---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- HareemFatima/stutteringdetection
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-stutteringdetection
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: stuttering
      type: HareemFatima/stutteringdetection
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9024390243902439
---

<!-- 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: 0.5717
- Accuracy: 0.9024

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8357        | 1.0   | 92   | 0.7812          | 0.8659   |
| 0.2951        | 2.0   | 184  | 0.3680          | 0.8902   |
| 0.097         | 3.0   | 276  | 0.4000          | 0.8659   |
| 0.0872        | 4.0   | 368  | 0.3953          | 0.9024   |
| 0.4557        | 5.0   | 460  | 0.4904          | 0.9024   |
| 0.0368        | 6.0   | 552  | 0.4972          | 0.9024   |
| 0.0074        | 7.0   | 644  | 0.5408          | 0.9146   |
| 0.0039        | 8.0   | 736  | 0.5460          | 0.9024   |
| 0.0036        | 9.0   | 828  | 0.5684          | 0.9024   |
| 0.0035        | 10.0  | 920  | 0.5717          | 0.9024   |


### Framework versions

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