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roberta-large-finetuned-TRAC-DS

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

  • Loss: 2.8198
  • Accuracy: 0.7190
  • Precision: 0.6955
  • Recall: 0.6979
  • F1: 0.6963

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-05
  • train_batch_size: 16
  • eval_batch_size: 32
  • seed: 43
  • 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
0.9538 1.0 612 0.8083 0.6111 0.6192 0.6164 0.5994
0.7924 2.0 1224 0.7594 0.6601 0.6688 0.6751 0.6424
0.6844 3.0 1836 0.6986 0.7042 0.6860 0.6969 0.6858
0.5715 3.99 2448 0.7216 0.7075 0.6957 0.6978 0.6925
0.45 4.99 3060 0.7963 0.7288 0.7126 0.7074 0.7073
0.352 5.99 3672 1.0824 0.7141 0.6999 0.6774 0.6818
0.2546 6.99 4284 1.0884 0.7230 0.7006 0.7083 0.7028
0.1975 7.99 4896 1.5338 0.7337 0.7090 0.7063 0.7074
0.1656 8.99 5508 1.8182 0.7100 0.6882 0.6989 0.6896
0.1358 9.98 6120 2.1623 0.7173 0.6917 0.6959 0.6934
0.1235 10.98 6732 2.3249 0.7141 0.6881 0.6914 0.6888
0.1003 11.98 7344 2.3474 0.7124 0.6866 0.6920 0.6887
0.0826 12.98 7956 2.3574 0.7083 0.6853 0.6959 0.6874
0.0727 13.98 8568 2.4989 0.7116 0.6858 0.6934 0.6883
0.0553 14.98 9180 2.8090 0.7026 0.6747 0.6710 0.6725
0.0433 15.97 9792 2.6647 0.7255 0.7010 0.7028 0.7018
0.0449 16.97 10404 2.6568 0.7247 0.7053 0.6997 0.7010
0.0373 17.97 11016 2.7632 0.7149 0.6888 0.6938 0.6909
0.0278 18.97 11628 2.8245 0.7124 0.6866 0.6930 0.6889
0.0288 19.97 12240 2.8198 0.7190 0.6955 0.6979 0.6963

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.10.1+cu111
  • Datasets 2.3.2
  • Tokenizers 0.12.1
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