roberta-base_LOGIC_LRTC

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

• Loss: 1.9419
• Accuracy: 0.6933
• Macro Precision: 0.6604
• Macro F1: 0.6503

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: 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
• num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro Precision	Macro F1
No log	1.0	116	1.5639	0.41	0.4728	0.3904
No log	2.0	232	1.1501	0.5833	0.5591	0.5659
No log	3.0	348	1.1315	0.64	0.6182	0.6220
No log	4.0	464	1.2345	0.67	0.6471	0.6237
0.9897	5.0	580	1.3952	0.62	0.5912	0.5960
0.9897	6.0	696	1.5032	0.68	0.6370	0.6407
0.9897	7.0	812	1.6859	0.6833	0.6437	0.6478
0.9897	8.0	928	1.9130	0.69	0.6638	0.6393
0.0959	9.0	1044	1.9246	0.68	0.6526	0.6459
0.0959	10.0	1160	1.8800	0.6867	0.6378	0.6392
0.0959	11.0	1276	1.9436	0.69	0.6623	0.6519
0.0959	12.0	1392	1.9419	0.6933	0.6604	0.6503

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2