USS-reward-model-baseline

This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1701
• Mse: 0.1701
• Mae: 0.2693
• R2: 0.1181
• Spearman Correlation: 0.2833

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: 2
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 10
• total_train_batch_size: 20
• 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: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Mae	R2	Spearman Correlation
7.0444	1.0	97	0.2106	0.2106	0.3080	-0.0916	nan
2.0581	2.0	194	0.1701	0.1701	0.2693	0.1181	0.2833
1.6404	3.0	291	0.1800	0.1800	0.3289	0.0670	0.3333
1.1040	4.0	388	0.2168	0.2168	0.3728	-0.1240	0.2631
0.5726	5.0	485	0.1988	0.1988	0.3510	-0.0306	0.2295
0.3258	6.0	582	0.1803	0.1803	0.3161	0.0651	0.1804
0.1733	7.0	679	0.1891	0.1891	0.3286	0.0195	0.1746
0.1145	8.0	776	0.1812	0.1812	0.3248	0.0608	0.2181
0.0574	9.0	873	0.1823	0.1823	0.3247	0.0547	0.2339
0.0217	10.0	970	0.1814	0.1814	0.3183	0.0594	0.2032

Framework versions

• Transformers 5.9.0
• Pytorch 2.12.0+cu130
• Datasets 4.8.5
• Tokenizers 0.22.2