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out_2

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6774
  • F1: 0.7444

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: 6e-06
  • train_batch_size: 3
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 48
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 5.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Accuracy Validation Loss
0.6448 0.21 500 0.6347 0.6498
0.6401 0.41 1000 0.6442 0.6312
0.6557 0.62 1500 0.6582 0.6314
0.5819 0.83 2000 0.6588 0.6320
0.6086 1.04 2500 0.6563 0.6343
0.6011 1.24 3000 0.6557 0.6165
0.5616 1.45 3500 0.6461 0.6376
0.5885 1.66 4000 0.6468 0.6304
0.6198 1.87 4500 0.6423 0.6448
0.5838 2.07 5000 0.6665 0.6320
0.5564 2.28 5500 0.6684 0.6428
0.5726 2.49 6000 0.6703 0.6401
0.5491 2.7 6500 0.6684 0.6455
0.5303 2.9 7000 0.6703 0.6339
0.497 3.11 7500 0.6607 0.6541
0.5041 3.32 8000 0.6760 0.6653
0.4978 3.53 8500 0.6696 0.6627
0.5272 3.73 9000 0.6677 0.6684
0.5487 3.94 9500 0.6760 0.6593
0.4998 4.15 10000 0.6747 0.6738
0.4626 4.36 10500 0.6753 0.6781
0.5202 4.56 11000 0.6722 0.6763
0.4623 4.77 11500 0.6728 0.6778
0.4383 4.98 12000 0.6741 0.6775

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.14.1
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