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gpt-imdb-dpo_annealing

This model is a fine-tuned version of lvwerra/gpt2-imdb on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3482
  • Rewards/chosen: -13.2925
  • Rewards/rejected: -37.2767
  • Rewards/accuracies: 0.9354
  • Rewards/margins: 23.9842
  • Logps/rejected: -302.0002
  • Logps/chosen: -248.9281
  • Logits/rejected: -38.9773
  • Logits/chosen: -40.1868

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: 1e-05
  • train_batch_size: 24
  • eval_batch_size: 24
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 150
  • training_steps: 7197

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.2713 0.21 500 0.3576 -0.9589 -2.8806 0.8417 1.9217 -300.2507 -247.4370 -34.9635 -36.2514
0.2605 0.42 1000 0.2876 -1.8668 -5.2245 0.8708 3.3577 -299.0920 -247.9165 -39.8673 -41.1403
0.134 0.63 1500 0.2827 -3.3220 -8.2599 0.8833 4.9379 -301.8662 -250.6212 -38.4289 -39.6488
0.2246 0.83 2000 0.2412 -3.0672 -9.5366 0.9000 6.4694 -297.1335 -246.0230 -36.9979 -38.2478
0.0612 1.04 2500 0.2382 -4.4276 -12.4767 0.9062 8.0491 -298.9408 -247.7763 -38.3549 -39.5684
0.2336 1.25 3000 0.2628 -5.5352 -15.3372 0.9042 9.8020 -299.9716 -248.3611 -39.0799 -40.3999
0.1755 1.46 3500 0.2670 -6.0750 -18.0326 0.9229 11.9576 -300.3778 -247.6266 -38.3635 -39.7127
0.34 1.67 4000 0.2499 -7.2657 -20.1377 0.9208 12.8719 -299.6307 -248.2345 -38.0993 -39.2549
0.1822 1.88 4500 0.3000 -7.9584 -22.7421 0.9271 14.7838 -299.8409 -247.9176 -38.7806 -39.9153
0.153 2.08 5000 0.2972 -9.4217 -26.8046 0.9333 17.3829 -302.0991 -248.7675 -38.2977 -39.5006
0.0004 2.29 5500 0.2962 -9.6704 -28.5833 0.9354 18.9129 -300.9727 -247.8805 -38.6801 -39.9033
0.0584 2.5 6000 0.3113 -11.3462 -31.8850 0.9375 20.5388 -301.8552 -248.8479 -38.5484 -39.7563
0.0304 2.71 6500 0.3441 -12.4687 -34.7986 0.9354 22.3299 -302.1741 -249.0562 -38.8388 -40.0519
0.223 2.92 7000 0.3482 -13.2925 -37.2767 0.9354 23.9842 -302.0002 -248.9281 -38.9773 -40.1868

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.1
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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