Update README.md

README.md

@@ -5,14 +5,12 @@ task_categories:
 language:
 - zh
 - en
-size_categories:
-- 1K<n<10K
 ---
 
 
 # SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images
 
-![SpecVQA 示例图](example/SpecVQA.jpg)
+![SpecVQA Example](example/SpecVQA.jpg)
 
 ## 1. Introduction
 
@@ -55,7 +53,7 @@ The final **3,100 QA-pairs** are classified into two critical categoriesbased on
 
 ## 3. Benchmark Evaluation
 
-This benchmark evaluates model performance on Visual Question Answering (VQA) tasks. Following the [ChartVLM](https://arxiv.org/abs/2402.12185), GPT-o4-mini serves solely as a judge to score the model’s predictions against the ground truth. A predefined error tolerance of 5 percentage points is applied: if the error falls within this range, the answer is considered correct; otherwise, it is marked incorrect. Accuracy is then computed based on these judgments. 
+This benchmark evaluates model performance on Visual Question Answering (VQA) tasks. Following the `ChartVLM`, GPT-o4-mini serves solely as a judge to score the model’s predictions against the ground truth. A predefined error tolerance of 5 percentage points is applied: if the error falls within this range, the answer is considered correct; otherwise, it is marked incorrect. Accuracy is then computed based on these judgments. 
 
 **Score Prompt**
 ```python