Update README.md

README.md

@@ -65,8 +65,9 @@ Each question entry is arranged as:
 ```
 {
         "question_id": id of the question,
-        "qtype": type of the question, for example: "rank" questions 
-        "figure_path": local path of the image if you download the image,
+        "question_type_name": question type name, for example: "extreme" questions 
+        "question_type_id": question type id, for example: 72 means "extreme" questions
+        "figure_id": id of the figure,
         "question": question,  
         "answer": answer,
         "instructions": instructions,
@@ -87,49 +88,54 @@ text_iunput: item["question"] + item["instructions"] (if any)
 
 where ``item`` is an entry of the dataset.
 
+
 # Evaluate
 
 You should store and evaluate model's response as:
 
 ```python
 # Example code for evaluate
-def build_question(query):#to build the question
+def build_question(query):
     question = ""
     if "prompt" in query:
-    	question = question + f"{query["prompt"]}\n"
-    question = question + f"{query["question"]}\n"
-    if "options" in query:
-        for _ in query["options"]:
-        	question = question + f"{_} {query['options'][_]}\n"
-	if "instructions" in query:
-    	question = question + query["instructions"]
+        question += f"{query['prompt']}\n"
+    question += f"{query['question']}\n"
+    if "options" in query and len(query["options"]) > 0:
+        for option in query["options"]:
+            question += f"{option}\n"
+    if "instructions" in query:
+        question += query["instructions"]
     return question
 
-with open("visual_basic.json","r",encode="utf-8") as f:
-	queries = json.load(f)
 
-for query in tqdm(queries):
+#### Run your model and save your answer
+
+Responses = {}
+
+for query in tqdm(ds):
     query_idx = query["question_id"]
     question_text = build_question(query)
-    chart_figure = query["url"]  # This should be a list of url
+    figure_path = query["url"]  # This should be a list of url for models that support url input
+
     """
         Note that for models that do not support url input, you may need to download images first.
         For example, for model like Qwen2.5-VL, you may need to down load the image first and pass the local image path to the model,
-        like: figure_path = YOUR_LOCAL_IMAGE_PATH OF query['figure_path']
+        like: figure_path = YOUR_LOCAL_IMAGE_PATH OF query['figure_id']
     """
-    # Replace with your model
-    response = model.generate(question_text, chart_figure)
 
+    # Replace with your model
+    response = model.generate(question_text, figure_path)
 
     Responses[query_idx] = {
-        "qtype": int(query["qtype"]),
+        "qtype": int(query["question_type_id"]),
         "answer": query["answer"],
         "question_id": query_idx,
         "response": response,
     }
 
 with open("./model_response.json", "w", encoding="utf-8") as f:
-    json.dump(Responses, f, indent = 2, ensure_ascii=False)
+    json.dump(Responses, f, indent=2, ensure_ascii=False)
+
 ```