nguyennghia0902/deberta-auto-grading

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

deberta-auto-grading

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

• —Loss: 0.4526
• —Accuracy: 0.8205
• —F1 Macro: 0.8005
• —F1 Incorrect: 0.8267
• —F1 Partial: 0.6995
• —F1 Correct: 0.8753

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: 32
• —evalbatchsize: 64
• —seed: 42
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 64
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 0.1
• —num_epochs: 5

Training results

Framework versions

• —Transformers 5.0.0
• —Pytorch 2.10.0+cu128
• —Datasets 4.8.3
• —Tokenizers 0.22.2