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model_24

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • eval_loss: 0.6165
  • eval_accuracy: 0.7775
  • eval_precision: 0.7770
  • eval_recall: 0.7775
  • eval_f1: 0.7771
  • eval_runtime: 42.58
  • eval_samples_per_second: 287.576
  • eval_steps_per_second: 17.99
  • epoch: 27.0
  • step: 82674

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 60
  • mixed_precision_training: Native AMP
  • label_smoothing_factor: 0.03

Framework versions

  • PEFT 0.11.1
  • Transformers 4.41.2
  • Pytorch 2.1.2
  • Datasets 2.19.2
  • Tokenizers 0.19.1
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