athirorg/USS-reward-model-hybrid-WRS

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USS-reward-model-hybrid-WRS

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: 0.3739
• —Mse: 0.4525
• —Mae: 0.5424
• —R2: -1.3457
• —Spearman Correlation: -0.0657

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
• —trainbatchsize: 2
• —evalbatchsize: 1
• —seed: 42
• —distributed_type: multi-GPU
• —gradientaccumulationsteps: 10
• —totaltrainbatch_size: 20
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 10
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

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