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distilhubert-finetuned-banglabeats

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

  • Loss: 1.4126
  • Accuracy: 0.8336

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: 6e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.9439 1.0 910 0.9274 0.6425
0.854 2.0 1820 0.7498 0.7260
0.4835 3.0 2730 0.6329 0.7706
0.6226 4.0 3640 0.6159 0.7934
0.456 5.0 4550 0.7118 0.7972
0.0565 6.0 5460 0.7994 0.8052
0.2605 7.0 6370 0.9735 0.8151
0.3635 8.0 7280 1.0618 0.8244
0.1879 9.0 8190 1.1644 0.8213
0.0292 10.0 9100 1.2543 0.8194
0.0002 11.0 10010 1.4084 0.8101
0.0006 12.0 10920 1.3823 0.8132
0.088 13.0 11830 1.4016 0.8256
0.0381 14.0 12740 1.3587 0.8225
0.0 15.0 13650 1.4242 0.8169
0.0 16.0 14560 1.4053 0.8275
0.0183 17.0 15470 1.4357 0.8318
0.0 18.0 16380 1.4123 0.8306
0.0098 19.0 17290 1.4077 0.8330
0.0 20.0 18200 1.4126 0.8336

Framework versions

  • Transformers 4.33.0
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.13.3
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Finetuned from

Dataset used to train thisisjibon/distilhubert-finetuned-banglabeats

Evaluation results