ilyaschaki-numrah/results_soft_label

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resultssoftlabel

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

• —Loss: 0.2341
• —Mae: 0.0495
• —Rmse: 0.1321
• —Pearson Correlation: 0.9561
• —Auc Roc: 0.9866
• —Average Precision: 0.9803
• —F1 At 0.5: 0.8754
• —Precision At 0.5: 0.7871
• —Recall At 0.5: 0.9862

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
• —trainbatchsize: 16
• —evalbatchsize: 64
• —seed: 42
• —optimizer: Use adamwtorchfused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 500
• —num_epochs: 3
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.57.1
• —Pytorch 2.9.0+cu128
• —Datasets 4.3.0
• —Tokenizers 0.22.1