deberta-v3-large-survey-fluency-rater

This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3349
  • Krippendorff: 0.4934
  • Spearman: 0.5298
  • Absolute Agreement: 0.4862
  • Agreement Within One: 0.8631

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: 16
  • 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_steps: 1000
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Krippendorff Spearman Absolute Agreement Agreement Within One
No log 1.0 50 1.9418 -0.2790 -0.0817 0.0417 0.9306
No log 2.0 100 1.9563 -0.3968 -0.3236 0.0417 0.8889
No log 3.0 150 1.9360 -0.2790 -0.0817 0.0417 0.9306
No log 4.0 200 1.9826 -0.3968 -0.3236 0.0417 0.8889
No log 5.0 250 1.9908 -0.3665 -0.3871 0.0556 0.7917
No log 6.0 300 1.8144 -0.2888 -0.1075 0.2778 0.7222
No log 7.0 350 1.9314 -0.1378 -0.0729 0.2639 0.75
No log 8.0 400 2.0468 -0.3631 -0.3911 0.1389 0.75
No log 9.0 450 2.0021 -0.1594 -0.1099 0.25 0.7639
1.536 10.0 500 2.0445 -0.2759 -0.1253 0.2639 0.7222
1.536 11.0 550 1.9957 -0.1583 -0.1012 0.2361 0.7917
1.536 12.0 600 2.0293 -0.2232 -0.1460 0.25 0.7639
1.536 13.0 650 2.0920 -0.2296 -0.2063 0.1667 0.8056
1.536 14.0 700 2.2456 -0.2580 -0.3090 0.2083 0.75
1.536 15.0 750 2.2635 -0.1791 -0.1913 0.2778 0.75
1.536 16.0 800 2.4517 -0.1779 -0.2208 0.25 0.7639
1.536 17.0 850 2.5741 -0.2354 -0.3037 0.2361 0.7361
1.536 18.0 900 2.6429 -0.1990 -0.2052 0.2361 0.7639
1.536 19.0 950 2.8684 -0.2073 -0.2513 0.1944 0.7639
0.7381 20.0 1000 2.9184 -0.2585 -0.2424 0.2639 0.7361

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1
  • Datasets 2.10.1
  • Tokenizers 0.12.1
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