README.md

---
license: cc-by-sa-4.0
base_model: klue/bert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-finetuned-ynat
  results: []
language:
- ko
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-finetuned-ynat

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3745
- F1: 0.8704

## Model description

뉴스 제목을 입력하면 뉴스의 카테고리를 예측  
label_map = {  
    'LABEL_0': 'IT/과학',  
    'LABEL_1': '경제',  
    'LABEL_2': '사회',  
    'LABEL_3': '생활문화',  
    'LABEL_4': '세계',  
    'LABEL_5': '스포츠',  
    'LABEL_6': '정치'  
}

## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 179  | 0.3909          | 0.8655 |
| No log        | 2.0   | 358  | 0.3788          | 0.8684 |
| 0.3774        | 3.0   | 537  | 0.3629          | 0.8699 |
| 0.3774        | 4.0   | 716  | 0.3776          | 0.8667 |
| 0.3774        | 5.0   | 895  | 0.3745          | 0.8704 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0