---
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
---

<!-- 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-finetuned-stutteringdetection

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/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