Create README.md

README.md

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+## Korean Grammatical Error Correction Model
+maintainer: [Soyoung Yoon](https://soyoung97.github.io/profile/)
+
+Official repository: [link](https://github.com/soyoung97/GEC-Korean)
+
+Dataset request form: [link](https://forms.gle/kF9pvJbLGvnh8ZnQ6)
+
+Demo: [link](https://huggingface.co/spaces/Soyoung97/gec-korean-demo)
+
+Colab demo: [link](https://colab.research.google.com/drive/1CL__3CpkhBzxWUbvsQmPTQWWu1cWmJHa?usp=sharing)
+
+
+### Sample code
+```
+import torch
+from transformers import PreTrainedTokenizerFast
+from transformers import BartForConditionalGeneration
+
+tokenizer = PreTrainedTokenizerFast.from_pretrained('Soyoung97/gec_kr')
+model = BartForConditionalGeneration.from_pretrained('Soyoung97/gec_kr')
+
+text = '한국어는어렵다.'
+
+raw_input_ids = tokenizer.encode(text)
+input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]
+corrected_ids = model.generate(torch.tensor([input_ids]),
+                                max_length=128,
+                                eos_token_id=1, num_beams=4,
+                                early_stopping=True, repetition_penalty=2.0)
+output_text = tokenizer.decode(corrected_ids.squeeze().tolist(), skip_special_tokens=True)
+
+
+output_text
+>>> '한국어는 어렵다.'
+```
+
+Special thanks to the [KoBART-summarization repository](https://huggingface.co/gogamza/kobart-summarization) (referenced from it)