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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
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 1
            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
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 1
            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
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 1
            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
            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 model trained on the 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

This model is an alias of robro612/modernbert_xtr_kd_256. It was KD-trained on lightonai/ms-marco-en-bge-gemma and contrastively trained on bclavie/msmarco-10m-triplets with XTR's k_train set to 256.

Model Description

Model Type: PyLate model
Document Length: 512 tokens
Query Length: 32 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim
Training Dataset:
- train
Language: en

Model Sources

Documentation: PyLate Documentation
Repository: PyLate on GitHub
Hugging Face: PyLate models on Hugging Face

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:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

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

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:

# 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:

# 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:

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 at 1a1ffe7
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
type	int	list	list
details	836: ~0.10% 3582: ~0.10% 4599: ~0.10% 4645: ~0.10% 4853: ~0.10% 5154: ~0.10% 7504: ~0.10% 12283: ~0.10% 12335: ~0.10% 12916: ~0.10% 14049: ~0.10% 14828: ~0.10% 15674: ~0.10% 15813: ~0.10% 16728: ~0.10% 22006: ~0.10% 23675: ~0.10% 24199: ~0.10% 25323: ~0.10% 28517: ~0.10% 29213: ~0.10% 32344: ~0.10% 34071: ~0.10% 34604: ~0.10% 35424: ~0.10% 35445: ~0.10% 36148: ~0.10% 37078: ~0.10% 37826: ~0.10% 38185: ~0.10% 40855: ~0.10% 42077: ~0.10% 43614: ~0.10% 45073: ~0.10% 46289: ~0.10% 47507: ~0.10% 48005: ~0.10% 48629: ~0.10% 48785: ~0.10% 49216: ~0.10% 49636: ~0.10% 49970: ~0.10% 52075: ~0.10% 52725: ~0.10% 54142: ~0.10% 54210: ~0.10% 55032: ~0.10% 59546: ~0.10% 60087: ~0.10% 60862: ~0.10% 60941: ~0.10% 61037: ~0.10% 61762: ~0.10% 62649: ~0.10% 63333: ~0.10% 64197: ~0.10% 64879: ~0.10% 67608: ~0.10% 67627: ~0.10% 69463: ~0.10% 70002: ~0.10% 70429: ~0.10% 72166: ~0.10% 72518: ~0.10% 72607: ~0.10% 72791: ~0.10% 73325: ~0.10% 74078: ~0.10% 74857: ~0.10% 75323: ~0.10% 75816: ~0.10% 76929: ~0.10% 77845: ~0.10% 77889: ~0.10% 78077: ~0.10% 78256: ~0.10% 78401: ~0.10% 78798: ~0.10% 80871: ~0.10% 81089: ~0.10% 82179: ~0.10% 82883: ~0.10% 84168: ~0.10% 86891: ~0.10% 88282: ~0.10% 89346: ~0.10% 89386: ~0.10% 90699: ~0.10% 90795: ~0.10% 91367: ~0.10% 91795: ~0.10% 92070: ~0.10% 92523: ~0.10% 92597: ~0.10% 92753: ~0.10% 92787: ~0.10% 96382: ~0.10% 96455: ~0.10% 97274: ~0.10% 97603: ~0.10% 100904: ~0.10% 101205: ~0.10% 101305: ~0.10% 102707: ~0.10% 103074: ~0.10% 105437: ~0.10% 108207: ~0.10% 109776: ~0.10% 112056: ~0.10% 112955: ~0.10% 112977: ~0.10% 113635: ~0.10% 115280: ~0.10% 115551: ~0.10% 116098: ~0.10% 117658: ~0.10% 120255: ~0.10% 120298: ~0.10% 121437: ~0.10% 123429: ~0.10% 125043: ~0.10% 125979: ~0.10% 126851: ~0.10% 128218: ~0.10% 128804: ~0.10% 129598: ~0.10% 131299: ~0.10% 132114: ~0.10% 133696: ~0.10% 134460: ~0.10% 137602: ~0.10% 137679: ~0.10% 138121: ~0.10% 138260: ~0.10% 138823: ~0.10% 139039: ~0.10% 140392: ~0.10% 140651: ~0.10% 142305: ~0.10% 145653: ~0.10% 145683: ~0.10% 145763: ~0.10% 150202: ~0.10% 151135: ~0.10% 152307: ~0.10% 152675: ~0.10% 153693: ~0.10% 154470: ~0.10% 155587: ~0.10% 157602: ~0.10% 157779: ~0.10% 158565: ~0.10% 159177: ~0.10% 159224: ~0.10% 159341: ~0.10% 159892: ~0.10% 161881: ~0.10% 162414: ~0.10% 163765: ~0.10% 165888: ~0.10% 168048: ~0.10% 168425: ~0.10% 168894: ~0.10% 169991: ~0.10% 170731: ~0.10% 171705: ~0.10% 176165: ~0.10% 176798: ~0.10% 180259: ~0.10% 181243: ~0.10% 182102: ~0.10% 182660: ~0.10% 183426: ~0.10% 183930: ~0.10% 184045: ~0.10% 184676: ~0.10% 185294: ~0.10% 186475: ~0.10% 187155: ~0.10% 188198: ~0.10% 191383: ~0.10% 192165: ~0.10% 193507: ~0.10% 194207: ~0.10% 195056: ~0.10% 197377: ~0.10% 198224: ~0.10% 198546: ~0.10% 202122: ~0.10% 203519: ~0.10% 206220: ~0.10% 209739: ~0.10% 210554: ~0.10% 212638: ~0.10% 213096: ~0.10% 213410: ~0.10% 214255: ~0.10% 217541: ~0.10% 219718: ~0.10% 220993: ~0.10% 223241: ~0.10% 224657: ~0.10% 227101: ~0.10% 227497: ~0.10% 227726: ~0.10% 228099: ~0.10% 228451: ~0.10% 230413: ~0.10% 231416: ~0.10% 233312: ~0.10% 234348: ~0.10% 235869: ~0.10% 237784: ~0.10% 240739: ~0.10% 246495: ~0.10% 246821: ~0.10% 248675: ~0.10% 249798: ~0.10% 249962: ~0.10% 249977: ~0.10% 250019: ~0.10% 250548: ~0.10% 251089: ~0.10% 254878: ~0.10% 255183: ~0.10% 255727: ~0.10% 256321: ~0.10% 258276: ~0.10% 260993: ~0.10% 261247: ~0.10% 262123: ~0.10% 262508: ~0.10% 266047: ~0.10% 267089: ~0.10% 267192: ~0.10% 268642: ~0.10% 269025: ~0.10% 273171: ~0.10% 273864: ~0.10% 274521: ~0.10% 274586: ~0.10% 275037: ~0.10% 275643: ~0.10% 276744: ~0.10% 277212: ~0.10% 277990: ~0.10% 279931: ~0.10% 280012: ~0.10% 281699: ~0.10% 282128: ~0.10% 283298: ~0.10% 284268: ~0.10% 285697: ~0.10% 285905: ~0.10% 287456: ~0.10% 287506: ~0.10% 288154: ~0.10% 289046: ~0.10% 292211: ~0.10% 292588: ~0.10% 293357: ~0.10% 293661: ~0.10% 294123: ~0.10% 299287: ~0.10% 300622: ~0.10% 302135: ~0.10% 303224: ~0.10% 304353: ~0.10% 304820: ~0.10% 310215: ~0.10% 310236: ~0.10% 310409: ~0.10% 311231: ~0.10% 312821: ~0.10% 314244: ~0.10% 314415: ~0.10% 314745: ~0.10% 316385: ~0.10% 316883: ~0.10% 317442: ~0.10% 318639: ~0.10% 318652: ~0.10% 320855: ~0.10% 321867: ~0.10% 322114: ~0.10% 323196: ~0.10% 324868: ~0.10% 327581: ~0.10% 329337: ~0.10% 331572: ~0.10% 331650: ~0.10% 331993: ~0.10% 332500: ~0.10% 334757: ~0.10% 336561: ~0.10% 336791: ~0.10% 337002: ~0.10% 338332: ~0.10% 338456: ~0.10% 339065: ~0.10% 339870: ~0.10% 340599: ~0.10% 341156: ~0.10% 342121: ~0.10% 342381: ~0.10% 343411: ~0.10% 344860: ~0.10% 345924: ~0.10% 346421: ~0.10% 346425: ~0.10% 348157: ~0.10% 351281: ~0.10% 351858: ~0.10% 352641: ~0.10% 353748: ~0.10% 357399: ~0.10% 359787: ~0.10% 359893: ~0.10% 360094: ~0.10% 360168: ~0.10% 361127: ~0.10% 362220: ~0.10% 362560: ~0.10% 366835: ~0.10% 367185: ~0.10% 369045: ~0.10% 371113: ~0.10% 376044: ~0.10% 376524: ~0.10% 377231: ~0.10% 377735: ~0.10% 378574: ~0.10% 379749: ~0.10% 379953: ~0.10% 381834: ~0.10% 