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roberta-hateful-meme

This model is a fine-tuned version of FacebookAI/roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.4643
  • Accuracy: 0.631
  • Precision: 0.6057
  • Recall: 0.6081
  • F1: 0.6066

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: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
No log 1.0 282 0.5978 0.696 0.6714 0.6698 0.6705
0.5742 2.0 564 0.6133 0.696 0.6691 0.6558 0.6599
0.5742 3.0 846 0.6326 0.694 0.6681 0.6385 0.6430
0.4682 4.0 1128 0.7503 0.662 0.6473 0.6564 0.6482
0.4682 5.0 1410 0.8875 0.687 0.6805 0.6004 0.5918
0.3907 6.0 1692 0.8072 0.651 0.6393 0.6489 0.6389
0.3907 7.0 1974 1.0383 0.639 0.6205 0.6266 0.6215
0.3445 8.0 2256 1.0678 0.643 0.6212 0.6257 0.6225
0.2996 9.0 2538 1.1221 0.649 0.6131 0.6025 0.6046
0.2996 10.0 2820 1.3531 0.646 0.6315 0.6397 0.6319
0.2749 11.0 3102 1.1258 0.655 0.6186 0.6031 0.6053
0.2749 12.0 3384 1.3594 0.641 0.6149 0.6166 0.6156
0.2635 13.0 3666 1.6264 0.637 0.5983 0.5884 0.5899
0.2635 14.0 3948 1.7278 0.636 0.6101 0.6120 0.6109
0.2382 15.0 4230 1.5670 0.635 0.6125 0.6165 0.6137
0.2387 16.0 4512 1.6334 0.63 0.6098 0.6149 0.6108
0.2387 17.0 4794 1.8990 0.632 0.5963 0.5908 0.5923
0.2214 18.0 5076 1.5244 0.644 0.6116 0.6073 0.6088
0.2214 19.0 5358 1.7948 0.639 0.6017 0.5928 0.5946
0.2138 20.0 5640 1.7595 0.638 0.6040 0.5991 0.6006
0.2138 21.0 5922 1.9552 0.633 0.6086 0.6114 0.6096
0.2038 22.0 6204 1.9481 0.636 0.6160 0.6214 0.6171
0.2038 23.0 6486 1.7951 0.648 0.6216 0.6227 0.6221
0.2003 24.0 6768 2.0499 0.636 0.6042 0.6016 0.6026
0.188 25.0 7050 2.3175 0.64 0.6147 0.6169 0.6156
0.188 26.0 7332 2.3284 0.634 0.6109 0.6145 0.6120
0.1838 27.0 7614 2.4239 0.634 0.6049 0.6046 0.6048
0.1838 28.0 7896 2.4116 0.639 0.6118 0.6127 0.6122
0.177 29.0 8178 2.4789 0.63 0.6052 0.6079 0.6062
0.177 30.0 8460 2.4643 0.631 0.6057 0.6081 0.6066

Framework versions

  • Transformers 4.30.2
  • Pytorch 1.13.1+cu117
  • Datasets 2.13.2
  • Tokenizers 0.11.0
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