roberta-base-outputs

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

  • Loss: 0.5836
  • Accuracy: 0.6636
  • F1: 0.6948
  • Precision: 0.6409
  • Recall: 0.7587

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: 8
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.6984 0.1778 1000 0.6931 0.5072 0.4296 0.5167 0.3677
0.6952 0.3556 2000 0.6932 0.4956 0.0032 0.6667 0.0016
0.6931 0.5333 3000 0.6922 0.5314 0.3417 0.5874 0.2409
0.6927 0.7111 4000 0.6901 0.5272 0.6625 0.5179 0.9192
0.6883 0.8889 5000 0.6792 0.5714 0.6346 0.5570 0.7373
0.6756 1.0667 6000 0.6521 0.6114 0.5702 0.6455 0.5107
0.6476 1.2444 7000 0.6317 0.627 0.6909 0.5939 0.8257
0.6278 1.4222 8000 0.6058 0.6474 0.6799 0.6276 0.7417
0.6134 1.6 9000 0.5959 0.6564 0.6909 0.6328 0.7607
0.6119 1.7778 10000 0.5870 0.6618 0.6933 0.6393 0.7571
0.6033 1.9556 11000 0.5836 0.6636 0.6948 0.6409 0.7587

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1
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