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
- Downloads last month
- 18
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for freud-sensei/headline_classification
Base model
klue/bert-base