freud-sensei/headline_classification

dummy-kosts

한국어 기사 헤드라인을 입력하면 7가지 주제 중 하나로 분류해 주는 모형입니다.

This model takes Korean article headlines as input and classifies them into one of seven topics.

This model is a fine-tuned version of klue/bert-base on klue/ynat. It achieves the following results on the evaluation set:

• Loss: 0.6433
• Accuracy: 0.8642

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4139	1.0	5710	0.4763	0.8528
0.3358	2.0	11420	0.5527	0.8650
0.2045	3.0	17130	0.6433	0.8642

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1