---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-donateacry
  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: 0.8932584269662921
---

<!-- 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-donateacry

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: 0.5034
- Accuracy: 0.8933

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 123
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9888  | 11   | 0.9525          | 0.7303   |
| No log        | 1.9775  | 22   | 1.2765          | 0.5393   |
| No log        | 2.9663  | 33   | 0.6634          | 0.7978   |
| No log        | 3.9551  | 44   | 0.6369          | 0.8202   |
| No log        | 4.9438  | 55   | 0.5328          | 0.8596   |
| No log        | 5.9326  | 66   | 0.5146          | 0.8652   |
| No log        | 6.9213  | 77   | 0.5200          | 0.8764   |
| No log        | 8.0     | 89   | 0.5213          | 0.8708   |
| No log        | 8.9888  | 100  | 0.6062          | 0.8596   |
| No log        | 9.9775  | 111  | 0.5938          | 0.8652   |
| No log        | 10.9663 | 122  | 0.5247          | 0.8652   |
| No log        | 11.9551 | 133  | 0.7004          | 0.8483   |
| No log        | 12.9438 | 144  | 0.5388          | 0.8876   |
| No log        | 13.9326 | 155  | 0.4856          | 0.8876   |
| No log        | 14.9213 | 166  | 0.5380          | 0.8764   |
| No log        | 16.0    | 178  | 0.5055          | 0.8876   |
| No log        | 16.9888 | 189  | 0.5217          | 0.8876   |
| No log        | 17.9775 | 200  | 0.5034          | 0.8933   |
| No log        | 18.9663 | 211  | 0.4745          | 0.8876   |
| No log        | 19.9551 | 222  | 0.4812          | 0.8876   |
| No log        | 20.9438 | 233  | 0.4709          | 0.8820   |
| No log        | 21.9326 | 244  | 0.4824          | 0.8876   |
| No log        | 22.9213 | 255  | 0.4819          | 0.8876   |
| No log        | 24.0    | 267  | 0.4877          | 0.8933   |
| No log        | 24.7191 | 275  | 0.4866          | 0.8933   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1