README.md

---
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: 0.2586

## 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>
모델이 가벼운 탓인지 정확도가 높지는 않아 보입니다.</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

### 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.63  | 100  | 2.9907          |
| No log        | 1.26  | 200  | 0.9196          |
| No log        | 1.89  | 300  | 0.5858          |
| No log        | 2.52  | 400  | 0.4351          |
| 2.4889        | 3.14  | 500  | 0.3693          |
| 2.4889        | 3.77  | 600  | 0.3356          |
| 2.4889        | 4.4   | 700  | 0.3182          |
| 2.4889        | 5.03  | 800  | 0.3017          |
| 2.4889        | 5.66  | 900  | 0.2949          |
| 0.3483        | 6.29  | 1000 | 0.2798          |
| 0.3483        | 6.92  | 1100 | 0.2748          |
| 0.3483        | 7.55  | 1200 | 0.2695          |
| 0.3483        | 8.18  | 1300 | 0.2649          |
| 0.3483        | 8.81  | 1400 | 0.2610          |
| 0.2753        | 9.43  | 1500 | 0.2586          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2