README.md

metadata

tags:
  - ColBERT
  - PyLate
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:640000
  - loss:Distillation
base_model: Alibaba-NLP/gte-modernbert-base
pipeline_tag: sentence-similarity
license: apache-2.0
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 model based on Alibaba-NLP/gte-modernbert-base
    results:
      - task:
          type: py-late-information-retrieval
          name: Py Late Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: MaxSim_accuracy@1
            value: 0.36
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.62
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.78
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.86
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.36
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.2333333333333333
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.20799999999999996
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.12799999999999997
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.18333333333333332
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.289
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.41566666666666663
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.49566666666666664
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.41477895139843374
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.526579365079365
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.33473812643311207
            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: 0.88
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.94
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.96
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.98
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.88
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.7133333333333334
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.6560000000000001
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.572
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.11798996781634019
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.23074158968531658
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.2961618059276896
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.4145532152487909
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.7295518860528665
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.9168571428571428
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.5883869727264871
            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: 0.92
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.98
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.98
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 1
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.92
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.35999999999999993
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.21599999999999994
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.10999999999999999
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.8566666666666667
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.96
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.96
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.98
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.9451911044041129
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.9522222222222223
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.9270501207729468
            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: 0.56
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.66
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.74
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.8
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.56
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.32666666666666666
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.25599999999999995
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.15199999999999997
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.30924603174603177
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.47840476190476194
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.5751746031746031
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.6411984126984127
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.5669909336903424
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.6359444444444444
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.5031998196513616
            name: Maxsim Map@100
      - task:
          type: py-late-information-retrieval
          name: Py Late Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: MaxSim_accuracy@1
            value: 0.92
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 1
            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: 0.92
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.58
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.35999999999999993
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.18599999999999994
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.46
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.87
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.9
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.93
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.9011747095216048
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.96
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.8591508921772081
            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: 0.54
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.68
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.74
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.92
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.54
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.22666666666666666
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.14800000000000002
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.092
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.54
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.68
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.74
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.92
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.7088869908160952
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.6446507936507936
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.6496349206349206
            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: 0.56
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.68
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.74
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.76
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.56
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.43333333333333335
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.39199999999999996
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.304
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.06640185752724687
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.10198877096622012
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.12839743828750172
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.15658989769166
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.3957047406068243
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.627
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.1917924344366858
            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: 0.64
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.82
            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: 0.64
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.2866666666666666
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.17999999999999997
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.1
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.61
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.78
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.82
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.88
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.7645227466201794
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.7390000000000001
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.7239323294755705
            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: 0.96
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 1
            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: 0.96
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.4
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.25599999999999995
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.13399999999999998
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.8473333333333334
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.9453333333333334
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.9693333333333334
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.9893333333333334
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.9691448095973965
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.9766666666666667
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.9551871794871795
            name: Maxsim Map@100
      - task:
          type: py-late-information-retrieval
          name: Py Late Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: MaxSim_accuracy@1
            value: 0.48
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.74
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.78
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.84
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.48
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.3999999999999999
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.292
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.19399999999999995
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.10066666666666667
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.24666666666666665
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.29966666666666664
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.39666666666666667
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.3986767701602276
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.6137222222222222
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.3163385555719993
            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: 0.3
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.62
            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: 0.3
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.20666666666666667
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.14
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.08199999999999999
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.3
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.62
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.7
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.82
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.5609089627577635
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.4774603174603175
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.4824361431413148
            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: 0.86
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.9
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.94
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.74
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.3
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.19599999999999995
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.10399999999999998
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.715
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.83
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.885
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.93
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.8371556505161787
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.8116666666666668
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.8048798701298702
            name: Maxsim Map@100
      - task:
          type: py-late-information-retrieval
          name: Py Late Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: MaxSim_accuracy@1
            value: 0.7755102040816326
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.9387755102040817
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.9795918367346939
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.9795918367346939
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.7755102040816326
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.6598639455782314
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.6571428571428573
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.5183673469387755
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.05176652252904378
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.13618168510556633
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.2193408037582337
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.33397423594107617
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.5926586898856947
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.8629251700680272
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.42574993112112997
            name: Maxsim Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: MaxSim_accuracy@1
            value: 0.6642700156985872
            name: Maxsim Accuracy@1
          - type: MaxSim_accuracy@3
            value: 0.8106750392464678
            name: Maxsim Accuracy@3
          - type: MaxSim_accuracy@5
            value: 0.8584301412872841
            name: Maxsim Accuracy@5
          - type: MaxSim_accuracy@10
            value: 0.9076609105180532
            name: Maxsim Accuracy@10
          - type: MaxSim_precision@1
            value: 0.6642700156985872
            name: Maxsim Precision@1
          - type: MaxSim_precision@3
            value: 0.39434850863422294
            name: Maxsim Precision@3
          - type: MaxSim_precision@5
            value: 0.3043956043956044
            name: Maxsim Precision@5
          - type: MaxSim_precision@10
            value: 0.20587441130298273
            name: Maxsim Precision@10
          - type: MaxSim_recall@1
            value: 0.3968003368937432
            name: Maxsim Recall@1
          - type: MaxSim_recall@3
            value: 0.5514089852047589
            name: Maxsim Recall@3
          - type: MaxSim_recall@5
            value: 0.6083647167549766
            name: Maxsim Recall@5
          - type: MaxSim_recall@10
            value: 0.6836909560189698
            name: Maxsim Recall@10
          - type: MaxSim_ndcg@10
            value: 0.6757959189252093
            name: Maxsim Ndcg@10
          - type: MaxSim_mrr@10
            value: 0.7495919239490669
            name: Maxsim Mrr@10
          - type: MaxSim_map@100
            value: 0.5971136381353681
            name: Maxsim Map@100

