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---

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: 0.898876404494382
---


<!-- 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: 0.4400
- Accuracy: 0.8989

## 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: 7



### Training results



| Training Loss | Epoch  | Step | Validation Loss | Accuracy |

|:-------------:|:------:|:----:|:---------------:|:--------:|

| No log        | 0.9888 | 11   | 0.9209          | 0.6910   |

| No log        | 1.9775 | 22   | 0.7430          | 0.7809   |

| No log        | 2.9663 | 33   | 0.7456          | 0.7978   |

| No log        | 3.9551 | 44   | 0.6411          | 0.7416   |

| No log        | 4.9438 | 55   | 0.4903          | 0.8652   |

| No log        | 5.9326 | 66   | 0.4881          | 0.8427   |

| No log        | 6.9213 | 77   | 0.4400          | 0.8989   |





### Framework versions



- Transformers 4.42.3

- Pytorch 2.3.1+cu118

- Datasets 2.20.0

- Tokenizers 0.19.1