README.md

metadata

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

distilhubert-tone-classification

This model is a fine-tuned version of 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