diff --git "a/README.md" "b/README.md"
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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: 0.28
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.6
+ 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: 0.28
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.21333333333333332
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.15600000000000003
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.11399999999999999
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.14166666666666664
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.28
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.3233333333333333
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.4433333333333333
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.3514515373411296
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.4419126984126984
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.26787129909036694
+ 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: 0.8
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.9
+ 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: 0.8
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.68
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.64
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.556
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.10146114576120233
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.1811253111210503
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.25584250683060056
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.38805909088160134
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6860171601389934
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.8573333333333334
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.5296825033597241
+ 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: 0.86
+ 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: 0.86
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.32666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.20799999999999996
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.10399999999999998
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.8166666666666668
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.9066666666666667
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.9566666666666667
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.9566666666666667
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.908144200292094
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.92
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8814609006793713
+ 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: 0.52
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.64
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.7
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.82
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.52
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.23199999999999996
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.144
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.3260793650793651
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.4550714285714285
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.5179523809523809
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.638452380952381
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5521515882834523
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6033809523809524
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.485613722262918
+ 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: 0.9
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.98
+ 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: 0.5333333333333333
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.3399999999999999
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.17399999999999996
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.45
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.8
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.85
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.87
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.8419623803570458
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9440000000000001
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.7820805143551045
+ 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: 0.68
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.76
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.86
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.52
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.22666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.15200000000000002
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.08599999999999998
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.52
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.68
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.76
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.86
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6811314480568632
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6250238095238094
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.6333976362474815
+ 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: 0.44
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.6
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.62
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.72
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.44
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3933333333333333
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.35200000000000004
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.264
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.04230874849281337
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.08146626368119848
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.09815206535035287
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.12479201912898642
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.33242903156565573
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.5284126984126984
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.15006576918781514
+ 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: 0.5
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.74
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.86
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.9
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.5
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.24666666666666667
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.17599999999999993
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.09599999999999997
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.49
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.7
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.82
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.86
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6841974176648971
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6358888888888888
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.6241807081807081
+ 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: 0.88
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.98
+ 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: 0.88
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3999999999999999
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.25999999999999995
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.13799999999999998
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.7673333333333333
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.9386666666666668
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.9793333333333334
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.9966666666666666
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.9419539850914371
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.934
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.9163896103896104
+ 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: 0.5
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.72
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.82
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.84
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.5
