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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

한국어 수학 문제를 입력하면, 문제 유형과 문제 유형에 대한 설명, 풀이(코드), 정답이 출력되도록 fine tuning 했습니다.</br>
문제 유형 종류로는 산술연산, 순서정하기, 조합하기, 수 찾기, 크기 비교, 도형이 있습니다.</br>
아직 원인은 잘 모르겠지만, 정확도가 높지는 않아보입니다..</br>

## Intended uses & limitations


## Training and evaluation data

[TUNiB.ai](https://tunib.ai/)에서 [github](https://github.com/tunib-ai/KMWP)에 공개한 train 데이터 셋으로 학습하였습니다.</br>

## Training procedure

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

	---
	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

	한국어 수학 문제를 입력하면, 문제 유형과 문제 유형에 대한 설명, 풀이(코드), 정답이 출력되도록 fine tuning 했습니다.</br>
	문제 유형 종류로는 산술연산, 순서정하기, 조합하기, 수 찾기, 크기 비교, 도형이 있습니다.</br>
	아직 원인은 잘 모르겠지만, 정확도가 높지는 않아보입니다..</br>

	## Intended uses & limitations


	## Training and evaluation data

	[TUNiB.ai](https://tunib.ai/)에서 [github](https://github.com/tunib-ai/KMWP)에 공개한 train 데이터 셋으로 학습하였습니다.</br>

	## Training procedure

	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