Upload modernbert_colbert_kd

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	---
	language:
	- en
	tags:
	- ColBERT
	- PyLate
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:640000
	- loss:Distillation
	datasets:
	- lightonai/ms-marco-en-bge-gemma
	pipeline_tag: sentence-similarity
	library_name: PyLate
	metrics:
	- MaxSim_accuracy@1
	- MaxSim_accuracy@3
	- MaxSim_accuracy@5
	- MaxSim_accuracy@10
	- MaxSim_precision@1
	- MaxSim_precision@3
	- MaxSim_precision@5
	- MaxSim_precision@10
	- MaxSim_recall@1
	- MaxSim_recall@3
	- MaxSim_recall@5
	- MaxSim_recall@10
	- MaxSim_ndcg@10
	- MaxSim_mrr@10
	- MaxSim_map@100
	model-index:
	- name: PyLate
	results:
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoClimateFEVER
	type: NanoClimateFEVER
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.24
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.42
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.56
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.76
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.24
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.14666666666666667
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.132
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.1
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.11499999999999998
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.205
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.2733333333333333
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.3906666666666666
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.2950902457523894
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.36876984126984125
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.22445703016815177
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoDBPedia
	type: NanoDBPedia
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.76
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.92
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.92
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.94
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.76
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.7199999999999999
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.64
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.5359999999999999
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.103349775455209
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.2069476173044798
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.26630033614450777
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.3798346720417632
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.6745044425577195
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.8420000000000001
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.5354371280529658
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoFEVER
	type: NanoFEVER
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.9
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.96
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 1.0
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 1.0
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.9
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.3399999999999999
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.21599999999999994
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.10999999999999999
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.8366666666666667
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.9233333333333333
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.97
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.98
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.9294789232192022
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.9366666666666665
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.9025750915750915
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoFiQA2018
	type: NanoFiQA2018
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.58
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.68
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.72
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.78
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.58
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.31999999999999995
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.244
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.13799999999999998
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.36607936507936506
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.48507142857142854
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.5518412698412698
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.6031746031746031
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.5639041299556308
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.6375793650793651
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.5136714023190043
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoHotpotQA
	type: NanoHotpotQA
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.92
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 1.0
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 1.0
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 1.0
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.92
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.5533333333333332
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.352
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.18199999999999997
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.46
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.83
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.88
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.91
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.8735671033500391
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.9533333333333333
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.819732728608772
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoMSMARCO
	type: NanoMSMARCO
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.52
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.72
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.78
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.92
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.52
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.24
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.15600000000000003
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.092
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.52
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.72
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.78
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.92
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.7115365744941191
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.6468571428571428
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.6512663906142167
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoNFCorpus
	type: NanoNFCorpus
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.48
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.62
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.68
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.74
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.48
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.42666666666666664
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.37200000000000005
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.28800000000000003
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.04445987936677032
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.08334318466845993
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.12387064834298472
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.15623137130300419
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.3662101077105874
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.5659126984126984
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.1629293985515298
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoNQ
	type: NanoNQ
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.52
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.84
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.86
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.88
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.52
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.2866666666666667
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.17999999999999997
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.09399999999999999
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.49
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.79
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.82
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.84
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.706413633867191
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.6778571428571428
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.6569910589410588
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoQuoraRetrieval
	type: NanoQuoraRetrieval
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.9
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.96
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.98
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 1.0
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.9
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.38666666666666655
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.25199999999999995
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.13799999999999998
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.7873333333333333
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.9146666666666667
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.956
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.9966666666666666
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.9423484210846561
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.9383333333333332
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.9161729437229437
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoSCIDOCS
	type: NanoSCIDOCS
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.48
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.68
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.7
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.84
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.48
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.32666666666666666
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.256
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.18599999999999997
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.10166666666666668
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.20266666666666666
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.26266666666666666
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.3796666666666667
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.37448789415335676
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.6007222222222223
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.28182998781809016
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoArguAna
	type: NanoArguAna
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.26
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.56
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.66
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.8
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.26
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.18666666666666668
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.132
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.08
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.26
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.56
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.66
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.8
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.5176675835157897
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.4284920634920634
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.43500479781656254
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoSciFact
	type: NanoSciFact
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.74
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.82
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.88
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.88
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.74
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.29333333333333333
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.19599999999999998
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.09799999999999999
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.715
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.805
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.87
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.87
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.8103600696147834
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.7906666666666666
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.792673895287103
	name: Maxsim Map@100
	- task:
	type: py-late-information-retrieval
	name: Py Late Information Retrieval
	dataset:
	name: NanoTouche2020
	type: NanoTouche2020
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.7755102040816326
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.9591836734693877
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.9795918367346939
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 1.0
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.7755102040816326
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.7210884353741496
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.6326530612244898
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.5306122448979592
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.05246741937655717
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.1459745060885227
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.20856404158297343
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.3416638417494836
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.6056555459991261
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.8646258503401361
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.4446312449677973
	name: Maxsim Map@100
	- task:
	type: nano-beir
	name: Nano BEIR
	dataset:
	name: NanoBEIR mean
	type: NanoBEIR_mean
	metrics:
	- type: MaxSim_accuracy@1
	value: 0.6211930926216641
	name: Maxsim Accuracy@1
	- type: MaxSim_accuracy@3
	value: 0.7799372056514914
	name: Maxsim Accuracy@3
	- type: MaxSim_accuracy@5
	value: 0.8245839874411303
	name: Maxsim Accuracy@5
	- type: MaxSim_accuracy@10
	value: 0.8876923076923078
	name: Maxsim Accuracy@10
	- type: MaxSim_precision@1
	value: 0.6211930926216641
	name: Maxsim Precision@1
	- type: MaxSim_precision@3
	value: 0.38059654631083195
	name: Maxsim Precision@3
	- type: MaxSim_precision@5
	value: 0.28928100470957613
	name: Maxsim Precision@5
	- type: MaxSim_precision@10
	value: 0.19789324960753532
	name: Maxsim Precision@10
	- type: MaxSim_recall@1
	value: 0.37323254661112065
	name: Maxsim Recall@1
	- type: MaxSim_recall@3
	value: 0.5286156464076582
	name: Maxsim Recall@3
	- type: MaxSim_recall@5
	value: 0.5863520227624412
	name: Maxsim Recall@5
	- type: MaxSim_recall@10
	value: 0.6590695760206811
	name: Maxsim Recall@10
	- type: MaxSim_ndcg@10
	value: 0.6439403596365069
	name: Maxsim Ndcg@10
	- type: MaxSim_mrr@10
	value: 0.7116781789638933
	name: Maxsim Mrr@10
	- type: MaxSim_map@100
	value: 0.5644133152648683
	name: Maxsim Map@100
	---

