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---
license: mit
base_model: gogamza/kobart-base-v2
tags:
- generated_from_trainer
model-index:
- name: KoBART_base_v2-trial2
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-trial2
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: 0.1820
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3889 | 0.11 | 50 | 0.5425 |
| 0.5339 | 0.22 | 100 | 0.4328 |
| 0.4609 | 0.32 | 150 | 0.4180 |
| 0.4631 | 0.43 | 200 | 0.4167 |
| 0.4065 | 0.54 | 250 | 0.3775 |
| 0.3898 | 0.65 | 300 | 0.3539 |
| 0.3637 | 0.76 | 350 | 0.3389 |
| 0.3347 | 0.87 | 400 | 0.3275 |
| 0.3428 | 0.97 | 450 | 0.3087 |
| 0.2871 | 1.08 | 500 | 0.3189 |
| 0.2843 | 1.19 | 550 | 0.3016 |
| 0.2685 | 1.3 | 600 | 0.2954 |
| 0.2603 | 1.41 | 650 | 0.2860 |
| 0.2636 | 1.52 | 700 | 0.2804 |
| 0.2586 | 1.62 | 750 | 0.2821 |
| 0.2485 | 1.73 | 800 | 0.2674 |
| 0.2483 | 1.84 | 850 | 0.2662 |
| 0.2322 | 1.95 | 900 | 0.2525 |
| 0.2052 | 2.06 | 950 | 0.2634 |
| 0.1838 | 2.16 | 1000 | 0.2472 |
| 0.1859 | 2.27 | 1050 | 0.2432 |
| 0.1887 | 2.38 | 1100 | 0.2392 |
| 0.1756 | 2.49 | 1150 | 0.2314 |
| 0.1697 | 2.6 | 1200 | 0.2332 |
| 0.1741 | 2.71 | 1250 | 0.2257 |
| 0.1665 | 2.81 | 1300 | 0.2204 |
| 0.1655 | 2.92 | 1350 | 0.2097 |
| 0.1539 | 3.03 | 1400 | 0.2141 |
| 0.126 | 3.14 | 1450 | 0.2129 |
| 0.1241 | 3.25 | 1500 | 0.2068 |
| 0.1266 | 3.35 | 1550 | 0.1999 |
| 0.1161 | 3.46 | 1600 | 0.1996 |
| 0.1183 | 3.57 | 1650 | 0.1943 |
| 0.1123 | 3.68 | 1700 | 0.1914 |
| 0.1096 | 3.79 | 1750 | 0.1881 |
| 0.1089 | 3.9 | 1800 | 0.1835 |
| 0.1096 | 4.0 | 1850 | 0.1803 |
| 0.0857 | 4.11 | 1900 | 0.1873 |
| 0.0833 | 4.22 | 1950 | 0.1857 |
| 0.0791 | 4.33 | 2000 | 0.1871 |
| 0.0825 | 4.44 | 2050 | 0.1852 |
| 0.0813 | 4.55 | 2100 | 0.1834 |
| 0.0806 | 4.65 | 2150 | 0.1830 |
| 0.0805 | 4.76 | 2200 | 0.1822 |
| 0.0786 | 4.87 | 2250 | 0.1820 |
| 0.0775 | 4.98 | 2300 | 0.1820 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
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