---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: klue_ynat_roberta_base_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: klue
      type: klue
      config: ynat
      split: validation
      args: ynat
    metrics:
    - name: F1
      type: f1
      value: 0.872014500465787
---

<!-- 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. -->

# klue_ynat_roberta_base_model

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3747
- F1: 0.8720

## Model description

Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.


## Intended uses & limitations

Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.

## Training and evaluation data

## How to use

_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)

```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")
```

## 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  | 0.4838          | 0.8444 |
| No log        | 2.0   | 358  | 0.3848          | 0.8659 |
| 0.4203        | 3.0   | 537  | 0.3778          | 0.8690 |
| 0.4203        | 4.0   | 716  | 0.3762          | 0.8702 |
| 0.4203        | 5.0   | 895  | 0.3747          | 0.8720 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3