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bertweet-base-cased-covid19-hateval

This model is a fine-tuned version of vinai/bertweet-covid19-base-cased on the HatEval dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4817
  • Accuracy: 0.773
  • F1: 0.7722

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: 1e-06
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Accuracy F1 Validation Loss
0.6925 0.99 70 0.573 0.3643 0.6827
0.6823 1.99 140 0.573 0.3643 0.6736
0.6713 2.99 210 0.587 0.3993 0.6568
0.6468 3.99 280 0.7 0.6708 0.6210
0.6047 4.99 350 0.732 0.7286 0.5785
0.5648 5.99 420 0.733 0.7319 0.5537
0.536 6.99 490 0.739 0.7381 0.5406
0.5175 7.99 560 0.744 0.7431 0.5308
0.5018 8.99 630 0.751 0.7504 0.5235
0.4874 9.99 700 0.749 0.7479 0.5145
0.4749 10.99 770 0.754 0.7533 0.5104
0.4666 11.99 840 0.761 0.7605 0.5052
0.456 12.99 910 0.761 0.7604 0.5017
0.4489 13.99 980 0.764 0.7635 0.4986
0.4375 14.99 1050 0.764 0.7625 0.4932
0.4319 15.99 1120 0.762 0.7608 0.4917
0.427 16.99 1190 0.77 0.7693 0.4918
0.4226 17.99 1260 0.772 0.7711 0.4889
0.4167 18.99 1330 0.769 0.7681 0.4874
0.4127 19.99 1400 0.768 0.7673 0.4868
0.4095 20.99 1470 0.774 0.7731 0.4836
0.4066 21.99 1540 0.77 0.7690 0.4829
0.405 22.99 1610 0.773 0.7721 0.4822
0.3993 23.99 1680 0.77 0.7692 0.4827
0.3977 24.99 1750 0.4831 0.772 0.7712
0.398 25.99 1820 0.4830 0.774 0.7733
0.3969 26.99 1890 0.4815 0.771 0.7701
0.3945 27.99 1960 0.4818 0.772 0.7712
0.3929 28.99 2030 0.4818 0.773 0.7722
0.3887 29.99 2100 0.4817 0.773 0.7722

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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