---
license: mit
base_model: gogamza/kobart-base-v2
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: kobart-base-v2-159-korean
  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-v2-159-korean

This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co./gogamza/kobart-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2628
- Rouge1: 0.3634
- Rouge2: 0.1451
- Rougel: 0.3566
- Rougelsum: 0.3563
- Gen Len: 20.0

## 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-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.2804        | 1.44  | 500  | 0.2805          | 0.3321 | 0.1273 | 0.3272 | 0.3276    | 19.9992 |
| 0.2141        | 2.88  | 1000 | 0.2472          | 0.3577 | 0.1381 | 0.3526 | 0.3525    | 20.0    |
| 0.1407        | 4.33  | 1500 | 0.2495          | 0.3615 | 0.1457 | 0.3543 | 0.3543    | 20.0    |
| 0.1206        | 5.77  | 2000 | 0.2508          | 0.3592 | 0.1448 | 0.3533 | 0.3532    | 20.0    |
| 0.0853        | 7.21  | 2500 | 0.2603          | 0.3623 | 0.147  | 0.3561 | 0.3562    | 20.0    |
| 0.0777        | 8.65  | 3000 | 0.2628          | 0.3634 | 0.1451 | 0.3566 | 0.3563    | 20.0    |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0