Update README.md

README.md

@@ -304,23 +304,30 @@ dataset_info:
   dataset_size: 1114583
 ---
 
+## 📌 Overview
 **Text2DistBench** is a reading comprehension benchmark for evaluating whether large language models can infer distributional knowledge from natural language evidence.
-Given metadata and a set of user comments about an entity (e.g., a movie or song), models must understand and response statistics such as:
+Given metadata and a set of user comments about an entity (e.g., a movie or song), models must infer statistics such as:
 stance/topic distribution, and most/second- frequent labels.
 
+---
+
+## ⚙️ Configurations
 The dataset is constructed from movie and music entities released between 2025-12-01 and 2026-03-01. 
 It includes two types of configurations:
 
-**Posterior configurations** include both metadata and user comments as evidence.  
-These configurations differ by sample size (50 or 100 comments) and task type (estimation, most_freq, second_freq):
-`posterior_sampled_<comment_num>_<task>`
+### 🟦 Posterior Configurations
+Include both metadata and user comments as evidence.  
+These configurations differ by sample size (50 or 100 comments) and task type (estimation, most_freq, second_freq).
+**Format:** `posterior_sampled_<comment_num>_<task>`
 
-**Prior configurations** contain only metadata without comments: `prior_<task>`
+### 🟨 Prior Configurations
+Include **only metadata (no comments)**. 
+**Format:** `prior_<task>`
 
 
 ---
 
-## Dataset Format
+## ⚙️ Dataset Format
 Each benchmark instance corresponds to a distributional reading comprehension question.
 ```
 {
@@ -339,7 +346,7 @@ Each benchmark instance corresponds to a distributional reading comprehension qu
 
 ---
 
-## Load the Dataset
+## ⚙️ Load the Dataset
 ```bash
 from datasets import load_dataset
 ds = load_dataset("frett/Text2DistBench", "posterior_sampled_50_estimation", split="test")