Update README.md - Add Korean

README.md

@@ -22,6 +22,8 @@ Ko-Deplot은 Google의 Pix2Struct 구조를 기반으로 한 한국어 Visual-QA
 # **Model Usage**
 You can run a prediction by querying an input image together with a question as follows:
 
+아래의 코드를 이용하여 모델 추론을 할 수 있습니다:
+
 ```python
 from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
 from PIL import Image
@@ -43,6 +45,8 @@ print(processor.decode(predictions[0], skip_special_tokens=True))
 
 Synthetic chart data from three libraries were used:
 
+세 개의 라이브러리에서 합성 차트 데이터를 생성하여 사용하였습니다:
+
 - [GenPlot](https://github.com/brendanartley/genplot)
 - [Chart.js](https://github.com/chartjs/Chart.js)
 - [Plotly](https://github.com/plotly/plotly.py)
@@ -51,12 +55,16 @@ Synthetic chart data from three libraries were used:
 
 The model was first exposed to a short warmup stage, following its [original paper](https://arxiv.org/pdf/2210.03347.pdf). It was then trained using the chart data for 50,000 steps.
 
+학습을 위해 처음 짧은 "warmup" 단계를 거쳐 한글을 학습시킨 후 50,000 스텝 동안 차트 데이터를 학습시켰습니다.
+
 # **Technical Specifications**
 
 ## Hardware
 
 Ko-Deplot was trained by using A100 80G. 
 
+A100 80G GPU를 이용하여 학습하였습니다.
+
 # **Contact**
 
 Any questions and suggestions, please use the discussion tab. If you want to contact us directly, email robin@nuua.ai.