emotion_classifier_v2

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0845

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: 2e-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
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.1412	0.1842	500	0.1350
0.1094	0.3685	1000	0.1059
0.1018	0.5527	1500	0.0961
0.0948	0.7369	2000	0.0917
0.096	0.9211	2500	0.0889
0.0901	1.1054	3000	0.0869
0.0832	1.2896	3500	0.0863
0.0857	1.4738	4000	0.0857
0.0857	1.6581	4500	0.0849
0.081	1.8423	5000	0.0845

Framework versions

• Transformers 4.53.2
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.2