distilhubert-finetuned-babycry-v7

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

  • Loss: 0.5864
  • Accuracy: {'accuracy': 0.8695652173913043}
  • F1: 0.8089
  • Precision: 0.7561
  • Recall: 0.8696

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: 0.001
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 8

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.7417 0.5435 25 0.5925 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.7226 1.0870 50 0.6167 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.5606 1.6304 75 0.6808 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.8858 2.1739 100 0.5850 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.6573 2.7174 125 0.5968 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.7942 3.2609 150 0.6142 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.7497 3.8043 175 0.5915 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.7408 4.3478 200 0.5899 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.6499 4.8913 225 0.5989 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.6725 5.4348 250 0.5865 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.6797 5.9783 275 0.5852 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.6553 6.5217 300 0.5861 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.6535 7.0652 325 0.5863 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696
0.7297 7.6087 350 0.5865 {'accuracy': 0.8695652173913043} 0.8089 0.7561 0.8696

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Tokenizers 0.19.1
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