GTE-ModernColBERT-v1

PyLate model based on Alibaba-NLP/gte-modernbert-base

This is a PyLate model trained on the ms-marco-en-bge-gemma dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

• Model Type: PyLate model
• Base model: Alibaba-NLP/gte-modernbert-base
• Document Length: 300 tokens
• Query Length: 32 tokens
• Output Dimensionality: 128 dimensions
• Similarity Function: MaxSim
• Training Dataset:
  • ms-marco-en-bge-gemma
• Language: English
• License: Apache 2.0

Document length

GTE-ModernColBERT has been trained with knowledge distillation on MS MARCO with a document length of 300 tokens, explaining its default value for documents length.

However, as illustrated in the ModernBERT paper, ColBERT models can generalize to documents lengths way beyond their training length and GTE-ModernColBERT actually yields results way above SOTA in long-context embedding benchmarks, see LongEmbed results.

Simply change adapt the document length parameter to your needs when loading the model:

model = models.ColBERT(
    model_name_or_path=lightonai/GTE-ModernColBERT-v1,
    document_length=8192,
)

ModernBERT itself has only been trained on 8K context length, but it seems that GTE-ModernColBERT can generalize to even bigger context sizes, though it is not guaranteed so please perform your own benches!

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': 299, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.

Indexing documents

First, load the ColBERT model and initialize the Voyager 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 Voyager index
index = indexes.Voyager(
    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.Voyager(
    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

BEIR Benchmark

GTE-ModernColBERT is the first model to outpeform ColBERT-small on the BEIR benchmark. As reproduction in the IR domain is challenging, we worked closely with Benjamin Clavié, the author of ColBERT-small to reproduce the evaluation setup of this model. Despite all these efforts and reducing to the maximum the difference in scores in most of the datasets, some are still a bit different. For this reason, we also report the results of ColBERT-small in the same setup we used to evaluate GTE-ModernColBERT for completness and fair comparison.

