mental-bert-base-uncased_pirina_reddit

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

• Loss: 0.1471
• Accuracy: 0.9539
• Precision Micro: 0.9539
• Precision Macro: 0.9460
• Recall Micro: 0.9539
• Recall Macro: 0.9436
• F1 Micro: 0.9539
• F1 Macro: 0.9448

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	0.2240	0.9214	0.9214	0.9107	0.9214	0.8996	0.9214	0.9048
No log	2.0	70	0.1414	0.9485	0.9485	0.9340	0.9485	0.9450	0.9485	0.9393
No log	3.0	105	0.1320	0.9512	0.9512	0.9461	0.9512	0.9365	0.9512	0.9411
No log	4.0	140	0.1420	0.9512	0.9512	0.9364	0.9512	0.9496	0.9512	0.9426
No log	5.0	175	0.1427	0.9485	0.9485	0.9439	0.9485	0.9319	0.9485	0.9376
No log	6.0	210	0.1462	0.9566	0.9566	0.9504	0.9566	0.9456	0.9566	0.9479
No log	7.0	245	0.1471	0.9539	0.9539	0.9460	0.9539	0.9436	0.9539	0.9448

Framework versions

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