Update README.md

README.md

@@ -97,6 +97,7 @@ Examples of our MM-IQ:
 You can download this dataset by the following command (make sure that you have installed [Huggingface Datasets](https://huggingface.co/docs/datasets/quickstart)):
 
 ```python
+from IPython.display import display, Image
 from datasets import load_dataset
 
 dataset = load_dataset("huanqia/MM-IQ")
@@ -110,8 +111,10 @@ print(dataset["test"][0])
 print(dataset["test"][0]['data_id']) # print the problem id 
 print(dataset["test"][0]['question']) # print the question text 
 print(dataset["test"][0]['answer']) # print the answer
-print(dataset["test"][0]['image']["bytes"]) # print the image raw bytes
 print(dataset["test"][0]['image']['path']) # print the image path
+# Display the image
+print("Image context:")
+display(Image(dataset["test"][0]['image']["bytes"]))
 ```
 
 We have uploaded a demo to illustrate how to access the MM-IQ dataset on Hugging Face, available at [hugging_face_dataset_demo.ipynb](https://github.com/AceCHQ/MMIQ/blob/main/mmiq/jupyter_notebook_demos/hugging_face_dataset_demo.ipynb).
@@ -121,7 +124,7 @@ We have uploaded a demo to illustrate how to access the MM-IQ dataset on Hugging
 
 ### Data Format
 
-The dataset is provided in json format and contains the following attributes:
+The dataset is provided in JSON format and contains the following attributes:
 
 ```json
 {
@@ -129,7 +132,7 @@ The dataset is provided in json format and contains the following attributes:
     "answer": [string] The correct answer for the problem,
     "data_id": [int] The problem id,
     "category": [string] The category of reasoning pattern,
-    "image": [dict] The image raw bytes and image path (corresponding to the image in data.zip),
+    "image": [dict] Containing image raw bytes and image path (corresponding to the image in data.zip),
 }
 ```