README.md

---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ko-en
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: kd4_opus-mt-ko-en
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: kde4
      type: kde4
      config: en-ko
      split: train
      args: en-ko
    metrics:
    - name: Bleu
      type: bleu
      value: 32.11616746914562
---

# kd4_opus-mt-ko-en

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3924
- Bleu: 32.1162

See [translation_ko_en.ipynb](https://github.com/chunwoolee0/ko-nlp/blob/main/translation_ko_en.ipynb)

## Model description

More information needed

## Intended uses & limitations

More information needed

## Usage

You can use this model directly with a pipeline for translation language modeling:

```python
>>> from transformers import pipeline
>>> translator = pipeline('translation',model='chunwoolee0/kd4_opus-mt-ko-e')
>>> translator("점심 식사 후에 산책가자.")

[{'translation_text': "Let's go for a walk after noon."}]

>>> translator("이 강좌는 허깅페이스가 만든 거야.")
[{'translation_text': 'This is a course by Huggingspace.'}]

>>> translator("오늘은 늦게 일어났다.")
[{'translation_text': "I'm up late today."}]
```
## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

Step	Training Loss
500	1.858500
1000	1.781400
1500	1.715200
2000	1.678100
2500	1.546600
3000	1.488700
3500	1.503500
4000	1.455100
4500	1.419100
5000	1.393400
5500	1.357100
6000	1.339400
TrainOutput(global_step=6474, training_loss=1.532715692246148, 
metrics={'train_runtime': 1035.7775, 'train_samples_per_second': 199.957, 
'train_steps_per_second': 6.25, 'total_flos': 2551308264603648.0, 
'train_loss': 1.532715692246148, 'epoch': 3.0})

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3