Update README.md

README.md

@@ -1,53 +1,49 @@
----
-license: mit
-task_categories:
-- question-answering
-- text-classification
-- text-retrieval
-language:
-- en
-tags:
-- academic
-- questions
-- evidence
-- papers
-size_categories:
-- 1K<n<10K
----
-
-# ELA Bench Dataset
-
-## Description
-This dataset contains academic questions with evidence passages extracted from research papers. Each question is paired with a relevant passage from the source paper that provides evidence for answering the question. This is a test dataset for evaluating question-answering systems on academic literature.
-
-## Dataset Structure
-The dataset contains 403 questions with the following fields:
-- `question_type`: Type of question (SA-MCQ, MA-MCQ, etc.)
-- `question`: The question text
-- `answer`: The correct answer
-- `relevant_passage`: Evidence passage extracted from the paper
-- `paper_content`: Full content of the source paper
-
-## Usage
-```python
-from datasets import load_dataset
-
-# Load the dataset
-dataset = load_dataset("KangKang625/ELAIPBench")
-
-# Access the data
-data = dataset['test']
-print(f"Number of questions: {len(data)}")
-print(f"First question: {data[0]['question']}")
-print(f"Paper content length: {len(data[0]['paper_content'])}")
-
-# Filter by question type
-mcq_questions = [item for item in data if item['question_type'] == 'SA-MCQ']
-print(f"Single-choice questions: {len(mcq_questions)}")
-```
-
-## Citation
-If you use this dataset, please cite the original ELA Bench paper.
-
-## License
-MIT License
+---
+license: mit
+task_categories:
+- question-answering
+- text-classification
+- text-retrieval
+language:
+- en
+tags:
+- academic
+- questions
+- evidence
+- papers
+size_categories:
+- 1K<n<10K
+---
+
+# ELAIPBench Dataset
+
+## Description
+This dataset contains academic questions with evidence passages extracted from research papers. Each question is paired with a relevant passage from the source paper that provides evidence for answering the question.
+
+## Dataset Structure
+The dataset contains 403 questions with the following fields:
+- `question_type`: Type of question (SA-MCQ, MA-MCQ, etc.)
+- `question`: The question text
+- `answer`: The correct answer
+- `relevant_passage`: Evidence passage extracted from the paper
+- `paper_content`: Full content of the source paper
+
+## Usage
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("KangKang625/ELAIPBench")
+
+# Access the data
+data = dataset['test']
+print(f"Number of questions: {len(data)}")
+print(f"First question: {data[0]['question']}")
+print(f"Paper content length: {len(data[0]['paper_content'])}")
+```
+
+## Citation
+If you use this dataset, please cite the original ELAIPBench paper.
+
+## License
+MIT License