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deberta-v3-large-survey-main_passage_consistency-rater-half-gpt4

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: 0.3709
  • Krippendorff: 0.7891
  • Spearman: 0.8055
  • Absolute Agreement: 0.9002
  • Agreement Within One: 0.9519

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 52 1.9290 -0.7901 nan 0.0556 1.0
No log 2.0 104 1.9260 -0.7901 nan 0.0556 1.0
No log 3.0 156 2.2136 -0.3366 nan 0.1667 0.8056
No log 4.0 208 3.0444 -0.3366 nan 0.1667 0.8056
No log 5.0 260 3.3406 -0.3366 nan 0.1667 0.8056
No log 6.0 312 3.6074 -0.3366 nan 0.1667 0.8056
No log 7.0 364 3.6395 -0.3366 nan 0.1667 0.8056
No log 8.0 416 3.5991 0.2720 0.1638 0.1389 0.8611
No log 9.0 468 3.7407 0.2147 0.1298 0.1389 0.8611
0.9989 10.0 520 3.8742 0.2347 0.2208 0.1389 0.8889
0.9989 11.0 572 3.7369 0.3144 0.3601 0.1667 0.8889
0.9989 12.0 624 3.9522 0.2347 0.2208 0.1389 0.8889
0.9989 13.0 676 3.4874 0.2598 0.3174 0.1944 0.8889
0.9989 14.0 728 4.1072 0.2420 0.1444 0.1389 0.8611
0.9989 15.0 780 4.6448 0.3090 0.3144 0.1389 0.9167
0.9989 16.0 832 4.2375 0.3130 0.2379 0.1389 0.9167
0.9989 17.0 884 4.4929 0.1724 0.1068 0.1111 0.9167
0.9989 18.0 936 4.2319 0.2867 0.1237 0.1389 0.8889
0.9989 19.0 988 4.3529 0.2572 0.0514 0.1111 0.8889
0.3366 20.0 1040 4.5277 0.2274 0.0428 0.1111 0.8611

Framework versions

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