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---
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
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