-base-15lang-cased
- seq_len: 512
- size: 138
- dim: 768
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- is_proprietary: false
- is_sentence_transformers_compatible: true
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- Geotrend__bert-base-25lang-cased:
- link: https://huggingface.co/Geotrend/bert-base-25lang-cased
- seq_len: 512
- size: 138
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- google-bert__bert-base-multilingual-cased:
- link: https://huggingface.co/google-bert/bert-base-multilingual-cased
- seq_len: 512
- size: 179
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- google-bert__bert-base-multilingual-uncased:
- link: https://huggingface.co/google-bert/bert-base-multilingual-uncased
- seq_len: 512
- size: 168
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- KB__bert-base-swedish-cased:
- link: https://huggingface.co/KB/bert-base-swedish-cased
- seq_len: 512
- size: 125
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- google-bert__bert-base-uncased:
- link: https://huggingface.co/google-bert/bert-base-uncased
- seq_len: 512
- size: 110
- dim: 768
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- is_proprietary: false
- is_sentence_transformers_compatible: true
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- BAAI__bge-base-en-v1.5:
- link: https://huggingface.co/BAAI/bge-base-en-v1.5
- seq_len: 512
- size: 109
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
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- BAAI__bge-base-en-v1.5-instruct:
- link: https://huggingface.co/BAAI/bge-base-en-v1.5
- seq_len: 512
- size: 109
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
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- BAAI__bge-base-zh-v1.5:
- link: https://huggingface.co/BAAI/bge-base-zh-v1.5
- seq_len: 512
- size: 102
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- bge-en-icl:
- link: https://huggingface.co/BAAI/bge-en-icl
- seq_len: 32768
- size: 7110
- dim: 4096
- is_external: false
- is_proprietary: false
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- bge-multilingual-gemma2:
- link: https://huggingface.co/BAAI/bge-multilingual-gemma2
- seq_len: 8192
- size: 9240
- dim: 3584
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: false
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- BAAI__bge-large-en-v1.5:
- link: https://huggingface.co/BAAI/bge-large-en-v1.5
- seq_len: 512
- size: 1340
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
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- BAAI__bge-large-en-v1.5-instruct:
- link: https://huggingface.co/BAAI/bge-large-en-v1.5
- seq_len: 512
- size: 1340
- dim: 1024
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- is_proprietary: false
- is_sentence_transformers_compatible: false
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- BAAI__bge-large-zh-noinstruct:
- link: https://huggingface.co/BAAI/bge-large-zh-noinstruct
- seq_len: 512
- size: 326
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- BAAI__bge-large-zh-v1.5:
- link: https://huggingface.co/BAAI/bge-large-zh-v1.5
- seq_len: 512
- size: 326
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- BAAI__bge-m3:
- link: https://huggingface.co/BAAI/bge-m3
- seq_len: 8192
- size: 567
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
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- BAAI__bge-m3-instruct:
- link: https://huggingface.co/BAAI/bge-m3
- seq_len: 8192
- size: 567
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
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- BAAI__bge-small-en-v1.5:
- link: https://huggingface.co/BAAI/bge-small-en-v1.5
- seq_len: 512
- size: 24
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- BAAI__bge-small-en-v1.5-instruct:
- link: https://huggingface.co/BAAI/bge-small-en-v1.5
- seq_len: 512
- size: 24
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- BAAI__bge-small-zh-v1.5:
- link: https://huggingface.co/BAAI/bge-small-zh-v1.5
- seq_len: 512
- size: 24
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- bm25:
- link: https://en.wikipedia.org/wiki/Okapi_BM25
- size: 0
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- bm25s:
- link: https://github.com/xhluca/bm25s
- size: 0
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- almanach__camembert-base:
- link: https://huggingface.co/almanach/camembert-base
- seq_len: 512
- size: 111
- dim: 512
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- almanach__camembert-large:
- link: https://huggingface.co/almanach/camembert-large
- seq_len: 512
- size: 338
- dim: 768
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- nthakur__contriever-base-msmarco:
- link: https://huggingface.co/nthakur/contriever-base-msmarco
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- facebook__contriever:
- link: https://huggingface.co/facebook/contriever
- seq_len: 512
- size: 438
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- facebook__contriever-instruct:
- link: https://huggingface.co/facebook/contriever
- seq_len: 512
- size: 438
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- T-Systems-onsite__cross-en-de-roberta-sentence-transformer:
- link: https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer
- seq_len: 514
- size: 278
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- chcaa__dfm-encoder-large-v1:
- link: https://huggingface.co/chcaa/dfm-encoder-large-v1
- seq_len: 512
- size: 355
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- Geotrend__distilbert-base-25lang-cased:
- link: https://huggingface.co/Geotrend/distilbert-base-25lang-cased
- seq_len: 512
- size: 110
- dim: 768
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- Geotrend__distilbert-base-en-fr-cased:
- link: https://huggingface.co/Geotrend/distilbert-base-en-fr-cased
- seq_len: 512
- size: 110
- dim: 768
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- Geotrend__distilbert-base-en-fr-es-pt-it-cased:
- link: https://huggingface.co/Geotrend/distilbert-base-en-fr-es-pt-it-cased
- seq_len: 512
- size: 110
- dim: 768
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- Geotrend__distilbert-base-fr-cased:
- link: https://huggingface.co/Geotrend/distilbert-base-fr-cased
- seq_len: 512
- size: 110
- dim: 768
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- distilbert__distilbert-base-uncased:
- link: https://huggingface.co/distilbert/distilbert-base-uncased
- seq_len: 512
- size: 110
- dim: 768
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__distiluse-base-multilingual-cased-v2:
- link: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2
- seq_len: 512
- size: 135
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- facebookresearch__dragon-plus:
- link: https://github.com/facebookresearch/dpr-scale/tree/main/dragon
- seq_len: 512
- size: 438
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- facebookresearch__dragon-plus-instruct:
- link: https://github.com/facebookresearch/dpr-scale/tree/main/dragon
- seq_len: 512
- size: 438
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- dwzhu__e5-base-4k:
- link: https://huggingface.co/dwzhu/e5-base-4k
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- intfloat__e5-base-v2:
- link: https://huggingface.co/intfloat/e5-base-v2
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- intfloat__e5-base:
- link: https://huggingface.co/intfloat/e5-base
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- intfloat__e5-large-v2:
- link: https://huggingface.co/intfloat/e5-large-v2
- seq_len: 512
- size: 335
- dim: 1024
- is_external: true
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- is_sentence_transformers_compatible: true
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- intfloat__e5-large:
- link: https://huggingface.co/intfloat/e5-large
- seq_len: 512
- size: 335
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- intfloat__e5-mistral-7b-instruct:
- link: https://huggingface.co/intfloat/e5-mistral-7b-instruct
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- size: 7111
- dim: 4096
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- is_sentence_transformers_compatible: true
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- intfloat__e5-mistral-7b-instruct-noinstruct:
- link: https://huggingface.co/intfloat/e5-mistral-7b-instruct-noinstruct
- seq_len: 32768
- size: 7111
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- intfloat__e5-small:
- link: https://huggingface.co/intfloat/e5-small
- seq_len: 512
- size: 33
- dim: 384
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- jonfd__electra-small-nordic:
- link: https://huggingface.co/jonfd/electra-small-nordic
- seq_len: 512
- size: 23
- dim: 256
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- KBLab__electra-small-swedish-cased-discriminator:
- link: https://huggingface.co/KBLab/electra-small-swedish-cased-discriminator
- seq_len: 512
- size: 16
- dim: 256
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- elastic__elser-v2:
- link: https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-elser.html
- seq_len: 512
- size: 110
- dim: 30522
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- google__flan-t5-base:
- link: https://huggingface.co/google/flan-t5-base
- seq_len: 512
- size: 220
- dim: -1
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- google__flan-t5-large:
- link: https://huggingface.co/google/flan-t5-large
- seq_len: 512
- size: 770
- dim: -1
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- flaubert__flaubert_base_cased:
- link: https://huggingface.co/flaubert/flaubert_base_cased
- seq_len: 512
- size: 138
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- flaubert__flaubert_base_uncased:
- link: https://huggingface.co/flaubert/flaubert_base_uncased
- seq_len: 512
- size: 138
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- flaubert__flaubert_large_cased:
- link: https://huggingface.co/flaubert/flaubert_large_cased
- seq_len: 512
- size: 372
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- deepset__gbert-base:
- link: https://huggingface.co/deepset/gbert-base
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- deepset__gbert-large:
- link: https://huggingface.co/deepset/gbert-large
- seq_len: 512
- size: 337
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- deepset__gelectra-base:
- link: https://huggingface.co/deepset/gelectra-base
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- deepset__gelectra-large:
- link: https://huggingface.co/deepset/gelectra-large
- seq_len: 512
- size: 335
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__average_word_embeddings_glove.6B.300d:
- link: https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d
- seq_len: N/A
- size: 120
- dim: 300
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- google-gecko__text-embedding-004-256:
- link: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models
- seq_len: 2048
- size: 1200
- dim: 256
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- google-gecko__text-embedding-004:
- link: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models
- seq_len: 2048
- size: 1200
- dim: 768
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- uklfr__gottbert-base:
- link: https://huggingface.co/uklfr/gottbert-base
- seq_len: 512
- size: 127
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- Alibaba-NLP__gte-Qwen1.5-7B-instruct:
- link: https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct
- seq_len: 32768
- size: 7099
- dim: 4096
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- Alibaba-NLP__gte-Qwen2-1.5B-instruct:
- link: https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct
- seq_len: 32768
- size: 1780
- dim: 1536
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- Alibaba-NLP__gte-Qwen2-7B-instruct:
- link: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct
- seq_len: 32768
- size: 7613
- dim: 3584
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- sentence-transformers__gtr-t5-base:
- link: https://huggingface.co/sentence-transformers/gtr-t5-base
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__gtr-t5-large:
- link: https://huggingface.co/sentence-transformers/gtr-t5-large
- seq_len: 512
- size: 168
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__gtr-t5-xl:
- link: https://huggingface.co/sentence-transformers/gtr-t5-xl
- seq_len: 512
- size: 1240
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__gtr-t5-xxl:
- link: https://huggingface.co/sentence-transformers/gtr-t5-xxl
- seq_len: 512
- size: 4865
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- ipipan__herbert-base-retrieval-v2:
- link: https://huggingface.co/ipipan/herbert-base-retrieval-v2
- seq_len: 514
- size: 125
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- hkunlp__instructor-base:
- link: https://huggingface.co/hkunlp/instructor-base
- seq_len: N/A
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- hkunlp__instructor-large:
- link: https://huggingface.co/hkunlp/instructor-large
- seq_len: 512
- size: 335
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- hkunlp__instructor-xl:
- link: https://huggingface.co/hkunlp/instructor-xl
- seq_len: 512
- size: 1241
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- jinaai__jina-embeddings-v2-base-en:
- link: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
- seq_len: 8192
- size: 275
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__average_word_embeddings_komninos:
- link: https://huggingface.co/sentence-transformers/average_word_embeddings_komninos
- seq_len: N/A
- size: 134
- dim: 300
- is_external: true
- is_proprietary: false
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- link: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
- seq_len: 4096
- size: 7000
- dim: -1
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- is_sentence_transformers_compatible: false
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- silk-road__luotuo-bert-medium:
- link: https://huggingface.co/silk-road/luotuo-bert-medium
- seq_len: 512
- size: 328
- dim: 768
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- is_proprietary: false
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- moka-ai__m3e-base:
- link: https://huggingface.co/moka-ai/m3e-base
- seq_len: 512
- size: 102
- dim: 768
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- is_proprietary: false
- is_sentence_transformers_compatible: true
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- moka-ai__m3e-large:
- link: https://huggingface.co/moka-ai/m3e-large
- seq_len: 512
- size: 102
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- mistralai__mistral-7b-instruct-v0.2:
- link: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
- seq_len: 4096
- size: 7240
- dim: -1
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- mistral__mistral-embed:
- link: https://docs.mistral.ai/guides/embeddings
- seq_len: null
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- castorini__monobert-large-msmarco:
- link: https://huggingface.co/castorini/monobert-large-msmarco
- seq_len: 512
- size: 770
- dim: -1
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- castorini__monot5-3b-msmarco-10k:
- link: https://huggingface.co/castorini/monot5-3b-msmarco-10k
- seq_len: 512
- size: 2480
- dim: -1
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- castorini__monot5-base-msmarco-10k:
- link: https://huggingface.co/castorini/monot5-base-msmarco-10k
- seq_len: 512
- size: 220
- dim: -1
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- sentence-transformers__msmarco-bert-co-condensor:
- link: https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__multi-qa-MiniLM-L6-cos-v1:
- link: https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1
- seq_len: 512
- size: 23
- dim: 384
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- intfloat__multilingual-e5-base:
- link: https://huggingface.co/intfloat/multilingual-e5-base
- seq_len: 514
- size: 278
- dim: 768
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- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- intfloat__multilingual-e5-large:
- link: https://huggingface.co/intfloat/multilingual-e5-large
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- size: 560
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- intfloat__multilingual-e5-large-instruct:
- link: https://huggingface.co/intfloat/multilingual-e5-large-instruct
- seq_len: 514
- size: 560
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- intfloat__multilingual-e5-small:
- link: https://huggingface.co/intfloat/multilingual-e5-small
- seq_len: 512
- size: 118
- dim: 384
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- NbAiLab__nb-bert-base:
- link: https://huggingface.co/NbAiLab/nb-bert-base
- seq_len: 512
- size: 179
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- NbAiLab__nb-bert-large:
- link: https://huggingface.co/NbAiLab/nb-bert-large
- seq_len: 512
- size: 355
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- nomic-ai__nomic-embed-text-v1:
- link: https://huggingface.co/nomic-ai/nomic-embed-text-v1
- seq_len: 8192
- size: 547
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- nomic-ai__nomic-embed-text-v1.5-128:
- link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
- seq_len: 8192
- size: 138
- dim: 128
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- nomic-ai__nomic-embed-text-v1.5-256:
- link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
- seq_len: 8192
- size: 138
- dim: 256
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- nomic-ai__nomic-embed-text-v1.5-512:
- link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
- seq_len: 8192
- size: 138
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- nomic-ai__nomic-embed-text-v1.5-64:
- link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
- seq_len: 8192
- size: 138
- dim: 64
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- ltg__norbert3-base:
- link: https://huggingface.co/ltg/norbert3-base
- seq_len: 512
- size: 131
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- ltg__norbert3-large:
- link: https://huggingface.co/ltg/norbert3-large
- seq_len: 512
- size: 368
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2:
- link: https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- seq_len: 512
- size: 118
- dim: 384
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__paraphrase-multilingual-mpnet-base-v2:
- link: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
- seq_len: 514
- size: 278
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- KBLab__sentence-bert-swedish-cased:
- link: https://huggingface.co/KBLab/sentence-bert-swedish-cased
- seq_len: 512
- size: 125
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- dangvantuan__sentence-camembert-base:
- link: https://huggingface.co/dangvantuan/sentence-camembert-base
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- dangvantuan__sentence-camembert-large:
- link: https://huggingface.co/dangvantuan/sentence-camembert-large
- seq_len: 512
- size: 337
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- Wissam42__sentence-croissant-llm-base:
- link: https://huggingface.co/Wissam42/sentence-croissant-llm-base
- seq_len: 2048
- size: 1280
- dim: 2048
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__sentence-t5-base:
- link: https://huggingface.co/sentence-transformers/sentence-t5-base
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__sentence-t5-large:
- link: https://huggingface.co/sentence-transformers/sentence-t5-large
- seq_len: 512
- size: 168
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
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- sentence-transformers__sentence-t5-xl:
- link: https://huggingface.co/sentence-transformers/sentence-t5-xl
- seq_len: 512
- size: 1240
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__sentence-t5-xxl:
- link: https://huggingface.co/sentence-transformers/sentence-t5-xxl
- seq_len: 512
- size: 4865
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- ipipan__silver-retriever-base-v1:
- link: https://huggingface.co/ipipan/silver-retriever-base-v1
- seq_len: 514
- size: 125
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sdadas__st-polish-paraphrase-from-distilroberta:
- link: https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta
- seq_len: 514
- size: 125
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sdadas__st-polish-paraphrase-from-mpnet:
- link: https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet
- seq_len: 514
- size: 125
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- stella_en_1.5B_v5:
- link: https://huggingface.co/dunzhang/stella_en_1.5B_v5
- seq_len: 512
- size: 1500
- dim: 8192
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- stella_en_400M_v5:
- link: https://huggingface.co/dunzhang/stella_en_400M_v5
- seq_len: 512
- size: 400
- dim: 8192
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: true
- princeton-nlp__sup-simcse-bert-base-uncased:
- link: https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- openai__text-embedding-3-large:
- link: https://openai.com/blog/new-embedding-models-and-api-updates
- seq_len: 8191
- size: null
- dim: 3072
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-embedding-3-large-instruct:
- link: https://openai.com/blog/new-embedding-models-and-api-updates
- seq_len: 8191
- size: null
- dim: 3072
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- is_sentence_transformers_compatible: false
- uses_instruct: true
- openai__text-embedding-3-large-256:
- link: https://openai.com/blog/new-embedding-models-and-api-updates
- seq_len: 8191
- size: null
- dim: 256
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- is_sentence_transformers_compatible: false
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- openai__text-embedding-3-small:
- link: https://openai.com/blog/new-embedding-models-and-api-updates
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- size: null
- dim: 1536
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- is_sentence_transformers_compatible: false
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- openai__text-embedding-3-small-instruct:
- link: https://openai.com/blog/new-embedding-models-and-api-updates
- seq_len: 8191
- size: null
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- openai__text-embedding-ada-002:
- link: https://openai.com/blog/new-and-improved-embedding-model
- seq_len: 8191
- size: null
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- openai__text-embedding-ada-002-instruct:
- link: https://openai.com/blog/new-and-improved-embedding-model
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- size: null
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- openai__text-search-ada-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
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- size: null
- dim: 1024
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- is_sentence_transformers_compatible: false
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- openai__text-search-ada-doc-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
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- is_sentence_transformers_compatible: false
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- text-search-ada-query-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 1024
- is_external: false
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-search-babbage-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 2048
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- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-search-curie-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 4096
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-search-davinci-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 12288
- is_external: true
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- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-similarity-ada-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 1024
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- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-similarity-babbage-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 2048
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- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-similarity-curie-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 4096
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- openai__text-similarity-davinci-001:
- link: https://openai.com/blog/introducing-text-and-code-embeddings
- seq_len: 2046
- size: null
- dim: 12288
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- orionweller__tart-dual-contriever-msmarco:
- link: https://huggingface.co/orionweller/tart-dual-contriever-msmarco
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- facebook__tart-full-flan-t5-xl:
- link: https://huggingface.co/facebook/tart-full-flan-t5-xl
- seq_len: 512
- size: 2480
- dim: -1
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: true
- shibing624__text2vec-base-chinese:
- link: https://huggingface.co/shibing624/text2vec-base-chinese
- seq_len: 512
- size: 102
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- shibing624__text2vec-base-multilingual:
- link: https://huggingface.co/shibing624/text2vec-base-multilingual
- seq_len: 256
- size: 118
- dim: 384
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: false
- uses_instruct: false
- shibing624__text2vec-large-chinese:
- link: https://huggingface.co/GanymedeNil/text2vec-large-chinese
- seq_len: 512
- size: 326
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- amazon__titan-embed-text-v1:
- link: https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html
- seq_len: 8000
- size: null
- dim: 1536
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- izhx__udever-bloom-1b1:
- link: https://huggingface.co/izhx/udever-bloom-1b1
- seq_len: 2048
- size: null
- dim: 1536
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- izhx__udever-bloom-560m:
- link: https://huggingface.co/izhx/udever-bloom-560m
- seq_len: 2048
- size: null
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- vprelovac__universal-sentence-encoder-multilingual-3:
- link: https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3
- seq_len: 512
- size: null
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- vprelovac__universal-sentence-encoder-multilingual-large-3:
- link: https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-large-3
- seq_len: 512
- size: null
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- princeton-nlp__unsup-simcse-bert-base-uncased:
- link: https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased
- seq_len: 512
- size: 110
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sentence-transformers__use-cmlm-multilingual:
- link: https://huggingface.co/sentence-transformers/use-cmlm-multilingual
- seq_len: 512
- size: 472
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- voyageai__voyage-2:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 4000
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- voyageai__voyage-code-2:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 16000
- size: null
- dim: 1536
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- voyageai__voyage-large-2-instruct:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 16000
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: true
- voyageai__voyage-law-2:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 16000
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- voyageai__voyage-lite-01-instruct:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 4000
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: true
- voyageai__voyage-lite-02-instruct:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 4000
- size: 1220
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: true
- voyageai__voyage-multilingual-2:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 32000
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- voyageai__voyage-3:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 32000
- size: null
- dim: 1024
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- voyageai__voyage-3-lite:
- link: https://docs.voyageai.com/embeddings/
- seq_len: 32000
- size: null
- dim: 512
- is_external: true
- is_proprietary: true
- is_sentence_transformers_compatible: false
- uses_instruct: false
- FacebookAI__xlm-roberta-base:
- link: https://huggingface.co/FacebookAI/xlm-roberta-base
- seq_len: 514
- size: 279
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- FacebookAI__xlm-roberta-large:
- link: https://huggingface.co/FacebookAI/xlm-roberta-large
- seq_len: 514
- size: 560
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- cointegrated__rubert-tiny:
- link: https://huggingface.co/cointegrated/rubert-tiny
- seq_len: 514
- size: 12
- dim: 512
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- cointegrated__rubert-tiny2:
- link: https://huggingface.co/cointegrated/rubert-tiny2
- seq_len: 514
- size: 29
- dim: 2048
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- ai-forever__sbert_large_mt_nlu_ru:
- link: https://huggingface.co/ai-forever/sbert_large_mt_nlu_ru
- seq_len: 514
- size: 427
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- ai-forever__sbert_large_nlu_ru:
- link: https://huggingface.co/ai-forever/sbert_large_nlu_ru
- seq_len: 512
- size: 427
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- deepvk__USER-base:
- link: https://huggingface.co/deepvk/USER-base
- seq_len: 512
- size: 124
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- deepvk__USER-bge-m3:
- link: https://huggingface.co/deepvk/USER-bge-m3
- seq_len: 8192
- size: 359
- dim: 1024
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- deepvk__deberta-v1-base:
- link: https://huggingface.co/deepvk/deberta-v1-base
- seq_len: 512
- size: 124
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sergeyzh__rubert-tiny-turbo:
- link: https://huggingface.co/sergeyzh/rubert-tiny-turbo
- seq_len: 2048
- size: 29
- dim: 312
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- sergeyzh__LaBSE-ru-turbo:
- link: https://huggingface.co/sergeyzh/LaBSE-ru-turbo
- seq_len: 512
- size: 128
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- DeepPavlov__distilrubert-small-cased-conversational:
- link: https://huggingface.co/DeepPavlov/distilrubert-small-cased-conversational
- seq_len: 512
- size: 106
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- DeepPavlov__rubert-base-cased:
- link: https://huggingface.co/DeepPavlov/rubert-base-cased
- seq_len: 512
- size: 180
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- DeepPavlov__rubert-base-cased-sentence:
- link: https://huggingface.co/DeepPavlov/rubert-base-cased-sentence
- seq_len: 512
- size: 180
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- cointegrated__LaBSE-en-ru:
- link: https://huggingface.co/cointegrated/LaBSE-en-ru
- seq_len: 512
- size: 129
- dim: 768
- is_external: true
- is_proprietary: false
- is_sentence_transformers_compatible: true
- uses_instruct: false
- cde-small-v1:
- link: https://huggingface.co/jxm/cde-small-v1
- seq_len: 512
- size: 143
- dim: 768
- is_external: false
- is_proprietary: false
- is_sentence_transformers_compatible: false
-models_to_skip:
-- michaelfeil/ct2fast-e5-large-v2
-- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse
-- newsrx/instructor-xl
-- sionic-ai/sionic-ai-v1
-- lsf1000/bge-evaluation
-- Intel/bge-small-en-v1.5-sst2
-- newsrx/instructor-xl-newsrx
-- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse
-- McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse
-- davidpeer/gte-small
-- goldenrooster/multilingual-e5-large
-- kozistr/fused-large-en
-- mixamrepijey/instructor-small
-- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised
-- DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext
-- Intel/bge-base-en-v1.5-sst2-int8-dynamic
-- morgendigital/multilingual-e5-large-quantized
-- BAAI/bge-small-en
-- ggrn/e5-small-v2
-- vectoriseai/gte-small
-- giulio98/placeholder
-- odunola/UAE-Large-VI
-- vectoriseai/e5-large-v2
-- gruber/e5-small-v2-ggml
-- Severian/nomic
-- arcdev/e5-mistral-7b-instruct
-- mlx-community/multilingual-e5-base-mlx
-- michaelfeil/ct2fast-bge-base-en-v1.5
-- Intel/bge-small-en-v1.5-sst2-int8-static
-- jncraton/stella-base-en-v2-ct2-int8
-- vectoriseai/multilingual-e5-large
-- rlsChapters/Chapters-SFR-Embedding-Mistral
-- arcdev/SFR-Embedding-Mistral
-- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised
-- McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised
-- vectoriseai/gte-base
-- mixamrepijey/instructor-models
-- GovCompete/e5-large-v2
-- ef-zulla/e5-multi-sml-torch
-- khoa-klaytn/bge-small-en-v1.5-angle
-- krilecy/e5-mistral-7b-instruct
-- vectoriseai/bge-base-en-v1.5
-- vectoriseai/instructor-base
-- jingyeom/korean_embedding_model
-- rizki/bgr-tf
-- barisaydin/bge-base-en
-- jamesgpt1/zzz
-- Malmuk1/e5-large-v2_Sharded
-- vectoriseai/ember-v1
-- Consensus/instructor-base
-- barisaydin/bge-small-en
-- barisaydin/gte-base
-- woody72/multilingual-e5-base
-- Einas/einas_ashkar
-- michaelfeil/ct2fast-bge-large-en-v1.5
-- vectoriseai/bge-small-en-v1.5
-- iampanda/Test
-- cherubhao/yogamodel
-- ieasybooks/multilingual-e5-large-onnx
-- jncraton/e5-small-v2-ct2-int8
-- radames/e5-large
-- khoa-klaytn/bge-base-en-v1.5-angle
-- Intel/bge-base-en-v1.5-sst2-int8-static
-- vectoriseai/e5-large
-- TitanML/jina-v2-base-en-embed
-- Koat/gte-tiny
-- binqiangliu/EmbeddingModlebgelargeENv1.5
-- beademiguelperez/sentence-transformers-multilingual-e5-small
-- sionic-ai/sionic-ai-v2
-- jamesdborin/jina-v2-base-en-embed
-- maiyad/multilingual-e5-small
-- dmlls/all-mpnet-base-v2
-- odunola/e5-base-v2
-- vectoriseai/bge-large-en-v1.5
-- vectoriseai/bge-small-en
-- karrar-alwaili/UAE-Large-V1
-- t12e/instructor-base
-- Frazic/udever-bloom-3b-sentence
-- Geolumina/instructor-xl
-- hsikchi/dump
-- recipe/embeddings
-- michaelfeil/ct2fast-bge-small-en-v1.5
-- ildodeltaRule/multilingual-e5-large
-- shubham-bgi/UAE-Large
-- BAAI/bge-large-en
-- michaelfeil/ct2fast-e5-small-v2
-- cgldo/semanticClone
-- barisaydin/gte-small
-- aident-ai/bge-base-en-onnx
-- jamesgpt1/english-large-v1
-- michaelfeil/ct2fast-e5-small
-- baseplate/instructor-large-1
-- newsrx/instructor-large
-- Narsil/bge-base-en
-- michaelfeil/ct2fast-e5-large
-- mlx-community/multilingual-e5-small-mlx
-- lightbird-ai/nomic
-- MaziyarPanahi/GritLM-8x7B-GGUF
-- newsrx/instructor-large-newsrx
-- dhairya0907/thenlper-get-large
-- barisaydin/bge-large-en
-- jncraton/bge-small-en-ct2-int8
-- retrainai/instructor-xl
-- BAAI/bge-base-en
-- gentlebowl/instructor-large-safetensors
-- d0rj/e5-large-en-ru
-- atian-chapters/Chapters-SFR-Embedding-Mistral
-- Intel/bge-base-en-v1.5-sts-int8-static
-- Intel/bge-base-en-v1.5-sts-int8-dynamic
-- jncraton/GIST-small-Embedding-v0-ct2-int8
-- jncraton/gte-tiny-ct2-int8
-- d0rj/e5-small-en-ru
-- vectoriseai/e5-small-v2
-- SmartComponents/bge-micro-v2
-- michaelfeil/ct2fast-gte-base
-- vectoriseai/e5-base-v2
-- Intel/bge-base-en-v1.5-sst2
-- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised
-- Research2NLP/electrical_stella
-- weakit-v/bge-base-en-v1.5-onnx
-- GovCompete/instructor-xl
-- barisaydin/text2vec-base-multilingual
-- Intel/bge-small-en-v1.5-sst2-int8-dynamic
-- jncraton/gte-small-ct2-int8
-- d0rj/e5-base-en-ru
-- barisaydin/gte-large
-- fresha/e5-large-v2-endpoint
-- vectoriseai/instructor-large
-- Severian/embed
-- vectoriseai/e5-base
-- mlx-community/multilingual-e5-large-mlx
-- vectoriseai/gte-large
-- anttip/ct2fast-e5-small-v2-hfie
-- michaelfeil/ct2fast-gte-large
-- gizmo-ai/Cohere-embed-multilingual-v3.0
-- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse
-- Kenknight1999/tungdd7_ft_e5
-- joteqwork/new_gsev0
-- vantagediscovery/jina-embeddings-v2-base-en
-- vantagediscovery/nomic-embed-text-v1
-- vantagediscovery/nomic-embed-text-v1.5
-- srikanthmalla/hkunlp-instructor-xl
-- afrideva/GIST-all-MiniLM-L6-v2-GGUF
-- nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF
-- agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF
-- ekorman-strive/bge-large-en-v1.5
-- raghavlight/SE_v1
-- liddlefish/privacyembeddingv2_bge_small
-- ahmet1338/finetuned_embedder
-- radia/snowflake-arctic-embed-l-Q4_K_M-GGUF
-- GregorBiswanger/GritLM-7B-Q4_K_M-GGUF
-- powermove72/GritLM-7B-Q4_K_M-GGUF
-- sunzx0810/gte-Qwen2-7B-instruct-Q5_K_M-GGUF
-- nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF
-- nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF
-- fishbone64/gte-Qwen2-7B-instruct-Q8_0-GGUF
-- tobchef/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF
-- liddlefish/privacy_embedding_rag
-- liddlefish/privacy_embedding_rag_10k_tmp
-- liddlefish/privacy_embedding_bge_small_synthetic
-- mxs980/gte-Qwen2-1.5B-instruct-Q8_0-GGUF
-- leonn71/gte-Qwen2-1.5B-instruct-Q6_K-GGUF
-- niancheng/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
-- niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF
-- cleatherbury/gte-Qwen2-7B-instruct-Q5_K_M-GGUF
-- T3LS/stella-mrl-large-zh-v3.5-1792d-1024
-- mxs980/b1ade-embed-Q8_0-GGUF
-- chihlunLee/NoInstruct-small-Embedding-v0-Q4_0-GGUF
-- bcastle/snowflake-arctic-embed-l-Q8_0-GGUF
-- niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF
-- brtaydin/gte-Qwen2-7B-instruct-Q5_K_M-GGUF
-- yishan-wang/snowflake-arctic-embed-s-Q8_0-GGUF
-- Pekarnick/e5-large-v2-Q4_K_M-GGUF
-- agier9/UAE-Large-V1-Q5_K_S-GGUF
-- archit28/bge-large-en-v1.5-Q4_K_S-GGUF
-- dimcha/mxbai-embed-large-v1-Q4_K_M-GGUF
-- djuna/jina-embeddings-v2-base-en-Q5_K_M-GGUF
-- djuna/jina-embeddings-v2-small-en-Q5_K_M-GGUF
-- djuna/stella-base-en-v2-Q5_K_M-GGUF
-- huoxu/bge-large-en-v1.5-Q8_0-GGUF
-- itod/UAE-Large-V1-Q8_0-GGUF
-- corto-ai/nomic-embed-text-v1
-- Intel/neural-embedding-v1
-- 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
-- liddlefish/privacy_embedding_rag_10k_base_12_final
-- liddlefish/privacy_embedding_rag_10k_base_15_final
-- liddlefish/privacy_embedding_rag_10k_base_checkpoint_2
-- liddlefish/privacy_embedding_rag_10k_base_final
-- tomaarsen/bge-base-en-v1.5
-- CAiRE/UniVaR-lambda-80
-- CAiRE/UniVaR-lambda-1
-- CAiRE/UniVaR-lambda-5
-- CAiRE/UniVaR-lambda-20
-- leeloolee/intention
-- yixuan-chia/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF
-- yixuan-chia/snowflake-arctic-embed-m-v1.5-Q4_K_M-GGUF
-- yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF
-- yixuan-chia/snowflake-arctic-embed-s-Q8_0-GGUF
-- VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF
-- chris-code/multilingual-e5-large-Q8_0-GGUF
-- soichisumi/multilingual-e5-large-Q8_0-GGUF
-- pruizpar/multilingual-e5-large-Q4_K_M-GGUF
-- yixuan-chia/snowflake-arctic-embed-l-Q8_0-GGUF
-- twadada/nmc-300-w30k-b10k
-- twadada/nmc-300-w50k-b30k
-- twadada/nmc-cls-200_correct
-- twadada/st-mpnet_original
-- twadada/nmc-cls-500_correct
-- twadada/nmc-cls-50_correct
-- twadada/nmc-cls-100_correct
-- twadada/nmc-wordpca
-- twadada/mpn-wordpca
-- twadada/lma-wordpca
-- twadada/lma-nobivec
-- twadada/lma-nopca
-- twadada/lma_noabtt
-- twadada/ft-lower-pca
-- twadada/ft-lower
-- twadada/nmc-prmt-and-emb-150k
-- twadada/s2v-gcp-pca
-- twadada/s2v-gcp
-- twadada/ft-gcp-pca
-- twadada/ft-gcp
-- twadada/lma3-correct
-- twadada/mpn-nopca
-- twadada/nmc-pmpt_correct
-- twadada/lma3-2
-- twadada/lma3
-- twadada/lma2
-- twadada/lma
-- twadada/mpn-noabtt
-- twadada/mpn-nobivec
-- twadada/nmc-prmt-and-emb
-- twadada/nmc-cls-300_correct
-- twadada/nmc-prmpt
-- twadada/kom2
-- twadada/nmc-cls-noabtt
-- twadada/nmc-cls-nobiv
-- twadada/nmc-cls-nopca
-- twadada/mpn
-- twadada/sup-cse
-- twadada/mxb
-- twadada/kom
-- twadada/un-sim
-- twadada/nmc-cls
-- twadada/komni
-- twadada/fasttext
-- twadada/s2v
-- twadada/tst2
-- twadada/tst
-- markaw/NV-Embed-v2
-- linhphanff/stella_en_1.5B_v5_clone
-- BN7002/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
-- DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge
-- Hiveurban/multilingual-e5-large-pooled
-- KeyurRamoliya/multilingual-e5-large-GGUF
-- SkyeTeam/stella_en_400m
-- yixuan-chia/snowflake-arctic-embed-m-long-Q8_0-GGUF
-- aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF
-- twadada/nmc-nignore15
-- twadada/nmc-nignore30
-- twadada/nmc-nignore50
-- twadada/nmc-300-w75k-b10k
-- twadada/nmc-300-w50k-b10k
-- bijaygurung/stella_en_400M_v5
-- bennegeek/stella_en_1.5B_v5
-- lgbird/stella_1.5B_custom
-- mmazeem/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
-- shanghung/stella-base-zh-v3-1792d
-- mav23/gte-Qwen2-7B-instruct-GGUF
-- Marqo/dunzhang-stella_en_400M_v5
-- mav23/gte-Qwen2-1.5B-instruct-GGUF
-- DivineNnamdi/jina-embeddings-v3
-- tomaarsen/mxbai-embed-large-v1-exported
-- yixuan-chia/multilingual-e5-large-instruct-gguf
-- Nextcloud-AI/multilingual-e5-large-instruct
-- aiacademyvn/multilingual-e5-large-instruct
-- katanemo/bge-large-en-v1.5
-- delwinn/stella_en_1.5B_v5
-- tensorblock/gte-Qwen2-7B-instruct-GGUF
-- tensorblock/gte-Qwen2-1.5B-instruct-GGUF
-- arkohut/jina-embeddings-v3
-- ISOISS/jina-embeddings-v3-tei
-- OrcaDB/cde-small-v1
-- tomaarsen/mxbai-embed-large-v1-test
-- OrcaDB/gte-base-en-v1.5
-- twine-network/NoInstruct-small-Embedding-v0-Q8_0-GGUF
-- twine-network/NoInstruct-small-Embedding-v0-Q5_K_M-GGUF
-- tomaarsen/bge-small-en-v1.5-copy
-- nnch/multilingual-e5-large-Q4_K_M-GGUF
-- apto-as/multilingual-e5-large-Q8_0-GGUF
-- gpustack/jina-embeddings-v2-base-en-GGUF
-- sheldonrobinson/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF
-- yixuan-chia/multilingual-e5-base-gguf
-- deepfile/multilingual-e5-small-onnx-qint8
-- deepfile/multilingual-e5-small-openvino
-- Marqo/multilingual-e5-small
-- mirekphd/gte-Qwen2-7B-instruct-Q2_K-GGUF
-- beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
-- MichielEeckhout/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
-- compressa-ai/Compressa-Embeddings
-- ilhamdprastyo/jina-embeddings-v3-tei
-- OysterHR/gte-base-en-v1.5
-- Saco93/Conan-embedding-v1-Q4_K_S-GGUF
-- lagoon999/Conan-embedding-v1-Q4_K_M-GGUF
-- lagoon999/xiaobu-embedding-v2-Q8_0-GGUF
-- mirekphd/gte-Qwen2-7B-instruct-Q2_K-GGUF
-- beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
-- MichielEeckhout/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
-- gpustack/jina-embeddings-v2-base-zh-GGUF
-- arkohut/jina-embeddings-v2-base-zh
-- silverjam/jina-embeddings-v2-base-zh
-- BookingCare/gte-multilingual-base-onnx-quantized
-- blevlabs/stella_en_v5
-- sigridjineth/stella_en_1.5B_v5_sigrid
-- google/Gemma-Embeddings-v1.0
-cross_encoders:
-- FollowIR-7B
-- flan-t5-base
-- flan-t5-large
-- monobert-large-msmarco
-- monot5-3b-msmarco-10k
-- monot5-base-msmarco-10k
-- llama-2-7b-chat
-- mistral-7b-instruct-v0.2
-- tart-full-flan-t5-xl
diff --git a/refresh.py b/refresh.py
deleted file mode 100644
index 51e8be4b113630f462ca2212869ca3ffaa7c0fb6..0000000000000000000000000000000000000000
--- a/refresh.py
+++ /dev/null
@@ -1,799 +0,0 @@
-from __future__ import annotations
-
-import json
-
-import os
-import re
-from functools import reduce
-from typing import Any
-
-import pandas as pd
-from datasets import load_dataset
-from huggingface_hub import hf_hub_download
-from huggingface_hub.repocard import metadata_load
-from tqdm.autonotebook import tqdm
-
-from envs import API, LEADERBOARD_CONFIG, MODEL_META, REPO_ID, RESULTS_REPO
-from utils.model_size import get_model_parameters_memory
-
-MODEL_CACHE = {}
-TASKS_CONFIG = LEADERBOARD_CONFIG["tasks"]
-BOARDS_CONFIG = LEADERBOARD_CONFIG["boards"]
-
-TASKS = list(TASKS_CONFIG.keys())
-PRETTY_NAMES = {
- "InstructionRetrieval": "Retrieval w/Instructions",
- "PairClassification": "Pair Classification",
- "BitextMining": "Bitext Mining",
-}
-
-TASK_TO_METRIC = {k: [v["metric"]] for k, v in TASKS_CONFIG.items()}
-# Add legacy metric names
-TASK_TO_METRIC["STS"].append("cos_sim_spearman")
-TASK_TO_METRIC["STS"].append("spearman")
-TASK_TO_METRIC["Summarization"].append("cos_sim_spearman")
-TASK_TO_METRIC["Summarization"].append("spearman")
-TASK_TO_METRIC["PairClassification"].append("ap")
-TASK_TO_METRIC["PairClassification"].append("cos_sim_ap")
-TASK_TO_METRIC["PairClassification"].append("cosine_ap")
-
-
-EXTERNAL_MODELS = {
- k for k, v in MODEL_META["model_meta"].items() if v.get("is_external", False)
-}
-EXTERNAL_MODEL_TO_LINK = {
- k: v["link"] for k, v in MODEL_META["model_meta"].items() if v.get("link", False)
-}
-EXTERNAL_MODEL_TO_DIM = {
- k: v["dim"] for k, v in MODEL_META["model_meta"].items() if v.get("dim", False)
-}
-EXTERNAL_MODEL_TO_SEQLEN = {
- k: v["seq_len"]
- for k, v in MODEL_META["model_meta"].items()
- if v.get("seq_len", False)
-}
-EXTERNAL_MODEL_TO_SIZE = {
- k: v["size"] for k, v in MODEL_META["model_meta"].items() if v.get("size", False)
-}
-PROPRIETARY_MODELS = {
- k for k, v in MODEL_META["model_meta"].items() if v.get("is_proprietary", False)
-}
-TASK_DESCRIPTIONS = {k: v["task_description"] for k, v in TASKS_CONFIG.items()}
-TASK_DESCRIPTIONS["Overall"] = "Overall performance across MTEB tasks."
-SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
- k
- for k, v in MODEL_META["model_meta"].items()
- if v.get("is_sentence_transformers_compatible", False)
-}
-MODELS_TO_SKIP = MODEL_META["models_to_skip"]
-CROSS_ENCODERS = MODEL_META["cross_encoders"]
-BI_ENCODERS = [
- k for k, _ in MODEL_META["model_meta"].items() if k not in CROSS_ENCODERS + ["bm25"]
-]
-INSTRUCT_MODELS = {
- k for k, v in MODEL_META["model_meta"].items() if v.get("uses_instruct", False)
-}
-NOINSTRUCT_MODELS = {
- k for k, v in MODEL_META["model_meta"].items() if not v.get("uses_instruct", False)
-}
-
-
-TASK_TO_TASK_TYPE = {task_category: [] for task_category in TASKS}
-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
-# model_infos_path = "model_infos.json"
-MODEL_INFOS = {}
-# if os.path.exists(model_infos_path):
-# with open(model_infos_path) as f:
-# MODEL_INFOS = json.load(f)
-
-
-def add_rank(df: pd.DataFrame) -> pd.DataFrame:
- cols_to_rank = [
- col
- for col in df.columns
- if col
- not in [
- "Model",
- "Model Size (Million Parameters)",
- "Memory Usage (GB, fp32)",
- "Embedding Dimensions",
- "Max Tokens",
- ]
- ]
- if len(cols_to_rank) == 1:
- df.sort_values(cols_to_rank[0], ascending=False, inplace=True)
- else:
- df.insert(
- len(df.columns) - len(cols_to_rank),
- "Average",
- df[cols_to_rank].mean(axis=1, skipna=False),
- )
- df.sort_values("Average", ascending=False, inplace=True)
- df.insert(0, "Rank", list(range(1, len(df) + 1)))
- df = df.round(2)
- # Fill NaN after averaging
- df.fillna("", inplace=True)
- return df
-
-
-def make_clickable_model(model_name: str, link: None | str = None) -> str:
- if link is None:
- link = "https://huggingface.co/" + model_name
- # Remove user from model name
- model_name = model_name.split("/")[-1]
- model_name = model_name.split("__")[-1]
- return f'{model_name}'
-
-
-def add_subset(examples):
- if not (examples["hf_subset"]) or (examples["hf_subset"] == "default"):
- examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"]
- else:
- examples["mteb_dataset_name_with_lang"] = (
- examples["mteb_dataset_name"] + f' ({examples["hf_subset"]})'
- )
- return examples
-
-
-def norm(names: list[str]) -> set[str]:
- return set([name.split()[0] for name in names])
-
-
-def add_task(examples):
- # Could be added to the dataset loading script instead
- task_name = examples["mteb_dataset_name"]
- task_type = None
- for task_category, task_list in TASK_TO_TASK_TYPE.items():
- if task_name in norm(task_list):
- task_type = task_category
- break
- if task_type is not None:
- examples["mteb_task"] = task_type
- else:
- print("WARNING: Task not found for dataset", examples["mteb_dataset_name"])
- examples["mteb_task"] = "Unknown"
- return examples
-
-
-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, 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.
- 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"])
- elif name.startswith("MIRACLReranking"):
- return bool(metric in ["NDCG@10(MIRACL)"])
- else:
- return bool(metric in expected_metrics)
-
-
-def get_dim_seq_size(model):
- siblings = model.siblings or []
- filenames = [sib.rfilename for sib in siblings]
- dim, seq = "", ""
- for filename in filenames:
- if re.match("\d+_Pooling/config.json", filename):
- st_config_path = hf_hub_download(model.modelId, filename=filename)
- dim = json.load(open(st_config_path)).get("word_embedding_dimension", "")
- break
- for filename in filenames:
- if re.match("\d+_Dense/config.json", filename):
- st_config_path = hf_hub_download(model.modelId, filename=filename)
- dim = json.load(open(st_config_path)).get("out_features", dim)
- if "config.json" in filenames:
- config_path = hf_hub_download(model.modelId, filename="config.json")
- config = json.load(open(config_path))
- if not dim:
- dim = config.get(
- "hidden_dim", config.get("hidden_size", config.get("d_model", ""))
- )
- seq = config.get(
- "n_positions",
- config.get(
- "max_position_embeddings",
- config.get("n_ctx", config.get("seq_length", "")),
- ),
- )
-
- if dim == "" or seq == "":
- raise Exception(f"Could not find dim or seq for model {model.modelId}")
-
- # Get model file size without downloading. Parameters in million parameters and memory in GB
- parameters, memory = get_model_parameters_memory(model)
- return dim, seq, parameters, memory
-
-
-def get_external_model_results():
- if os.path.exists("EXTERNAL_MODEL_RESULTS.json"):
- with open("EXTERNAL_MODEL_RESULTS.json") as f:
- EXTERNAL_MODEL_RESULTS = json.load(f)
- # Update with models not contained
- models_to_run = []
- for model in EXTERNAL_MODELS:
- if model not in EXTERNAL_MODEL_RESULTS:
- models_to_run.append(model)
- EXTERNAL_MODEL_RESULTS[model] = {
- k: {v[0]: []} for k, v in TASK_TO_METRIC.items()
- }
-
- ## only if we want to re-calculate all instead of using the cache... it's likely they haven't changed
- ## but if your model results have changed, delete it from the "EXTERNAL_MODEL_RESULTS.json" file
- else:
- EXTERNAL_MODEL_RESULTS = {
- model: {k: {v[0]: []} for k, v in TASK_TO_METRIC.items()}
- for model in EXTERNAL_MODELS
- }
- models_to_run = EXTERNAL_MODELS
-
- pbar = tqdm(models_to_run, desc="Fetching external model results")
- for model in pbar:
- pbar.set_description(f"Fetching external model results for {model!r}")
- try:
- ds = load_dataset(
- RESULTS_REPO,
- model,
- trust_remote_code=True,
- download_mode="force_redownload",
- verification_mode="no_checks",
- )
- except (KeyError, ValueError) as e:
- model_tmp = "__".join([MODEL_META["model_meta"][model]["link"].split("/")[-2], model])
- ds = load_dataset(
- RESULTS_REPO,
- model_tmp,
- trust_remote_code=True,
- download_mode="force_redownload",
- verification_mode="no_checks",
- )
- except ValueError as e:
- print(f"Can't fined model {model} in results repository. Exception: {e}")
- continue
-
- ds = ds.map(add_subset)
- ds = ds.map(add_task)
- base_dict = {
- "Model": make_clickable_model(
- model,
- link=EXTERNAL_MODEL_TO_LINK.get(
- model, f"https://huggingface.co/spaces/{REPO_ID}"
- ),
- )
- }
-
- for task, metrics in TASK_TO_METRIC.items():
- ds_sub = ds.filter(lambda x: filter_metric_external(x, task, metrics))[
- "test"
- ]
- curent_task_metrics = ds_sub.unique("metric")
- for metric in curent_task_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}
- )
- #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:
- json.dump(dict(sorted(EXTERNAL_MODEL_RESULTS.items())), f, indent=4)
-
- return EXTERNAL_MODEL_RESULTS
-
-
-def download_or_use_cache(modelId: str):
- global MODEL_CACHE
- if modelId in MODEL_CACHE:
- return MODEL_CACHE[modelId]
- try:
- readme_path = hf_hub_download(modelId, filename="README.md", etag_timeout=30)
- except Exception:
- print(f"ERROR: Could not fetch metadata for {modelId}, trying again")
- readme_path = hf_hub_download(modelId, filename="README.md", etag_timeout=30)
- meta = metadata_load(readme_path)
- MODEL_CACHE[modelId] = meta
- return meta
-
-
-def simplify_dataset_name(name):
- return name.replace("MTEB ", "").replace(" (default)", "")
-
-
-def get_mteb_data(
- tasks: list = ["Clustering"],
- langs: list = [],
- datasets: list = [],
- fillna: bool = True,
- add_emb_dim: bool = True,
- task_to_metric: dict = TASK_TO_METRIC,
- rank: bool = True,
-) -> pd.DataFrame:
- global MODEL_INFOS
-
- with open("EXTERNAL_MODEL_RESULTS.json", "r") as f:
- external_model_results = json.load(f)
-
- api = API
- models = list(api.list_models(filter="mteb", full=True))
- # Legacy names changes; Also fetch the old results & merge later
- if "MLSUMClusteringP2P (fr)" in datasets:
- datasets.append("MLSUMClusteringP2P")
- if "MLSUMClusteringS2S (fr)" in datasets:
- datasets.append("MLSUMClusteringS2S")
- if "PawsXPairClassification (fr)" in datasets:
- datasets.append("PawsX (fr)")
- # Initialize list to models that we cannot fetch metadata from
- df_list = []
- for model in external_model_results:
- 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_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:
- res = {
- k: v
- for d in results_list
- for k, v in d.items()
- if (k == "Model") or any([x in k for x in datasets])
- }
- elif langs:
- # Would be cleaner to rely on an extra language column instead
- langs_format = [f"({lang})" for lang in langs]
- res = {
- k: v
- for d in results_list
- for k, v in d.items()
- if any([k.split(" ")[-1] in (k, x) for x in langs_format])
- }
- else:
- res = {k: v for d in results_list for k, v in d.items()}
- # Model & at least one result
- if len(res) > 1:
- if add_emb_dim:
- res["Model Size (Million Parameters)"] = EXTERNAL_MODEL_TO_SIZE.get(
- model, ""
- )
- res["Memory Usage (GB, fp32)"] = (
- round(res["Model Size (Million Parameters)"] * 1e6 * 4 / 1024**3, 2)
- if res["Model Size (Million Parameters)"] != ""
- else ""
- )
- res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "")
- res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "")
- df_list.append(res)
-
- pbar = tqdm(models, desc="Fetching model metadata")
- for model in pbar:
- if model.modelId in MODELS_TO_SKIP:
- continue
- pbar.set_description(f"Fetching {model.modelId!r} metadata")
- meta = download_or_use_cache(model.modelId)
- MODEL_INFOS[model.modelId] = {"metadata": meta}
- if "model-index" not in meta:
- continue
- # meta['model-index'][0]["results"] is list of elements like:
- # {
- # "task": {"type": "Classification"},
- # "dataset": {
- # "type": "mteb/amazon_massive_intent",
- # "name": "MTEB MassiveIntentClassification (nb)",
- # "config": "nb",
- # "split": "test",
- # },
- # "metrics": [
- # {"type": "accuracy", "value": 39.81506388702084},
- # {"type": "f1", "value": 38.809586587791664},
- # ],
- # },
- # Use "get" instead of dict indexing to skip incompat metadata instead of erroring out
- if len(datasets) > 0:
- task_results = [
- sub_res
- for sub_res in meta["model-index"][0]["results"]
- if (sub_res.get("task", {}).get("type", "") in tasks)
- and any(
- [x in sub_res.get("dataset", {}).get("name", "") for x in datasets]
- )
- ]
- elif langs:
- task_results = [
- sub_res
- for sub_res in meta["model-index"][0]["results"]
- if (sub_res.get("task", {}).get("type", "") in tasks)
- and (
- sub_res.get("dataset", {}).get("config", "default")
- in ("default", *langs)
- )
- ]
- else:
- task_results = [
- sub_res
- for sub_res in meta["model-index"][0]["results"]
- if (sub_res.get("task", {}).get("type", "") in tasks)
- ]
- try:
- out = [
- {
- simplify_dataset_name(res["dataset"]["name"]): [
- round(score["value"], 2)
- for score in res["metrics"]
- if filter_metric_fetched(
- simplify_dataset_name(res["dataset"]["name"]),
- score["type"],
- task_to_metric.get(res["task"]["type"]),
- res["dataset"]["split"],
- )
- ][0]
- }
- for res in task_results
- ]
- except Exception as e:
- if 'ILKT' in model.modelId: continue
- print("ERROR", model.modelId, e)
- continue
- out = {k: v for d in out for k, v in d.items()}
- out["Model"] = make_clickable_model(model.modelId)
- # Model & at least one result
- if len(out) > 1:
- if add_emb_dim:
- # The except clause triggers on gated repos, we can use external metadata for those
- try:
- MODEL_INFOS[model.modelId]["dim_seq_size"] = list(get_dim_seq_size(model))
- except:
- name_without_org = model.modelId.split("/")[-1]
- # EXTERNAL_MODEL_TO_SIZE[name_without_org] refers to millions of parameters, so for memory usage
- # we multiply by 1e6 to get just the number of parameters, then by 4 to get the number of bytes
- # given fp32 precision (4 bytes per float), then divide by 1024**3 to get the number of GB
- MODEL_INFOS[model.modelId]["dim_seq_size"] = (
- EXTERNAL_MODEL_TO_DIM.get(name_without_org, ""),
- EXTERNAL_MODEL_TO_SEQLEN.get(name_without_org, ""),
- EXTERNAL_MODEL_TO_SIZE.get(name_without_org, ""),
- round(
- EXTERNAL_MODEL_TO_SIZE[name_without_org]
- * 1e6
- * 4
- / 1024**3,
- 2,
- )
- if name_without_org in EXTERNAL_MODEL_TO_SIZE
- else "",
- )
- (
- out["Embedding Dimensions"],
- out["Max Tokens"],
- out["Model Size (Million Parameters)"],
- out["Memory Usage (GB, fp32)"],
- ) = tuple(MODEL_INFOS[model.modelId]["dim_seq_size"])
- df_list.append(out)
- model_siblings = model.siblings or []
- if (
- model.library_name == "sentence-transformers"
- or "sentence-transformers" in model.tags
- or "modules.json" in {file.rfilename for file in model_siblings}
- ):
- SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"])
-
- # # Save & cache MODEL_INFOS
- # with open("model_infos.json", "w") as f:
- # json.dump(MODEL_INFOS, f)
-
- df = pd.DataFrame(df_list)
- # If there are any models that are the same, merge them
- # E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one
- df = df.groupby("Model", as_index=False).first()
- # Put 'Model' column first
- cols = sorted(list(df.columns))
- base_columns = [
- "Model",
- "Model Size (Million Parameters)",
- "Memory Usage (GB, fp32)",
- "Embedding Dimensions",
- "Max Tokens",
- ]
- if len(datasets) > 0:
- # Update legacy column names to be merged with newer ones
- # Update 'MLSUMClusteringP2P (fr)' with values from 'MLSUMClusteringP2P'
- if ("MLSUMClusteringP2P (fr)" in datasets) and ("MLSUMClusteringP2P" in cols):
- df["MLSUMClusteringP2P (fr)"] = df["MLSUMClusteringP2P (fr)"].fillna(
- df["MLSUMClusteringP2P"]
- )
- datasets.remove("MLSUMClusteringP2P")
- if ("MLSUMClusteringS2S (fr)" in datasets) and ("MLSUMClusteringS2S" in cols):
- df["MLSUMClusteringS2S (fr)"] = df["MLSUMClusteringS2S (fr)"].fillna(
- df["MLSUMClusteringS2S"]
- )
- datasets.remove("MLSUMClusteringS2S")
- if ("PawsXPairClassification (fr)" in datasets) and ("PawsX (fr)" in cols):
- # for the first bit no model has it, hence no column for it. We can remove this in a month or so
- if "PawsXPairClassification (fr)" not in cols:
- df["PawsXPairClassification (fr)"] = df["PawsX (fr)"]
- else:
- df["PawsXPairClassification (fr)"] = df[
- "PawsXPairClassification (fr)"
- ].fillna(df["PawsX (fr)"])
- # make all the columns the same
- datasets.remove("PawsX (fr)")
- cols.remove("PawsX (fr)")
- df.drop(columns=["PawsX (fr)"], inplace=True)
-
- # Filter invalid columns
- cols = [col for col in cols if col in base_columns + datasets]
- i = 0
- for column in base_columns:
- if column in cols:
- cols.insert(i, cols.pop(cols.index(column)))
- i += 1
- df = df[cols]
- if rank:
- df = add_rank(df)
- if fillna:
- df.fillna("", inplace=True)
- return df
-
-
-# Get dict with a task list for each task category
-# E.g. {"Classification": ["AmazonMassiveIntentClassification (en)", ...], "PairClassification": ["SprintDuplicateQuestions", ...]}
-def get_mteb_average(task_dict: dict) -> tuple[Any, dict]:
- all_tasks = reduce(lambda x, y: x + y, task_dict.values())
- DATA_OVERALL = get_mteb_data(
- tasks=list(task_dict.keys()),
- datasets=all_tasks,
- fillna=False,
- add_emb_dim=True,
- rank=False,
- )
- # Debugging:
- # DATA_OVERALL.to_csv("overall.csv")
- DATA_OVERALL.insert(
- 1,
- f"Average ({len(all_tasks)} datasets)",
- DATA_OVERALL[all_tasks].mean(axis=1, skipna=False),
- )
-
- for i, (task_category, task_category_list) in enumerate(task_dict.items()):
- DATA_OVERALL.insert(
- i + 2,
- f"{task_category} Average ({len(task_category_list)} datasets)",
- DATA_OVERALL[task_category_list].mean(axis=1, skipna=False),
- )
- DATA_OVERALL.sort_values(
- f"Average ({len(all_tasks)} datasets)", ascending=False, inplace=True
- )
- # Start ranking from 1
- DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1)))
-
- DATA_OVERALL = DATA_OVERALL.round(2)
-
- DATA_TASKS = {}
- for task_category, task_category_list in task_dict.items():
- DATA_TASKS[task_category] = add_rank(
- DATA_OVERALL[
- ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"] + task_category_list
- ]
- )
- DATA_TASKS[task_category] = DATA_TASKS[task_category][
- DATA_TASKS[task_category].iloc[:, 4:].ne("").any(axis=1)
- ]
-
- # Fill NaN after averaging
- DATA_OVERALL.fillna("", inplace=True)
-
- data_overall_rows = [
- "Rank",
- "Model",
- "Model Size (Million Parameters)",
- "Memory Usage (GB, fp32)",
- "Embedding Dimensions",
- "Max Tokens",
- f"Average ({len(all_tasks)} datasets)",
- ]
- for task_category, task_category_list in task_dict.items():
- data_overall_rows.append(
- f"{task_category} Average ({len(task_category_list)} datasets)"
- )
-
- DATA_OVERALL = DATA_OVERALL[data_overall_rows]
- DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)]
-
- return DATA_OVERALL, DATA_TASKS
-
-
-def refresh_leaderboard() -> tuple[list, dict]:
- """
- The main code to refresh and calculate results for the leaderboard. It does this by fetching the results from the
- external models and the models in the leaderboard, then calculating the average scores for each task category.
