Update README.md

README.md

@@ -9,21 +9,21 @@ metrics:
 - wer
 - cer
 widget:
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/random_2.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_2.jpg
   example_title: 랜덤 문장 1
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/random_6.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_6.jpg
   example_title: 랜덤 문장 2
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/chatbot_3.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_3.jpg
   example_title: 챗봇 1
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/chatbot_5.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_5.jpg
   example_title: 챗봇 2
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/news_1.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.jpg
   example_title: 뉴스 1
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/news_3.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_3.jpg
   example_title: 뉴스 2
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/nsmc_1.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_1.jpg
   example_title: 영화 리뷰 1
-- src: https://raw.githubusercontent.com/daekeun-ml/sm-kornlp-usecases/main/trocr/sample_imgs/nsmc_2.jpg
+- src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_2.jpg
   example_title: 영화 리뷰 2
 ---
 
@@ -37,9 +37,11 @@ TrOCR has not yet released a multilingual model including Korean, so we trained
 
 ### Text data
 We created training data by processing three types of datasets.
+
 - News summarization dataset: https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko
 - Naver Movie Sentiment Classification: https://github.com/e9t/nsmc
 - Chatbot dataset: https://github.com/songys/Chatbot_data
+  
 For efficient data collection, each sentence was separated by a sentence separator library (Kiwi Python wrapper; https://github.com/bab2min/kiwipiepy), and as a result, 637,401 samples were collected.
 
 ### Image Data
@@ -76,7 +78,7 @@ processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
 model = VisionEncoderDecoderModel.from_pretrained("daekeun-ml/ko-trocr-base-nsmc-news-chatbot")
 tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/ko-trocr-base-nsmc-news-chatbot")
 
-url = "https://raw.githubusercontent.com/aws-samples/aws-ai-ml-workshop-kr/master/sagemaker/sm-kornlp/trocr/sample_imgs/news_1.jpg"
+url = "https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.jpg"
 response = requests.get(url)
 img = Image.open(BytesIO(response.content))