distilhubert-finetuned-grade

This model is a fine-tuned version of ntu-spml/distilhubert on the PQVD dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7494
  • Accuracy: 0.6703

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6335 1.0 92 0.5993 0.6593
0.4618 2.0 184 0.6011 0.6703
0.3811 3.0 276 0.7119 0.5604
0.4686 4.0 368 0.7788 0.6593
0.2984 5.0 460 0.8130 0.6044
0.2004 6.0 552 0.8343 0.6484
0.3806 7.0 644 0.9339 0.6264
0.1813 8.0 736 1.1104 0.5055
0.1335 9.0 828 1.1915 0.6813
0.2548 10.0 920 1.2242 0.6703
0.5109 11.0 1012 1.4604 0.6923
0.155 12.0 1104 1.5649 0.6593
0.1678 13.0 1196 1.6666 0.6703
0.1302 14.0 1288 1.7023 0.6813
0.0068 15.0 1380 1.7494 0.6703

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.3.2
  • Tokenizers 0.21.0
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Evaluation results