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license: cc-by-4.0 |
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--- |
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# **KoQuality-Polyglot-5.8b** |
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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. |
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## Open Ko-LLM LeaderBoard |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6152b4b9ecf3ca6ab820e325/iYzR_mdvkcjnVquho0Y9R.png" width= "1000px" title="νμ κ°μμ§"> |
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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**. |
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## Overall Average accuracy score of the KoBEST datasets |
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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. |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/t5x4PphoNb-tW3iCzXXHT.png" width= "500px"> |
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| Model | 0-shot | 1-shot | 2-shot | 5-shot | 10-shot |
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| --- | --- | --- | --- | --- | --- | |
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| polyglot-ko-5.8b | 0.4734 | 0.5929 | 0.6120 | 0.6388 | 0.6295 |
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| koalpcaca-polyglot-5.8b | 0.4731 | 0.5284 | 0.5721 | 0.6054 | 0.6042 |
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| kullm-polyglot-5.8b | 0.4415 | 0.6030 | 0.5849 | 0.6252 | 0.6451 |
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| koquality-polyglot-5.8b | 0.4530 | 0.6050 | 0.6351 | 0.6420 | 0.6457 |
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## Evaluation results |
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### COPA (F1) |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/QAie0x99S8-KEKvK0I_uZ.png" width= "500px"> |
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### BoolQ (F1) |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/CtEWEQ5BBS05V9cDWA7kp.png" width= "500px"> |
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### HellaSwag (F1) |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/cHws6qWkDlTfs5GVcQvtN.png" width= "500px"> |
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### SentiNeg (F1) |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/VEG15XXOIbzJyQAusLa4B.png" width= "500px"> |
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### WiC (F1) |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/hV-uADJiydkVQOyYysej9.png" width= "500px"> |
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## Training hyperparameters |
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- learning_rate: 5e-5 |
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- train_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU (A100 80G) |
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- num_devices: 4 |
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- gradient_accumulation_steps: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2.0 |
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## Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.11.0 |
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- deepspeed 0.9.5 |
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## Citation |
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``` |
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@misc{2023koqaulity, |
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title = {KoQuality: Curation of High-quality Instruction Data for Korean Language Models}, |
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author = {Na, Yohan and Kim, Dahye and Chae, Dong-Kyu}, |
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journal={Proceedings of the 35th Annual Conference on Human and Cognitive Language Technology (HCLT 2023)}, |
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pages={}, |
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year = {2023}, |
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} |
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``` |
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More details can be found here: [github.com/nayohan/KoQuality](https://github.com/nayohan/KoQuality) |
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