---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-RHD_Dataset
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8048780487804879
---

<!-- 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-RHD_Dataset

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9447
- Accuracy: 0.8049

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0412        | 1.0   | 46   | 1.0084          | 0.6829   |
| 0.8547        | 2.0   | 92   | 0.8433          | 0.6585   |
| 0.7936        | 3.0   | 138  | 0.7128          | 0.7073   |
| 0.5984        | 4.0   | 184  | 0.7778          | 0.7317   |
| 0.3888        | 5.0   | 230  | 0.6361          | 0.7317   |
| 0.4947        | 6.0   | 276  | 0.7471          | 0.7805   |
| 0.1663        | 7.0   | 322  | 0.8244          | 0.7561   |
| 0.1379        | 8.0   | 368  | 0.7986          | 0.8049   |
| 0.0405        | 9.0   | 414  | 0.8892          | 0.8049   |
| 0.0229        | 10.0  | 460  | 0.9447          | 0.8049   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0