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---
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
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