	# PyLate

	This is a [PyLate](https://github.com/lightonai/pylate) model trained on the [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

	## Model Details

	### Model Description
	- Model Type: PyLate model
	<!-- - Base model: [Unknown](https://huggingface.co/unknown) -->
	- Document Length: 512 tokens
	- Query Length: 32 tokens
	- Output Dimensionality: 128 tokens
	- Similarity Function: MaxSim
	- Training Dataset:
	- [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma)
	- Language: en
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [PyLate Documentation](https://lightonai.github.io/pylate/)
	- Repository: [PyLate on GitHub](https://github.com/lightonai/pylate)
	- Hugging Face: [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)

	### Full Model Architecture

	```
	ColBERT(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
	(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
	)
	```

	## Usage
	First install the PyLate library:

	```bash
	pip install -U pylate
	```

	### Retrieval

	Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.

	#### Indexing documents

	Load the ColBERT model and initialize the PLAID index, then encode and index your documents:

	```python
	from pylate import indexes, models, retrieve

	# Step 1: Load the ColBERT model
	model = models.ColBERT(
	model_name_or_path="pylate_model_id",
	)

	# Step 2: Initialize the PLAID index
	index = indexes.PLAID(
	index_folder="pylate-index",
	index_name="index",
	override=True, # This overwrites the existing index if any
	)

	# Step 3: Encode the documents
	documents_ids = ["1", "2", "3"]
	documents = ["document 1 text", "document 2 text", "document 3 text"]

	documents_embeddings = model.encode(
	documents,
	batch_size=32,
	is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
	show_progress_bar=True,
	)

	# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
	index.add_documents(
	documents_ids=documents_ids,
	documents_embeddings=documents_embeddings,
	)
	```

	Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

	```python
	# To load an index, simply instantiate it with the correct folder/name and without overriding it
	index = indexes.PLAID(
	index_folder="pylate-index",
	index_name="index",
	)
	```

	#### Retrieving top-k documents for queries

	Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
	To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

	```python
	# Step 1: Initialize the ColBERT retriever
	retriever = retrieve.ColBERT(index=index)

	# Step 2: Encode the queries
	queries_embeddings = model.encode(
	["query for document 3", "query for document 1"],
	batch_size=32,
	is_query=True, # # Ensure that it is set to False to indicate that these are queries
	show_progress_bar=True,
	)

	# Step 3: Retrieve top-k documents
	scores = retriever.retrieve(
	queries_embeddings=queries_embeddings,
	k=10, # Retrieve the top 10 matches for each query
	)
	```

