multilabel_mood_distilbert

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0852
• F1: 0.5646
• Precision: 0.7199
• Recall: 0.4644

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: 32
• eval_batch_size: 32
• 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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall
0.138	1.0	1357	0.0932	0.5104	0.7194	0.3955
0.0888	2.0	2714	0.0860	0.5498	0.7359	0.4389
0.0807	3.0	4071	0.0852	0.5646	0.7199	0.4644

Framework versions

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