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.36666666666666664
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.284
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.182
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.10566666666666666
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.22666666666666666
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.29166666666666663
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.37166666666666665
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.3831880691359888
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6239999999999999
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.29330717965633857
+ 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: 0.16
+ 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: 0.76
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.16
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.18666666666666668
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.13200000000000003
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.07600000000000001
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.16
+ 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: 0.76
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.461790847680295
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.365547619047619
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.3743204230050349
+ 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: 0.84
+ 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: 0.18799999999999997
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.09799999999999999
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.705
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.795
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.83
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.87
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.8015954255022331
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.786388888888889
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.780149020175336
+ 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: 0.7959183673469388
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.9795918367346939
+ 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: 0.7959183673469388
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.7142857142857143
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.6653061224489795
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.5244897959183673
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.054514709716006485
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.14494235624957325
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2233145114601526
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.3397163035501697
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6049106751999275
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.8770651117589893
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.4446934096215408
+ name: Maxsim Map@100
+ - task:
+ type: nano-beir
+ name: Nano BEIR
+ dataset:
+ name: NanoBEIR mean
+ type: NanoBEIR_mean
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.6073783359497645
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.7815070643642071
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.8322762951334379
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.8861538461538461
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.6073783359497645
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3754578754578754
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.2911773940345369
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.19665306122448975
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.36005363864482465
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.5192004122787115
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.5820201126610375
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.652257932911267
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6331479820238474
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.7033041538959905
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.5510163612470269
+ 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
+
+- **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
+
+
+### 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,
+)
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Py Late Information Retrieval
+* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']`
+* Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator
+
+| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
+|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
+| MaxSim_accuracy@1 | 0.28 | 0.8 | 0.86 | 0.52 | 0.9 | 0.52 | 0.44 | 0.5 | 0.88 | 0.5 | 0.16 | 0.74 | 0.7959 |
+| MaxSim_accuracy@3 | 0.6 | 0.9 | 0.96 | 0.64 | 0.98 | 0.68 | 0.6 | 0.74 | 0.98 | 0.72 | 0.56 | 0.82 | 0.9796 |
+| MaxSim_accuracy@5 | 0.66 | 0.92 | 1.0 | 0.7 | 1.0 | 0.76 | 0.62 | 0.86 | 1.0 | 0.82 | 0.66 | 0.84 | 0.9796 |
+| MaxSim_accuracy@10 | 0.8 | 0.94 | 1.0 | 0.82 | 1.0 | 0.86 | 0.72 | 0.9 | 1.0 | 0.84 | 0.76 | 0.88 | 1.0 |
+| MaxSim_precision@1 | 0.28 | 0.8 | 0.86 | 0.52 | 0.9 | 0.52 | 0.44 | 0.5 | 0.88 | 0.5 | 0.16 | 0.74 | 0.7959 |
+| MaxSim_precision@3 | 0.2133 | 0.68 | 0.3267 | 0.3 | 0.5333 | 0.2267 | 0.3933 | 0.2467 | 0.4 | 0.3667 | 0.1867 | 0.2933 | 0.7143 |
+| MaxSim_precision@5 | 0.156 | 0.64 | 0.208 | 0.232 | 0.34 | 0.152 | 0.352 | 0.176 | 0.26 | 0.284 | 0.132 | 0.188 | 0.6653 |
+| MaxSim_precision@10 | 0.114 | 0.556 | 0.104 | 0.144 | 0.174 | 0.086 | 0.264 | 0.096 | 0.138 | 0.182 | 0.076 | 0.098 | 0.5245 |
+| MaxSim_recall@1 | 0.1417 | 0.1015 | 0.8167 | 0.3261 | 0.45 | 0.52 | 0.0423 | 0.49 | 0.7673 | 0.1057 | 0.16 | 0.705 | 0.0545 |
+| MaxSim_recall@3 | 0.28 | 0.1811 | 0.9067 | 0.4551 | 0.8 | 0.68 | 0.0815 | 0.7 | 0.9387 | 0.2267 | 0.56 | 0.795 | 0.1449 |
+| MaxSim_recall@5 | 0.3233 | 0.2558 | 0.9567 | 0.518 | 0.85 | 0.76 | 0.0982 | 0.82 | 0.9793 | 0.2917 | 0.66 | 0.83 | 0.2233 |
+| MaxSim_recall@10 | 0.4433 | 0.3881 | 0.9567 | 0.6385 | 0.87 | 0.86 | 0.1248 | 0.86 | 0.9967 | 0.3717 | 0.76 | 0.87 | 0.3397 |
+| **MaxSim_ndcg@10** | **0.3515** | **0.686** | **0.9081** | **0.5522** | **0.842** | **0.6811** | **0.3324** | **0.6842** | **0.942** | **0.3832** | **0.4618** | **0.8016** | **0.6049** |
+| MaxSim_mrr@10 | 0.4419 | 0.8573 | 0.92 | 0.6034 | 0.944 | 0.625 | 0.5284 | 0.6359 | 0.934 | 0.624 | 0.3655 | 0.7864 | 0.8771 |
+| MaxSim_map@100 | 0.2679 | 0.5297 | 0.8815 | 0.4856 | 0.7821 | 0.6334 | 0.1501 | 0.6242 | 0.9164 | 0.2933 | 0.3743 | 0.7801 | 0.4447 |
+
+#### Nano BEIR
+* Dataset: `NanoBEIR_mean`
+* Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator
+
+| Metric | Value |
+|:--------------------|:-----------|
+| MaxSim_accuracy@1 | 0.6074 |
+| MaxSim_accuracy@3 | 0.7815 |
+| MaxSim_accuracy@5 | 0.8323 |
+| MaxSim_accuracy@10 | 0.8862 |
+| MaxSim_precision@1 | 0.6074 |
+| MaxSim_precision@3 | 0.3755 |
+| MaxSim_precision@5 | 0.2912 |
+| MaxSim_precision@10 | 0.1967 |
+| MaxSim_recall@1 | 0.3601 |
+| MaxSim_recall@3 | 0.5192 |
+| MaxSim_recall@5 | 0.582 |
+| MaxSim_recall@10 | 0.6523 |
+| **MaxSim_ndcg@10** | **0.6331** |
+| MaxSim_mrr@10 | 0.7033 |
+| MaxSim_map@100 | 0.551 |
+
+
+
+
+
+## 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: query_id, document_ids, and scores
+* Approximate statistics based on the first 1000 samples:
+ | | query_id | document_ids | scores |
+ |:--------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
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| | |
+* Samples:
+ | query_id | document_ids | scores |
+ |:--------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
+ | 685613 | [7546874, 1176459, 197677, 2306318, 8541504, ...] | [0.9999999992804947, 0.24845418756716053, 0.7594154013647826, 0.26644182105618575, 0.390668914839766, ...] |
+ | 237784 | [6366584, 4034101, 2325374, 6914618, 6042146, ...] | [0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...] |
+ | 904294 | [448408, 8743975, 49600, 7339401, 2714261, ...] | [0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...] |
+* Loss: pylate.losses.distillation.Distillation
+
+### 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
+Click to expand
+
+- `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`: {}
+
+