---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- audio-classification
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: demo_LID_ntu-spml_distilhubert
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: common_language
      type: common_language
      config: full
      split: validation
      args: full
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6554008152173914
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# demo_LID_ntu-spml_distilhubert

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2545
- Accuracy: 0.6554

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 1
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 9.6557        | 0.9989 | 693  | 2.6549          | 0.2614   |
| 6.1707        | 1.9989 | 1386 | 1.8478          | 0.4681   |
| 3.7871        | 2.9989 | 2079 | 1.6941          | 0.5474   |
| 2.7966        | 3.9989 | 2772 | 1.8580          | 0.5579   |
| 1.5871        | 4.9989 | 3465 | 1.6663          | 0.6140   |
| 0.7355        | 5.9989 | 4158 | 1.9491          | 0.6155   |
| 0.4492        | 6.9989 | 4851 | 2.0594          | 0.6379   |
| 0.1528        | 7.9989 | 5544 | 2.1739          | 0.6403   |
| 0.0468        | 8.9989 | 6237 | 2.3125          | 0.6505   |
| 0.0045        | 9.9989 | 6930 | 2.2545          | 0.6554   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0