---
license: apache-2.0
base_model: chunwoolee0/ke_t5_base_bongsoo_ko_en
tags:
- generated_from_trainer
model-index:
- name: ke_t5_base_bongsoo_ko_en_epoch2
  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. -->

# ke_t5_base_bongsoo_ko_en_epoch2

This model is a fine-tuned version of [chunwoolee0/ke_t5_base_bongsoo_ko_en](https://huggingface.co/chunwoolee0/ke_t5_base_bongsoo_ko_en) 
on  [bongsoo/news_news_talk_en_ko](https://huggingface.co/datasets/bongsoo/news_talk_ko_en) dataset.

## Model description

KE-T5 is a pretrained-model of t5 text-to-text transfer transformers 
using the Korean and English corpus developed by KETI (한국전자연구원).
The vocabulary used by KE-T5 consists of 64,000 sub-word tokens 
and was created using Google's sentencepiece. 
The Sentencepiece model was trained to cover 99.95% of a 30GB corpus 
with an approximate 7:3 mix of Korean and English.

## Intended uses & limitations

Translation from Korean to English :  epoch = 2

```python
>>> from transformers import pipeline
>>> translator = pipeline('translation', model='chunwoolee0/ke_t5_base_bongsoo_en_ko')

>>> translator("나는 습관적으로 점심식사 후에 산책을 한다.")
[{'translation_text': 'I habitally walk after lunch.'}]

>>> translator("이 강좌는 허깅페이스가 만든 거야.")
[{'translation_text': 'This class was created by Huggface.'}]

>>> translator("오늘은 늦게 일어났다.")
[{'translation_text': 'This day I woke up earlier.'}]
```


## Training and evaluation data

[bongsoo/news_news_talk_en_ko](https://huggingface.co/datasets/bongsoo/news_talk_ko_en)

train : 360000 rows
test:    20000 rows
validation 20000 rows

## Training procedure

Use chunwoolee0/ke_t5_base_bongsoo_ko_en as a pretrained model checkpoint.
max_token_length is set to 64 for stable training.
learing rate is reduced from 0.0005 for epoch 1 to 0.00002 here.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log        | 1.0   | 5625 | 1.6646          | 12.5566 |

TrainOutput(global_step=5625, training_loss=1.8157017361111112, 
metrics={'train_runtime': 11137.6996, 'train_samples_per_second': 32.323, 
'train_steps_per_second': 0.505, 'total_flos': 2.056934156746752e+16, 
'train_loss': 1.8157017361111112, 'epoch': 1.0})

### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3