---
license: mit
base_model: gogamza/kobart-base-v2
tags:
- generated_from_trainer
model-index:
- name: KoBART_base_v2-trial
  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. -->

# KoBART_base_v2-trial

This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co./gogamza/kobart-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1815

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4147        | 0.11  | 50   | 0.5490          |
| 0.5457        | 0.22  | 100  | 0.4810          |
| 0.4642        | 0.32  | 150  | 0.3971          |
| 0.4364        | 0.43  | 200  | 0.3955          |
| 0.4111        | 0.54  | 250  | 0.3851          |
| 0.3888        | 0.65  | 300  | 0.3438          |
| 0.3586        | 0.76  | 350  | 0.3290          |
| 0.3304        | 0.87  | 400  | 0.3201          |
| 0.3337        | 0.97  | 450  | 0.2992          |
| 0.2677        | 1.08  | 500  | 0.3161          |
| 0.2576        | 1.19  | 550  | 0.2981          |
| 0.2467        | 1.3   | 600  | 0.2846          |
| 0.2369        | 1.41  | 650  | 0.2674          |
| 0.226         | 1.52  | 700  | 0.2529          |
| 0.2204        | 1.62  | 750  | 0.2446          |
| 0.204         | 1.73  | 800  | 0.2400          |
| 0.2071        | 1.84  | 850  | 0.2262          |
| 0.1911        | 1.95  | 900  | 0.2153          |
| 0.1591        | 2.06  | 950  | 0.2121          |
| 0.1338        | 2.16  | 1000 | 0.2090          |
| 0.1312        | 2.27  | 1050 | 0.1986          |
| 0.1336        | 2.38  | 1100 | 0.1947          |
| 0.1205        | 2.49  | 1150 | 0.1903          |
| 0.1162        | 2.6   | 1200 | 0.1867          |
| 0.1187        | 2.71  | 1250 | 0.1840          |
| 0.1171        | 2.81  | 1300 | 0.1821          |
| 0.1149        | 2.92  | 1350 | 0.1815          |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0