# ALERT!!!

[leadawon/jeju-ko-nmt-v6](https://huggingface.co/leadawon/jeju-ko-nmt-v6) is better than leadawon/jeju-ko-nmt-v8

6버전 성능이 더 좋습니다!!!!!!!!!!!!!!!!!!!!!!!!!!!!!


# tag

---
tags:
- generated_from_trainer
model-index:
- name: jeju-ko-nmt-v8
  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-v8

This model is a fine-tuned version of [leadawon/jeju-ko-nmt-v7](https://huggingface.co/leadawon/jeju-ko-nmt-v7) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2448

## 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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2684        | 0.04  | 500   | 0.2568          |
| 0.2468        | 0.08  | 1000  | 0.2547          |
| 0.2167        | 0.12  | 1500  | 0.2540          |
| 0.1966        | 0.16  | 2000  | 0.2535          |
| 0.1846        | 0.2   | 2500  | 0.2533          |
| 0.1727        | 0.24  | 3000  | 0.2535          |
| 0.1746        | 0.28  | 3500  | 0.2522          |
| 0.1726        | 0.32  | 4000  | 0.2521          |
| 0.1722        | 0.36  | 4500  | 0.2519          |
| 0.1731        | 0.4   | 5000  | 0.2515          |
| 0.1701        | 0.44  | 5500  | 0.2518          |
| 0.168         | 0.48  | 6000  | 0.2515          |
| 0.1706        | 0.52  | 6500  | 0.2509          |
| 0.1659        | 0.56  | 7000  | 0.2514          |
| 0.1702        | 0.6   | 7500  | 0.2509          |
| 0.1667        | 0.64  | 8000  | 0.2510          |
| 0.1661        | 0.68  | 8500  | 0.2508          |
| 0.1647        | 0.72  | 9000  | 0.2510          |
| 0.1632        | 0.76  | 9500  | 0.2510          |
| 0.1655        | 0.8   | 10000 | 0.2506          |
| 0.1645        | 0.84  | 10500 | 0.2508          |
| 0.1617        | 0.88  | 11000 | 0.2508          |
| 0.1627        | 0.91  | 11500 | 0.2511          |
| 0.2764        | 0.95  | 12000 | 0.2478          |
| 0.2755        | 0.99  | 12500 | 0.2462          |
| 0.2275        | 1.03  | 13000 | 0.2464          |
| 0.2201        | 1.07  | 13500 | 0.2463          |
| 0.2207        | 1.11  | 14000 | 0.2463          |
| 0.2202        | 1.15  | 14500 | 0.2462          |
| 0.2194        | 1.19  | 15000 | 0.2460          |
| 0.2177        | 1.23  | 15500 | 0.2461          |
| 0.2187        | 1.27  | 16000 | 0.2460          |
| 0.2184        | 1.31  | 16500 | 0.2459          |
| 0.2182        | 1.35  | 17000 | 0.2457          |
| 0.219         | 1.39  | 17500 | 0.2458          |
| 0.2206        | 1.43  | 18000 | 0.2455          |
| 0.2211        | 1.47  | 18500 | 0.2455          |
| 0.2164        | 1.51  | 19000 | 0.2455          |
| 0.2202        | 1.55  | 19500 | 0.2454          |
| 0.2208        | 1.59  | 20000 | 0.2452          |
| 0.2208        | 1.63  | 20500 | 0.2450          |
| 0.2204        | 1.67  | 21000 | 0.2450          |
| 0.2193        | 1.71  | 21500 | 0.2450          |
| 0.221         | 1.75  | 22000 | 0.2451          |
| 0.2168        | 1.79  | 22500 | 0.2450          |
| 0.2169        | 1.83  | 23000 | 0.2449          |
| 0.218         | 1.87  | 23500 | 0.2449          |
| 0.2196        | 1.91  | 24000 | 0.2449          |
| 0.2218        | 1.95  | 24500 | 0.2448          |
| 0.2199        | 1.99  | 25000 | 0.2448          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2