Model	Average	FiQA2018	NFCorpus	TREC-COVID	Touche2020	ArguAna	QuoraRetrieval	SCIDOCS	SciFact	NQ	ClimateFEVER	HotpotQA	DBPedia	CQADupstack	FEVER	MSMARCO
GTE-ModernColBERT	54.67	45.28	37.93	83.59	31.23	48.51	86.61	19.06	76.34	61.8	30.62	77.32	48.03	41	87.44	45.32
ColBERT-small (reported)	53.79	41.15	37.3	84.59	25.69	50.09	87.72	18.42	74.77	59.1	33.07	76.11	45.58	38.75	90.96	43.5
JinaColBERT-v2		40.8	34.6	83.4	27.4	36.6	88.7	18.6	67.8	64	23.9	76.6	47.1		80.5
ColBERT-small (rerunned)	53.35	41.01	36.86	83.14	24.95	46.76	87.89	18.72	74.02	59.42	32.83	76.88	46.36	39.36	88.66	43.44

LongEmbed Benchmark

GTE-ModernColBERT has been trained with knowledge distillation on MS MARCO with a document length of 300 tokens, explaining its default value for documents length. However, as illustrated in the ModernBERT paper, ColBERT models can generalize to documents lengths way beyond their training length and GTE-ModernColBERT actually yields results way above SOTA (almost 10 points above previous SOTA) in long-context embedding benchmark:

Model	Mean	LEMBNarrativeQARetrieval	LEMBNeedleRetrieval	LEMBPasskeyRetrieval	LEMBQMSumRetrieval	LEMBSummScreenFDRetrieval	LEMBWikimQARetrieval
GTE-ModernColBERT (with 32k document length)	88.39	78.82	92.5	92	72.17	94.98	99.87
voyage-multilingual-2	79.17	64.694	75.25	97	51.495	99.105	87.489
inf-retriever-v1	73.19	60.702	61.5	78.75	55.072	97.387	85.751
snowflake-arctic-embed-l-v2,0	63.73	43.632	50.25	77.25	40.04	96.383	74.843
gte-multilingual-base	62.12	52.358	42.25	55.5	43.033	95.499	84.078
jasper_en_vision_language_v1	60.93	37.928	55	62.25	41.186	97.206	72.025
bge-m3	58.73	45.761	40.25	59	35.543	94.089	77.726
jina-embeddings-v3	55.66	34.297	64	38	39.337	92.334	66.018
e5-base-4k	54.51	30.03	37.75	65.25	31.268	93.868	68.875
gte-Qwen2-7B-instruct	47.24	45.46	31	38.5	31.272	76.08	61.151

ModernBERT itself has only been trained on 8K context length, but it seems that GTE-ModernColBERT can generalize to even bigger context sizes, though it is not guaranteed so please perform your own benches!

PyLate Information Retrieval

• Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and 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	0.36	0.88	0.92	0.56	0.92	0.54	0.56	0.64	0.96	0.48	0.3	0.74	0.7755
MaxSim_accuracy@3	0.62	0.94	0.98	0.66	1.0	0.68	0.68	0.82	1.0	0.74	0.62	0.86	0.9388
MaxSim_accuracy@5	0.78	0.96	0.98	0.74	1.0	0.74	0.74	0.86	1.0	0.78	0.7	0.9	0.9796
MaxSim_accuracy@10	0.86	0.98	1.0	0.8	1.0	0.92	0.76	0.9	1.0	0.84	0.82	0.94	0.9796
MaxSim_precision@1	0.36	0.88	0.92	0.56	0.92	0.54	0.56	0.64	0.96	0.48	0.3	0.74	0.7755
MaxSim_precision@3	0.2333	0.7133	0.36	0.3267	0.58	0.2267	0.4333	0.2867	0.4	0.4	0.2067	0.3	0.6599
MaxSim_precision@5	0.208	0.656	0.216	0.256	0.36	0.148	0.392	0.18	0.256	0.292	0.14	0.196	0.6571
MaxSim_precision@10	0.128	0.572	0.11	0.152	0.186	0.092	0.304	0.1	0.134	0.194	0.082	0.104	0.5184
MaxSim_recall@1	0.1833	0.118	0.8567	0.3092	0.46	0.54	0.0664	0.61	0.8473	0.1007	0.3	0.715	0.0518
MaxSim_recall@3	0.289	0.2307	0.96	0.4784	0.87	0.68	0.102	0.78	0.9453	0.2467	0.62	0.83	0.1362
MaxSim_recall@5	0.4157	0.2962	0.96	0.5752	0.9	0.74	0.1284	0.82	0.9693	0.2997	0.7	0.885	0.2193
MaxSim_recall@10	0.4957	0.4146	0.98	0.6412	0.93	0.92	0.1566	0.88	0.9893	0.3967	0.82	0.93	0.334
MaxSim_ndcg@10	0.4148	0.7296	0.9452	0.567	0.9012	0.7089	0.3957	0.7645	0.9691	0.3987	0.5609	0.8372	0.5927
MaxSim_mrr@10	0.5266	0.9169	0.9522	0.6359	0.96	0.6447	0.627	0.739	0.9767	0.6137	0.4775	0.8117	0.8629
MaxSim_map@100	0.3347	0.5884	0.9271	0.5032	0.8592	0.6496	0.1918	0.7239	0.9552	0.3163	0.4824	0.8049	0.4257

