hubert-emotion-classifier-pt-en-v2
This model is a fine-tuned version of facebook/hubert-base-ls960 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6230
- Accuracy: 0.8521
- F1: 0.8536
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: 3e-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_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2824 | 1.0 | 254 | 0.4829 | 0.8797 | 0.8806 |
| 0.3608 | 2.0 | 508 | 0.8904 | 0.7978 | 0.8006 |
| 0.3792 | 3.0 | 762 | 0.6753 | 0.8442 | 0.8443 |
| 0.3076 | 4.0 | 1016 | 0.6230 | 0.8521 | 0.8536 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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