---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- PQVD
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-grade
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: PQVD
      type: PQVD
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6703296703296703
---

<!-- 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-grade

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the PQVD dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7494
- Accuracy: 0.6703

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6335        | 1.0   | 92   | 0.5993          | 0.6593   |
| 0.4618        | 2.0   | 184  | 0.6011          | 0.6703   |
| 0.3811        | 3.0   | 276  | 0.7119          | 0.5604   |
| 0.4686        | 4.0   | 368  | 0.7788          | 0.6593   |
| 0.2984        | 5.0   | 460  | 0.8130          | 0.6044   |
| 0.2004        | 6.0   | 552  | 0.8343          | 0.6484   |
| 0.3806        | 7.0   | 644  | 0.9339          | 0.6264   |
| 0.1813        | 8.0   | 736  | 1.1104          | 0.5055   |
| 0.1335        | 9.0   | 828  | 1.1915          | 0.6813   |
| 0.2548        | 10.0  | 920  | 1.2242          | 0.6703   |
| 0.5109        | 11.0  | 1012 | 1.4604          | 0.6923   |
| 0.155         | 12.0  | 1104 | 1.5649          | 0.6593   |
| 0.1678        | 13.0  | 1196 | 1.6666          | 0.6703   |
| 0.1302        | 14.0  | 1288 | 1.7023          | 0.6813   |
| 0.0068        | 15.0  | 1380 | 1.7494          | 0.6703   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0