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README.md

@@ -102,9 +102,6 @@ dataset = load_dataset("ByteDance-Seed/AInsteinBench")
 msb_dataset = load_dataset("ByteDance-Seed/AInsteinBench", "msb_type")
 et_dataset = load_dataset("ByteDance-Seed/AInsteinBench", "et_type")
 
-# Load difficulty annotations
-difficulty = load_dataset("ByteDance-Seed/AInsteinBench", "difficulty_tag")
-
 # Access samples
 for sample in dataset['train']:
     print(f"ID: {sample['question_id']}")
@@ -112,14 +109,6 @@ for sample in dataset['train']:
     print(f"Task: {sample['description']}")
 ```
 
-### Difficulty Tags
-
-The `difficulty_tag` subset provides difficulty annotations for each question with two metrics:
-- **`engineeringDifficultyScore`** (1-5): Software engineering complexity
-- **`semanticDepthScore`** (1-5): Domain-specific scientific knowledge required
-
-Match difficulty tags to questions using `repo` and `pr_number` fields.
-
 ### Evaluation
 
 For evaluation scripts and detailed usage, please visit the [AInsteinBench GitHub repository](https://github.com/ByteDance-Seed/AInsteinBench).