ynat_model

This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the klue-ynat dataset. It achieves the following results on the evaluation set:

• eval_loss: 2.0522
• eval_accuracy: 0.0917
• eval_precision: 0.0131
• eval_recall: 0.1429
• eval_f1: 0.0240
• eval_runtime: 14.6861
• eval_samples_per_second: 620.111
• eval_steps_per_second: 38.812
• epoch: 1.0
• step: 2855

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
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Framework versions

• Transformers 4.54.1
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4