---
language:
- ko
metrics:
- accuracy
library_name: transformers
---

# KLUE Robeta-base for legal documents

<!-- Provide a quick summary of what the model is/does. -->

- KLUE/Robeta-Base Model을 판결문으로 이뤄진 legal_text_merged02_light.txt 파일을 사용하여 재학습 시킨 모델입니다.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** J.Park @ KETI
- **Model type:** klue/roberta-base
- **Language(s) (NLP):** korean
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### 학습 방법

```python
base_model = 'klue/roberta-base'
base_tokenizer = 'klue/roberta-base'

from transformers import RobertaTokenizer, RobertaForMaskedLM
from transformers import AutoModel, AutoTokenizer
model = RobertaForMaskedLM.from_pretrained(base_model)
tokenizer = AutoTokenizer.from_pretrained(base_tokenizer)

from transformers import LineByLineTextDataset
dataset = LineByLineTextDataset(
    tokenizer=tokenizer,
    file_path=fpath_dataset,
    block_size=512,
)

from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=True, mlm_probability=0.15
)

from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
    output_dir=output_dir,
    overwrite_output_dir=True,
    num_train_epochs=5,
    per_device_train_batch_size=18,
    save_steps=100,
    save_total_limit=2,
    seed=1
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=dataset
)

train_metrics = trainer.train()
trainer.save_model(output_dir)
trainer.push_to_hub()
```