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metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-donateacry
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8820224719101124

distilhubert-finetuned-donateacry

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

  • Loss: 0.4880
  • Accuracy: 0.8820

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: 8
  • eval_batch_size: 8
  • seed: 123
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 12

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9888 11 0.9569 0.7135
No log 1.9775 22 0.8898 0.6798
No log 2.9663 33 0.7790 0.7528
No log 3.9551 44 0.7517 0.8258
No log 4.9438 55 0.6255 0.8539
No log 5.9326 66 0.6212 0.8258
No log 6.9213 77 0.5533 0.8596
No log 8.0 89 0.6533 0.8427
No log 8.9888 100 0.5997 0.8539
No log 9.9775 111 0.5749 0.8764
No log 10.9663 122 0.4880 0.8820
No log 11.8652 132 0.4965 0.8820

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.3.1+cu118
  • Datasets 2.20.0
  • Tokenizers 0.19.1