metadata

tags:
  - generated_from_trainer
datasets:
  - superb
metrics:
  - accuracy
model-index:
  - name: trillsson3-ft-keyword-spotting
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: superb
          type: superb
          config: ks
          split: train
          args: ks
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9024713150926743

trillsson3-ft-keyword-spotting

This model is a fine-tuned version of vumichien/nonsemantic-speech-trillsson3 on the superb dataset. It achieves the following results on the evaluation set:

Loss: 0.3322
Accuracy: 0.9025

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: 0.0003
train_batch_size: 32
eval_batch_size: 64
seed: 0
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1824	1.0	798	0.6478	0.7489
0.7448	2.0	1596	0.4274	0.8728
0.7089	3.0	2394	0.3723	0.8950
0.6781	4.0	3192	0.3563	0.9041
0.6386	5.0	3990	0.3441	0.8986
0.6342	6.0	4788	0.3380	0.8994
0.6275	7.0	5586	0.3376	0.8982
0.6349	8.0	6384	0.3333	0.9014
0.6261	9.0	7182	0.3295	0.9025
0.6188	10.0	7980	0.3322	0.9025

Framework versions

Transformers 4.23.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2