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gpt-imdb-jsd-beta_0.1

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

  • Step: 7000
  • Loss: 0.1422
  • Rewards/chosen: -6.6308
  • Rewards/rejected: -12.9931
  • Rewards/accuracies: 0.9396
  • Rewards/margins: 6.3623
  • Logps/rejected: -393.6160
  • Logps/chosen: -301.5730
  • Logits/rejected: -40.9101
  • Logits/chosen: -42.7380

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
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.2783 0.21 500 0.3575 -1.6510 -3.6200 0.8458 1.9690 -299.8852 -251.7749 -34.0335 -35.2131
0.3254 0.42 1000 0.2845 -2.6765 -5.5357 0.8771 2.8593 -319.0428 -262.0301 -41.3238 -42.6399
0.187 0.63 1500 0.2520 -4.2045 -7.9801 0.8875 3.7756 -343.4868 -277.3105 -36.4710 -37.8971
0.2236 0.83 2000 0.1916 -3.9591 -8.0388 0.9313 4.0797 -344.0737 -274.8567 -35.8180 -37.3586
0.1544 1.04 2500 0.1671 -4.7747 -9.4384 0.9333 4.6637 -358.0689 -283.0118 -38.2421 -39.6906
0.285 1.25 3000 0.1728 -5.7913 -11.0242 0.9271 5.2329 -373.9274 -293.1786 -39.8869 -41.8088
0.3249 1.46 3500 0.1585 -5.3924 -11.0092 0.9313 5.6168 -373.7777 -289.1895 -41.4103 -43.3052
0.2288 1.67 4000 0.1544 -5.7770 -11.2642 0.9333 5.4872 -376.3274 -293.0356 -39.3995 -41.1619
0.1367 1.88 4500 0.1463 -5.6038 -11.2632 0.9312 5.6594 -376.3172 -291.3033 -38.0074 -39.7695
0.1596 2.08 5000 0.1489 -6.3796 -12.4737 0.9312 6.0941 -388.4222 -299.0610 -39.8571 -41.5072
0.035 2.29 5500 0.1413 -6.2472 -12.4489 0.9375 6.2017 -388.1746 -297.7371 -40.1165 -41.9028
0.1528 2.5 6000 0.1452 -6.7167 -13.0974 0.9354 6.3807 -394.6590 -302.4318 -39.9707 -41.8089
0.1269 2.71 6500 0.1427 -6.6508 -13.0564 0.9458 6.4056 -394.2490 -301.7733 -40.7866 -42.6209
0.2239 2.92 7000 0.1422 -6.6308 -12.9931 0.9396 6.3623 -393.6160 -301.5730 -40.9101 -42.7380

Framework versions

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