run-2

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1449
  • Accuracy: 0.75
  • Precision: 0.7115
  • Recall: 0.7093
  • F1: 0.7103

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.9838 1.0 50 0.8621 0.645 0.6536 0.6130 0.6124
0.7134 2.0 100 0.8124 0.7 0.6628 0.6421 0.6483
0.4911 3.0 150 0.8571 0.7 0.6726 0.6314 0.6361
0.3104 4.0 200 0.8228 0.76 0.7298 0.7367 0.7294
0.1942 5.0 250 1.1132 0.76 0.7282 0.7031 0.7119
0.1409 6.0 300 1.2218 0.685 0.6516 0.6560 0.6524
0.0976 7.0 350 1.3648 0.715 0.6984 0.7044 0.6946
0.0791 8.0 400 1.5985 0.745 0.7183 0.7113 0.7124
0.0647 9.0 450 1.8884 0.725 0.6818 0.6761 0.6785
0.0275 10.0 500 1.8639 0.725 0.6979 0.7008 0.6958
0.0329 11.0 550 1.8831 0.72 0.6816 0.6869 0.6838
0.0169 12.0 600 2.1426 0.73 0.6864 0.6776 0.6794
0.0072 13.0 650 2.2483 0.725 0.7187 0.7054 0.6968
0.0203 14.0 700 2.2901 0.735 0.6986 0.6885 0.6921
0.0093 15.0 750 2.3134 0.725 0.6830 0.6666 0.6723
0.0089 16.0 800 2.1598 0.73 0.6919 0.6860 0.6885
0.0061 17.0 850 2.0879 0.75 0.7129 0.7132 0.7125
0.0024 18.0 900 2.1285 0.745 0.7062 0.7071 0.7049
0.0043 19.0 950 2.1386 0.74 0.7001 0.7003 0.6985
0.0028 20.0 1000 2.1449 0.75 0.7115 0.7093 0.7103

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.1+cu116
  • Tokenizers 0.13.2
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