mental-bert-base-cased_pirina_reddit

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

• Loss: 0.1369
• Accuracy: 0.9485
• Precision Micro: 0.9485
• Precision Macro: 0.9491
• Recall Micro: 0.9485
• Recall Macro: 0.9267
• F1 Micro: 0.9485
• F1 Macro: 0.9369

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Micro	Precision Macro	Recall Micro	Recall Macro	F1 Micro	F1 Macro
No log	1.0	35	0.2602	0.9079	0.9079	0.9014	0.9079	0.8742	0.9079	0.8862
No log	2.0	70	0.1596	0.9485	0.9485	0.9395	0.9485	0.9372	0.9485	0.9383
No log	3.0	105	0.1494	0.9458	0.9458	0.9471	0.9458	0.9222	0.9458	0.9334
No log	4.0	140	0.1259	0.9566	0.9566	0.9482	0.9566	0.9482	0.9566	0.9482
No log	5.0	175	0.1322	0.9512	0.9512	0.9511	0.9512	0.9313	0.9512	0.9404
No log	6.0	210	0.1670	0.9404	0.9404	0.9431	0.9404	0.9131	0.9404	0.9264
No log	7.0	245	0.1369	0.9485	0.9485	0.9491	0.9485	0.9267	0.9485	0.9369

Framework versions

• Transformers 4.44.2
• Pytorch 2.5.0a0+872d972e41.nv24.08
• Datasets 2.21.0
• Tokenizers 0.19.1