Update README.md

README.md

@@ -22,9 +22,9 @@ Please check out our [checkpoint_STORM](https://huggingface.co/datasets/ttlyy/OR
 ## Dataset
 | Data file name | Size |
 | --- | ---: |
-| [ORD_instruct_MAX_527k.jsonl](https://huggingface.co/datasets/ttlyy/ORD/blob/main/ORD/IO_qwen_train_oc_527k.jsonl) | 383 MB |
-| [ORD_instruct_Lite_123k.json](https://huggingface.co/datasets/ttlyy/ORD/blob/main/ORD/IO_qwen_train_oc_123k.jsonl) | 87.1 MB |
-| [ORD_instruct_Test_80k.json](https://huggingface.co/datasets/ttlyy/ORD/blob/main/ORD/IO_qwen_test_oc_80k.jsonl) | 58.4 MB |
+| [STORM_instruct_MAX_527k.jsonl](https://huggingface.co/datasets/ttlyy/ORD/blob/main/ORD/IO_qwen_train_oc_527k.jsonl) | 383 MB |
+| [STORM_instruct_Lite_123k.json](https://huggingface.co/datasets/ttlyy/ORD/blob/main/ORD/IO_qwen_train_oc_123k.jsonl) | 87.1 MB |
+| [STORM_instruct_Test_80k.json](https://huggingface.co/datasets/ttlyy/ORD/blob/main/ORD/IO_qwen_test_oc_80k.jsonl) | 58.4 MB |
 ### Pretraining Dataset
 To ensure a robust foundation for different visual rating tasks, our STORM data collection deliberately integrates a diverse selection of data including image quality assessment (IQA), image aesthetic assessment (IAA), facial age estimation (FAE), medical disease grading (MDG), and image historical date estimation (HDE). 
 These data domains are intentionally chosen to cultivate a comprehensive skill set across varied visual rating tasks.
@@ -50,7 +50,7 @@ Specifically, each data sample originally consists of a simple question and a co
 An example with the original VQA and our proposed coarse-to-fine CoT process is shown in the following figure. Meanwhile, we adopt the form of text + numbers for the labels to enhance semantic understanding.
 ![A data example with the original VQA compared with our coarse-to-fine CoT VQA.](./example.png)
 
-### ORD Prompts
+### STORM Prompts
 *Generating the dataset for IQA*
 ```
 <image> You are now an advanced Image Quality Evaluator, and your task is to assess the quality of the provided image. Please evaluate the image’s quality based on a 5-rate scale: rate0(Bad), rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent). Please provide the coarse category that can help you answer the question better. Please first coarsely categorise the image: rate0-1(Below Fair), rate2(Fair), rate3-4(Above Fair). Based on the coarse classification, proceed to make a final rate prediction. The specific steps are as follows:
@@ -120,7 +120,7 @@ def write_jsonl(data, filename):
         json_str = json.dumps(data, ensure_ascii=False)  
         f.write(json_str + '\n')
 
-file_path = 'ORD/IO_qwen_test_vqa_oc_80k.jsonl'
+file_path = 'STORM/IO_qwen_test_vqa_oc_80k.jsonl'
 output_json = "answer.jsonl"
 
 with open(file_path, 'r') as file: