README.md

---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- accuracy
model-index:
- name: klue_nli_roberta_base_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: klue
      type: klue
      config: nli
      split: validation
      args: nli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8653333333333333
---

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# klue_nli_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: 0.6867
- Accuracy: 0.8653

## Model description

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


## Intended uses & limitations

## How to use
*NOTE*: Use BertTokenizer instead of RobertaTokenizer. (AutoTokenizer will load BertTokenizer)


from transformers import AutoModel, AutoTokenizer

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

## Training and evaluation data


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5988        | 1.0   | 782  | 0.4378          | 0.8363   |
| 0.2753        | 2.0   | 1564 | 0.4169          | 0.851    |
| 0.1735        | 3.0   | 2346 | 0.5267          | 0.8607   |
| 0.0956        | 4.0   | 3128 | 0.6275          | 0.8683   |
| 0.0708        | 5.0   | 3910 | 0.6867          | 0.8653   |


### Framework versions

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