 Evaluation results  We evaluate Polyglot-Ko-1.3B on [KOBEST dataset](https://arxiv.org/abs/2204.04541), a benchmark with 5 downstream tasks, against comparable models such as skt/ko-gpt-trinity-1.2B-v0.5, kakaobrain/kogpt and facebook/xglm-7.5B, using the prompts provided in the paper.   The following tables show the results when the number of few-shot examples differ. You can reproduce these results using the [polyglot branch of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot) and the following scripts. For a fair comparison, all models were run under the same conditions and using the same prompts. In the tables, `n` refers to the number of few-shot examples.   ```console python main.py \    --model gpt2 \    --model_args pretrained='EleutherAI/polyglot-ko-1.3b' \    --tasks kobest_copa,kobest_hellaswag \    --num_fewshot $YOUR_NUM_FEWSHOT \    --batch_size $YOUR_BATCH_SIZE \    --device $YOUR_DEVICE \    --output_path $/path/to/output/ ```  **We show model performance on COPA and HellaSwag. On the other three tasks, the evaluated models all performed similarly close to random guessing.**  