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@KennethEnevoldsen KennethEnevoldsen commented May 9, 2026 •
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This PR add various results

The target is

The goal is not to finish everything, simply add a large part of the results in one go.

Continuation of #528

Checklist

  • My model has a model sheet, report, or similar
  • My model has a reference implementation in mteb/models/model_implementations/, this can be as an API. Instruction on how to add a model can be found here
    • No, but there is an existing PR ___
  • The results submitted are obtained using the reference implementation
  • My model is available, either as a publicly accessible API or publicly on e.g., Huggingface
  • I solemnly swear that for all results submitted I have not trained on the evaluation dataset including training splits. If I have, I have disclosed it clearly.

Kenneth added 6 commits May 9, 2026 15:11
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Model Results Comparison

Reference models: intfloat/multilingual-e5-large, google/gemini-embedding-001
New models evaluated: KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5, Qwen/Qwen3-Embedding-0.6B, google/gemini-embedding-2-preview, google/siglip-base-patch16-224, manveertamber/cadet-embed-base-v1
Tasks: AfriSentiClassification, BUCC.v2, BibleNLPBitextMining, BornholmBitextMining, BrightAopsRetrieval, BrightBiologyLongRetrieval, BrightBiologyRetrieval, BrightEarthScienceLongRetrieval, BrightEarthScienceRetrieval, BrightEconomicsLongRetrieval, BrightEconomicsRetrieval, BrightLeetcodeRetrieval, BrightPonyLongRetrieval, BrightPonyRetrieval, BrightPsychologyLongRetrieval, BrightPsychologyRetrieval, BrightRoboticsLongRetrieval, BrightRoboticsRetrieval, BrightStackoverflowLongRetrieval, BrightStackoverflowRetrieval, BrightSustainableLivingLongRetrieval, BrightSustainableLivingRetrieval, BrightTheoremQAQuestionsRetrieval, BrightTheoremQATheoremsRetrieval, BulgarianStoreReviewSentimentClassfication, CzechProductReviewSentimentClassification, DBpediaClassification, DiaBlaBitextMining, EstonianValenceClassification, FEVERHardNegatives, FilipinoShopeeReviewsClassification, FinancialPhrasebankClassification, FloresBitextMining, GreekLegalCodeClassification, GujaratiNewsClassification, IN22GenBitextMining, IndicGenBenchFloresBitextMining, IndonesianIdClickbaitClassification, ItaCaseholdClassification, KorSarcasmClassification, KurdishSentimentClassification, LccSentimentClassification, MacedonianTweetSentimentClassification, NTREXBitextMining, NollySentiBitextMining, NorwegianCourtsBitextMining, NusaTranslationBitextMining, NusaXBitextMining, PoemSentimentClassification, SentimentAnalysisHindi, Tatoeba, ToxicConversationsClassification, TweetSentimentClassification, TweetTopicSingleClassification

Results for KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5

task_name KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5 intfloat/multilingual-e5-large Max result Model with max result In Training Data
TweetSentimentClassification 0.4935 0.503 0.6570 codefuse-ai/F2LLM-v2-14B False
Average 0.4935 0.503 0.6570 nan -

Training datasets: ATEC, AmazonCounterfactualClassification, AmazonCounterfactualVNClassification, AmazonPolarityClassification, AmazonPolarityClassification.v2, AmazonPolarityVNClassification, AmazonReviewsClassification, AmazonReviewsVNClassification, ArXivHierarchicalClusteringP2P, ArXivHierarchicalClusteringS2S, ArxivClusteringP2P, ArxivClusteringP2P.v2, ArxivClusteringS2S, BQ, Banking77Classification, Banking77Classification.v2, Banking77VNClassification, BiorxivClusteringP2P, BiorxivClusteringP2P.v2, BiorxivClusteringS2S, BiorxivClusteringS2S.v2, CQADupstack, CodeFeedbackMT, CodeFeedbackST, ContractNLIConfidentialityOfAgreementLegalBenchClassification, ContractNLIExplicitIdentificationLegalBenchClassification, ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, ContractNLILimitedUseLegalBenchClassification, ContractNLINoLicensingLegalBenchClassification, ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, ContractNLIPermissibleCopyLegalBenchClassification, ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, ContractNLIReturnOfConfidentialInformationLegalBenchClassification, ContractNLISharingWithEmployeesLegalBenchClassification, ContractNLISharingWithThirdPartiesLegalBenchClassification, ContractNLISurvivalOfObligationsLegalBenchClassification, DBPedia, DBPedia-Fa, DBPedia-NL, DBPedia-PL, DBPedia-PLHardNegatives, DBPedia-VN, DBPediaHardNegatives, DBPediaHardNegatives.v2, ESCIReranking, EmotionClassification, EmotionClassification.v2, EmotionVNClassification, FEVER, FEVER-FaHardNegatives, FEVER-NL, FEVER-VN, FEVERHardNegatives, FEVERHardNegatives.v2, FiQA-PL, FiQA2018, FiQA2018-Fa, FiQA2018-Fa.v2, FiQA2018-NL, FiQA2018-VN, HUMEArxivClusteringP2P, HUMEEmotionClassification, HUMEToxicConversationsClassification, HUMETweetSentimentExtractionClassification, HotpotQA, HotpotQA-Fa, HotpotQA-FaHardNegatives, HotpotQA-NL, HotpotQA-PL, HotpotQA-PLHardNegatives, HotpotQA-VN, HotpotQAHardNegatives, HotpotQAHardNegatives.v2, ImdbClassification, ImdbClassification.v2, ImdbVNClassification, MIRACLJaRetrievalLite, MIRACLReranking, MIRACLRetrieval, MIRACLRetrievalHardNegatives, MIRACLRetrievalHardNegatives.v2, MSMARCO, MSMARCO-Fa, MSMARCO-FaHardNegatives, MSMARCO-PL, MSMARCO-PLHardNegatives, MSMARCO-VN, MSMARCOHardNegatives, MSMARCOv2, MTOPDomainClassification, MTOPDomainVNClassification, MTOPIntentClassification, MTOPIntentVNClassification, MassiveIntentClassification, MassiveIntentVNClassification, MassiveScenarioClassification, MassiveScenarioVNClassification, MedrxivClusteringP2P, MedrxivClusteringP2P.v2, MedrxivClusteringS2S, MedrxivClusteringS2S.v2, MrTidyRetrieval, MrTyDiJaRetrievalLite, MultiLongDocReranking, MultiLongDocRetrieval, MultilingualSentiment, MultilingualSentiment.v2, NFCorpus, NFCorpus-Fa, NFCorpus-NL, NFCorpus-NL.v2, NFCorpus-PL, NFCorpus-VN, NQ, NQ-Fa, NQ-FaHardNegatives, NQ-NL, NQ-PL, NQ-PLHardNegatives, NQ-VN, NQHardNegatives, NanoDBPedia-VN, NanoDBPediaRetrieval, NanoFEVER-VN, NanoFEVERRetrieval, NanoFiQA2018Retrieval, NanoHotpotQA-VN, NanoHotpotQARetrieval, NanoMSMARCO-VN, NanoMSMARCORetrieval, NanoNFCorpusRetrieval, NanoNQ-VN, NanoNQRetrieval, NanoQuoraRetrieval, NanoSciFactRetrieval, PawsXPairClassification, Quora-NL, Quora-PL, Quora-PLHardNegatives, QuoraRetrieval, QuoraRetrieval-Fa, QuoraRetrieval-Fa.v2, QuoraRetrievalHardNegatives, QuoraRetrievalHardNegatives.v2, Reddit-Clustering, Reddit-Clustering-P2P, SciFact, SciFact-Fa, SciFact-Fa.v2, SciFact-NL, SciFact-NL.v2, SciFact-PL, SciFact-VN, Stackexchange-Clustering, Stackexchange-Clustering-P2P, TRECCOVID, TRECCOVID-Fa, TRECCOVID-Fa.v2, TRECCOVID-NL, TRECCOVID-PL, TRECCOVID-VN, ToxicConversationsClassification, ToxicConversationsClassification.v2, ToxicConversationsVNClassification, TweetSentimentExtractionClassification, TweetSentimentExtractionClassification.v2, TweetSentimentExtractionVNClassification, TwentyNewsgroups-Clustering, YahooAnswersTopicsClassification, YahooAnswersTopicsClassification.v2, mMARCO-NL

Results for Qwen/Qwen3-Embedding-0.6B

task_name Qwen/Qwen3-Embedding-0.6B google/gemini-embedding-001 intfloat/multilingual-e5-large Max result Model with max result In Training Data
FEVERHardNegatives 0.8726 0.8898 0.8379 0.9453 ByteDance-Seed/Seed1.5-Embedding True
TweetSentimentClassification 0.4813 nan 0.503 0.6570 codefuse-ai/F2LLM-v2-14B False
Average 0.677 0.8898 0.6704 0.8012 nan -

Training datasets: CMedQAv2-reranking, CmedqaRetrieval, CodeSearchNet, DuRetrieval, FEVER, FEVER-FaHardNegatives, FEVER-NL, FEVER-VN, FEVERHardNegatives, FEVERHardNegatives.v2, HotpotQA, HotpotQA-Fa, HotpotQA-FaHardNegatives, HotpotQA-NL, HotpotQA-PL, HotpotQA-PLHardNegatives, HotpotQA-VN, HotpotQAHardNegatives, HotpotQAHardNegatives.v2, MIRACLJaRetrievalLite, MIRACLReranking, MIRACLRetrieval, MIRACLRetrievalHardNegatives, MIRACLRetrievalHardNegatives.v2, MMarcoReranking, MSMARCO, MSMARCO-Fa, MSMARCO-FaHardNegatives, MSMARCO-PL, MSMARCO-PLHardNegatives, MSMARCO-VN, MSMARCOHardNegatives, MSMARCOv2, MrTidyRetrieval, MrTyDiJaRetrievalLite, NQ, NQ-Fa, NQ-FaHardNegatives, NQ-NL, NQ-PL, NQ-PLHardNegatives, NQ-VN, NQHardNegatives, NanoFEVER-VN, NanoFEVERRetrieval, NanoHotpotQA-VN, NanoHotpotQARetrieval, NanoMSMARCO-VN, NanoMSMARCORetrieval, NanoNQ-VN, NanoNQRetrieval, T2Retrieval

Results for google/gemini-embedding-2-preview

task_name google/gemini-embedding-001 google/gemini-embedding-2-preview intfloat/multilingual-e5-large Max result Model with max result In Training Data
BUCC.v2 0.9899 1.0000 0.9878 0.9905 codefuse-ai/F2LLM-v2-8B False
BibleNLPBitextMining 0.2072 0.5298 0.1665 0.9899 deepvk/USER-bge-m3 False
BornholmBitextMining 0.5169 0.9167 0.4416 0.7798 jinaai/jina-embeddings-v5-text-small False
BulgarianStoreReviewSentimentClassfication 0.7813 0.7319 0.6385 0.8159 microsoft/harrier-oss-v1-27b False
CzechProductReviewSentimentClassification 0.6816 0.6435 0.5714 0.7667 Bytedance/Seed1.6-embedding-1215 False
DBpediaClassification 0.9476 0.8865 0.8828 0.9926 Qwen/Qwen3-Embedding-4B False
DiaBlaBitextMining 0.8723 0.9963 0.8483 0.8882 codefuse-ai/F2LLM-v2-14B False
EstonianValenceClassification 0.5352 0.4581 0.4289 0.6764 microsoft/harrier-oss-v1-27b False
FilipinoShopeeReviewsClassification 0.4845 0.4243 0.3527 0.5279 microsoft/harrier-oss-v1-27b False
FinancialPhrasebankClassification 0.8864 0.8447 0.8394 0.9519 microsoft/harrier-oss-v1-0.6b False
FloresBitextMining 0.8371 0.9824 0.8108 0.9087 SamilPwC-AXNode-GenAI/PwC-Embedding_expr False
GreekLegalCodeClassification 0.4376 0.3386 0.3713 0.8052 Bytedance/Seed1.6-embedding-1215 False
GujaratiNewsClassification 0.9205 0.9010 0.7674 0.9343 Bytedance/Seed1.6-embedding-1215 False
IN22GenBitextMining 0.9375 0.9953 0.7675 0.9375 google/gemini-embedding-001 False
IndicGenBenchFloresBitextMining 0.9677 0.9690 0.8875 0.9881 Sailesh97/Hinvec False
IndonesianIdClickbaitClassification 0.67 0.6256 0.6122 0.7560 nvidia/llama-embed-nemotron-8b False
LccSentimentClassification 0.6993 0.5933 0.594 0.7687 Alibaba-NLP/gte-Qwen2-7B-instruct False
NTREXBitextMining 0.9364 0.9881 0.914 0.9592 microsoft/harrier-oss-v1-27b False
NollySentiBitextMining 0.6871 0.7837 0.675 0.8376 microsoft/harrier-oss-v1-27b False
NorwegianCourtsBitextMining 0.9342 1.0000 0.9404 0.9481 jinaai/jina-embeddings-v5-text-nano False
NusaTranslationBitextMining 0.7752 0.9316 0.672 0.9222 Qwen/Qwen3-Embedding-8B False
NusaXBitextMining 0.8252 0.8393 0.7267 0.9056 Bytedance/Seed1.6-embedding-1215 False
PoemSentimentClassification 0.5966 0.4756 0.5067 0.8642 Bytedance/Seed1.6-embedding-1215 False
SentimentAnalysisHindi 0.7606 0.5818 0.642 0.8070 microsoft/harrier-oss-v1-27b False
Tatoeba 0.8197 0.9947 0.7573 0.9659 SamilPwC-AXNode-GenAI/PwC-Embedding_expr False
ToxicConversationsClassification 0.8875 0.7352 0.6601 0.9759 voyageai/voyage-3-m-exp False
TweetTopicSingleClassification 0.7111 0.6699 0.6532 0.8631 jinaai/jina-embeddings-v5-text-small False
Average 0.7521 0.7717 0.671 0.8714 nan -

Model have high performance on these tasks: BUCC.v2,Tatoeba,NTREXBitextMining,NorwegianCourtsBitextMining,IN22GenBitextMining,NusaTranslationBitextMining,FloresBitextMining,DiaBlaBitextMining,BornholmBitextMining

Training datasets: FEVER, FEVER-FaHardNegatives, FEVER-NL, FEVER-VN, FEVERHardNegatives, FEVERHardNegatives.v2, HotpotQA, HotpotQA-Fa, HotpotQA-FaHardNegatives, HotpotQA-NL, HotpotQA-PL, HotpotQA-PLHardNegatives, HotpotQA-VN, HotpotQAHardNegatives, HotpotQAHardNegatives.v2, MIRACLJaRetrievalLite, MIRACLReranking, MIRACLRetrieval, MIRACLRetrievalHardNegatives, MIRACLRetrievalHardNegatives.v2, NQ, NQ-Fa, NQ-FaHardNegatives, NQ-NL, NQ-PL, NQ-PLHardNegatives, NQ-VN, NQHardNegatives, NanoFEVER-VN, NanoFEVERRetrieval, NanoHotpotQA-VN, NanoHotpotQARetrieval, NanoNQ-VN, NanoNQRetrieval

Results for google/siglip-base-patch16-224

task_name google/gemini-embedding-001 google/siglip-base-patch16-224 intfloat/multilingual-e5-large Max result Model with max result In Training Data
AfriSentiClassification 0.5356 0.5124 0.455 0.5688 tencent/KaLM-Embedding-Gemma3-12B-2511 False
BUCC.v2 0.9899 1.0000 0.9878 0.9905 codefuse-ai/F2LLM-v2-8B False
BibleNLPBitextMining 0.2072 0.5298 0.1665 0.9899 deepvk/USER-bge-m3 False
BornholmBitextMining 0.5169 0.9167 0.4416 0.7798 jinaai/jina-embeddings-v5-text-small False
BulgarianStoreReviewSentimentClassfication 0.7813 0.7319 0.6385 0.8159 microsoft/harrier-oss-v1-27b False
CzechProductReviewSentimentClassification 0.6816 0.6435 0.5714 0.7667 Bytedance/Seed1.6-embedding-1215 False
DBpediaClassification 0.9476 0.8865 0.8828 0.9926 Qwen/Qwen3-Embedding-4B False
DiaBlaBitextMining 0.8723 0.9963 0.8483 0.8882 codefuse-ai/F2LLM-v2-14B False
EstonianValenceClassification 0.5352 0.4581 0.4289 0.6764 microsoft/harrier-oss-v1-27b False
FilipinoShopeeReviewsClassification 0.4845 0.4243 0.3527 0.5279 microsoft/harrier-oss-v1-27b False
FinancialPhrasebankClassification 0.8864 0.8447 0.8394 0.9519 microsoft/harrier-oss-v1-0.6b False
FloresBitextMining 0.8371 0.9824 0.8108 0.9087 SamilPwC-AXNode-GenAI/PwC-Embedding_expr False
GreekLegalCodeClassification 0.4376 0.3386 0.3713 0.8052 Bytedance/Seed1.6-embedding-1215 False
GujaratiNewsClassification 0.9205 0.9010 0.7674 0.9343 Bytedance/Seed1.6-embedding-1215 False
IN22GenBitextMining 0.9375 0.9953 0.7675 0.9375 google/gemini-embedding-001 False
IndicGenBenchFloresBitextMining 0.9677 0.9690 0.8875 0.9881 Sailesh97/Hinvec False
IndonesianIdClickbaitClassification 0.67 0.6256 0.6122 0.7560 nvidia/llama-embed-nemotron-8b False
ItaCaseholdClassification 0.733 0.5127 0.6679 0.9439 bigscience/sgpt-bloom-7b1-msmarco False
KorSarcasmClassification 0.6051 0.6358 0.5679 0.8190 ICT-TIME-and-Querit/BOOM_4B_v1 False
KurdishSentimentClassification 0.8639 0.7964 0.7708 0.9403 Bytedance/Seed1.6-embedding-1215 False
LccSentimentClassification 0.6993 0.5933 0.594 0.7687 Alibaba-NLP/gte-Qwen2-7B-instruct False
MacedonianTweetSentimentClassification 0.7183 0.6325 0.6192 0.7547 Qwen/Qwen3-Embedding-4B False
NTREXBitextMining 0.9364 0.9881 0.914 0.9592 microsoft/harrier-oss-v1-27b False
NollySentiBitextMining 0.6871 0.7837 0.675 0.8376 microsoft/harrier-oss-v1-27b False
NorwegianCourtsBitextMining 0.9342 1.0000 0.9404 0.9481 jinaai/jina-embeddings-v5-text-nano False
NusaTranslationBitextMining 0.7752 0.9316 0.672 0.9222 Qwen/Qwen3-Embedding-8B False
NusaXBitextMining 0.8252 0.8393 0.7267 0.9056 Bytedance/Seed1.6-embedding-1215 False
PoemSentimentClassification 0.5966 0.4756 0.5067 0.8642 Bytedance/Seed1.6-embedding-1215 False
SentimentAnalysisHindi 0.7606 0.5818 0.642 0.8070 microsoft/harrier-oss-v1-27b False
Tatoeba 0.8197 0.9947 0.7573 0.9659 SamilPwC-AXNode-GenAI/PwC-Embedding_expr False
ToxicConversationsClassification 0.8875 0.7352 0.6601 0.9759 voyageai/voyage-3-m-exp False
TweetTopicSingleClassification 0.7111 0.6699 0.6532 0.8631 jinaai/jina-embeddings-v5-text-small False
Average 0.7426 0.7477 0.6624 0.8611 nan -

Model have high performance on these tasks: BUCC.v2,Tatoeba,NTREXBitextMining,NorwegianCourtsBitextMining,IN22GenBitextMining,NusaTranslationBitextMining,FloresBitextMining,DiaBlaBitextMining,BornholmBitextMining

Results for manveertamber/cadet-embed-base-v1

task_name intfloat/multilingual-e5-large manveertamber/cadet-embed-base-v1 Max result Model with max result In Training Data
BrightAopsRetrieval 0.0722 0.0755 0.0825 lightonai/Reason-ModernColBERT False
BrightBiologyLongRetrieval 0.0194 0.2532 0.2557 sentence-transformers/all-mpnet-base-v2 False
BrightBiologyRetrieval 0.0174 0.2129 0.3387 lightonai/Reason-ModernColBERT False
BrightEarthScienceLongRetrieval 0.2155 0.3348 0.3405 sentence-transformers/all-mpnet-base-v2 False
BrightEarthScienceRetrieval 0.1506 0.3452 0.4170 lightonai/Reason-ModernColBERT False
BrightEconomicsLongRetrieval 0.1359 0.1408 0.2087 BAAI/bge-large-en-v1.5 False
BrightEconomicsRetrieval 0.0706 0.1912 0.2455 lightonai/Reason-ModernColBERT False
BrightLeetcodeRetrieval 0.2787 0.2793 0.3086 lightonai/Reason-ModernColBERT False
BrightPonyLongRetrieval 0.0234 0.0284 0.0338 minishlab/potion-multilingual-128M False
BrightPonyRetrieval 0.1302 0.0747 0.1517 BAAI/bge-m3 False
BrightPsychologyLongRetrieval 0.0594 0.1555 0.1931 BAAI/bge-m3 False
BrightPsychologyRetrieval 0.0879 0.2123 0.3104 lightonai/Reason-ModernColBERT False
BrightRoboticsLongRetrieval 0.0792 0.1287 0.1238 BAAI/bge-m3 False
BrightRoboticsRetrieval 0.1112 0.1547 0.2181 lightonai/Reason-ModernColBERT False
BrightStackoverflowLongRetrieval 0.1581 0.1282 0.2350 mteb/baseline-bm25s False
BrightStackoverflowRetrieval 0.0694 0.1365 0.2425 lightonai/Reason-ModernColBERT False
BrightSustainableLivingLongRetrieval 0.081 0.1847 0.1852 mteb/baseline-bm25s False
BrightSustainableLivingRetrieval 0.0961 0.1515 0.2021 lightonai/Reason-ModernColBERT False
BrightTheoremQAQuestionsRetrieval 0.1296 0.1526 0.2004 sentence-transformers/all-mpnet-base-v2 False
BrightTheoremQATheoremsRetrieval 0.0549 0.0762 0.1078 sentence-transformers/all-mpnet-base-v2 False
Average 0.102 0.1708 0.2201 nan -

Model have high performance on these tasks: BrightRoboticsLongRetrieval

Training datasets: ArguAna, ArguAna-Fa, ArguAna-Fa.v2, ArguAna-NL, ArguAna-NL.v2, ArguAna-PL, ArguAna-VN, CMedQAv1-reranking, CMedQAv2-reranking, CmedqaRetrieval, CodeSearchNet, DBPedia, DBPedia-Fa, DBPedia-NL, DBPedia-PL, DBPedia-PLHardNegatives, DBPedia-VN, DBPediaHardNegatives, DBPediaHardNegatives.v2, DuRetrieval, FEVER, FEVER-FaHardNegatives, FEVER-NL, FEVER-VN, FEVERHardNegatives, FEVERHardNegatives.v2, HotpotQA, HotpotQA-Fa, HotpotQA-FaHardNegatives, HotpotQA-NL, HotpotQA-PL, HotpotQA-PLHardNegatives, HotpotQA-VN, HotpotQAHardNegatives, HotpotQAHardNegatives.v2, LeCaRDv2, MIRACLJaRetrievalLite, MIRACLReranking, MIRACLRetrieval, MIRACLRetrievalHardNegatives, MIRACLRetrievalHardNegatives.v2, MMarcoReranking, MSMARCO, MSMARCO-Fa, MSMARCO-FaHardNegatives, MSMARCO-PL, MSMARCO-PLHardNegatives, MSMARCO-VN, MSMARCOHardNegatives, MSMARCOv2, MrTidyRetrieval, MrTyDiJaRetrievalLite, NQ, NQ-Fa, NQ-FaHardNegatives, NQ-NL, NQ-PL, NQ-PLHardNegatives, NQ-VN, NQHardNegatives, NanoArguAnaRetrieval, NanoDBPedia-VN, NanoDBPediaRetrieval, NanoFEVER-VN, NanoFEVERRetrieval, NanoHotpotQA-VN, NanoHotpotQARetrieval, NanoMSMARCO-VN, NanoMSMARCORetrieval, NanoNQ-VN, NanoNQRetrieval, T2Reranking, T2Retrieval, mMARCO-NL

Note: Content truncated due to GitHub API limits. See the full report in the workflow artifacts.

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@KennethEnevoldsen