---
library_name: transformers
license: cc-by-4.0
base_model: paust/pko-t5-base
tags:
- generated_from_trainer
model-index:
- name: correction
  results: []
---

<!-- 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. -->

# Basic Inference
```python
from transformers import T5TokenizerFast, T5ForConditionalGeneration

tokenizer = T5TokenizerFast.from_pretrained('ij5/whitespace-correction')
model = T5ForConditionalGeneration.from_pretrained('ij5/whitespace-correction')

def fix_whitespace(text):
    inputs = f"띄어쓰기 교정: {text}"
    tokenized = tokenizer(inputs, max_length=128, truncation=True, return_tensors='pt').to('cuda')
    output_ids = model.generate(
        input_ids=tokenized['input_ids'],
        attention_mask=tokenized['attention_mask'],
        max_length=128,
    )
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)


print(fix_whitespace("흔들 리는 가지 사이로 불쑥 바람의 형상 이 드 러나기라도 할 것처럼."))
# result: 흔들리는 가지 사이로 불쑥 바람의 형상이 드러나기라도 할 것처럼.
```

# correction

This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0160

## Model description

More information needed

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0243        | 1.0   | 1688 | 0.0183          |
| 0.0172        | 2.0   | 3376 | 0.0165          |
| 0.0126        | 3.0   | 5064 | 0.0160          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0