readme: ---
  license: apache-2.0
  tags:
  - audio-classification
  - generated_from_trainer
  datasets:
  - superb
  metrics:
  - accuracy
  model-index:
  - name: distilhubert-ft-keyword-spotting
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # distilhubert-ft-keyword-spotting
  
  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the superb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1163
  - Accuracy: 0.9706
  
  ## 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: 3e-05
  - train_batch_size: 256
  - eval_batch_size: 32
  - seed: 0
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.8176        | 1.0   | 200  | 0.7718          | 0.8116   |
  | 0.2364        | 2.0   | 400  | 0.2107          | 0.9662   |
  | 0.1198        | 3.0   | 600  | 0.1374          | 0.9678   |
  | 0.0891        | 4.0   | 800  | 0.1163          | 0.9706   |
  | 0.085         | 5.0   | 1000 | 0.1180          | 0.9690   |
  
  
  ### Framework versions
  
  - Transformers 4.12.0.dev0
  - Pytorch 1.9.1+cu111
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  