First working version

.gitattributes

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+images/**/*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python virtual environment
+hf-env/
+venv/
+env/
+*.venv
+
+# Python cache files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IDE / Editor specific files
+.idea/
+.vscode/
+*.sublime-project
+*.sublime-workspace
+
+# OS generated files
+.DS_Store
+Thumbs.db
+
+# Results (optional - uncomment if you don't want to track results in Git)
+# results/
+
+# Configuration files containing secrets (uncomment if you use these names)
+# configs/openrouter_config.yaml
+.env*
+.env
+
+# Logs
+logs/
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+# Other
+*.swp
+*~

README.md

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+# JEE/NEET LLM Benchmark Dataset
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) <!-- Choose your license -->
+
+## Dataset Description
+
+This repository contains a benchmark dataset designed for evaluating the capabilities of Large Language Models (LLMs) on questions from the Joint Entrance Examination (JEE) and the National Eligibility cum Entrance Test (NEET) conducted in India. These are highly competitive entrance examinations for engineering and medical colleges, respectively.
+
+The questions are presented in image format (`.png`) as they appear in the original papers. The dataset includes metadata linking each image to its corresponding exam details, subject, question type, and correct answer(s).
+
+**Current Data:**
+*   NEET 2024 (Code T3)
+
+## How to Use
+
+### Using `datasets` Library
+
+The dataset is designed to be loaded using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the evaluation split
+dataset = load_dataset("your-username/your-repo-name", split='test') # Replace with your HF repo name
+
+# Example: Access the first question
+example = dataset[0]
+image = example["image"]
+question_id = example["question_id"]
+subject = example["subject"]
+correct_answers = example["correct_answer"]
+
+print(f"Question ID: {question_id}")
+print(f"Subject: {subject}")
+print(f"Correct Answer(s): {correct_answers}")
+# Display the image (requires Pillow)
+# image.show()
+```
+
+### Manual Usage (Benchmark Scripts)
+
+This repository contains scripts to run the benchmark evaluation directly:
+
+1.  **Clone the repository:**
+    ```bash
+    # Replace with your actual Hugging Face repository URL
+    git clone https://huggingface.co/datasets/Reja1/jee-neet-benchmark
+    cd your-repo-name
+    # Ensure Git LFS is installed and pull large files if necessary
+    # git lfs pull
+    ```
+2.  **Install dependencies:**
+    ```bash
+    # It's recommended to use a virtual environment
+    python -m venv venv
+    # source venv/bin/activate # or .\venv\Scripts\activate on Windows
+    pip install -r requirements.txt
+    ```
+3.  **Configure API Key:**
+    *   Create a file named `.env` in the root directory of the project (`your-repo-name/`).
+    *   Add your OpenRouter API key to this file:
+        ```dotenv
+        OPENROUTER_API_KEY=your_actual_openrouter_api_key
+        ```
+    *   **Important:** The `.gitignore` file is already configured to prevent committing the `.env` file. Never commit your API keys directly.
+4.  **Configure Models:**
+    *   Edit the `configs/benchmark_config.yaml` file.
+    *   Modify the `openrouter_models` list to include the specific model identifiers (e.g., `"openai/gpt-4o"`, `"google/gemini-pro-vision"`) you want to evaluate. Ensure these models support vision input on OpenRouter.
+    *   You can also adjust other parameters like `max_tokens` and `request_timeout` if needed.
+5.  **Run the benchmark:**
+    *   Execute the runner script from the root directory:
+        ```bash
+        python src/benchmark_runner.py --config configs/benchmark_config.yaml
+        ```
+    *   You can override the models list from the command line:
+        ```bash
+        python src/benchmark_runner.py --config configs/benchmark_config.yaml --models "openai/gpt-4o" "google/gemini-pro-vision"
+        ```
+    *   You can specify a different output directory:
+        ```bash
+        python src/benchmark_runner.py --config configs/benchmark_config.yaml --output_dir my_custom_results
+        ```
+6.  **Check Results:**
+    *   Results for each model will be saved in subdirectories within the `results/` folder (or your custom output directory).
+    *   Each model's folder (e.g., `results/openai_gpt-4o/`) will contain:
+        *   `predictions.jsonl`: Detailed results for each question (prediction, ground truth, raw response).
+        *   `summary.json`: Overall accuracy and statistics for that model.
+
+## Dataset Structure
+
+*   **`data/metadata.jsonl`**: Contains metadata for each question image. Each line is a JSON object with fields like `image_path`, `question_id`, `exam_name`, `exam_year`, `exam_code`, `subject`, `question_type`, `correct_answer`.
+*   **`images/`**: Contains subdirectories for each exam set (e.g., `images/NEET_2024_T3/`), holding the `.png` question images.
+*   **`src/`**: Python source code for running the benchmark (data loading, LLM interaction, evaluation).
+*   **`configs/`**: Configuration files for the benchmark.
+*   **`results/`**: Directory where benchmark results (LLM outputs) will be stored.
+*   **`jee_neet_benchmark_dataset.py`**: Hugging Face `datasets` loading script.
+*   **`requirements.txt`**: Python dependencies.
+*   **`README.md`**: This file.
+
+## Data Fields
+
+The dataset contains the following fields (accessible via `datasets`):
+
+*   `image`: The question image (`datasets.Image`).
+*   `question_id`: Unique identifier for the question (string).
+*   `exam_name`: Name of the exam (e.g., "NEET", "JEE Main") (string).
+*   `exam_year`: Year of the exam (int).
+*   `exam_code`: Specific paper code/session (e.g., "T3", "S1") (string).
+*   `subject`: Subject (e.g., "Physics", "Chemistry", "Biology", "Mathematics") (string).
+*   `question_type`: Type of question (e.g., "MCQ", "Multiple Correct") (string).
+*   `correct_answer`: List containing the correct answer index/indices (e.g., `[2]`, `[1, 3]`) (list of int).
+
+## Limitations
+
+[TODO: Describe any known limitations, e.g., OCR quality if applicable, coverage of exams/subjects, potential biases.]
+
+## Citation
+
+If you use this dataset or benchmark code, please cite:
+
+```bibtex
+@misc{your_name_or_org_year_jeeneetbenchmark,
+  title={JEE/NEET LLM Benchmark},
+  author={Md Rejaullah},
+  year={2025},
+  howpublished={\\url{https://huggingface.co/datasets/Reja1/jee-neet-benchmark}},
+}
+```
+
+## License
+
+This dataset and associated code are licensed under the [MIT License](https://opensource.org/licenses/MIT).

configs/benchmark_config.yaml

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+# List of OpenRouter model identifiers to evaluate.
+# Find identifiers at: https://openrouter.ai/docs#models
+# Ensure the models support vision input.
+openrouter_models:
+  - "google/gemini-2.5-pro-preview-03-25"
+  #- "openai/gpt-4o"
+  # - "google/gemini-pro-vision" # Example - uncomment or add others
+  # - "anthropic/claude-3-opus" # Example - check vision support and access
+  # - "anthropic/claude-3-sonnet"
+  # - "anthropic/claude-3-haiku"
+
+# Path to the dataset loading script relative to the repository root.
+# This should point to the .py file that defines the dataset.
+dataset_path: "jee_neet_benchmark_dataset.py"
+
+# Base directory where results for each model will be saved.
+results_base_dir: "results"
+
+# Optional: API request parameters
+# Maximum tokens to generate in the response. Increase to allow model more room.
+max_tokens: 15000
+# Timeout for each API request in seconds.
+#request_timeout: 200

data/metadata.jsonl

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+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_001.png", "question_id": "NEET_2024_T3_001", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [1]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_002.png", "question_id": "NEET_2024_T3_002", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_003.png", "question_id": "NEET_2024_T3_003", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_004.png", "question_id": "NEET_2024_T3_004", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [2]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_005.png", "question_id": "NEET_2024_T3_005", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [4]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_006.png", "question_id": "NEET_2024_T3_006", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [1]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_007.png", "question_id": "NEET_2024_T3_007", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [4]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_008.png", "question_id": "NEET_2024_T3_008", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_009.png", "question_id": "NEET_2024_T3_009", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [1]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_010.png", "question_id": "NEET_2024_T3_010", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [1]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_011.png", "question_id": "NEET_2024_T3_011", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [2]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_012.png", "question_id": "NEET_2024_T3_012", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_013.png", "question_id": "NEET_2024_T3_013", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]}
+{"image_path": "images/NEET_2024_T3/NEET_2024_T3_014.png", "question_id": "NEET_2024_T3_014", "exam_name": "NEET", "exam_year": 2024, "subject": "Physics", "question_type": "MCQ", "correct_answer": [3]}
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