384039: ~0.10% 384364: ~0.10% 384398: ~0.10% 384751: ~0.10% 385758: ~0.10% 385893: ~0.10% 386098: ~0.10% 387205: ~0.10% 387374: ~0.10% 388450: ~0.10% 388589: ~0.10% 388593: ~0.10% 389571: ~0.10% 389572: ~0.10% 391531: ~0.10% 391857: ~0.10% 393174: ~0.10% 393426: ~0.10% 396601: ~0.10% 396905: ~0.10% 397801: ~0.10% 398011: ~0.10% 398132: ~0.10% 398721: ~0.10% 399016: ~0.10% 401601: ~0.10% 403876: ~0.10% 403897: ~0.10% 404830: ~0.10% 406102: ~0.10% 406397: ~0.10% 407151: ~0.10% 409373: ~0.10% 410084: ~0.10% 410859: ~0.10% 411693: ~0.10% 411984: ~0.10% 412214: ~0.10% 412560: ~0.10% 413117: ~0.10% 416391: ~0.10% 417066: ~0.10% 417198: ~0.10% 417751: ~0.10% 417778: ~0.10% 420257: ~0.10% 420787: ~0.10% 421001: ~0.10% 421045: ~0.10% 421354: ~0.10% 428114: ~0.10% 429057: ~0.10% 429459: ~0.10% 430319: ~0.10% 431215: ~0.10% 431332: ~0.10% 431488: ~0.10% 432097: ~0.10% 432283: ~0.10% 434131: ~0.10% 434934: ~0.10% 435353: ~0.10% 437793: ~0.10% 438297: ~0.10% 438806: ~0.10% 439016: ~0.10% 439129: ~0.10% 439217: ~0.10% 439755: ~0.10% 440343: ~0.10% 440506: ~0.10% 441030: ~0.10% 441509: ~0.10% 443408: ~0.10% 443686: ~0.10% 445516: ~0.10% 445999: ~0.10% 447039: ~0.10% 447219: ~0.10% 447298: ~0.10% 453040: ~0.10% 453745: ~0.10% 454869: ~0.10% 456224: ~0.10% 456251: ~0.10% 457065: ~0.10% 459890: ~0.10% 460010: ~0.10% 463716: ~0.10% 465235: ~0.10% 470470: ~0.10% 471875: ~0.10% 472462: ~0.10% 474016: ~0.10% 479266: ~0.10% 479360: ~0.10% 480621: ~0.10% 483014: ~0.10% 484553: ~0.10% 485031: ~0.10% 485828: ~0.10% 486664: ~0.10% 488266: ~0.10% 489488: ~0.10% 490992: ~0.10% 491894: ~0.10% 491983: ~0.10% 492620: ~0.10% 493035: ~0.10% 493461: ~0.10% 494255: ~0.10% 496473: ~0.10% 496474: ~0.10% 496516: ~0.10% 496813: ~0.10% 496853: ~0.10% 499553: ~0.10% 499565: ~0.10% 499737: ~0.10% 500057: ~0.10% 500546: ~0.10% 501510: ~0.10% 501978: ~0.10% 503905: ~0.10% 510559: ~0.10% 511473: ~0.10% 512440: ~0.10% 513832: ~0.10% 514106: ~0.10% 514902: ~0.10% 515053: ~0.10% 515507: ~0.10% 516205: ~0.10% 517903: ~0.10% 518096: ~0.10% 520796: ~0.10% 521570: ~0.10% 522112: ~0.10% 523814: ~0.10% 525505: ~0.10% 525583: ~0.10% 525764: ~0.10% 528105: ~0.10% 530985: ~0.10% 532014: ~0.10% 534952: ~0.10% 538836: ~0.10% 539326: ~0.10% 539504: ~0.10% 541861: ~0.10% 542925: ~0.10% 543525: ~0.10% 544853: ~0.10% 545091: ~0.10% 546527: ~0.10% 546753: ~0.10% 548007: ~0.10% 548100: ~0.10% 554548: ~0.10% 555064: ~0.10% 560255: ~0.10% 560711: ~0.10% 561084: ~0.10% 561114: ~0.10% 561329: ~0.10% 561838: ~0.10% 561946: ~0.10% 564894: ~0.10% 566884: ~0.10% 568110: ~0.10% 569541: ~0.10% 570881: ~0.10% 571286: ~0.10% 571515: ~0.10% 571577: ~0.10% 572354: ~0.10% 573015: ~0.10% 573283: ~0.10% 577767: ~0.10% 578249: ~0.10% 578805: ~0.10% 580872: ~0.10% 581072: ~0.10% 581684: ~0.10% 582341: ~0.10% 583169: ~0.10% 583322: ~0.10% 583889: ~0.10% 584173: ~0.10% 585406: ~0.10% 585523: ~0.10% 585660: ~0.10% 587005: ~0.10% 587399: ~0.10% 588010: ~0.10% 588337: ~0.10% 590946: ~0.10% 593319: ~0.10% 595246: ~0.10% 597157: ~0.10% 597215: ~0.10% 597368: ~0.10% 597453: ~0.10% 598538: ~0.10% 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~0.10% 1184313: ~0.10% 1185090: ~0.10% 1185669: ~0.10%	size: 16 elements	size: 16 elements

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: {}