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deberta-v3-large-survey-cross_passage_consistency-rater-all-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: 0.4020
  • Krippendorff: 0.8066
  • Spearman: 0.8930
  • Absolute Agreement: 0.8848
  • Agreement Within One: 0.9447

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 1.8997 -0.2987 0.2073 0.0556 0.9028
No log 2.0 110 1.8795 -0.3017 -0.0377 0.0833 0.9028
No log 3.0 165 1.8738 -0.0086 0.0516 0.2778 0.7778
No log 4.0 220 1.8663 -0.0943 -0.0377 0.3472 0.7361
No log 5.0 275 1.8618 -0.0943 -0.0377 0.3472 0.7361
No log 6.0 330 2.0154 -0.2704 nan 0.3194 0.6667
No log 7.0 385 1.9047 -0.2704 nan 0.3194 0.6667
No log 8.0 440 1.7587 -0.2704 nan 0.3194 0.6667
No log 9.0 495 1.6830 -0.2704 nan 0.3194 0.6667
1.409 10.0 550 1.6436 -0.2704 nan 0.3194 0.6667
1.409 11.0 605 1.6273 -0.2704 nan 0.3194 0.6667
1.409 12.0 660 1.5649 -0.2505 0.1100 0.3333 0.6667
1.409 13.0 715 1.5027 -0.0705 0.4448 0.4167 0.6806
1.409 14.0 770 1.5082 0.0375 0.4258 0.4028 0.6944
1.409 15.0 825 1.3945 0.0492 0.5924 0.4861 0.7083
1.409 16.0 880 1.3575 0.0458 0.5905 0.4861 0.6806
1.409 17.0 935 1.3444 0.0492 0.5924 0.4861 0.7083
1.409 18.0 990 1.2574 0.1791 0.5461 0.5278 0.7778
0.7706 19.0 1045 1.2676 0.0357 0.5486 0.5139 0.7361
0.7706 20.0 1100 1.2345 0.0376 0.5548 0.5278 0.7222

Framework versions

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