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---
license: apache-2.0
configs:
- config_name: jan
data_files:
- split: test
path: main2025-jan.jsonl
- config_name: apr
data_files:
- split: test
path: main2025-apr.jsonl
---
# JEE Mains 2025 Math Evaluation Set
## 🧾 Dataset Summary
This dataset contains **475 math questions** from the **official JEE Mains 2025** examination, covering both **January** and **April** sessions. It is curated to benchmark mathematical reasoning models under high-stakes exam conditions.
---
## 🚀 How to Load the Dataset
You can load the evaluation data using the `datasets` library from Hugging Face:
```python
from datasets import load_dataset
# Load January session evaluation set
jan_data = load_dataset("PhysicsWallahAI/JEE-Main-2025-Math", "jan", split="test")
# Load April session evaluation set
apr_data = load_dataset("PhysicsWallahAI/JEE-Main-2025-Math", "apr", split="test")
```
---
## 📂 Dataset Structure
Each sample is stored as a JSON object with the following fields:
| Field Name | Type | Description |
|--------------------|-----------|-----------------------------------------------------------------------------|
| `question` | `string` | Math problem text (can include LaTeX) |
| `answer` | `string` | Final answer (NAT or symbolic form) |
| `question_type` | `int` | `0 = Numerical Answer Type`, `1 = Multiple Choice Question` |
| `options` | `list` | List of answer choices (present only for MCQ) |
| `correct_options` | `list` | Indices of correct options in `options[]` (for MCQ only) |
| `additional_data` | `dict` | Placeholder for extended fields used during model training or evaluation. |
| `metadata` | `dict` | Optional metadata providing contextual information about the question. |
---
## 📊 Dataset Statistics
| Split | Papers | Questions |
| ------------ | ------ | --------- |
| January 2025 | 10 | 250 |
| April 2025 | 9 | 225 |
| **Total** | 19 | **475** |
* **MCQs**: \~80%
* **NATs**: \~20%
---
## 📥 Source
All questions were sourced from **official JEE Mains 2025** mathematics papers publicly released by **NTA**. Answer keys were cross-verified with NTA final answer releases.
---
## 💼 Intended Uses
* Benchmarking Indian math LLMs
* Evaluating symbolic + numeric reasoning
* Comparing SFT/RLHF/retrieval-based models on real exams
---
## ⚠️ Limitations
* Limited to mathematics domain
---
## 📄 Citation
```bibtex
@misc{jee2025math,
title = {JEE Mains 2025 Math Evaluation Set},
author = {Physics Wallah AI Research},
year = {2025},
note = {Official JEE Mains 2025 math questions curated for evaluating educational language models},
howpublished = {\url{https://huggingface.co/datasets/PhysicsWallahAI/JEE-Main-2025-Math}},
}
``` |