- """
-
- # get external model results and cache them
- # NOTE: if your model results have changed, use this function to refresh them (see inside for details)
- get_external_model_results()
-
- boards_data = {}
- all_data_tasks = []
- pbar_tasks = tqdm(
- BOARDS_CONFIG.items(),
- desc="Fetching leaderboard results for ???",
- total=len(BOARDS_CONFIG),
- leave=True,
- )
- for board, board_config in pbar_tasks:
- # Optional fetch only for a specific board
- # if board != "ru": 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()
- if board_config["has_overall"]:
- data_overall, data_tasks = get_mteb_average(board_config["tasks"])
- boards_data[board]["data_overall"] = data_overall
- boards_data[board]["data_tasks"] = data_tasks
- all_data_tasks.extend(data_tasks.values())
- else:
- for task_category, task_category_list in board_config["tasks"].items():
- data_task_category = get_mteb_data(
- tasks=[task_category], datasets=task_category_list
- )
- 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
- of the item and then save the DFs as jsonl files
-
- Args:
- item: The item to save
- item_name: The name of the item
- """
- main_folder = item_name
-
- if isinstance(item, list):
- for i, v in enumerate(item):
- write_out_results(v, os.path.join(main_folder, str(i)))
-
- elif isinstance(item, dict):
- for key, value in item.items():
- if isinstance(value, dict):
- write_out_results(value, os.path.join(main_folder, key))
- elif isinstance(value, list):
- for i, v in enumerate(value):
- write_out_results(v, os.path.join(main_folder, key + str(i)))
- else:
- write_out_results(value, os.path.join(main_folder, key))
-
- elif isinstance(item, pd.DataFrame):
- print(f"Saving {main_folder} to {main_folder}/default.jsonl")
- os.makedirs(main_folder, exist_ok=True)
-
- if "index" not in item.columns:
- item.reset_index(inplace=True)
- item.to_json(f"{main_folder}/default.jsonl", orient="records", lines=True)
-
- elif isinstance(item, str):
- print(f"Saving {main_folder} to {main_folder}/default.txt")
- os.makedirs(main_folder, exist_ok=True)
- with open(f"{main_folder}/default.txt", "w") as f:
- f.write(item)
-
- elif item is None:
- # write out an empty file
- print(f"Saving {main_folder} to {main_folder}/default.txt")
- os.makedirs(main_folder, exist_ok=True)
- with open(f"{main_folder}/default.txt", "w") as f:
- f.write("")
-
- else:
- raise Exception(f"Unknown type {type(item)}")
-
-
-def load_results(data_path: str) -> list | dict | pd.DataFrame | str | None:
- """
- Do the reverse of `write_out_results` to reconstruct the item
-
- Args:
- data_path: The path to the data to load
-
- Returns:
- The loaded data
- """
- if os.path.isdir(data_path):
- # if the folder just has numbers from 0 to N, load as a list
- all_files_in_dir = list(os.listdir(data_path))
- if set(all_files_in_dir) == set([str(i) for i in range(len(all_files_in_dir))]):
- ### the list case
- return [
- load_results(os.path.join(data_path, str(i)))
- for i in range(len(os.listdir(data_path)))
- ]
- else:
- if len(all_files_in_dir) == 1:
- file_name = all_files_in_dir[0]
- if file_name == "default.jsonl":
- return load_results(os.path.join(data_path, file_name))
- else: ### the dict case
- return {file_name: load_results(os.path.join(data_path, file_name))}
- else:
- return {
- file_name: load_results(os.path.join(data_path, file_name))
- for file_name in all_files_in_dir
- }
-
- elif data_path.endswith(".jsonl"):
- df = pd.read_json(data_path, orient="records", lines=True)
- if "index" in df.columns:
- df = df.set_index("index")
- if "Memory Usage (GB, fp32)" in df.columns:
- df["Memory Usage (GB, fp32)"] = df["Memory Usage (GB, fp32)"].map(lambda value: round(value, 2) if isinstance(value, float) else value)
- return df
-
- else:
- with open(data_path, "r") as f:
- data = f.read()
- if data == "":
- return None
- else:
- return data
-
-
-if __name__ == "__main__":
- print("Refreshing leaderboard statistics...")
- all_data_tasks, boards_data = refresh_leaderboard()
- print("Done calculating, saving...")
- # save them so that the leaderboard can use them. They're quite complex though
- # but we can't use pickle files because of git-lfs.
- write_out_results(all_data_tasks, "all_data_tasks")
- write_out_results(boards_data, "boards_data")
-
- # to load them use
- # all_data_tasks = load_results("all_data_tasks")
- # boards_data = load_results("boards_data")
- print("Done saving results!")
diff --git a/requirements.txt b/requirements.txt
index 4c91b9d7a06f41382bf13b94e51870f0eb7125a5..7eed8a9948ba4c8784626a78c409dbb16332963d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,3 @@
-git+https://github.com/EuDs63/ffmpy.git@333a19ee4d21f32537c0508aa1942ef1aa7afe24
-gradio[oauth]==4.20.0
-datasets
-pandas
-huggingface_hub
-tqdm
+gradio==4.20.0
uvicorn>=0.14.0
fastapi<0.113.0
\ No newline at end of file
diff --git a/utils/model_size.py b/utils/model_size.py
deleted file mode 100644
index f6eb6873349ec837b6d61d39d74339b18125b11f..0000000000000000000000000000000000000000
--- a/utils/model_size.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-import re
-import traceback
-from huggingface_hub.hf_api import ModelInfo, get_safetensors_metadata, model_info as get_model_info, get_hf_file_metadata, hf_hub_url
-from huggingface_hub import hf_hub_download
-
-# Map model IDs to the number of bytes used for one parameter. So, 4 bytes for fp32, 2 bytes for fp16, etc.
-# By default, we assume that the model is stored in fp32.
-KNOWN_BYTES_PER_PARAM = {
- "dwzhu/e5-base-4k": 2,
-}
-
-def get_model_parameters_memory(model_info: ModelInfo):
- '''Get the size of the model in million of parameters.'''
- try:
- safetensors = get_safetensors_metadata(model_info.id)
- except Exception as e:
- pass
- else:
- num_parameters = sum(safetensors.parameter_count.values())
- return round(num_parameters / 1e6), round(num_parameters * 4 / 1024**3, 2)
-
- filenames = [sib.rfilename for sib in model_info.siblings]
- if "pytorch_model.bin" in filenames:
- url = hf_hub_url(model_info.id, filename="pytorch_model.bin")
- meta = get_hf_file_metadata(url)
- bytes_per_param = KNOWN_BYTES_PER_PARAM.get(model_info.id, 4)
- num_params = round(meta.size / bytes_per_param / 1e6)
- size_gb = round(meta.size * (4 / bytes_per_param) / 1024**3, 2)
- return num_params, size_gb
-
- if "pytorch_model.bin.index.json" in filenames:
- index_path = hf_hub_download(model_info.id, filename="pytorch_model.bin.index.json")
- """
- {
- "metadata": {
- "total_size": 28272820224
- },....
- """
- size = json.load(open(index_path))
- bytes_per_param = KNOWN_BYTES_PER_PARAM.get(model_info.id, 4)
- if ("metadata" in size) and ("total_size" in size["metadata"]):
- return round(size["metadata"]["total_size"] / bytes_per_param / 1e6), round(size["metadata"]["total_size"] / 1024**3, 2)
-
- raise Exception(f"Could not find the model parameters for {model_info.id}")