---
license: mit
base_model: hyunwoongko/kobart
tags:
- generated_from_trainer
model-index:
- name: qa_kor_math_2
  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_2

This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.co./hyunwoongko/kobart) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1234

## 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  | 3.2887          |
| No log        | 1.13  | 200  | 0.8359          |
| No log        | 1.69  | 300  | 0.4944          |
| No log        | 2.26  | 400  | 0.3843          |
| 2.4704        | 2.82  | 500  | 0.3349          |
| 2.4704        | 3.39  | 600  | 0.3005          |
| 2.4704        | 3.95  | 700  | 0.2768          |
| 2.4704        | 4.52  | 800  | 0.2641          |
| 2.4704        | 5.08  | 900  | 0.2479          |
| 0.3213        | 5.65  | 1000 | 0.2335          |
| 0.3213        | 6.21  | 1100 | 0.2208          |
| 0.3213        | 6.78  | 1200 | 0.2117          |
| 0.3213        | 7.34  | 1300 | 0.2041          |
| 0.3213        | 7.91  | 1400 | 0.1964          |
| 0.2503        | 8.47  | 1500 | 0.1876          |
| 0.2503        | 9.04  | 1600 | 0.1790          |
| 0.2503        | 9.6   | 1700 | 0.1745          |
| 0.2503        | 10.17 | 1800 | 0.1673          |
| 0.2503        | 10.73 | 1900 | 0.1623          |
| 0.2141        | 11.3  | 2000 | 0.1579          |
| 0.2141        | 11.86 | 2100 | 0.1527          |
| 0.2141        | 12.43 | 2200 | 0.1494          |
| 0.2141        | 12.99 | 2300 | 0.1438          |
| 0.2141        | 13.56 | 2400 | 0.1427          |
| 0.1873        | 14.12 | 2500 | 0.1386          |
| 0.1873        | 14.69 | 2600 | 0.1347          |
| 0.1873        | 15.25 | 2700 | 0.1334          |
| 0.1873        | 15.82 | 2800 | 0.1321          |
| 0.1873        | 16.38 | 2900 | 0.1295          |
| 0.1718        | 16.95 | 3000 | 0.1276          |
| 0.1718        | 17.51 | 3100 | 0.1263          |
| 0.1718        | 18.08 | 3200 | 0.1255          |
| 0.1718        | 18.64 | 3300 | 0.1244          |
| 0.1718        | 19.21 | 3400 | 0.1240          |
| 0.1628        | 19.77 | 3500 | 0.1234          |


### Framework versions

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