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator

Metric	Value
MaxSim_accuracy@1	0.6643
MaxSim_accuracy@3	0.8107
MaxSim_accuracy@5	0.8584
MaxSim_accuracy@10	0.9077
MaxSim_precision@1	0.6643
MaxSim_precision@3	0.3943
MaxSim_precision@5	0.3044
MaxSim_precision@10	0.2059
MaxSim_recall@1	0.3968
MaxSim_recall@3	0.5514
MaxSim_recall@5	0.6084
MaxSim_recall@10	0.6837
MaxSim_ndcg@10	0.6758
MaxSim_mrr@10	0.7496
MaxSim_map@100	0.5971

Training Details

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• learning_rate: 3e-05
• bf16: 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: 3e-05
• 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
• max_steps: -1
• 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: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 6
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: True
• dataloader_num_workers: 0
• 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}
• 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: None
• 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
• 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: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: 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: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	NanoClimateFEVER_MaxSim_ndcg@10	NanoDBPedia_MaxSim_ndcg@10	NanoFEVER_MaxSim_ndcg@10	NanoFiQA2018_MaxSim_ndcg@10	NanoHotpotQA_MaxSim_ndcg@10	NanoMSMARCO_MaxSim_ndcg@10	NanoNFCorpus_MaxSim_ndcg@10	NanoNQ_MaxSim_ndcg@10	NanoQuoraRetrieval_MaxSim_ndcg@10	NanoSCIDOCS_MaxSim_ndcg@10	NanoArguAna_MaxSim_ndcg@10	NanoSciFact_MaxSim_ndcg@10	NanoTouche2020_MaxSim_ndcg@10	NanoBEIR_mean_MaxSim_ndcg@10
0.004	20	0.0493	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.008	40	0.0434	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.012	60	0.0324	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.016	80	0.0238	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.02	100	0.0202	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.024	120	0.0186	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.028	140	0.0172	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.032	160	0.0164	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.036	180	0.0157	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.04	200	0.0153	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.044	220	0.0145	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.048	240	0.014	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.052	260	0.0138	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.056	280	0.0135	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.06	300	0.0132	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.064	320	0.0129	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.068	340	0.0126	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.072	360	0.0123	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.076	380	0.0122	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.08	400	0.012	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.084	420	0.0121	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.088	440	0.0115	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.092	460	0.0113	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.096	480	0.0112	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1	500	0.0111	0.3085	0.6309	0.9206	0.5303	0.8618	0.6893	0.3703	0.7163	0.9548	0.3885	0.4682	0.7930	0.5982	0.6331
0.104	520	0.0109	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.108	540	0.0109	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.112	560	0.0109	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.116	580	0.0105	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.12	600	0.0102	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.124	620	0.0104	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.128	640	0.0103	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.132	660	0.01	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.136	680	0.0101	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.14	700	0.0098	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.144	720	0.0097	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.148	740	0.0097	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.152	760	0.0096	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.156	780	0.0096	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.16	800	0.0094	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.164	820	0.0096	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.168	840	0.0095	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.172	860	0.0093	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.176	880	0.0092	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.18	900	0.0093	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.184	920	0.009	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.188	940	0.009	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.192	960	0.0089	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.196	980	0.0089	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2	1000	0.0089	0.3148	0.6586	0.9335	0.5374	0.8810	0.6805	0.3746	0.7368	0.9486	0.3955	0.4824	0.8219	0.6089	0.6442
0.204	1020	0.0088	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.208	1040	0.0089	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.212	1060	0.0088	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.216	1080	0.0086	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.22	1100	0.0087	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.224	1120	0.0088	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.228	1140	0.0086	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.232	1160	0.0086	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.236	1180	0.0084	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.24	1200	0.0086	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.244	1220	0.0085	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.248	1240	0.0084	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.252	1260	0.0084	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.256	1280	0.0081	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.26	1300	0.0083	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.264	1320	0.0084	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.268	1340	0.0082	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.272	1360	0.0082	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.276	1380	0.008	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.28	1400	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.284	1420	0.0079	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.288	1440	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.292	1460	0.0081	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.296	1480	0.0081	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3	1500	0.0079	0.3510	0.6590	0.9285	0.5463	0.8893	0.6853	0.3800	0.7370	0.9513	0.3980	0.5268	0.8268	0.6130	0.6533
0.304	1520	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.308	1540	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.312	1560	0.0077	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.316	1580	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.32	1600	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.324	1620	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.328	1640	0.0078	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.332	1660	0.0076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.336	1680	0.0076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.34	1700	0.0077	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.344	1720	0.0076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.348	1740	0.0074	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.352	1760	0.0074	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.356	1780	0.0075	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.36	1800	0.0076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.364	1820	0.0075	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.368	1840	0.0073	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.372	1860	0.0075	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.376	1880	0.0073	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.38	1900	0.0074	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.384	1920	0.0072	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.388	1940	0.0072	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.392	1960	0.0071	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.396	1980	0.0073	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4	2000	0.0071	0.3551	0.6807	0.9311	0.5340	0.8951	0.7019	0.3767	0.7460	0.9559	0.3912	0.5121	0.8245	0.6058	0.6546
