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reward-model-out

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

  • eval_loss: 0.6737
  • eval_accuracy: 0.6041
  • eval_precision: 0.6041
  • eval_recall: 1.0
  • eval_f1: 0.7532
  • eval_runtime: 23.9877
  • eval_samples_per_second: 32.85
  • eval_steps_per_second: 5.503
  • epoch: 0.35
  • step: 4500

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: 0.0001
  • train_batch_size: 6
  • eval_batch_size: 6
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.14.1
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