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bert-16

This model is a fine-tuned version of deepset/bert-base-cased-squad2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 10.2625

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
11.3013 0.09 5 12.2645
11.351 0.18 10 12.1394
10.8708 0.27 15 12.0185
10.9286 0.36 20 11.9023
10.8502 0.45 25 11.7895
10.806 0.55 30 11.6807
10.0507 0.64 35 11.5771
10.6057 0.73 40 11.4770
10.1224 0.82 45 11.3818
9.9648 0.91 50 11.2900
9.7027 1.0 55 11.2029
9.8434 1.09 60 11.1193
9.8999 1.18 65 11.0406
10.0717 1.27 70 10.9654
9.5874 1.36 75 10.8939
9.9629 1.45 80 10.8264
9.6759 1.55 85 10.7628
9.6001 1.64 90 10.7023
9.4276 1.73 95 10.6462
9.5867 1.82 100 10.5940
9.1448 1.91 105 10.5456
9.4274 2.0 110 10.5012
9.3621 2.09 115 10.4610
9.1924 2.18 120 10.4240
9.318 2.27 125 10.3911
9.4253 2.36 130 10.3618
9.0476 2.45 135 10.3361
9.3817 2.55 140 10.3147
8.9517 2.64 145 10.2968
9.4026 2.73 150 10.2828
9.1891 2.82 155 10.2723
9.1735 2.91 160 10.2654
9.3527 3.0 165 10.2625

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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