0.404	2020	0.0073	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.408	2040	0.0072	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.412	2060	0.0071	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.416	2080	0.0073	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.42	2100	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.424	2120	0.0071	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.428	2140	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.432	2160	0.0071	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.436	2180	0.0071	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.44	2200	0.007	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.444	2220	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.448	2240	0.0071	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.452	2260	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.456	2280	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.46	2300	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.464	2320	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.468	2340	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.472	2360	0.0068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.476	2380	0.0068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.48	2400	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.484	2420	0.0068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.488	2440	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.492	2460	0.0068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.496	2480	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5	2500	0.0068	0.3647	0.6883	0.9435	0.5624	0.8946	0.7065	0.3815	0.7709	0.9658	0.3993	0.5631	0.8371	0.6076	0.6681
0.504	2520	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.508	2540	0.0068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.512	2560	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.516	2580	0.0068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.52	2600	0.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.524	2620	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.528	2640	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.532	2660	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.536	2680	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.54	2700	0.0068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.544	2720	0.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.548	2740	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.552	2760	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.556	2780	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.56	2800	0.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.564	2820	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.568	2840	0.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.572	2860	0.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.576	2880	0.0065	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.58	2900	0.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.584	2920	0.0065	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.588	2940	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.592	2960	0.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.596	2980	0.0065	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6	3000	0.0064	0.3585	0.7081	0.9409	0.5474	0.8915	0.7037	0.3796	0.7763	0.9540	0.4038	0.5628	0.8424	0.6042	0.6672
0.604	3020	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.608	3040	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.612	3060	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.616	3080	0.0065	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.62	3100	0.0065	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.624	3120	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.628	3140	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.632	3160	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.636	3180	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.64	3200	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.644	3220	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.648	3240	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.652	3260	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.656	3280	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.66	3300	0.0064	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.664	3320	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.668	3340	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.672	3360	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.676	3380	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.68	3400	0.0063	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.684	3420	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.688	3440	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.692	3460	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.696	3480	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7	3500	0.0061	0.3783	0.7080	0.9441	0.5603	0.8902	0.7022	0.3824	0.7780	0.9612	0.3995	0.5414	0.8450	0.6049	0.6689
0.704	3520	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.708	3540	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.712	3560	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.716	3580	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.72	3600	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.724	3620	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.728	3640	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.732	3660	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.736	3680	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.74	3700	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.744	3720	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.748	3740	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.752	3760	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.756	3780	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.76	3800	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.764	3820	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.768	3840	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.772	3860	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.776	3880	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.78	3900	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.784	3920	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.788	3940	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.792	3960	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.796	3980	0.0061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8	4000	0.0059	0.3820	0.7028	0.9441	0.5722	0.8890	0.7135	0.3825	0.7790	0.9659	0.4012	0.5425	0.8446	0.6085	0.6714
