mental-roberta-large-tqacd

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

• Loss: 5.1025
• F1 Macro: 0.2135
• Precision: 0.2435
• Recall: 0.2081
• Accuracy: 0.3960

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: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	Precision	Recall	Accuracy
No log	1.0	114	2.3883	0.0709	0.0731	0.1136	0.1683
No log	2.0	228	2.2740	0.1730	0.1903	0.2120	0.2921
No log	3.0	342	2.1808	0.2186	0.2406	0.2767	0.2723
No log	4.0	456	2.1832	0.2306	0.2621	0.2427	0.3069
2.0598	5.0	570	2.4082	0.2319	0.2299	0.2796	0.3218
2.0598	6.0	684	2.6410	0.2590	0.2524	0.2830	0.3812
2.0598	7.0	798	3.2373	0.2529	0.2765	0.2467	0.3861
2.0598	8.0	912	3.5088	0.2629	0.2753	0.2573	0.3911
0.2954	9.0	1026	4.4566	0.2175	0.2695	0.2074	0.3812
0.2954	10.0	1140	5.1025	0.2135	0.2435	0.2081	0.3960

Framework versions

• Transformers 4.57.1
• Pytorch 2.8.0+cu128
• Datasets 4.4.1
• Tokenizers 0.22.1