README.md

---
language:
- ko
license: apache-2.0
tags:
- generated_from_trainer
- polyglot-ko
- gpt-neox
- KoQuality
datasets:
- DILAB-HYU/KoQuality
pipeline_tag: text-generation
base_model: EleutherAI/polyglot-ko-5.8b
model-index:
- name: KoAlpaca-Polyglot-5.8B
  results: []
---
# **KoQuality-Polyglot-5.8b**

KoQuality-Polyglot-5.8b is a fine-tuned iteration of the [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) model, specifically trained on the [KoQuality dataset](https://huggingface.co/datasets/DILAB-HYU/KoQuality). Notably, when excluding models employing COT datasets, KoQuality-Polyglot-5.8b exhibits exceptional performance  in same size models, even though it operates with a relatively small dataset.

## Open Ko-LLM LeaderBoard
<img src="https://cdn-uploads.huggingface.co/production/uploads/6152b4b9ecf3ca6ab820e325/iYzR_mdvkcjnVquho0Y9R.png"  width= "1000px"  title="하얀 강아지">

Our approach centers around leveraging high-quality instruction datasets to deepen our understanding of commands, all the while preserving the performance of the Pre-trained Language Model (PLM). Compared to alternative models, we have achieved this with minimal learning, **utilizing only 1% of the dataset, which equates to 4006 instructions**.

## Overall Average accuracy score of the KoBEST datasets

We use [KoBEST benchmark](https://huggingface.co/datasets/skt/kobest_v1) datasets(BoolQ, COPA, HellaSwag, SentiNeg, WiC) to compare the performance of our best model and other models accuracy. Our model outperforms other models in the average accuracy score of the KoBEST datasets.
<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/t5x4PphoNb-tW3iCzXXHT.png"  width= "500px">



| Model | 0-shot | 1-shot | 2-shot | 5-shot | 10-shot
| --- | --- | --- | --- | --- | --- |
| polyglot-ko-5.8b | 0.4734 | 0.5929 | 0.6120 | 0.6388 | 0.6295
| koalpcaca-polyglot-5.8b | 0.4731 | 0.5284 | 0.5721 | 0.6054 | 0.6042
| kullm-polyglot-5.8b | 0.4415 | 0.6030 | 0.5849 | 0.6252 | 0.6451
| koquality-polyglot-5.8b | 0.4530 | 0.6050 | 0.6351 | 0.6420 | 0.6457

## Evaluation results
### COPA (F1)
<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/QAie0x99S8-KEKvK0I_uZ.png"  width= "500px">

### BoolQ (F1)
<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/CtEWEQ5BBS05V9cDWA7kp.png" width= "500px">

### HellaSwag (F1)
<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/cHws6qWkDlTfs5GVcQvtN.png" width= "500px">

### SentiNeg (F1)
<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/VEG15XXOIbzJyQAusLa4B.png" width= "500px">

### WiC (F1)
<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/hV-uADJiydkVQOyYysej9.png" width= "500px">


## Training hyperparameters
- learning_rate: 5e-5
- train_batch_size: 4
- seed: 42
- distributed_type: multi-GPU (A100 80G) + No offloading
- num_devices: 4
- gradient_accumulation_steps: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0

## Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.11.0
- deepspeed 0.9.5

## Citation

```
@misc{2023koqaulity,
  title = {KoQuality: Curation of High-quality Instruction Data for Korean Language Models},
  author = {Na, Yohan and Kim, Dahye and Chae, Dong-Kyu},
  journal={Proceedings of the 35th Annual Conference on Human and Cognitive Language Technology (HCLT 2023)},
  pages={306-311},
  year = {2023},
}
```

More details can be found here: [github.com/nayohan/KoQuality](https://github.com/nayohan/KoQuality)
<br>