---
tags:
- generated_from_trainer
model-index:
- name: jeju-ko-nmt-v6-word-v1
  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. -->

# jeju-ko-nmt-v6-word-v1

This model is a fine-tuned version of [leadawon/jeju-ko-nmt-v6](https://huggingface.co/leadawon/jeju-ko-nmt-v6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6390

## 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-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8194        | 0.03  | 500   | 0.7499          |
| 0.7814        | 0.06  | 1000  | 0.7211          |
| 0.7559        | 0.09  | 1500  | 0.7036          |
| 0.7441        | 0.12  | 2000  | 0.7017          |
| 0.741         | 0.15  | 2500  | 0.6865          |
| 0.7329        | 0.18  | 3000  | 0.6825          |
| 0.7341        | 0.21  | 3500  | 0.6754          |
| 0.7358        | 0.24  | 4000  | 0.6671          |
| 0.723         | 0.28  | 4500  | 0.6641          |
| 0.7229        | 0.31  | 5000  | 0.6639          |
| 0.726         | 0.34  | 5500  | 0.6642          |
| 0.7219        | 0.37  | 6000  | 0.6577          |
| 0.7213        | 0.4   | 6500  | 0.6552          |
| 0.7192        | 0.43  | 7000  | 0.6627          |
| 0.7154        | 0.46  | 7500  | 0.6582          |
| 0.7108        | 0.49  | 8000  | 0.6570          |
| 0.7154        | 0.52  | 8500  | 0.6530          |
| 0.7178        | 0.55  | 9000  | 0.6601          |
| 0.7046        | 0.58  | 9500  | 0.6480          |
| 0.7127        | 0.61  | 10000 | 0.6507          |
| 0.6985        | 0.64  | 10500 | 0.6493          |
| 0.7093        | 0.67  | 11000 | 0.6500          |
| 0.7055        | 0.7   | 11500 | 0.6469          |
| 0.7061        | 0.73  | 12000 | 0.6534          |
| 0.7018        | 0.76  | 12500 | 0.6458          |
| 0.7027        | 0.8   | 13000 | 0.6424          |
| 0.7068        | 0.83  | 13500 | 0.6387          |
| 0.7044        | 0.86  | 14000 | 0.6441          |
| 0.7028        | 0.89  | 14500 | 0.6413          |
| 0.6992        | 0.92  | 15000 | 0.6359          |
| 0.7007        | 0.95  | 15500 | 0.6479          |
| 0.6887        | 0.98  | 16000 | 0.6416          |
| 0.7053        | 1.01  | 16500 | 0.6381          |
| 0.6795        | 1.04  | 17000 | 0.6391          |
| 0.6812        | 1.07  | 17500 | 0.6392          |
| 0.6826        | 1.1   | 18000 | 0.6405          |
| 0.6775        | 1.13  | 18500 | 0.6391          |
| 0.6798        | 1.16  | 19000 | 0.6378          |
| 0.6895        | 1.19  | 19500 | 0.6359          |
| 0.687         | 1.22  | 20000 | 0.6364          |
| 0.6887        | 1.25  | 20500 | 0.6357          |
| 0.6758        | 1.28  | 21000 | 0.6356          |
| 0.6732        | 1.32  | 21500 | 0.6368          |
| 0.6832        | 1.35  | 22000 | 0.6381          |
| 0.6805        | 1.38  | 22500 | 0.6347          |
| 0.6821        | 1.41  | 23000 | 0.6373          |
| 0.6828        | 1.44  | 23500 | 0.6401          |
| 0.678         | 1.47  | 24000 | 0.6402          |
| 0.6892        | 1.5   | 24500 | 0.6358          |
| 0.6855        | 1.53  | 25000 | 0.6339          |
| 0.6748        | 1.56  | 25500 | 0.6363          |
| 0.6734        | 1.59  | 26000 | 0.6361          |
| 0.6748        | 1.62  | 26500 | 0.6348          |
| 0.6812        | 1.65  | 27000 | 0.6355          |
| 0.6844        | 1.68  | 27500 | 0.6380          |
| 0.6794        | 1.71  | 28000 | 0.6393          |
| 0.6834        | 1.74  | 28500 | 0.6390          |
| 0.6843        | 1.77  | 29000 | 0.6411          |
| 0.6732        | 1.8   | 29500 | 0.6414          |
| 0.6758        | 1.84  | 30000 | 0.6395          |
| 0.6782        | 1.87  | 30500 | 0.6407          |
| 0.6787        | 1.9   | 31000 | 0.6402          |
| 0.6752        | 1.93  | 31500 | 0.6393          |
| 0.6769        | 1.96  | 32000 | 0.6392          |
| 0.6768        | 1.99  | 32500 | 0.6390          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2