	### Reranking
	If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

	```python
	from pylate import rank, models

	queries = [
	"query A",
	"query B",
	]

	documents = [
	["document A", "document B"],
	["document 1", "document C", "document B"],
	]

	documents_ids = [
	[1, 2],
	[1, 3, 2],
	]

	model = models.ColBERT(
	model_name_or_path="pylate_model_id",
	)

	queries_embeddings = model.encode(
	queries,
	is_query=True,
	)

	documents_embeddings = model.encode(
	documents,
	is_query=False,
	)

	reranked_documents = rank.rerank(
	documents_ids=documents_ids,
	queries_embeddings=queries_embeddings,
	documents_embeddings=documents_embeddings,
	)
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Py Late Information Retrieval
	* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']`
	* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>

	\| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \|
	\|:--------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\|
	\| MaxSim_accuracy@1 \| 0.24 \| 0.76 \| 0.9 \| 0.58 \| 0.92 \| 0.52 \| 0.48 \| 0.52 \| 0.9 \| 0.48 \| 0.26 \| 0.74 \| 0.7755 \|
	\| MaxSim_accuracy@3 \| 0.42 \| 0.92 \| 0.96 \| 0.68 \| 1.0 \| 0.72 \| 0.62 \| 0.84 \| 0.96 \| 0.68 \| 0.56 \| 0.82 \| 0.9592 \|
	\| MaxSim_accuracy@5 \| 0.56 \| 0.92 \| 1.0 \| 0.72 \| 1.0 \| 0.78 \| 0.68 \| 0.86 \| 0.98 \| 0.7 \| 0.66 \| 0.88 \| 0.9796 \|
	\| MaxSim_accuracy@10 \| 0.76 \| 0.94 \| 1.0 \| 0.78 \| 1.0 \| 0.92 \| 0.74 \| 0.88 \| 1.0 \| 0.84 \| 0.8 \| 0.88 \| 1.0 \|
	\| MaxSim_precision@1 \| 0.24 \| 0.76 \| 0.9 \| 0.58 \| 0.92 \| 0.52 \| 0.48 \| 0.52 \| 0.9 \| 0.48 \| 0.26 \| 0.74 \| 0.7755 \|
	\| MaxSim_precision@3 \| 0.1467 \| 0.72 \| 0.34 \| 0.32 \| 0.5533 \| 0.24 \| 0.4267 \| 0.2867 \| 0.3867 \| 0.3267 \| 0.1867 \| 0.2933 \| 0.7211 \|
	\| MaxSim_precision@5 \| 0.132 \| 0.64 \| 0.216 \| 0.244 \| 0.352 \| 0.156 \| 0.372 \| 0.18 \| 0.252 \| 0.256 \| 0.132 \| 0.196 \| 0.6327 \|
	\| MaxSim_precision@10 \| 0.1 \| 0.536 \| 0.11 \| 0.138 \| 0.182 \| 0.092 \| 0.288 \| 0.094 \| 0.138 \| 0.186 \| 0.08 \| 0.098 \| 0.5306 \|
	\| MaxSim_recall@1 \| 0.115 \| 0.1033 \| 0.8367 \| 0.3661 \| 0.46 \| 0.52 \| 0.0445 \| 0.49 \| 0.7873 \| 0.1017 \| 0.26 \| 0.715 \| 0.0525 \|
	\| MaxSim_recall@3 \| 0.205 \| 0.2069 \| 0.9233 \| 0.4851 \| 0.83 \| 0.72 \| 0.0833 \| 0.79 \| 0.9147 \| 0.2027 \| 0.56 \| 0.805 \| 0.146 \|
	\| MaxSim_recall@5 \| 0.2733 \| 0.2663 \| 0.97 \| 0.5518 \| 0.88 \| 0.78 \| 0.1239 \| 0.82 \| 0.956 \| 0.2627 \| 0.66 \| 0.87 \| 0.2086 \|
	\| MaxSim_recall@10 \| 0.3907 \| 0.3798 \| 0.98 \| 0.6032 \| 0.91 \| 0.92 \| 0.1562 \| 0.84 \| 0.9967 \| 0.3797 \| 0.8 \| 0.87 \| 0.3417 \|
	\| MaxSim_ndcg@10 \| 0.2951 \| 0.6745 \| 0.9295 \| 0.5639 \| 0.8736 \| 0.7115 \| 0.3662 \| 0.7064 \| 0.9423 \| 0.3745 \| 0.5177 \| 0.8104 \| 0.6057 \|
	\| MaxSim_mrr@10 \| 0.3688 \| 0.842 \| 0.9367 \| 0.6376 \| 0.9533 \| 0.6469 \| 0.5659 \| 0.6779 \| 0.9383 \| 0.6007 \| 0.4285 \| 0.7907 \| 0.8646 \|
	\| MaxSim_map@100 \| 0.2245 \| 0.5354 \| 0.9026 \| 0.5137 \| 0.8197 \| 0.6513 \| 0.1629 \| 0.657 \| 0.9162 \| 0.2818 \| 0.435 \| 0.7927 \| 0.4446 \|

	#### Nano BEIR
	* Dataset: `NanoBEIR_mean`
	* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| MaxSim_accuracy@1 \| 0.6212 \|
	\| MaxSim_accuracy@3 \| 0.7799 \|
	\| MaxSim_accuracy@5 \| 0.8246 \|
	\| MaxSim_accuracy@10 \| 0.8877 \|
	\| MaxSim_precision@1 \| 0.6212 \|
	\| MaxSim_precision@3 \| 0.3806 \|
