---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- Nooon/Donate_a_cry
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-Donate_a_cry
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: Donate_a_cry
      type: Nooon/Donate_a_cry
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9347826086956522
---

<!-- 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-Donate_a_cry

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the Donate_a_cry dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3214
- Accuracy: 0.9348

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7814        | 1.0   | 52   | 0.3964          | 0.9348   |
| 0.7018        | 2.0   | 104  | 0.3576          | 0.9348   |
| 0.6262        | 3.0   | 156  | 0.3551          | 0.9348   |
| 0.4743        | 4.0   | 208  | 0.3489          | 0.9348   |
| 0.6709        | 5.0   | 260  | 0.3983          | 0.9348   |
| 0.5627        | 6.0   | 312  | 0.3430          | 0.9348   |
| 0.6891        | 7.0   | 364  | 0.3549          | 0.9348   |
| 0.7408        | 8.0   | 416  | 0.3505          | 0.9348   |
| 0.4493        | 9.0   | 468  | 0.3161          | 0.9348   |
| 0.2918        | 10.0  | 520  | 0.3214          | 0.9348   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2