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metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - arisha/stuttering
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-stutteringdetection
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: stuttering
          type: arisha/stuttering
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7692307692307693

distilhubert-finetuned-stutteringdetection

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

  • Loss: 0.8952
  • Accuracy: 0.7692

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.1755 1.0 102 1.1561 0.5275
0.9759 2.0 204 0.9051 0.6703
0.5208 3.0 306 0.7956 0.7143
0.3765 4.0 408 0.7282 0.8022
0.2368 5.0 510 0.6921 0.8022
0.1761 6.0 612 0.8270 0.7582
0.3561 7.0 714 0.8967 0.7253
0.2222 8.0 816 0.8201 0.8022
0.0303 9.0 918 0.9433 0.7473
0.019 10.0 1020 0.8952 0.7692

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1