0.804	4020	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.808	4040	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.812	4060	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.816	4080	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.82	4100	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.824	4120	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.828	4140	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.832	4160	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.836	4180	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.84	4200	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.844	4220	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.848	4240	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.852	4260	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.856	4280	0.0057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.86	4300	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.864	4320	0.006	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.868	4340	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.872	4360	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.876	4380	0.0057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.88	4400	0.0059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.884	4420	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.888	4440	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.892	4460	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.896	4480	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9	4500	0.0059	0.3703	0.7111	0.9441	0.5555	0.8886	0.7251	0.3934	0.7632	0.9671	0.4052	0.5390	0.8442	0.6068	0.6703
0.904	4520	0.0057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.908	4540	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.912	4560	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.916	4580	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.92	4600	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.924	4620	0.0057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.928	4640	0.0057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.932	4660	0.0057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.936	4680	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.94	4700	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.944	4720	0.0055	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.948	4740	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.952	4760	0.0055	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.956	4780	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.96	4800	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.964	4820	0.0057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.968	4840	0.0058	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.972	4860	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.976	4880	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.98	4900	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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0.988	4940	0.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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1.904	9520	0.0044	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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1.996	9980	0.0044	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.02	10100	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.08	10400	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.088	10440	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.096	10480	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1	10500	0.0043	0.4131	0.7248	0.9443	0.5673	0.9022	0.7101	0.3982	0.7736	0.9652	0.4000	0.5649	0.8322	0.6020	0.6768
2.104	10520	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.108	10540	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.112	10560	0.0044	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.116	10580	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.12	10600	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.18	10900	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.196	10980	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.204	11020	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.22	11100	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.228	11140	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.232	11160	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2360	11180	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.24	11200	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2440	11220	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.26	11300	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2640	11320	0.0043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.2720	11360	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.276	11380	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2800	11400	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.284	11420	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.288	11440	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.292	11460	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.296	11480	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3	11500	0.0041	0.4129	0.7232	0.9443	0.5662	0.9020	0.7100	0.3936	0.7655	0.9750	0.3956	0.5633	0.8368	0.5952	0.6757
2.304	11520	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.308	11540	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.312	11560	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.316	11580	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.32	11600	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.324	11620	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.328	11640	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.332	11660	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.336	11680	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.34	11700	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.344	11720	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.348	11740	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.356	11780	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.36	11800	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.364	11820	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.368	11840	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.376	11880	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.38	11900	0.0042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.388	11940	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.392	11960	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.396	11980	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4	12000	0.0041	0.4152	0.7204	0.9443	0.5601	0.8967	0.7104	0.3978	0.7688	0.9751	0.3918	0.5609	0.8368	0.5988	0.6752
2.404	12020	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.408	12040	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.412	12060	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.416	12080	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.42	12100	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.424	12120	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.428	12140	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.432	12160	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.436	12180	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.44	12200	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.444	12220	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.448	12240	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.452	12260	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.46	12300	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.468	12340	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.476	12380	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.48	12400	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.484	12420	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.488	12440	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.496	12480	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5	12500	0.004	0.4194	0.7222	0.9443	0.5677	0.9031	0.7103	0.3955	0.7726	0.9708	0.3966	0.5573	0.8380	0.5966	0.6765
