multilabel-mental-health-classifier-v3-iter3

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

• Loss: 0.0013
• Precision Macro: 1.0
• Recall Macro: 0.9998
• F1 Macro: 0.9999
• Precision Micro: 1.0
• Recall Micro: 0.9997
• F1 Micro: 0.9999

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: 764
• 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Macro	Recall Macro	F1 Macro	Precision Micro	Recall Micro	F1 Micro
0.1681	1.0	201	0.0308	0.9702	0.9923	0.9809	0.9849	0.9911	0.9880
0.0523	2.0	402	0.0158	0.9916	0.9956	0.9935	0.9970	0.9946	0.9958
0.0285	3.0	603	0.0121	0.9892	0.9969	0.9929	0.9929	0.9976	0.9953
0.01	4.0	804	0.0034	0.9992	0.9998	0.9995	0.9994	0.9997	0.9996
0.0074	5.0	1005	0.0026	1.0	0.9999	0.9999	1.0	0.9997	0.9999
0.0045	6.0	1206	0.0021	1.0	0.9996	0.9998	1.0	0.9994	0.9997
0.0031	7.0	1407	0.0015	1.0	1.0	1.0	1.0	1.0	1.0
0.0022	8.0	1608	0.0013	1.0	1.0	1.0	1.0	1.0	1.0
0.0018	9.0	1809	0.0014	1.0	0.9998	0.9999	1.0	0.9997	0.9999
0.0017	10.0	2010	0.0013	1.0	0.9998	0.9999	1.0	0.9997	0.9999

Framework versions

• Transformers 4.56.2
• Pytorch 2.4.1
• Tokenizers 0.22.1