mental-roberta_stress_classification

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

• Loss: 0.0096
• Accuracy: 0.9984
• F1: 0.9984
• Precision: 0.9984
• Recall: 0.9984

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0006	1.0	8000	0.0239	0.9966	0.9966	0.9966	0.9966
0.0002	2.0	16000	0.0096	0.9984	0.9984	0.9984	0.9984

Framework versions

• Transformers 4.38.0
• Pytorch 2.2.1+cu121
• Datasets 2.14.7
• Tokenizers 0.15.2