2.504	12520	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.508	12540	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.512	12560	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.516	12580	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.52	12600	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.524	12620	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.528	12640	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.532	12660	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.536	12680	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.54	12700	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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2.548	12740	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.552	12760	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.556	12780	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.56	12800	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.564	12820	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.568	12840	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.572	12860	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.576	12880	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.58	12900	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.584	12920	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.588	12940	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.592	12960	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.596	12980	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6	13000	0.004	0.4201	0.7257	0.9443	0.5676	0.9012	0.7103	0.3984	0.7577	0.9705	0.4011	0.5609	0.8366	0.5990	0.6764
2.604	13020	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.608	13040	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.612	13060	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.616	13080	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.62	13100	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.624	13120	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.628	13140	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.632	13160	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.636	13180	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.64	13200	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.644	13220	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.648	13240	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.652	13260	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.656	13280	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.66	13300	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.664	13320	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.668	13340	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.672	13360	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.676	13380	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.68	13400	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.684	13420	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6880	13440	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.692	13460	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6960	13480	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7	13500	0.004	0.4158	0.7244	0.9452	0.5662	0.9012	0.7042	0.3966	0.7709	0.9705	0.3919	0.5640	0.8370	0.5941	0.6755
2.7040	13520	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.708	13540	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7120	13560	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.716	13580	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7200	13600	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.724	13620	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7280	13640	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.732	13660	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7360	13680	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.74	13700	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7440	13720	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.748	13740	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.752	13760	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7560	13780	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.76	13800	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7640	13820	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.768	13840	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7720	13860	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.776	13880	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7800	13900	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.784	13920	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7880	13940	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.792	13960	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7960	13980	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8	14000	0.0039	0.4230	0.7262	0.9443	0.5669	0.9028	0.7100	0.3930	0.7645	0.9750	0.3998	0.5635	0.8366	0.5975	0.6772
2.8040	14020	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.808	14040	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8120	14060	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.816	14080	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.82	14100	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.824	14120	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.828	14140	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.832	14160	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.836	14180	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.84	14200	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.844	14220	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.848	14240	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.852	14260	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.856	14280	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.86	14300	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.864	14320	0.0041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.868	14340	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.872	14360	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.876	14380	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.88	14400	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.884	14420	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.888	14440	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.892	14460	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.896	14480	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9	14500	0.004	0.4177	0.7296	0.9452	0.5663	0.9012	0.7095	0.3917	0.7645	0.9708	0.3985	0.5609	0.8369	0.5952	0.6760
2.904	14520	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.908	14540	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.912	14560	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.916	14580	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.92	14600	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.924	14620	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.928	14640	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.932	14660	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.936	14680	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.94	14700	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.944	14720	0.0038	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.948	14740	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.952	14760	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.956	14780	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.96	14800	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.964	14820	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.968	14840	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.972	14860	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.976	14880	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.98	14900	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.984	14920	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.988	14940	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.992	14960	0.0039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.996	14980	0.004	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.0	15000	0.0039	0.4148	0.7296	0.9452	0.5670	0.9012	0.7089	0.3957	0.7645	0.9691	0.3987	0.5609	0.8372	0.5927	0.6758

Framework Versions

• Python: 3.11.10
• Sentence Transformers: 3.5.0.dev0
• Transformers: 4.48.2
• PyTorch: 2.5.1+cu124
• Accelerate: 1.1.1
• Datasets: 2.21.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084"
}

PyLate

@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}

GTE-ModernColBERT

@misc{GTE-ModernColBERT,
title={GTE-ModernColBERT},
author={Chaffin, Antoine},
url={https://huggingface.co/lightonai/GTE-ModernColBERT-v1},
year={2025}
}