README.md

metadata

metrics:
  - f1
  - accuracy
model-index:
  - name: aha_class
    results: []

aha_class

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

• Loss: 0.0885
• F1: 0.9580
• Roc Auc: 0.9679
• Accuracy: 0.9391

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
No log	1.0	42	0.1723	0.95	0.9635	0.9217
No log	2.0	84	0.1160	0.9576	0.9659	0.9391
No log	3.0	126	0.1064	0.9492	0.9595	0.9304
No log	4.0	168	0.0974	0.9540	0.9657	0.9304
No log	5.0	210	0.0968	0.9580	0.9679	0.9304
No log	6.0	252	0.0885	0.9580	0.9679	0.9391
No log	7.0	294	0.1005	0.9580	0.9679	0.9391
No log	8.0	336	0.0921	0.9664	0.9743	0.9478
No log	9.0	378	0.1055	0.9580	0.9679	0.9391
No log	10.0	420	0.0988	0.9664	0.9743	0.9478
No log	11.0	462	0.0993	0.9664	0.9743	0.9478

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.0+cu121
• Datasets 2.18.0
• Tokenizers 0.19.1