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
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Model tree for freud-sensei/headline_classification
Base model
klue/bert-base