File size: 2,010 Bytes
87499a5 2ab8898 ba98fb8 c9e09ae 22b9989 c9e09ae ba98fb8 5b0c529 c9e09ae af15cca ba98fb8 c9e09ae 22b9989 ba98fb8 2ab8898 ba98fb8 2ab8898 ba98fb8 2ab8898 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
---
license: cc-by-4.0
---
# **KoQuality-Polyglot-5.8b**
KoQuality-Polyglot-5.8b is an auto-regressive language model that conducts instruction tuning with KoQuality datasets on Polyglot-5.8b model.
### Average accuracy score of the KoBEST datasets
Our best model is trained on [KoQuality dataset](https://huggingface.co/datasets/DILAB-HYU/KoQuality), which is curated by proposed method (len_group=5, k=100, method=ppl_sampling).
We use KoBEST benchmark datasets(KoBEST_boolq, KoBEST_copa, KoBEST_hellaswag, KoBEST_sentineg, KoBEST_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/q4cCUCzRJa3m2f7oxI_FY.png style="max-width: 500px; width: 300%"/>
| Model | 0-shot | 1-shot | 2-shot | 5-shot | 10-shot
| --- | --- | --- | --- | --- | --- |
| koquality-polyglot-5.8b | 0.5472 | 0.5979 | 0.6260 | 0.6486 | 0.6535
| polyglot-ko-5.8b | 0.5587 | 0.5977 | 0.6138 | 0.6431 | 0.6457
| koalpcaca-polyglot-5.8b | 0.5085 | 0.5561 | 0.5768 | 0.6097 | 0.6059
| kullm-polyglot-5.8b | 0.5409 | 0.6072 | 0.5945 | 0.6345 | 0.6530
<img src=https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/7EKl1OAgKgPBFcSlGzBiW.png style="max-width: 800px; width: 400%"/>
## Training hyperparameters
- learning_rate: 5e-5
- train_batch_size: 4
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- 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
## 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={},
year = {2023},
}
```
<br> |