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bert-10

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: 9.5797

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: 2e-07
  • train_batch_size: 8
  • eval_batch_size: 8
  • 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
10.8556 0.05 5 12.3235
10.8413 0.09 10 12.2591
11.0649 0.14 15 12.1778
11.6408 0.18 20 12.0989
11.3732 0.23 25 12.0213
10.5122 0.28 30 11.9458
10.6594 0.32 35 11.8691
10.745 0.37 40 11.7928
10.8256 0.41 45 11.7163
10.1627 0.46 50 11.6430
10.9907 0.5 55 11.5703
10.1394 0.55 60 11.4997
9.6059 0.6 65 11.4287
9.4972 0.64 70 11.3621
10.2252 0.69 75 11.2949
10.4887 0.73 80 11.2288
9.9616 0.78 85 11.1638
9.5775 0.83 90 11.1003
9.5971 0.87 95 11.0381
9.5745 0.92 100 10.9773
9.3218 0.96 105 10.9178
9.4906 1.01 110 10.8597
9.1168 1.06 115 10.8030
9.8009 1.1 120 10.7465
9.3632 1.15 125 10.6915
8.9858 1.19 130 10.6399
9.2904 1.24 135 10.5874
9.5344 1.28 140 10.5370
9.0034 1.33 145 10.4871
9.3024 1.38 150 10.4384
8.7905 1.42 155 10.3920
8.9329 1.47 160 10.3465
8.9834 1.51 165 10.3027
8.7307 1.56 170 10.2607
8.6729 1.61 175 10.2200
9.1849 1.65 180 10.1794
9.1618 1.7 185 10.1400
8.9048 1.74 190 10.1023
8.9427 1.79 195 10.0655
9.1052 1.83 200 10.0294
9.1123 1.88 205 9.9938
9.0476 1.93 210 9.9604
8.5532 1.97 215 9.9285
8.7871 2.02 220 9.8977
8.5984 2.06 225 9.8690
8.7009 2.11 230 9.8414
8.9376 2.16 235 9.8146
8.3535 2.2 240 9.7906
8.5805 2.25 245 9.7675
8.4641 2.29 250 9.7463
8.3975 2.34 255 9.7263
8.7698 2.39 260 9.7070
8.3541 2.43 265 9.6901
8.5443 2.48 270 9.6743
8.1539 2.52 275 9.6595
7.9856 2.57 280 9.6459
8.2532 2.61 285 9.6333
8.2116 2.66 290 9.6221
8.9557 2.71 295 9.6119
8.0754 2.75 300 9.6032
7.9534 2.8 305 9.5956
8.5578 2.84 310 9.5899
8.6403 2.89 315 9.5848
8.1103 2.94 320 9.5817
8.3785 2.98 325 9.5797

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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