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---
license: mit
base_model: gogamza/kobart-base-v2
tags:
- generated_from_trainer
model-index:
- name: qa_kor_math
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. -->
# qa_kor_math
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: 0.3294
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.56 | 100 | 3.5725 |
| No log | 1.13 | 200 | 1.2367 |
| No log | 1.69 | 300 | 0.7100 |
| No log | 2.26 | 400 | 0.5420 |
| 2.4974 | 2.82 | 500 | 0.5891 |
| 2.4974 | 3.39 | 600 | 0.5370 |
| 2.4974 | 3.95 | 700 | 0.4738 |
| 2.4974 | 4.52 | 800 | 0.4985 |
| 2.4974 | 5.08 | 900 | 0.4540 |
| 0.3445 | 5.65 | 1000 | 0.4439 |
| 0.3445 | 6.21 | 1100 | 0.4261 |
| 0.3445 | 6.78 | 1200 | 0.4007 |
| 0.3445 | 7.34 | 1300 | 0.3739 |
| 0.3445 | 7.91 | 1400 | 0.3937 |
| 0.26 | 8.47 | 1500 | 0.3550 |
| 0.26 | 9.04 | 1600 | 0.3623 |
| 0.26 | 9.6 | 1700 | 0.3944 |
| 0.26 | 10.17 | 1800 | 0.3669 |
| 0.26 | 10.73 | 1900 | 0.3628 |
| 0.217 | 11.3 | 2000 | 0.3703 |
| 0.217 | 11.86 | 2100 | 0.3580 |
| 0.217 | 12.43 | 2200 | 0.3318 |
| 0.217 | 12.99 | 2300 | 0.3199 |
| 0.217 | 13.56 | 2400 | 0.3537 |
| 0.1916 | 14.12 | 2500 | 0.3198 |
| 0.1916 | 14.69 | 2600 | 0.3317 |
| 0.1916 | 15.25 | 2700 | 0.3333 |
| 0.1916 | 15.82 | 2800 | 0.3280 |
| 0.1916 | 16.38 | 2900 | 0.3269 |
| 0.1737 | 16.95 | 3000 | 0.3315 |
| 0.1737 | 17.51 | 3100 | 0.3346 |
| 0.1737 | 18.08 | 3200 | 0.3290 |
| 0.1737 | 18.64 | 3300 | 0.3317 |
| 0.1737 | 19.21 | 3400 | 0.3282 |
| 0.1637 | 19.77 | 3500 | 0.3294 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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