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deberta-v3-large-survey-fluency-rater-all

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: 0.7178
  • Krippendorff: 0.9045
  • Spearman: 0.8801
  • Absolute Agreement: 0.7396
  • Agreement Within One: 0.9608

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 55 2.0214 -0.6902 0.2469 0.0139 1.0
No log 2.0 110 2.0118 -0.6398 0.1978 0.0278 1.0
No log 3.0 165 2.0147 -0.6585 -0.0351 0.0278 0.9583
No log 4.0 220 2.0186 -0.6499 -0.1548 0.0139 0.9306
No log 5.0 275 1.7283 -0.2880 nan 0.2917 0.7222
No log 6.0 330 1.7079 -0.2454 0.1527 0.3056 0.7222
No log 7.0 385 1.6519 -0.0919 -0.0671 0.3889 0.7639
No log 8.0 440 1.6403 0.1140 -0.0254 0.4028 0.7778
No log 9.0 495 1.6711 -0.1939 -0.0213 0.3333 0.75
1.5874 10.0 550 1.6714 -0.0236 0.1527 0.3056 0.7361
1.5874 11.0 605 1.5761 -0.0843 -0.1552 0.4028 0.7639
1.5874 12.0 660 1.4782 0.2760 0.1495 0.4444 0.7917
1.5874 13.0 715 1.4679 0.1322 -0.0231 0.4028 0.7778
1.5874 14.0 770 1.4314 0.1322 -0.0231 0.4028 0.7778
1.5874 15.0 825 1.3915 0.3094 0.2541 0.4583 0.7917
1.5874 16.0 880 1.3383 0.3143 0.3726 0.4583 0.7917
1.5874 17.0 935 1.2967 0.1861 0.3025 0.4583 0.7778
1.5874 18.0 990 1.3368 0.1848 0.4152 0.4722 0.8056
0.9758 19.0 1045 1.2081 0.2376 0.4096 0.4861 0.8056
0.9758 20.0 1100 1.1952 0.2625 0.4872 0.5 0.8056

Framework versions

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