	\| MaxSim_precision@5 \| 0.2893 \|
	\| MaxSim_precision@10 \| 0.1979 \|
	\| MaxSim_recall@1 \| 0.3732 \|
	\| MaxSim_recall@3 \| 0.5286 \|
	\| MaxSim_recall@5 \| 0.5864 \|
	\| MaxSim_recall@10 \| 0.6591 \|
	\| MaxSim_ndcg@10 \| 0.6439 \|
	\| MaxSim_mrr@10 \| 0.7117 \|
	\| MaxSim_map@100 \| 0.5644 \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### train

	* Dataset: [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma) at [1a1ffe7](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma/tree/1a1ffe7cde403016be12ae532b249965b2293114)
	* Size: 640,000 training samples
	* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query_id \| document_ids \| scores \|
	\|:--------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------\|:------------------------------------\|
	\| type \| int \| list \| list \|
	\| details \| <ul><li>836: ~0.10%</li><li>3582: ~0.10%</li><li>4599: ~0.10%</li><li>4645: ~0.10%</li><li>4853: ~0.10%</li><li>5154: ~0.10%</li><li>7504: ~0.10%</li><li>12283: ~0.10%</li><li>12335: ~0.10%</li><li>12916: ~0.10%</li><li>14049: ~0.10%</li><li>14828: ~0.10%</li><li>15674: ~0.10%</li><li>15813: ~0.10%</li><li>16728: ~0.10%</li><li>22006: ~0.10%</li><li>23675: ~0.10%</li><li>24199: ~0.10%</li><li>25323: ~0.10%</li><li>28517: ~0.10%</li><li>29213: ~0.10%</li><li>32344: ~0.10%</li><li>34071: ~0.10%</li><li>34604: ~0.10%</li><li>35424: ~0.10%</li><li>35445: ~0.10%</li><li>36148: ~0.10%</li><li>37078: ~0.10%</li><li>37826: ~0.10%</li><li>38185: ~0.10%</li><li>40855: ~0.10%</li><li>42077: ~0.10%</li><li>43614: ~0.10%</li><li>45073: ~0.10%</li><li>46289: ~0.10%</li><li>47507: ~0.10%</li><li>48005: ~0.10%</li><li>48629: ~0.10%</li><li>48785: ~0.10%</li><li>49216: ~0.10%</li><li>49636: ~0.10%</li><li>49970: ~0.10%</li><li>52075: ~0.10%</li><li>52725: ~0.10%</li><li>54142: ~0.10%</li><li>54210: ~0.10%</li><li>55032: ~0.10%</li><li>59546: ~0.10%</li><li>60087: ~0.10%</li><li>60862: ~0.10%</li><li>60941: ~0.10%</li><li>61037: ~0.10%</li><li>61762: ~0.10%</li><li>62649: ~0.10%</li><li>63333: ~0.10%</li><li>64197: ~0.10%</li><li>64879: ~0.10%</li><li>67608: ~0.10%</li><li>67627: ~0.10%</li><li>69463: ~0.10%</li><li>70002: ~0.10%</li><li>70429: ~0.10%</li><li>72166: ~0.10%</li><li>72518: ~0.10%</li><li>72607: ~0.10%</li><li>72791: ~0.10%</li><li>73325: ~0.10%</li><li>74078: ~0.10%</li><li>74857: ~0.10%</li><li>75323: ~0.10%</li><li>75816: ~0.10%</li><li>76929: ~0.10%</li><li>77845: ~0.10%</li><li>77889: ~0.10%</li><li>78077: ~0.10%</li><li>78256: ~0.10%</li><li>78401: ~0.10%</li><li>78798: ~0.10%</li><li>80871: ~0.10%</li><li>81089: ~0.10%</li><li>82179: ~0.10%</li><li>82883: ~0.10%</li><li>84168: ~0.10%</li><li>86891: ~0.10%</li><li>88282: ~0.10%</li><li>89346: ~0.10%</li><li>89386: ~0.10%</li><li>90699: ~0.10%</li><li>90795: ~0.10%</li><li>91367: ~0.10%</li><li>91795: ~0.10%</li><li>92070: ~0.10%</li><li>92523: ~0.10%</li><li>92597: ~0.10%</li><li>92753: ~0.10%</li><li>92787: ~0.10%</li><li>96382: ~0.10%</li><li>96455: ~0.10%</li><li>97274: ~0.10%</li><li>97603: ~0.10%</li><li>100904: ~0.10%</li><li>101205: ~0.10%</li><li>101305: ~0.10%</li><li>102707: ~0.10%</li><li>103074: ~0.10%</li><li>105437: ~0.10%</li><li>108207: ~0.10%</li><li>109776: ~0.10%</li><li>112056: ~0.10%</li><li>112955: ~0.10%</li><li>112977: ~0.10%</li><li>113635: ~0.10%</li><li>115280: ~0.10%</li><li>115551: ~0.10%</li><li>116098: ~0.10%</li><li>117658: ~0.10%</li><li>120255: ~0.10%</li><li>120298: ~0.10%</li><li>121437: ~0.10%</li><li>123429: ~0.10%</li><li>125043: ~0.10%</li><li>125979: ~0.10%</li><li>126851: ~0.10%</li><li>128218: ~0.10%</li><li>128804: ~0.10%</li><li>129598: ~0.10%</li><li>131299: ~0.10%</li><li>132114: ~0.10%</li><li>133696: ~0.10%</li><li>134460: ~0.10%</li><li>137602: ~0.10%</li><li>137679: ~0.10%</li><li>138121: ~0.10%</li><li>138260: ~0.10%</li><li>138823: ~0.10%</li><li>139039: ~0.10%</li><li>140392: ~0.10%</li><li>140651: ~0.10%</li><li>142305: ~0.10%</li><li>145653: ~0.10%</li><li>145683: ~0.10%</li><li>145763: ~0.10%</li><li>150202: ~0.10%</li><li>151135: ~0.10%</li><li>152307: ~0.10%</li><li>152675: ~0.10%</li><li>153693: ~0.10%</li><li>154470: ~0.10%</li><li>155587: ~0.10%</li><li>157602: ~0.10%</li><li>157779: ~0.10%</li><li>158565: ~0.10%</li><li>159177: ~0.10%</li><li>159224: ~0.10%</li><li>159341: ~0.10%</li><li>159892: ~0.10%</li><li>161881: ~0.10%</li><li>162414: ~0.10%</li><li>163765: ~0.10%</li><li>165888: ~0.10%</li><li>168048: ~0.10%</li><li>168425: ~0.10%</li><li>168894: ~0.10%</li><li>169991: ~0.10%</li><li>170731: ~0.10%</li><li>171705: ~0.10%</li><li>176165: ~0.10%</li><li>176798: ~0.10%</li><li>180259: ~0.10%</li><li>181243: ~0.10%</li><li>182102: ~0.10%</li><li>182660: ~0.10%</li><li>183426: ~0.10%</li><li>183930: ~0.10%</li><li>184045: ~0.10%</li><li>184676: ~0.10%</li><li>185294: ~0.10%</li><li>186475: ~0.10%</li><li>187155: ~0.10%</li><li>188198: ~0.10%</li><li>191383: ~0.10%</li><li>192165: ~0.10%</li><li>193507: ~0.10%</li><li>194207: ~0.10%</li><li>195056: ~0.10%</li><li>197377: ~0.10%</li><li>198224: ~0.10%</li><li>198546: ~0.10%</li><li>202122: ~0.10%</li><li>203519: ~0.10%</li><li>206220: ~0.10%</li><li>209739: ~0.10%</li><li>210554: ~0.10%</li><li>212638: ~0.10%</li><li>213096: ~0.10%</li><li>213410: ~0.10%</li><li>214255: ~0.10%</li><li>217541: ~0.10%</li><li>219718: ~0.10%</li><li>220993: ~0.10%</li><li>223241: ~0.10%</li><li>224657: ~0.10%</li><li>227101: ~0.10%</li><li>227497: ~0.10%</li><li>227726: ~0.10%</li><li>228099: ~0.10%</li><li>228451: ~0.10%</li><li>230413: ~0.10%</li><li>231416: ~0.10%</li><li>233312: ~0.10%</li><li>234348: ~0.10%</li><li>235869: ~0.10%</li><li>237784: ~0.10%</li><li>240739: ~0.10%</li><li>246495: ~0.10%</li><li>246821: ~0.10%</li><li>248675: ~0.10%</li><li>249798: ~0.10%</li><li>249962: ~0.10%</li><li>249977: ~0.10%</li><li>250019: ~0.10%</li><li>250548: ~0.10%</li><li>251089: ~0.10%</li><li>254878: ~0.10%</li><li>255183: ~0.10%</li><li>255727: ~0.10%</li><li>256321: ~0.10%</li><li>258276: ~0.10%</li><li>260993: ~0.10%</li><li>261247: ~0.10%</li><li>262123: ~0.10%</li><li>262508: ~0.10%</li><li>266047: ~0.10%</li><li>267089: ~0.10%</li><li>267192: ~0.10%</li><li>268642: ~0.10%</li><li>269025: ~0.10%</li><li>273171: ~0.10%</li><li>273864: ~0.10%</li><li>274521: ~0.10%</li><li>274586: ~0.10%</li><li>275037: ~0.10%</li><li>275643: ~0.10%</li><li>276744: ~0.10%</li><li>277212: ~0.10%</li><li>277990: ~0.10%</li><li>279931: ~0.10%</li><li>280012: ~0.10%</li><li>281699: ~0.10%</li><li>282128: ~0.10%</li><li>283298: ~0.10%</li><li>284268: ~0.10%</li><li>285697: ~0.10%</li><li>285905: ~0.10%</li><li>287456: ~0.10%</li><li>287506: ~0.10%</li><li>288154: ~0.10%</li><li>289046: ~0.10%</li><li>292211: ~0.10%</li><li>292588: ~0.10%</li><li>293357: ~0.10%</li><li>293661: ~0.10%</li><li>294123: ~0.10%</li><li>299287: ~0.10%</li><li>300622: ~0.10%</li><li>302135: ~0.10%</li><li>303224: ~0.10%</li><li>304353: ~0.10%</li><li>304820: ~0.10%</li><li>310215: ~0.10%</li><li>310236: ~0.10%</li><li>310409: ~0.10%</li><li>311231: ~0.10%</li><li>312821: ~0.10%</li><li>314244: ~0.10%</li><li>314415: ~0.10%</li><li>314745: ~0.10%</li><li>316385: ~0.10%</li><li>316883: ~0.10%</li><li>317442: ~0.10%</li><li>318639: ~0.10%</li><li>318652: ~0.10%</li><li>320855: ~0.10%</li><li>321867: ~0.10%</li><li>322114: ~0.10%</li><li>323196: ~0.10%</li><li>324868: ~0.10%</li><li>327581: ~0.10%</li><li>329337: ~0.10%</li><li>331572: ~0.10%</li><li>331650: ~0.10%</li><li>331993: ~0.10%</li><li>332500: ~0.10%</li><li>334757: ~0.10%</li><li>336561: ~0.10%</li><li>336791: ~0.10%</li><li>337002: ~0.10%</li><li>338332: ~0.10%</li><li>338456: ~0.10%</li><li>339065: ~0.10%</li><li>339870: ~0.10%</li><li>340599: ~0.10%</li><li>341156: ~0.10%</li><li>342121: ~0.10%</li><li>342381: 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~0.10%</li><li>778375: ~0.10%</li><li>781066: ~0.10%</li><li>782328: ~0.10%</li><li>783231: ~0.10%</li><li>784413: ~0.10%</li><li>785781: ~0.10%</li><li>786250: ~0.10%</li><li>786845: ~0.10%</li><li>788012: ~0.10%</li><li>791857: ~0.10%</li><li>792788: ~0.10%</li><li>793182: ~0.10%</li><li>794187: ~0.10%</li><li>794308: ~0.10%</li><li>794318: ~0.10%</li><li>796097: ~0.10%</li><li>796117: ~0.10%</li><li>797182: ~0.10%</li><li>798215: ~0.10%</li><li>802050: ~0.10%</li><li>802669: ~0.10%</li><li>804168: ~0.10%</li><li>804253: ~0.10%</li><li>804461: ~0.10%</li><li>805743: ~0.10%</li><li>808416: ~0.10%</li><li>808455: ~0.10%</li><li>810577: ~0.10%</li><li>811702: ~0.10%</li><li>811843: ~0.10%</li><li>815923: ~0.10%</li><li>816475: ~0.10%</li><li>818312: ~0.10%</li><li>818521: ~0.10%</li><li>819278: ~0.10%</li><li>820890: ~0.10%</li><li>821615: ~0.10%</li><li>823136: ~0.10%</li><li>823735: ~0.10%</li><li>829476: ~0.10%</li><li>830591: ~0.10%</li><li>832433: ~0.10%</li><li>832597: ~0.10%</li><li>833053: ~0.10%</li><li>835043: ~0.10%</li><li>835759: ~0.10%</li><li>837731: ~0.10%</li><li>837942: ~0.10%</li><li>839448: ~0.10%</li><li>840228: ~0.10%</li><li>840417: ~0.10%</li><li>841851: ~0.10%</li><li>843327: ~0.10%</li><li>843622: ~0.10%</li><li>844870: ~0.10%</li><li>846084: ~0.10%</li><li>846807: ~0.10%</li><li>847076: ~0.10%</li><li>847535: ~0.10%</li><li>847977: ~0.10%</li><li>848075: ~0.10%</li><li>848326: ~0.10%</li><li>852725: ~0.10%</li><li>853465: ~0.10%</li><li>856427: ~0.10%</li><li>857186: ~0.10%</li><li>858377: ~0.10%</li><li>858543: ~0.10%</li><li>860426: ~0.10%</li><li>863804: ~0.10%</li><li>866039: ~0.10%</li><li>866406: ~0.10%</li><li>867180: ~0.10%</li><li>868280: ~0.10%</li><li>872156: ~0.10%</li><li>872791: ~0.10%</li><li>872953: ~0.10%</li><li>872959: ~0.10%</li><li>875015: ~0.10%</li><li>876522: ~0.10%</li><li>878407: ~0.10%</li><li>878710: ~0.10%</li><li>878855: ~0.10%</li><li>880495: ~0.10%</li><li>882732: ~0.10%</li><li>884335: ~0.10%</li><li>884941: ~0.10%</li><li>885893: ~0.10%</li><li>886713: ~0.10%</li><li>887068: ~0.10%</li><li>887751: ~0.10%</li><li>888027: ~0.10%</li><li>890152: ~0.10%</li><li>891137: ~0.10%</li><li>891890: ~0.10%</li><li>892662: ~0.10%</li><li>892973: ~0.10%</li><li>893360: ~0.10%</li><li>893915: ~0.10%</li><li>893976: ~0.10%</li><li>894324: ~0.10%</li><li>895709: ~0.10%</li><li>897065: ~0.10%</li><li>898387: ~0.10%</li><li>899291: ~0.10%</li><li>899604: ~0.10%</li><li>900513: ~0.10%</li><li>900619: ~0.10%</li><li>901170: ~0.10%</li><li>902794: ~0.10%</li><li>903238: ~0.10%</li><li>904294: ~0.10%</li><li>904520: ~0.10%</li><li>904992: ~0.10%</li><li>907212: ~0.10%</li><li>908062: ~0.10%</li><li>908561: ~0.10%</li><li>911034: ~0.10%</li><li>911982: ~0.10%</li><li>913716: ~0.10%</li><li>914819: ~0.10%</li><li>915750: ~0.10%</li><li>915766: ~0.10%</li><li>916125: ~0.10%</li><li>916648: ~0.10%</li><li>917285: ~0.10%</li><li>918194: ~0.10%</li><li>926035: ~0.10%</li><li>927726: ~0.10%</li><li>929821: ~0.10%</li><li>930300: ~0.10%</li><li>930796: ~0.10%</li><li>931617: ~0.10%</li><li>932719: ~0.10%</li><li>933784: ~0.10%</li><li>934378: ~0.10%</li><li>935900: ~0.10%</li><li>936118: ~0.10%</li><li>936336: ~0.10%</li><li>937231: ~0.10%</li><li>938420: ~0.10%</li><li>939184: ~0.10%</li><li>939567: ~0.10%</li><li>941588: ~0.10%</li><li>944093: ~0.10%</li><li>944912: ~0.10%</li><li>945069: ~0.10%</li><li>945659: ~0.10%</li><li>946110: ~0.10%</li><li>950044: ~0.10%</li><li>954101: ~0.10%</li><li>954147: ~0.10%</li><li>958697: ~0.10%</li><li>959530: ~0.10%</li><li>961721: ~0.10%</li><li>963582: ~0.10%</li><li>964471: ~0.10%</li><li>965026: ~0.10%</li><li>966573: ~0.10%</li><li>967330: ~0.10%</li><li>968346: ~0.10%</li><li>970649: ~0.10%</li><li>970873: ~0.10%</li><li>971636: ~0.10%</li><li>971664: ~0.10%</li><li>973555: ~0.10%</li><li>973851: ~0.10%</li><li>974207: ~0.10%</li><li>976896: ~0.10%</li><li>981402: ~0.10%</li><li>983723: ~0.10%</li><li>984358: ~0.10%</li><li>984653: ~0.10%</li><li>987107: ~0.10%</li><li>987167: ~0.10%</li><li>994360: ~0.10%</li><li>995049: ~0.10%</li><li>1002688: ~0.10%</li><li>1004305: ~0.10%</li><li>1004650: ~0.10%</li><li>1004849: ~0.10%</li><li>1005118: ~0.10%</li><li>1005614: ~0.10%</li><li>1005626: ~0.10%</li><li>1005669: ~0.10%</li><li>1006835: ~0.10%</li><li>1011008: ~0.10%</li><li>1012299: ~0.10%</li><li>1014010: ~0.10%</li><li>1014030: ~0.10%</li><li>1016549: ~0.10%</li><li>1017016: ~0.10%</li><li>1017335: ~0.10%</li><li>1018386: ~0.10%</li><li>1020640: ~0.10%</li><li>1021041: ~0.10%</li><li>1021411: ~0.10%</li><li>1025341: ~0.10%</li><li>1025423: ~0.10%</li><li>1025767: ~0.10%</li><li>1026066: ~0.10%</li><li>1026434: ~0.10%</li><li>1027516: ~0.10%</li><li>1027703: ~0.10%</li><li>1028119: ~0.10%</li><li>1028642: ~0.10%</li><li>1031554: ~0.10%</li><li>1032300: ~0.10%</li><li>1033639: ~0.10%</li><li>1033660: ~0.10%</li><li>1034832: ~0.10%</li><li>1035274: ~0.10%</li><li>1037432: ~0.10%</li><li>1037536: ~0.10%</li><li>1037759: ~0.10%</li><li>1039860: ~0.10%</li><li>1041131: ~0.10%</li><li>1041892: ~0.10%</li><li>1043066: ~0.10%</li><li>1044326: ~0.10%</li><li>1044905: ~0.10%</li><li>1047848: ~0.10%</li><li>1048534: ~0.10%</li><li>1049477: ~0.10%</li><li>1050531: ~0.10%</li><li>1052073: ~0.10%</li><li>1052617: ~0.10%</li><li>1054049: ~0.10%</li><li>1055142: ~0.10%</li><li>1056933: ~0.10%</li><li>1057358: ~0.10%</li><li>1057911: ~0.10%</li><li>1061411: ~0.10%</li><li>1062328: ~0.10%</li><li>1062485: ~0.10%</li><li>1062534: ~0.10%</li><li>1062794: ~0.10%</li><li>1063269: ~0.10%</li><li>1063467: ~0.10%</li><li>1064568: ~0.10%</li><li>1064868: ~0.10%</li><li>1065481: ~0.10%</li><li>1065565: ~0.10%</li><li>1067970: ~0.10%</li><li>1068014: ~0.10%</li><li>1070203: ~0.10%</li><li>1070708: ~0.10%</li><li>1072038: ~0.10%</li><li>1072214: ~0.10%</li><li>1074885: ~0.10%</li><li>1075308: ~0.10%</li><li>1078872: ~0.10%</li><li>1078979: ~0.10%</li><li>1079266: ~0.10%</li><li>1079736: ~0.10%</li><li>1080075: ~0.10%</li><li>1081716: ~0.10%</li><li>1137391: ~0.10%</li><li>1138530: ~0.10%</li><li>1139697: ~0.10%</li><li>1140119: ~0.10%</li><li>1140869: ~0.10%</li><li>1141527: ~0.10%</li><li>1144693: ~0.10%</li><li>1145425: ~0.10%</li><li>1149162: ~0.10%</li><li>1149207: ~0.10%</li><li>1150086: ~0.10%</li><li>1150398: ~0.10%</li><li>1150731: ~0.10%</li><li>1151256: ~0.10%</li><li>1151403: ~0.10%</li><li>1152236: ~0.10%</li><li>1153693: ~0.10%</li><li>1155859: ~0.10%</li><li>1156918: ~0.10%</li><li>1158007: ~0.10%</li><li>1158559: ~0.10%</li><li>1158952: ~0.10%</li><li>1159165: ~0.10%</li><li>1161242: ~0.10%</li><li>1163227: ~0.10%</li><li>1166023: ~0.10%</li><li>1166231: ~0.10%</li><li>1167002: ~0.10%</li><li>1169844: ~0.10%</li><li>1170663: ~0.10%</li><li>1171580: ~0.10%</li><li>1172072: ~0.10%</li><li>1172083: ~0.10%</li><li>1173371: ~0.10%</li><li>1173809: ~0.10%</li><li>1174049: ~0.10%</li><li>1175044: ~0.10%</li><li>1175745: ~0.10%</li><li>1176061: ~0.10%</li><li>1176414: ~0.10%</li><li>1176993: ~0.10%</li><li>1177449: ~0.10%</li><li>1178311: ~0.10%</li><li>1179029: ~0.10%</li><li>1179069: ~0.10%</li><li>1180579: ~0.10%</li><li>1181077: ~0.10%</li><li>1183293: ~0.10%</li><li>1184313: ~0.10%</li><li>1185090: ~0.10%</li><li>1185669: ~0.10%</li></ul> \| <ul><li>size: 16 elements</li></ul> \| <ul><li>size: 16 elements</li></ul> \|
	* Samples:
	\| query_id \| document_ids \| scores \|
	\|:--------------------\|:----------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------\|
	\| <code>685613</code> \| <code>[7546874, 1176459, 197677, 2306318, 8541504, ...]</code> \| <code>[0.9999999992804947, 0.24845418756716053, 0.7594154013647826, 0.26644182105618575, 0.390668914839766, ...]</code> \|
	\| <code>237784</code> \| <code>[6366584, 4034101, 2325374, 6914618, 6042146, ...]</code> \| <code>[0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...]</code> \|
	\| <code>904294</code> \| <code>[448408, 8743975, 49600, 7339401, 2714261, ...]</code> \| <code>[0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...]</code> \|
	* Loss: <code>pylate.losses.distillation.Distillation</code>

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `learning_rate`: 4e-06
	- `max_steps`: 20000
	- `fp16`: True
	- `dataloader_drop_last`: True
	- `dataloader_num_workers`: 8
	- `ddp_find_unused_parameters`: False
	- `torch_compile`: True
	- `torch_compile_backend`: inductor
	- `eval_on_start`: True

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 8
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 4e-06
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 3.0
	- `max_steps`: 20000
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: True
	- `dataloader_num_workers`: 8
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `parallelism_config`: None
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: False
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: None
	- `hub_always_push`: False
	- `hub_revision`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: True
	- `torch_compile_backend`: inductor
	- `torch_compile_mode`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: True
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>