---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- Emo-Codec/CREMA-D_synth
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilhubert-tone-classification
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: CREMA-D
      type: Emo-Codec/CREMA-D_synth
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7024128686327078
    - name: Precision
      type: precision
      value: 0.7036509389001218
    - name: Recall
      type: recall
      value: 0.7024128686327078
    - name: F1
      type: f1
      value: 0.6970142752522046
---

<!-- 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-tone-classification

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the CREMA-D dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1479
- Accuracy: 0.7024
- Precision: 0.7037
- Recall: 0.7024
- F1: 0.6970

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.339         | 1.0   | 442  | 1.3491          | 0.4987   | 0.5533    | 0.4987 | 0.4664 |
| 1.0008        | 2.0   | 884  | 1.0219          | 0.6408   | 0.6668    | 0.6408 | 0.6373 |
| 0.7673        | 3.0   | 1326 | 0.9572          | 0.6676   | 0.6870    | 0.6676 | 0.6557 |
| 0.5888        | 4.0   | 1768 | 0.8830          | 0.6890   | 0.6930    | 0.6890 | 0.6889 |
| 0.4396        | 5.0   | 2210 | 1.0893          | 0.6810   | 0.7064    | 0.6810 | 0.6738 |
| 0.2987        | 6.0   | 2652 | 1.0561          | 0.6810   | 0.6892    | 0.6810 | 0.6738 |
| 0.2009        | 7.0   | 3094 | 1.1421          | 0.6836   | 0.6944    | 0.6836 | 0.6769 |
| 0.1345        | 8.0   | 3536 | 1.1479          | 0.7024   | 0.7037    | 0.7024 | 0.6970 |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1