---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- EdwardLin2023/AESDD
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-AESDD
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: aesdd
      type: aesdd
      config: AESDD
      split: train
      args: AESDD
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9016393442622951
---

<!-- 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-AESDD

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the aesdd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4389
- Accuracy: 0.9016

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1249        | 1.0   | 68   | 1.1905          | 0.5082   |
| 0.7441        | 2.0   | 136  | 0.8850          | 0.6721   |
| 0.5941        | 3.0   | 204  | 0.6579          | 0.8361   |
| 0.4349        | 4.0   | 272  | 0.9638          | 0.6721   |
| 0.2612        | 5.0   | 340  | 0.5081          | 0.8689   |
| 0.1883        | 6.0   | 408  | 0.6223          | 0.8197   |
| 0.0978        | 7.0   | 476  | 0.4671          | 0.8689   |
| 0.0425        | 8.0   | 544  | 0.4338          | 0.8852   |
| 0.0264        | 9.0   | 612  | 0.4488          | 0.8525   |
| 0.0219        | 10.0  | 680  | 0.4389          | 0.9016   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3