Modify : Delete Widget and Add Results

README.md

@@ -2,10 +2,6 @@
 language:
 - ko
 pipeline_tag: image-to-text
-widget:
-    - src: sample.jpg
-    output:
-        text: "대상:\n제목: 2011-2021 보건복지 분야 일자리의 <unk>증\n유형: 단일형 일반 세로 <unk>대형\n| 보건(천 명) | 복지(천 명)\n1분위 | 29.7 | 178.4\n2분위 | 70.8 | 97.3\n3분위 | 86.4 | 61.3\n4분위 | 28.2 | 16.0\n5분위 | 52.3 | 0.9"
 ---
 
 # **deplot_kr**
@@ -42,8 +38,24 @@ print(processor.batch_decode(deplot_generated_ids, skip_special_token=True)[0])
 
 ```
 
+**Model Input Image**
+![model_input_image](./sample.jpg)
 
-### Preprocessing
+**Model Output - Prediction**
+
+대상:     
+제목: 2011-2021 보건복지 분야 일자리의 <unk>증    
+유형: 단일형 일반 세로 <unk>대형    
+| 보건(천 명) | 복지(천 명)    
+1분위 | 29.7 | 178.4    
+2분위 | 70.8 | 97.3    
+3분위 | 86.4 | 61.3    
+4분위 | 28.2 | 16.0    
+5분위 | 52.3 | 0.9    
+     
+     
+
+### **Preprocessing**
 
 According to [Liu et al.(2023)](https://arxiv.org/pdf/2212.10505.pdf)...     
 
@@ -52,13 +64,13 @@ According to [Liu et al.(2023)](https://arxiv.org/pdf/2212.10505.pdf)...
 - \n : seperating rows (행 구분)       
 
 
-### Train
+### **Train**
 
 The model was trained in a TPU environment.
 - num_warmup_steps : 1,000
 - num_training_steps : 40,000 
 
-## Evaluation Results
+## **Evaluation Results**
 
 This model achieves the following results: