With more clear summary report and reattempt mechanism
Browse files- README.md +20 -5
- configs/benchmark_config.yaml +2 -2
- results/google_gemini-2.5-pro-preview-03-25/predictions.jsonl +0 -200
- results/google_gemini-2.5-pro-preview-03-25/summary.json +0 -7
- results/google_gemini-2.5-pro-preview-03-25_20250505_091752/predictions.jsonl +200 -0
- results/google_gemini-2.5-pro-preview-03-25_20250505_091752/summary.json +62 -0
- results/google_gemini-2.5-pro-preview-03-25_20250505_091752/summary.md +25 -0
- src/benchmark_runner.py +383 -92
- src/evaluation.py +217 -2
- src/llm_interface.py +61 -15
- src/utils.py +49 -36
README.md
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@@ -74,7 +74,7 @@ This repository contains scripts to run the benchmark evaluation directly:
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```
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* You can override the models list from the command line:
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```bash
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python src/benchmark_runner.py --config configs/benchmark_config.yaml --models "openai/gpt-4o" "google/gemini-pro-
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```
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* You can specify a different output directory:
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```bash
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```
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6. **Check Results:**
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* Results for each model will be saved in subdirectories within the `results/` folder (or your custom output directory).
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* Each model's folder (e.g., `results/
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* `predictions.jsonl`: Detailed results for each question (prediction, ground truth, raw response).
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* `summary.json`: Overall accuracy and statistics for that model.
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## Dataset Structure
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* `question_type`: Type of question (e.g., "MCQ", "Multiple Correct") (string).
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* `correct_answer`: List containing the correct answer index/indices (e.g., `[2]`, `[1, 3]`) (list of int).
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## Limitations
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## Citation
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If you use this dataset or benchmark code, please cite:
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```bibtex
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@misc{
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title={JEE/NEET LLM Benchmark},
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author={Md Rejaullah},
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year={2025},
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}
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```
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## License
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This dataset and associated code are licensed under the [MIT License](https://opensource.org/licenses/MIT).
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```
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* You can override the models list from the command line:
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```bash
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python src/benchmark_runner.py --config configs/benchmark_config.yaml --models "openai/gpt-4o" "google/gemini-2.5-pro-preview-03-25"
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```
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* You can specify a different output directory:
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```bash
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```
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6. **Check Results:**
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* Results for each model will be saved in subdirectories within the `results/` folder (or your custom output directory).
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* Each model's folder (e.g., `results/provider/modelname_YYYYMMDD_HHMMSS`) will contain:
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* `predictions.jsonl`: Detailed results for each question (prediction, ground truth, raw response).
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* `summary.json`: Overall accuracy and statistics for that model.
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* Sample benchmark results for some models can be found in the `results/` folder.
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## Pros
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* **Multimodal Reasoning:** Uses images of questions directly, testing the multimodal reasoning capability of the model.
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* **Reattempt Mechanism:** Implements a reattempt mechanism to encourage the model to provide the final answer within `<answer>` tags.
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* **Reproducibility:** Easily reproducible with simple commands and an OpenRouter API key.
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* **Model Flexibility:** Allows testing of various models available through OpenRouter.
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## Dataset Structure
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* `question_type`: Type of question (e.g., "MCQ", "Multiple Correct") (string).
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* `correct_answer`: List containing the correct answer index/indices (e.g., `[2]`, `[1, 3]`) (list of int).
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## Cons / Current Limitations
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* **Early Development:** The benchmark is still in its early stages of development.
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* **Limited Data:** Currently, only one exam question paper (NEET 2024 T3) is available. More exam papers are planned for future inclusion.
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## Citation
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If you use this dataset or benchmark code, please cite:
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```bibtex
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@misc{rejaullah_2025_jeeneetbenchmark,
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title={JEE/NEET LLM Benchmark},
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author={Md Rejaullah},
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year={2025},
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}
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```
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## Contact
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For questions, suggestions, or collaboration, feel free to reach out:
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* **X (Twitter):** [https://x.com/RejaullahmdMd](https://x.com/RejaullahmdMd)
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## License
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This dataset and associated code are licensed under the [MIT License](https://opensource.org/licenses/MIT).
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configs/benchmark_config.yaml
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results_base_dir: "results"
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# Optional: API request parameters
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# Maximum tokens to generate in the response.
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max_tokens:
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# Timeout for each API request in seconds.
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#request_timeout: 200
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results_base_dir: "results"
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# Optional: API request parameters
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# Maximum tokens to generate in the response. Keep it low as we expect only numbers.
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max_tokens: 64000
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# Timeout for each API request in seconds.
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#request_timeout: 200
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results/google_gemini-2.5-pro-preview-03-25/predictions.jsonl
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{"question_id": "NEET_2024_T3_001", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_002", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_003", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_004", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_005", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_006", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_007", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` C\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_008", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_009", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_010", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n{\"reasoning\": \"The question asks about the result of using white light instead of monochromatic light in Young's double-slit experiment. White light consists of multiple wavelengths (colors). In Young's experiment, the condition for constructive interference (bright fringes) is path difference = n\u03bb, and for destructive interference (dark fringes) is path difference = (n+1/2)\u03bb. At the center of the screen, the path difference from both slits is zero. For zero path difference, all wavelengths interfere constructively. Therefore, the central fringe (n=0) will be bright and formed by the superposition of all colors, resulting in a white fringe. Away from the center, the positions of bright fringes depend on the wavelength (y_bright = n\u03bbD/d). Since different colors have different wavelengths, their bright fringes will occur at slightly different positions for n \u2260 0. This leads to the formation of colored fringes flanking the central white fringe. For example, the first-order bright fringe (n=1) will be spread out, with violet (shortest visible wavelength) closest to the center and red (longest visible wavelength) farthest. Only the first few orders of fringes will be distinctly colored before they overlap significantly and wash out. Option (1) correctly describes this phenomenon: a central bright white fringe surrounded by a few colored fringes. Option (2) is incorrect because fringe width (\u03b2 = \u03bbD/d) depends on wavelength, so fringes for different colors (and the composite colored fringes) will not have equal widths. Option (3) is incorrect because an interference pattern is observed, albeit different from the monochromatic case. Option (4) is incorrect because the central fringe is bright, not dark (assuming 'dear' is a typo for 'dark').\", \"correct_options\": [\"1\"]}\n```\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_011", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_012", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_013", "ground_truth": [3], "predicted_answer": [4], "raw_response": "```\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_014", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_015", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n{\n \"reasoning\": \"The user wants to find the refractive index (n) of the prism based on the given information and diagram, and then identify the corresponding option number.\\n\\n1. **Analyze the setup:** A light ray enters face AB of a right-angled prism (A=90\u00b0) with an angle of incidence i = 30\u00b0. The ray travels parallel to the base BC and emerges along face AC (angle of emergence e = 90\u00b0).\\n2. **Apply Snell's Law at face AB:** Let r1 be the angle of refraction at face AB. The refractive index of air is n_air \u2248 1.\\n n_air * sin(i) = n * sin(r1)\\n 1 * sin(30\u00b0) = n * sin(r1)\\n 1/2 = n * sin(r1) (Equation 1)\\n3. **Apply Snell's Law at face AC:** Let r2 be the angle of incidence at face AC. The ray emerges along the face, so the angle of emergence e = 90\u00b0.\\n n * sin(r2) = n_air * sin(e)\\n n * sin(r2) = 1 * sin(90\u00b0)\\n n * sin(r2) = 1 * 1\\n n * sin(r2) = 1 (Equation 2)\\n4. **Use Prism Geometry:** For a prism with angle A, the sum of the internal angles of refraction and incidence is equal to the prism angle: A = r1 + r2. Since the prism is right-angled at A, A = 90\u00b0.\\n r1 + r2 = 90\u00b0\\n Therefore, r2 = 90\u00b0 - r1.\\n5. **Substitute r2 into Equation 2:**\\n n * sin(90\u00b0 - r1) = 1\\n Using the trigonometric identity sin(90\u00b0 - \u03b8) = cos(\u03b8):\\n n * cos(r1) = 1 (Equation 3)\\n6. **Solve for n using Equations 1 and 3:** We have two equations:\\n n * sin(r1) = 1/2\\n n * cos(r1) = 1\\n Square both equations and add them:\\n (n * sin(r1))\u00b2 + (n * cos(r1))\u00b2 = (1/2)\u00b2 + 1\u00b2\\n n\u00b2 * sin\u00b2(r1) + n\u00b2 * cos\u00b2(r1) = 1/4 + 1\\n n\u00b2 * (sin\u00b2(r1) + cos\u00b2(r1)) = 5/4\\n Using the identity sin\u00b2(\u03b8) + cos\u00b2(\u03b8) = 1:\\n n\u00b2 * 1 = 5/4\\n n\u00b2 = 5/4\\n n = sqrt(5/4) (Refractive index must be positive)\\n n = sqrt(5) / sqrt(4)\\n n = sqrt(5) / 2\\n7. **Match the result with the options:**\\n (1) sqrt(3)/4\\n (2) sqrt(3)/2\\n (3) sqrt(5)/4\\n (4) sqrt(5)/2\\n The calculated refractive index n = sqrt(5) / 2 matches option (4).\\n8. **Format the output:** The correct option number is 4.\",\n \"answer\": \"<answer>4</answer>\"\n}\n```\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_016", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_017", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_018", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_019", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_020", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_021", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_022", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_023", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_024", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_025", "ground_truth": [1, 3], "predicted_answer": null, "raw_response": "```json\n{\"thought\": \"1. **Analyze Statement I:** \\\"Atoms are electrically neutral as they contain equal number of positive and negative charges.\\\" An atom has protons (positive) in the nucleus and electrons (negative) orbiting. In a neutral atom, the number of protons equals the number of electrons. Thus, the total positive charge cancels the total negative charge, making the atom electrically neutral. Statement I is correct.\n\n2. **Analyze Statement II:** \\\"Atoms of each element are stable and emit their characteristic spectrum.\\\" Atoms are generally stable in their ground state. However, they only emit a characteristic spectrum when their electrons are excited to higher energy levels and then fall back to lower levels, releasing energy as photons. They do not continuously emit spectra in their stable ground state. Also, not all atoms are inherently stable (e.g., radioactive isotopes). Therefore, Statement II is incorrect as a general, unconditional statement.\n\n3. **Evaluate the Options:**\n * Option (1): Statement I is correct but Statement II is incorrect. This aligns with the analysis.\n * Option (2): Statement I is incorrect but Statement II is correct. This contradicts the analysis.\n * Option (3): Both Statement I and Statement II are correct. This contradicts the analysis.\n * Option (4): Both Statement I and Statement II are incorrect. This contradicts the analysis.\n\n4. **Determine the Correct Integer:** The analysis shows that Statement I is correct and Statement II is incorrect. This corresponds to option (1).\n\n5. **Format the Output:** Enclose the integer 1 within <answer> tags.\", \"answer\": \"<answer>1</answer>\"}\n```", "parse_successful": false, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_026", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_027", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_028", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```python\nimport math\n\n# Define the given values\nradius_cm = 4.5 # cm\nsurface_tension = 0.07 # Nm^-1\n\n# Convert radius to meters\nradius_m = radius_cm / 100.0 # m\n\n# Calculate the circumference (length of contact)\nlength_contact = 2 * math.pi * radius_m\n\n# Calculate the excess force required\n# The force acts along the circumference\n# F = T * L\nforce = surface_tension * length_contact\n\n# Calculate the numerical value\nforce_value = 0.07 * (2 * math.pi * (4.5 / 100.0))\n# force_value = 0.07 * 2 * 3.14159 * 0.045\n# force_value = 0.14 * 3.14159 * 0.045\n# force_value = 0.43982 * 0.045\n# force_value = 0.0197919 N\n\n# Convert the force to milliNewtons (mN)\n# 1 N = 1000 mN\nforce_mN = force_value * 1000\n# force_mN = 19.7919 mN\n\n# Round the value to match the options\nforce_mN_rounded = round(force_mN, 1) # Round to one decimal place as in option (3)\n\n# Compare with options\n# (1) 1.98 mN\n# (2) 99 N\n# (3) 19.8 mN\n# (4) 198 N\n\n# The calculated value 19.7919 mN is approximately 19.8 mN.\n\ncorrect_option = 3\n```\n\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_029", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_030", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_031", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_032", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_033", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_034", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_035", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_036", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` C\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_037", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_038", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
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{"question_id": "NEET_2024_T3_039", "ground_truth": [3], "predicted_answer": [4], "raw_response": "1. **Analyze the question:** The question asks to identify the correct circuit diagram that represents a Wheatstone bridge capable of achieving balance. This means we need to find the circuit(s) with the correct topology for a Wheatstone bridge measurement, where the source, resistors, and detector(s) are placed correctly. The bridge doesn't necessarily need to be balanced with the given resistance values.\n\n2. **Analyze the options:**\n * A standard Wheatstone bridge consists of four resistors (forming a diamond or square), a voltage source connected across one pair of opposite junctions, and a detector (like a galvanometer) connected across the other pair of opposite junctions. The balance condition (zero current through the detector) is achieved when the ratios of resistances in adjacent arms are equal (e.g., R1/R2 = R3/R4).\n * **Option (1):** The voltage source (E) is connected vertically. The galvanometer (G) is connected horizontally between the resistor junctions. However, the detector (D) is placed in series with the 5\u03a9 resistor (R4). This series placement is incorrect for a standard bridge detector.\n * **Option (2):** The voltage source (E) is connected horizontally. The galvanometer (G) is connected vertically between the top and bottom junctions. Similar to option (1), the detector (D) is incorrectly placed in series with the 5\u03a9 resistor (R4).\n * **Option (3):** The voltage source (E) is connected horizontally. Both the galvanometer (G) and the detector (D) are connected in parallel across the vertical junctions (top and bottom). This topology (source across one diagonal, detector(s) across the other) is a valid Wheatstone bridge configuration.\n * **Option (4):** The voltage source (E) is connected vertically. Both the galvanometer (G) and the detector (D) are connected in parallel across the horizontal junctions (left and right midpoints). This topology is also a valid Wheatstone bridge configuration and is perhaps the most common way it is drawn.\n\n3. **Determine the correct option(s):** Options (1) and (2) are incorrect because the detector D is misplaced in series with an arm resistor. Options (3) and (4) both represent topologically correct Wheatstone bridge circuits. In both, the source is connected across one pair of opposite nodes, and the detectors (G and D shown in parallel, which is redundant but topologically valid) are connected across the other pair of opposite nodes. Since the question asks for the correct circuit which *can* achieve balance, and both (3) and (4) are valid configurations, we need to consider if there's a reason to prefer one or if the intended answer might select the most conventional representation. Option (4) is the most conventional diagram. However, *both* 3 and 4 depict circuits that represent a Wheatstone bridge structure capable of balance if the resistance values were appropriate. Assuming the question expects the standard, well-formed bridge circuit diagrams, both 3 and 4 fit. If only one answer can be selected, often the most standard representation (Option 4) is intended. But without further clarification or context suggesting only one is correct, both topologies are valid. Let's check if any option *is* balanced with the given values (although the question asks which *can* achieve balance).\n * Option 3 Balance: (10/15) vs (10/5) or (10/10) vs (15/5). Neither matches.\n * Option 4 Balance: (10/15) vs (10/5) or (10/10) vs (15/5). Neither matches.\n Since neither circuit is balanced as shown, the question must refer to the correctness of the circuit topology. Both 3 and 4 have correct topology. In multiple-choice questions where more than one option seems plausible based on strict criteria, sometimes the most standard or common representation is considered the single \"best\" answer. Option 4 is generally the most common depiction. However, it is possible that the exam allows multiple correct answers or intended both 3 and 4. Given the constraint to provide integer numbers, and the possibility of ambiguity, if only one answer is accepted, option 4 is the most likely intended answer due to convention. If multiple answers were allowed, it would be 3 and 4. Assuming a single best answer based on conventional representation.\n\nLet's assume the question expects the most standard configuration.\nFinal check: Option 4 has source top-bottom, detector left-right. Resistors 10, 10, 15, 5. Topology is correct. Option 3 has source left-right, detector top-bottom. Resistors 10, 10, 15, 5. Topology is correct. Options 1 and 2 have Detector D in series with resistor - incorrect topology. So, 3 and 4 are potentially correct. Given it's a single-choice question format usually, 4 is the most standard representation.\n\nReconsidering common errors or interpretations: sometimes the labelling implies ratios. For example, in Circuit 4, ratio 10/15 compared to 10/5; or 10/10 compared to 15/5. No balance. The wording \"can achieve the bridge balance\" usually refers to the structural possibility.\n\nLet's select 4 as the most standard representation of a correct Wheatstone bridge topology.\n\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 40 |
-
{"question_id": "NEET_2024_T3_040", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 41 |
-
{"question_id": "NEET_2024_T3_041", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 42 |
-
{"question_id": "NEET_2024_T3_042", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```cpp\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 43 |
-
{"question_id": "NEET_2024_T3_043", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 44 |
-
{"question_id": "NEET_2024_T3_044", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 45 |
-
{"question_id": "NEET_2024_T3_045", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` Mmh ``` <answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 46 |
-
{"question_id": "NEET_2024_T3_046", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 47 |
-
{"question_id": "NEET_2024_T3_047", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 48 |
-
{"question_id": "NEET_2024_T3_048", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 49 |
-
{"question_id": "NEET_2024_T3_049", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 50 |
-
{"question_id": "NEET_2024_T3_050", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 51 |
-
{"question_id": "NEET_2024_T3_051", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 52 |
-
{"question_id": "NEET_2024_T3_052", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 53 |
-
{"question_id": "NEET_2024_T3_053", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 54 |
-
{"question_id": "NEET_2024_T3_054", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 55 |
-
{"question_id": "NEET_2024_T3_055", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` Phemistry\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 56 |
-
{"question_id": "NEET_2024_T3_056", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 57 |
-
{"question_id": "NEET_2024_T3_057", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 58 |
-
{"question_id": "NEET_2024_T3_058", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 59 |
-
{"question_id": "NEET_2024_T3_059", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 60 |
-
{"question_id": "NEET_2024_T3_060", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 61 |
-
{"question_id": "NEET_2024_T3_061", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 62 |
-
{"question_id": "NEET_2024_T3_062", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 63 |
-
{"question_id": "NEET_2024_T3_063", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 64 |
-
{"question_id": "NEET_2024_T3_064", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 65 |
-
{"question_id": "NEET_2024_T3_065", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 66 |
-
{"question_id": "NEET_2024_T3_066", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 67 |
-
{"question_id": "NEET_2024_T3_067", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 68 |
-
{"question_id": "NEET_2024_T3_068", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 69 |
-
{"question_id": "NEET_2024_T3_069", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 70 |
-
{"question_id": "NEET_2024_T3_070", "ground_truth": [1], "predicted_answer": [1], "raw_response": "``` C2 molecule bonding clarification:\nMolecular Orbital Theory for C2 (8 valence electrons):\n(\u03c32s)\u00b2 (\u03c3*2s)\u00b2 (\u03c02py)\u00b2 (\u03c02pz)\u00b2\nBond Order = (Number of bonding electrons - Number of antibonding electrons) / 2\nBond Order = (2 + 2 + 2 - 2) / 2 = 6 / 2 = 3 -> incorrect count, it's (2+4 - 2)/2 = 4/2 = 2.\nThe bonding consists of two \u03c0 bonds from (\u03c02py)\u00b2 and (\u03c02pz)\u00b2. The \u03c32p orbital is higher in energy than the \u03c0 orbitals for C2.\nSo, C2 has a bond order of 2, consisting solely of two \u03c0 bonds according to simple MO theory for second-period homonuclear diatomics up to N2.\n\nMatching:\nA. Ethane (C-C): Single bond = one \u03c3-bond -> III\nB. Ethene (C=C): Double bond = one \u03c3-bond and one \u03c0-bond -> IV\nC. Carbon molecule (C\u2082): Two \u03c0-bonds -> II\nD. Ethyne (C\u2261C): Triple bond = one \u03c3-bond and two \u03c0-bonds -> I\n\nMatches: A-III, B-IV, C-II, D-I\n\nChecking options:\n(1) A-III, B-IV, C-II, D-I -> This matches.\n(2) A-III, B-IV, C-I, D-II\n(3) A-I, B-IV, C-II, D-III\n(4) A-IV, B-III, C-II, D-I\n\nThe correct option is (1).\n```\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 71 |
-
{"question_id": "NEET_2024_T3_071", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 72 |
-
{"question_id": "NEET_2024_T3_072", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n```\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 73 |
-
{"question_id": "NEET_2024_T3_073", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 74 |
-
{"question_id": "NEET_2024_T3_074", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 75 |
-
{"question_id": "NEET_2024_T3_075", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 76 |
-
{"question_id": "NEET_2024_T3_076", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 77 |
-
{"question_id": "NEET_2024_T3_077", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n\"<answer>3</answer>\"\n```", "parse_successful": true, "api_call_successful": true}
|
| 78 |
-
{"question_id": "NEET_2024_T3_078", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 79 |
-
{"question_id": "NEET_2024_T3_079", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 80 |
-
{"question_id": "NEET_2024_T3_080", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 81 |
-
{"question_id": "NEET_2024_T3_081", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 82 |
-
{"question_id": "NEET_2024_T3_082", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 83 |
-
{"question_id": "NEET_2024_T3_083", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 84 |
-
{"question_id": "NEET_2024_T3_084", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 85 |
-
{"question_id": "NEET_2024_T3_085", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` D:\\code-generation\\Grounded_generation\\Exam_Analysis\\extraction\\21637196\\ocr_image.png\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 86 |
-
{"question_id": "NEET_2024_T3_086", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```<answer>4</answer>```", "parse_successful": true, "api_call_successful": true}
|
| 87 |
-
{"question_id": "NEET_2024_T3_087", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 88 |
-
{"question_id": "NEET_2024_T3_088", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n{\n \"query\": \"The plot of osmotic pressure (\u03a0) vs concentration (mol L\u207b\u00b9) for a solution gives a straight line with slope 25.73 L bar mol\u207b\u00b9. The temperature at which the osmotic pressure measurement is done is: (Use R = 0.083 L bar mol\u207b\u00b9 K\u207b\u00b9)\",\n \"options\": {\n \"1\": \"25.73\u00b0C\",\n \"2\": \"12.05\u00b0C\",\n \"3\": \"37\u00b0C\",\n \"4\": \"310\u00b0C\"\n },\n \"solution\": {\n \"step1\": \"Recall the van't Hoff equation for osmotic pressure: \u03a0 = iCRT. Assuming a non-electrolyte or that concentration refers to the total solute particle concentration, we can often use \u03a0 = CRT.\",\n \"step2\": \"Identify the variables from the plot described. The plot is \u03a0 vs C. This matches the form y = mx + b, where \u03a0 is y, C is x.\",\n \"step3\": \"Comparing \u03a0 = CRT to y = mx, we see that the slope (m) of the \u03a0 vs C plot is equal to RT.\",\n \"step4\": \"Use the given values: slope = 25.73 L bar mol\u207b\u00b9 and R = 0.083 L bar mol\u207b\u00b9 K\u207b\u00b9.\",\n \"step5\": \"Set the slope equal to RT: 25.73 L bar mol\u207b\u00b9 = (0.083 L bar mol\u207b\u00b9 K\u207b\u00b9) * T.\",\n \"step6\": \"Solve for the absolute temperature T (in Kelvin): T = (25.73 L bar mol\u207b\u00b9) / (0.083 L bar mol\u207b\u00b9 K\u207b\u00b9) = 25.73 / 0.083 K \u2248 310 K.\",\n \"step7\": \"Convert the temperature from Kelvin to Celsius: T(\u00b0C) = T(K) - 273.15. Using 273 for approximation: T(\u00b0C) = 310 K - 273 = 37\u00b0C.\",\n \"step8\": \"Compare the calculated temperature with the given options: Option (3) is 37\u00b0C.\",\n \"final_answer\": 3\n }\n}\n```\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 89 |
-
{"question_id": "NEET_2024_T3_089", "ground_truth": [4], "predicted_answer": [1, 2, 3, 4], "raw_response": "``` C-H KMnO4/H+ OH OH (1) (major product) 'P\" CH-CH (2) C-C (3) CHO (4)COOH ```", "parse_successful": true, "api_call_successful": true}
|
| 90 |
-
{"question_id": "NEET_2024_T3_090", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 91 |
-
{"question_id": "NEET_2024_T3_091", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 92 |
-
{"question_id": "NEET_2024_T3_092", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 93 |
-
{"question_id": "NEET_2024_T3_093", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 94 |
-
{"question_id": "NEET_2024_T3_094", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 95 |
-
{"question_id": "NEET_2024_T3_095", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 96 |
-
{"question_id": "NEET_2024_T3_096", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 97 |
-
{"question_id": "NEET_2024_T3_097", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` MSON ```\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 98 |
-
{"question_id": "NEET_2024_T3_098", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 99 |
-
{"question_id": "NEET_2024_T3_099", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 100 |
-
{"question_id": "NEET_2024_T3_100", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 101 |
-
{"question_id": "NEET_2024_T3_101", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```xml\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 102 |
-
{"question_id": "NEET_2024_T3_102", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 103 |
-
{"question_id": "NEET_2024_T3_103", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 104 |
-
{"question_id": "NEET_2024_T3_104", "ground_truth": [1], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 105 |
-
{"question_id": "NEET_2024_T3_105", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 106 |
-
{"question_id": "NEET_2024_T3_106", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 107 |
-
{"question_id": "NEET_2024_T3_107", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 108 |
-
{"question_id": "NEET_2024_T3_108", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 109 |
-
{"question_id": "NEET_2024_T3_109", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 110 |
-
{"question_id": "NEET_2024_T3_110", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 111 |
-
{"question_id": "NEET_2024_T3_111", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 112 |
-
{"question_id": "NEET_2024_T3_112", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 113 |
-
{"question_id": "NEET_2024_T3_113", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 114 |
-
{"question_id": "NEET_2024_T3_114", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 115 |
-
{"question_id": "NEET_2024_T3_115", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 116 |
-
{"question_id": "NEET_2024_T3_116", "ground_truth": [2], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 117 |
-
{"question_id": "NEET_2024_T3_117", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 118 |
-
{"question_id": "NEET_2024_T3_118", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 119 |
-
{"question_id": "NEET_2024_T3_119", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 120 |
-
{"question_id": "NEET_2024_T3_120", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 121 |
-
{"question_id": "NEET_2024_T3_121", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 122 |
-
{"question_id": "NEET_2024_T3_122", "ground_truth": [2], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 123 |
-
{"question_id": "NEET_2024_T3_123", "ground_truth": [3], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 124 |
-
{"question_id": "NEET_2024_T3_124", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 125 |
-
{"question_id": "NEET_2024_T3_125", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 126 |
-
{"question_id": "NEET_2024_T3_126", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 127 |
-
{"question_id": "NEET_2024_T3_127", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 128 |
-
{"question_id": "NEET_2024_T3_128", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 129 |
-
{"question_id": "NEET_2024_T3_129", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 130 |
-
{"question_id": "NEET_2024_T3_130", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 131 |
-
{"question_id": "NEET_2024_T3_131", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 132 |
-
{"question_id": "NEET_2024_T3_132", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 133 |
-
{"question_id": "NEET_2024_T3_133", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 134 |
-
{"question_id": "NEET_2024_T3_134", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 135 |
-
{"question_id": "NEET_2024_T3_135", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 136 |
-
{"question_id": "NEET_2024_T3_136", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 137 |
-
{"question_id": "NEET_2024_T3_137", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 138 |
-
{"question_id": "NEET_2024_T3_138", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 139 |
-
{"question_id": "NEET_2024_T3_139", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 140 |
-
{"question_id": "NEET_2024_T3_140", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 141 |
-
{"question_id": "NEET_2024_T3_141", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 142 |
-
{"question_id": "NEET_2024_T3_142", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 143 |
-
{"question_id": "NEET_2024_T3_143", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 144 |
-
{"question_id": "NEET_2024_T3_144", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 145 |
-
{"question_id": "NEET_2024_T3_145", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 146 |
-
{"question_id": "NEET_2024_T3_146", "ground_truth": [1], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 147 |
-
{"question_id": "NEET_2024_T3_147", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 148 |
-
{"question_id": "NEET_2024_T3_148", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 149 |
-
{"question_id": "NEET_2024_T3_149", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 150 |
-
{"question_id": "NEET_2024_T3_150", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 151 |
-
{"question_id": "NEET_2024_T3_151", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 152 |
-
{"question_id": "NEET_2024_T3_152", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 153 |
-
{"question_id": "NEET_2024_T3_153", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 154 |
-
{"question_id": "NEET_2024_T3_154", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 155 |
-
{"question_id": "NEET_2024_T3_155", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 156 |
-
{"question_id": "NEET_2024_T3_156", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 157 |
-
{"question_id": "NEET_2024_T3_157", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 158 |
-
{"question_id": "NEET_2024_T3_158", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```shell\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 159 |
-
{"question_id": "NEET_2024_T3_159", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 160 |
-
{"question_id": "NEET_2024_T3_160", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 161 |
-
{"question_id": "NEET_2024_T3_161", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 162 |
-
{"question_id": "NEET_2024_T3_162", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 163 |
-
{"question_id": "NEET_2024_T3_163", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 164 |
-
{"question_id": "NEET_2024_T3_164", "ground_truth": [2], "predicted_answer": [3], "raw_response": "``` BASH\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 165 |
-
{"question_id": "NEET_2024_T3_165", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 166 |
-
{"question_id": "NEET_2024_T3_166", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 167 |
-
{"question_id": "NEET_2024_T3_167", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 168 |
-
{"question_id": "NEET_2024_T3_168", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 169 |
-
{"question_id": "NEET_2024_T3_169", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 170 |
-
{"question_id": "NEET_2024_T3_170", "ground_truth": [4], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 171 |
-
{"question_id": "NEET_2024_T3_171", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 172 |
-
{"question_id": "NEET_2024_T3_172", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 173 |
-
{"question_id": "NEET_2024_T3_173", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 174 |
-
{"question_id": "NEET_2024_T3_174", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```swift\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 175 |
-
{"question_id": "NEET_2024_T3_175", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 176 |
-
{"question_id": "NEET_2024_T3_176", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 177 |
-
{"question_id": "NEET_2024_T3_177", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 178 |
-
{"question_id": "NEET_2024_T3_178", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 179 |
-
{"question_id": "NEET_2024_T3_179", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 180 |
-
{"question_id": "NEET_2024_T3_180", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 181 |
-
{"question_id": "NEET_2024_T3_181", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 182 |
-
{"question_id": "NEET_2024_T3_182", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 183 |
-
{"question_id": "NEET_2024_T3_183", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 184 |
-
{"question_id": "NEET_2024_T3_184", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 185 |
-
{"question_id": "NEET_2024_T3_185", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 186 |
-
{"question_id": "NEET_2024_T3_186", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 187 |
-
{"question_id": "NEET_2024_T3_187", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 188 |
-
{"question_id": "NEET_2024_T3_188", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 189 |
-
{"question_id": "NEET_2024_T3_189", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` MSON\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 190 |
-
{"question_id": "NEET_2024_T3_190", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 191 |
-
{"question_id": "NEET_2024_T3_191", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 192 |
-
{"question_id": "NEET_2024_T3_192", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 193 |
-
{"question_id": "NEET_2024_T3_193", "ground_truth": [3], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 194 |
-
{"question_id": "NEET_2024_T3_194", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 195 |
-
{"question_id": "NEET_2024_T3_195", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true}
|
| 196 |
-
{"question_id": "NEET_2024_T3_196", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true}
|
| 197 |
-
{"question_id": "NEET_2024_T3_197", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true}
|
| 198 |
-
{"question_id": "NEET_2024_T3_198", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true}
|
| 199 |
-
{"question_id": "NEET_2024_T3_199", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
|
| 200 |
-
{"question_id": "NEET_2024_T3_200", "ground_truth": [1], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true}
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results/google_gemini-2.5-pro-preview-03-25/summary.json
DELETED
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@@ -1,7 +0,0 @@
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-
{
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-
"model_name": "google/gemini-2.5-pro-preview-03-25",
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-
"total_questions": 200,
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-
"successful_predictions": 199,
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"api_failures": 0,
|
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-
"accuracy": 0.9396984924623115
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-
}
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results/google_gemini-2.5-pro-preview-03-25_20250505_091752/predictions.jsonl
ADDED
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@@ -0,0 +1,200 @@
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| 1 |
+
{"question_id": "NEET_2024_T3_001", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 2 |
+
{"question_id": "NEET_2024_T3_002", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 3 |
+
{"question_id": "NEET_2024_T3_003", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` P\n\u2191\nd\nC\n300 kPa\n100 kPa\nb\na\n3\n3\n100 cm\n400 cm\nV-\n```\n\nThe question asks for the work done by the gas along the path *bc*.\nWork done by the gas is given by the integral W = \u222b P dV.\nAlong the path *bc*, the process goes from state *b* to state *c*.\nFrom the diagram:\nState *b*: V_b = 400 cm\u00b3, P_b = 100 kPa\nState *c*: V_c = 400 cm\u00b3, P_c = 300 kPa\n\nSince the volume is constant along the path *bc* (V_b = V_c = 400 cm\u00b3), the change in volume dV is 0.\nTherefore, the work done W_bc = \u222b P dV = P \u0394V = P * (V_c - V_b) = P * (400 cm\u00b3 - 400 cm\u00b3) = 0.\nThe work done by the gas along the path *bc* is zero.\n\nComparing this with the options:\n(1) -90 J\n(2) -60 J\n(3) zero\n(4) 30 J\n\nThe correct option is (3).\n\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 4 |
+
{"question_id": "NEET_2024_T3_004", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 5 |
+
{"question_id": "NEET_2024_T3_005", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 6 |
+
{"question_id": "NEET_2024_T3_006", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 7 |
+
{"question_id": "NEET_2024_T3_007", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 8 |
+
{"question_id": "NEET_2024_T3_008", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 9 |
+
{"question_id": "NEET_2024_T3_009", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 10 |
+
{"question_id": "NEET_2024_T3_010", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "raw_response": "``` Mnemonic:\n1. Analyze the question: Replace monochromatic light with white light in YDSE.\n2. Apply physics concepts:\n - Central fringe (path difference = 0): Constructive interference for ALL wavelengths. Result: White light.\n - Other fringes (path difference \u2260 0): Position depends on wavelength (y = m\u03bbD/d). Different colors form fringes at different positions. Result: Colored fringes.\n - Fringe width (\u03b2 = \u03bbD/d): Depends on wavelength. Fringes are not of equal width for different colors.\n3. Evaluate options:\n - (1) Central bright white fringe, surrounded by colored fringes. -> Matches explanation.\n - (2) All bright fringes equal width. -> Incorrect, width depends on \u03bb.\n - (3) Pattern disappears. -> Incorrect, pattern forms but is colored.\n - (4) Central \"dear\" fringe (typo). Central fringe is white.\n4. Select the best option: (1) is the most accurate description. ```\n\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 11 |
+
{"question_id": "NEET_2024_T3_011", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` C\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 12 |
+
{"question_id": "NEET_2024_T3_012", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 13 |
+
{"question_id": "NEET_2024_T3_013", "subject": "Physics", "ground_truth": [3], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 14 |
+
{"question_id": "NEET_2024_T3_014", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 15 |
+
{"question_id": "NEET_2024_T3_015", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` SiliBocon\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 16 |
+
{"question_id": "NEET_2024_T3_016", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 17 |
+
{"question_id": "NEET_2024_T3_017", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 18 |
+
{"question_id": "NEET_2024_T3_018", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 19 |
+
{"question_id": "NEET_2024_T3_019", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 20 |
+
{"question_id": "NEET_2024_T3_020", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 21 |
+
{"question_id": "NEET_2024_T3_021", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 22 |
+
{"question_id": "NEET_2024_T3_022", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 23 |
+
{"question_id": "NEET_2024_T3_023", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 24 |
+
{"question_id": "NEET_2024_T3_024", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 25 |
+
{"question_id": "NEET_2024_T3_025", "subject": "Physics", "ground_truth": [1, 3], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 26 |
+
{"question_id": "NEET_2024_T3_026", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 27 |
+
{"question_id": "NEET_2024_T3_027", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 28 |
+
{"question_id": "NEET_2024_T3_028", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 29 |
+
{"question_id": "NEET_2024_T3_029", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` T = 2\u03c0 * sqrt(I / (M * B)) ```\nWhere:\n* T is the time period of oscillation\n* I is the moment of inertia\n* M is the magnetic moment\n* B is the magnetic field strength\n\nFirst, calculate the time period T:\nThe needle performs 20 complete oscillations in 5 seconds.\n``` T = Time / Number of oscillations = 5 s / 20 = 1/4 s = 0.25 s ```\n\nGiven values:\n* B = 0.049 T\n* I = 9.8 x 10\u207b\u2076 kg m\u00b2\n* T = 0.25 s\n* M = x * 10\u207b\u2075 Am\u00b2\n\nRearrange the formula to solve for M:\n``` T\u00b2 = (2\u03c0)\u00b2 * (I / (M * B)) ```\n``` T\u00b2 = 4\u03c0\u00b2 * I / (M * B) ```\n``` M = 4\u03c0\u00b2 * I / (T\u00b2 * B) ```\n\nSubstitute the values:\n``` M = 4\u03c0\u00b2 * (9.8 * 10\u207b\u2076) / ((0.25)\u00b2 * 0.049) ```\n``` M = 4\u03c0\u00b2 * (9.8 * 10\u207b\u2076) / (0.0625 * 0.049) ```\n``` M = 4\u03c0\u00b2 * (9.8 * 10\u207b\u2076) / 0.0030625 ```\n\nLet's simplify the calculation: 0.049 = 49 * 10\u207b\u00b3, 9.8 = 98 * 10\u207b\u00b9, 0.0625 = 1/16.\n``` M = 4\u03c0\u00b2 * (9.8 * 10\u207b\u2076) / ((1/16) * 0.049) ```\n``` M = 4\u03c0\u00b2 * 16 * (9.8 * 10\u207b\u2076) / 0.049 ```\n``` M = 64\u03c0\u00b2 * (9.8 / 0.049) * 10\u207b\u2076 ```\n``` M = 64\u03c0\u00b2 * (980 / 49) * 10\u207b\u2076 ```\n``` M = 64\u03c0\u00b2 * 20 * 10\u207b\u2076 ```\n``` M = 1280\u03c0\u00b2 * 10\u207b\u2076 ```\n``` M = 12.80\u03c0\u00b2 * 10\u207b\u2074 ```\n``` M = 1.280\u03c0\u00b2 * 10\u207b\u00b3 ```\n``` M = 0.1280\u03c0\u00b2 * 10\u207b\u00b2 ```\n``` M = 1280\u03c0\u00b2 * 10\u207b\u2076 Am\u00b2 ```\n\nWe are given that M = x * 10\u207b\u2075 Am\u00b2. Let's convert our result to this form:\n``` M = 1280 * 10\u207b\u00b9 * \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2 ```\n``` M = 128.0 \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2 ```\n\nWait, let me recalculate 64 * 20.\n64 * 20 = 1280.\nSo, M = 1280 \u03c0\u00b2 * 10\u207b\u2076 Am\u00b2\nConvert to 10\u207b\u2075:\nM = (1280 / 10) \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2\nM = 128 \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2\n\nComparing M = 128 \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2 with M = x * 10\u207b\u2075 Am\u00b2:\nx = 128 \u03c0\u00b2\n\nLooking at the options:\n(1) 50 \u03c0\u00b2\n(2) 1280 \u03c0\u00b2\n(3) 5 \u03c0\u00b2\n(4) 128 \u03c0\u00b2\n\nThe value of x is 128 \u03c0\u00b2, which corresponds to option (4).\n\nLet me recheck the calculation:\nM = 4\u03c0\u00b2 * I / (T\u00b2 * B)\nM = 4\u03c0\u00b2 * (9.8 x 10\u207b\u2076) / ( (1/4)\u00b2 * 0.049 )\nM = 4\u03c0\u00b2 * (9.8 x 10\u207b\u2076) / ( (1/16) * 0.049 )\nM = 4\u03c0\u00b2 * 16 * (9.8 x 10\u207b\u2076) / 0.049\nM = 64\u03c0\u00b2 * (9.8 / 0.049) * 10\u207b\u2076\nM = 64\u03c0\u00b2 * 200 * 10\u207b\u2076\nM = 12800 \u03c0\u00b2 * 10\u207b\u2076\nM = 12800 * 10\u207b\u00b9 \u03c0\u00b2 * 10\u207b\u2075\nM = 1280 \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2\n\nComparing M = 1280 \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2 with M = x * 10\u207b\u2075 Am\u00b2:\nx = 1280 \u03c0\u00b2\n\nSo the correct option is (2).\n\nLet's review the calculations once more to be sure.\nT = 5/20 = 1/4 s\nB = 0.049 T\nI = 9.8 x 10\u207b\u2076 kg m\u00b2\nM = x * 10\u207b\u2075 Am\u00b2\n\nT = 2\u03c0 sqrt(I / MB)\nT\u00b2 = 4\u03c0\u00b2 I / (MB)\nM = 4\u03c0\u00b2 I / (T\u00b2 B)\nM = 4\u03c0\u00b2 (9.8 x 10\u207b\u2076) / ((1/4)\u00b2 * 0.049)\nM = 4\u03c0\u00b2 (9.8 x 10\u207b\u2076) / ((1/16) * 0.049)\nM = 4\u03c0\u00b2 * 16 * (9.8 x 10\u207b\u2076) / 0.049\nM = 64\u03c0\u00b2 * (9.8/0.049) * 10\u207b\u2076\nM = 64\u03c0\u00b2 * 200 * 10\u207b\u2076\nM = 12800 \u03c0\u00b2 * 10\u207b\u2076\nM = (12800 / 10) \u03c0\u00b2 * 10\u207b\u2075\nM = 1280 \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2\n\nComparing M = x * 10\u207b\u2075 Am\u00b2\nx = 1280 \u03c0\u00b2\n\nThe calculation seems correct. The value of x is 1280 \u03c0\u00b2. This corresponds to option (2).\n\nFinal check:\nT = 0.25 s\nT\u00b2 = 0.0625 s\u00b2\nB = 0.049 T\nI = 9.8 x 10\u207b\u2076 kg m\u00b2\nM = 4\u03c0\u00b2 * (9.8e-6) / (0.0625 * 0.049)\nM = 4\u03c0\u00b2 * (9.8e-6) / 0.0030625\nM = 4\u03c0\u00b2 * 3200 * 10\u207b\u2076\nM = 12800 \u03c0\u00b2 * 10\u207b\u2076\nM = 1.28 \u03c0\u00b2 * 10\u207b\u00b2\nM = 1280 \u03c0\u00b2 * 10\u207b\u2075 Am\u00b2\nx = 1280 \u03c0\u00b2\nOption (2) is correct.<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 30 |
+
{"question_id": "NEET_2024_T3_030", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 31 |
+
{"question_id": "NEET_2024_T3_031", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
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| 32 |
+
{"question_id": "NEET_2024_T3_032", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` M\u2070L\u2070T\u2070 ```\n\n* **Strain:** Strain is defined as the ratio of change in dimension (length, area, volume) to the original dimension. For example, longitudinal strain = \u0394L/L. Since both numerator and denominator have dimensions of length [L], strain is dimensionless: ``` [L]/[L] = [M\u2070L\u2070T\u2070] ```.\n* **Arc:** Arc length is a measure of distance along a curve, so it has dimensions of length: ``` [L] ```.\n* **Angular speed (\u03c9):** Angular speed is the rate of change of angle (\u03b8) with respect to time (t), \u03c9 = d\u03b8/dt. Angle (plane angle) is dimensionless, and time has dimensions [T]. So, angular speed has dimensions: ``` [M\u2070L\u2070T\u2070] / [T] = [T\u207b\u00b9] ```.\n* **Stress:** Stress is defined as force (F) per unit area (A). Force has dimensions [MLT\u207b\u00b2] and area has dimensions [L\u00b2]. So, stress has dimensions: ``` [MLT\u207b\u00b2] / [L\u00b2] = [ML\u207b\u00b9T\u207b\u00b2] ```.\n* **Angle (plane angle, \u03b8):** Plane angle is defined as the ratio of the arc length (s) subtended to the radius (r), \u03b8 = s/r. Arc length has dimensions [L] and radius has dimensions [L]. So, angle is dimensionless: ``` [L]/[L] = [M\u2070L\u2070T\u2070] ```.\n\nThe question asks for quantities that have the same dimensions as a solid angle. A solid angle (\u03a9) is defined as the ratio of the subtended area (A) on a sphere to the square of the radius (r\u00b2), \u03a9 = A/r\u00b2. Area has dimensions [L\u00b2] and radius squared has dimensions [L\u00b2]. Thus, solid angle is dimensionless: ``` [L\u00b2] / [L\u00b2] = [M\u2070L\u2070T\u2070] ```.\n\nWe need to find the option where both quantities are dimensionless.\n\n* Option (1): Strain (dimensionless), Arc ([L]). Not both dimensionless.\n* Option (2): Angular speed ([T\u207b\u00b9]), Stress ([ML\u207b\u00b9T\u207b\u00b2]). Neither is dimensionless.\n* Option (3): Strain (dimensionless), Angle (dimensionless). Both are dimensionless, matching the dimensions of a solid angle.\n* Option (4): Stress ([ML\u207b\u00b9T\u207b\u00b2]), Angle (dimensionless). Not both dimensionless.\n\nTherefore, the correct option is (3) because both strain and angle are dimensionless, just like a solid angle.\n\n```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 33 |
+
{"question_id": "NEET_2024_T3_033", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 34 |
+
{"question_id": "NEET_2024_T3_034", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 35 |
+
{"question_id": "NEET_2024_T3_035", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 36 |
+
{"question_id": "NEET_2024_T3_036", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 37 |
+
{"question_id": "NEET_2024_T3_037", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 38 |
+
{"question_id": "NEET_2024_T3_038", "subject": "Physics", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
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| 39 |
+
{"question_id": "NEET_2024_T3_039", "subject": "Physics", "ground_truth": [3], "predicted_answer": [4], "raw_response": "``` C\n1. Analyze the Question: The question asks to identify the circuit diagram that represents a Wheatstone bridge configuration capable of achieving balance.\n2. Analyze the Options: Four circuit diagrams labeled (1), (2), (3), and (4) are provided. All four diagrams depict a Wheatstone bridge circuit with four resistors (10\u03a9, 10\u03a9, 15\u03a9, 5\u03a9), a galvanometer (G), a component labeled 'D', and a voltage source (E).\n3. Check Balance Condition: The condition for a balanced Wheatstone bridge is R1/R2 = R3/R4, where R1, R2, R3, R4 are the resistors in the arms. Using the values given (labeling TL=10, TR=10, BL=15, BR=5):\n * TL/BL = 10/15 = 2/3\n * TR/BR = 10/5 = 2\n * Since 2/3 \u2260 2, the bridge with these specific *fixed* resistor values is not balanced.\n4. Interpret \"Can Achieve Balance\": The question asks which circuit *can* achieve balance. This might imply either:\n * One of the components is variable.\n * The diagram represents the correct *setup* for a potentially balanceable bridge, even if the values shown don't balance it.\n5. Analyze Component Placement:\n * Source E: Connected correctly across two opposite vertices in all options.\n * Galvanometer G: Connected correctly between the other two opposite vertices (the central branch) in all options.\n * Component D:\n * In options 1, 2, and 3, 'D' is placed in series with the 5\u03a9 resistor in the bottom-right arm.\n * In option 4, 'D' is placed in series with the galvanometer 'G' in the central branch.\n6. Identify Structural Correctness for Detection: The central branch (where G is located) is where the balance detector (typically a galvanometer) is placed to sense zero current. Placing an additional component 'D', possibly related to detection, in this same central branch (as in option 4) maintains the correct structural location for the detection part of the circuit. Placing 'D' in series with one of the main bridge arms (as in options 1, 2, 3) is non-standard for a *detector*. If 'D' were a variable resistor *for balancing*, then placement in an arm (like 1, 2, 3) would be logical. However, typically, a component labeled 'D' alongside 'G' would imply Detector.\n7. Consider Identical Options: Options (1), (2), and (3) are electrically and visually identical diagrams. In a multiple-choice question with a single correct answer, if one of these were correct, they all would be. This suggests that either the question/options are flawed or that option (4) must be the intended answer based on some principle. The principle most likely relates to the correct *placement* of detection components (G and D) in the central bridge arm.\n8. Conclusion: Although the specific values don't lead to balance, option 4 represents the most standard configuration regarding the placement of detection components (G and implicitly D if it's also for detection) within the central branch of the Wheatstone bridge circuit.\n\n```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 40 |
+
{"question_id": "NEET_2024_T3_040", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 41 |
+
{"question_id": "NEET_2024_T3_041", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` C\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 42 |
+
{"question_id": "NEET_2024_T3_042", "subject": "Physics", "ground_truth": [4], "predicted_answer": "SKIP", "raw_response": "<answer>SKIP</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "skipped", "marks_awarded": 0}
|
| 43 |
+
{"question_id": "NEET_2024_T3_043", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 44 |
+
{"question_id": "NEET_2024_T3_044", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 45 |
+
{"question_id": "NEET_2024_T3_045", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 46 |
+
{"question_id": "NEET_2024_T3_046", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 47 |
+
{"question_id": "NEET_2024_T3_047", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 48 |
+
{"question_id": "NEET_2024_T3_048", "subject": "Physics", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 49 |
+
{"question_id": "NEET_2024_T3_049", "subject": "Physics", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```cpp\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 50 |
+
{"question_id": "NEET_2024_T3_050", "subject": "Physics", "ground_truth": [4], "predicted_answer": "SKIP", "raw_response": "<answer>SKIP</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "skipped", "marks_awarded": 0}
|
| 51 |
+
{"question_id": "NEET_2024_T3_051", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 52 |
+
{"question_id": "NEET_2024_T3_052", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```xml\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 53 |
+
{"question_id": "NEET_2024_T3_053", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 54 |
+
{"question_id": "NEET_2024_T3_054", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 55 |
+
{"question_id": "NEET_2024_T3_055", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 56 |
+
{"question_id": "NEET_2024_T3_056", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 57 |
+
{"question_id": "NEET_2024_T3_057", "subject": "Chemistry", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 58 |
+
{"question_id": "NEET_2024_T3_058", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 59 |
+
{"question_id": "NEET_2024_T3_059", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 60 |
+
{"question_id": "NEET_2024_T3_060", "subject": "Chemistry", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 61 |
+
{"question_id": "NEET_2024_T3_061", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 62 |
+
{"question_id": "NEET_2024_T3_062", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 63 |
+
{"question_id": "NEET_2024_T3_063", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 64 |
+
{"question_id": "NEET_2024_T3_064", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```c\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 65 |
+
{"question_id": "NEET_2024_T3_065", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 66 |
+
{"question_id": "NEET_2024_T3_066", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 67 |
+
{"question_id": "NEET_2024_T3_067", "subject": "Chemistry", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 68 |
+
{"question_id": "NEET_2024_T3_068", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 69 |
+
{"question_id": "NEET_2024_T3_069", "subject": "Chemistry", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n```json\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 70 |
+
{"question_id": "NEET_2024_T3_070", "subject": "Chemistry", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 71 |
+
{"question_id": "NEET_2024_T3_071", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```xml\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 72 |
+
{"question_id": "NEET_2024_T3_072", "subject": "Chemistry", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 73 |
+
{"question_id": "NEET_2024_T3_073", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 74 |
+
{"question_id": "NEET_2024_T3_074", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n[\n {\"box_2d\": [94, 118, 117, 149], \"text\": \"74.\"},\n {\"box_2d\": [117, 118, 202, 956], \"text\": \"In which of the following equilibria, Kp and Ke are\"},\n {\"box_2d\": [217, 116, 255, 298], \"text\": \"NOT equal?\"},\n {\"box_2d\": [266, 116, 316, 701], \"text\": \"(1) CO(g) + H2O(g)CO2(g) + H2(g)\"},\n {\"box_2d\": [396, 115, 447, 671], \"text\": \"(2) 2 BrCl(g) Br2(g) + Cl2(g)\"},\n {\"box_2d\": [529, 116, 571, 148], \"text\": \"(3)\"},\n {\"box_2d\": [531, 189, 572, 280], \"text\": \"PCl5(g)\"},\n {\"box_2d\": [554, 400, 596, 650], \"text\": \"PCl3(g) + Cl2(g)\"},\n {\"box_2d\": [656, 116, 699, 147], \"text\": \"(4)\"},\n {\"box_2d\": [658, 188, 712, 598], \"text\": \"H2(g) + I2(g) = 2 HI(g)\"}\n]\n```\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 75 |
+
{"question_id": "NEET_2024_T3_075", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 76 |
+
{"question_id": "NEET_2024_T3_076", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` C6H11+\n<answer>2</answer>```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 77 |
+
{"question_id": "NEET_2024_T3_077", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 78 |
+
{"question_id": "NEET_2024_T3_078", "subject": "Chemistry", "ground_truth": [1], "predicted_answer": [1], "raw_response": "``` D. **Cl2(g) \u2192 2Cl(g):** One mole of gas (Cl2) forms two moles of gas (2Cl). The number of moles of gas increases (\u0394n_g = 2 - 1 = 1 > 0), leading to an increase in entropy. Entropy increases.\n\nTherefore, entropy increases in processes A, C, and D.\n\nLooking at the options:\n(1) A, C and D\n(2) C and D\n(3) A and C\n(4) A, B and D\n\nThe correct option includes A, C, and D. This corresponds to option (1).\n\n```<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 79 |
+
{"question_id": "NEET_2024_T3_079", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 80 |
+
{"question_id": "NEET_2024_T3_080", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 81 |
+
{"question_id": "NEET_2024_T3_081", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 82 |
+
{"question_id": "NEET_2024_T3_082", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` C\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 83 |
+
{"question_id": "NEET_2024_T3_083", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 84 |
+
{"question_id": "NEET_2024_T3_084", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` DRAFT\n1. **Analyze the question:** The question asks to identify the most stable carbocation among the four given chemical structures. Carbocation stability increases with the number of alkyl groups attached to the positively charged carbon atom due to inductive effects and hyperconjugation. The order of stability is generally: tertiary (3\u00b0) > secondary (2\u00b0) > primary (1\u00b0) > methyl. Resonance can further enhance stability.\n\n2. **Analyze the options:**\n * **(1)** This is a primary carbocation. The positively charged carbon (-CH\u2082\u207a) is attached to only one other carbon atom (the cyclopentyl ring karbon).\n * **(2)** This is a tertiary carbocation. The positively charged carbon (within the cyclohexane ring) is attached to three other carbon atoms (two within the ring and one from the methyl group).\n * **(3)** This is a secondary carbocation. The positively charged carbon (-CH\u207a-) is attached to two other carbon atoms (one from the ethyl group -CH\u2082- and one from the isopropyl group -CH-). Specifically, it is 3-methylpentan-3-yl cation based on the drawing: CH3-CH2-CH(+)-CH(CH3)2. This is incorrect reading of the structure. Actually it is CH3-CH2-CH(+)-CH(CH3)2 - yes, this is secondary.\n * **(4)** This is a secondary carbocation. The positively charged carbon (-CH\u207a-) is attached to two other carbon atoms (one from the methyl group -CH\u2083 and one from the isobutyl group -CH\u2082-). Specifically, it is 4-methylpentan-2-yl cation: CH3-CH(+)-CH2-CH(CH3)2.\n\n3. **Compare stability:** Based on the order of stability (tertiary > secondary > primary), the tertiary carbocation in option (2) is the most stable. Options (1) is primary, and options (3) and (4) are secondary.\n\n4. **Determine the correct integer:** The correct option is (2).\n\n5. **Format the answer:** <answer>2</answer>\n```\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 85 |
+
{"question_id": "NEET_2024_T3_085", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 86 |
+
{"question_id": "NEET_2024_T3_086", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n```json\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 87 |
+
{"question_id": "NEET_2024_T3_087", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 88 |
+
{"question_id": "NEET_2024_T3_088", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` Memento ```python\nimport math\n\n# Given information\nslope = 25.73 # L bar mol^-1\nR = 0.083 # L bar mol^-1 K^-1\n\n# Formula relating osmotic pressure (\u03a0), concentration (C), and temperature (T)\n# \u03a0 = i * C * R * T\n# Plot of \u03a0 vs C gives a straight line with slope = i * R * T\n# Assuming i=1 (van't Hoff factor for non-electrolyte or ideal solution)\n# Slope = R * T\n\n# Calculate Temperature in Kelvin (T_K)\nT_K = slope / R\nT_K = 25.73 / 0.083\nT_K = 310.0 # Kelvin\n\n# Convert Temperature to Celsius (T_C)\n# T_C = T_K - 273.15\n# More commonly in these types of problems, 273 is used for conversion.\nT_C_approx = T_K - 273\nT_C_approx = 310 - 273\nT_C_approx = 37 # degrees Celsius\n\nT_C_exact = T_K - 273.15\nT_C_exact = 310.0 - 273.15\nT_C_exact = 36.85 # degrees Celsius\n\n# Check the options\noption1 = 25.73 # \u00b0C\noption2 = 12.05 # \u00b0C\noption3 = 37 # \u00b0C\noption4 = 310 # \u00b0C\n\n# The calculated temperature (36.85\u00b0C or approximately 37\u00b0C) matches option (3).\n\n# Select the option number corresponding to 37\u00b0C.\ncorrect_option_number = 3\n```\n\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 89 |
+
{"question_id": "NEET_2024_T3_089", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` C6H11\n|\nC=CH + KMnO4/H+ -> 'P' (major product)\n| |\nH C6H11\n```\n\n1. **Analyze the reaction:** The reaction involves an alkene (1,2-dicyclohexylethylene) reacting with potassium permanganate (KMnO4) in acidic conditions (H+). Hot, acidic KMnO4 is a strong oxidizing agent that causes oxidative cleavage of the carbon-carbon double bond.\n2. **Determine the products of oxidative cleavage:** The double bond (C=C) is broken. Each carbon atom that was part of the double bond gets oxidized.\n * If a carbon atom of the double bond has one hydrogen attached (R-CH=), it gets oxidized to a carboxylic acid group (-COOH).\n * If a carbon atom of the double bond has two hydrogens attached (=CH2), it gets oxidized to carbon dioxide (CO2).\n * If a carbon atom of the double bond has no hydrogens attached (R-C(R')=), it gets oxidized to a ketone group (=O).\n3. **Apply to the specific substrate:** In the given alkene, both carbon atoms of the double bond are identical in terms of substitution. Each carbon is attached to one hydrogen atom and one cyclohexyl group (C6H11).\n ```\n C6H11-CH=CH-C6H11\n ```\n Cleavage occurs at the double bond. The left `C6H11-CH=` fragment will be oxidized to `C6H11-COOH`. The right `=CH-C6H11` fragment will also be oxidized to `C6H11-COOH`.\n4. **Identify the major product 'P':** The reaction breaks the molecule into two identical fragments, each being cyclohexanecarboxylic acid. Therefore, the major product 'P' is cyclohexanecarboxylic acid.\n5. **Match with the options:**\n * Option (1) is 1,2-dicyclohexyl-1,2-ethanediol. This is the product of syn-dihydroxylation (e.g., cold, dilute, alkaline KMnO4 or OsO4). Not correct for hot/acidic KMnO4.\n * Option (2) is 1,2-dicyclohexylethanedione. This would imply oxidation without cleavage, or cleavage of a different starting material. Not correct.\n * Option (3) is cyclohexanecarbaldehyde. Aldehydes are intermediates but are readily oxidized to carboxylic acids by strong oxidizing agents like hot/acidic KMnO4. Not the final major product.\n * Option (4) is cyclohexanecarboxylic acid. This matches the expected product of oxidative cleavage.\n\nTherefore, the correct option is (4).\n\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 90 |
+
{"question_id": "NEET_2024_T3_090", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n{\n \"step_by_step_thinking\": \"Step 1: Analyze Statement I. Statement I says '[Co(NH3)6]3+ is a homoleptic complex whereas [Co(NH3)4Cl2]+ is a heteroleptic complex.' A homoleptic complex has only one type of ligand attached to the central metal ion. In [Co(NH3)6]3+, only NH3 ligands are attached to Co. Thus, it is homoleptic. A heteroleptic complex has more than one type of ligand attached to the central metal ion. in [Co(NH3)4Cl2]+, both NH3 and Cl ligands are attached to Co. Thus, it is heteroleptic. Therefore, Statement I is true.\\nStep 2: Analyze Statement II. Statement II says 'Complex [Co(NH3)6]3+ has only one kind of ligands but [Co(NH3)4Cl2]+ has more than one kind of ligands.' As established in Step 1, [Co(NH3)6]3+ has only NH3 ligands (one kind). Also, [Co(NH3)4Cl2]+ has NH3 and Cl ligands (two kinds, which is more than one kind). Therefore, Statement II is true.\\nStep 3: Evaluate the options based on the truthfulness of the statements. Since both Statement I and Statement II are true, we need to find the option that reflects this.\\nOption (1) says Statement I is true but Statement II is false. This is incorrect.\\nOption (2) says Statement I is false but Statement II is true. This is incorrect.\\nOption (3) says Both Statement I and Statement II are true. This is correct.\\nOption (4) says Both Statement I and Statement II are false. This is incorrect.\\nStep 4: Identify the correct option number. The correct option is (3).\\nStep 5: Format the final answer as requested.\",\n \"final_answer\": \"<answer>3</answer>\"\n}\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 91 |
+
{"question_id": "NEET_2024_T3_091", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 92 |
+
{"question_id": "NEET_2024_T3_092", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 93 |
+
{"question_id": "NEET_2024_T3_093", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 94 |
+
{"question_id": "NEET_2024_T3_094", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` ELECTION ANALYSIS\n1. **Step 1:** 1-Iodopropane (CH\u2083-CH\u2082-CH\u2082-I) reacts with sodium cyanide (NaCN). This is an SN2 reaction where the cyanide ion (CN\u207b) replaces the iodide ion (I\u207b). The product A is butanenitrile (CH\u2083-CH\u2082-CH\u2082-CN).\n2. **Step 2:** Butanenitrile (A) undergoes partial hydrolysis with hydroxide ions (OH\u207b). Partial hydrolysis of a nitrile yields an amide. The product B is butanamide (CH\u2083-CH\u2082-CH\u2082-CONH\u2082).\n3. **Step 3:** Butanamide (B) reacts with sodium hydroxide (NaOH) and bromine (Br\u2082). This is the Hofmann rearrangement (or Hofmann degradation) reaction. In this reaction, an amide is converted to a primary amine with one less carbon atom. Butanamide (4 carbons) is converted to propylamine (CH\u2083-CH\u2082-CH\u2082-NH\u2082), which has 3 carbons. Product C is propylamine.\n\n**Checking the Options:**\n* (1) butanamide: This is intermediate B.\n* (2) \u03b1-bromobutanoic acid: Incorrect product.\n* (3) propylamine: This is the final product C (CH\u2083-CH\u2082-CH\u2082-NH\u2082).\n* (4) butylamine: Incorrect product (same number of carbons as B, Hofmann degradation removes one carbon).\n\nTherefore, the correct option is (3).\n```\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 95 |
+
{"question_id": "NEET_2024_T3_095", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 96 |
+
{"question_id": "NEET_2024_T3_096", "subject": "Chemistry", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```xml\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 97 |
+
{"question_id": "NEET_2024_T3_097", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [1], "raw_response": "```php\n```\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 98 |
+
{"question_id": "NEET_2024_T3_098", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 99 |
+
{"question_id": "NEET_2024_T3_099", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` MSON\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 100 |
+
{"question_id": "NEET_2024_T3_100", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 101 |
+
{"question_id": "NEET_2024_T3_101", "subject": "Botany", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 102 |
+
{"question_id": "NEET_2024_T3_102", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "1. **Analyze the question:** The question describes a conservation method where threatened species are removed from their natural habitat and kept in a controlled, protected environment (like zoos, botanical gardens, seed banks). This method is known as *ex-situ* conservation (off-site conservation).\n\n2. **Analyze the options:**\n * (1) Semi-conservative method: Refers to DNA replication. Incorrect.\n * (2) Sustainable development: A broad economic and environmental policy concept. Incorrect.\n * (3) *in-situ* conservation: Means conservation within the natural habitat (e.g., national parks, wildlife sanctuaries). This is the opposite of the method described in the question. Incorrect.\n * (4) Biodiversity conservation: This is the overall effort to preserve the variety of life on Earth. It encompasses both *in-situ* and *ex-situ* strategies.\n\n3. **Determine the correct option:** The specific type of conservation described in the question is *ex-situ* conservation. This option is not provided. Among the given choices, *in-situ* conservation (3) is the opposite approach. Biodiversity conservation (4) is a broad term that includes *ex-situ* conservation as one of its strategies. Options (1) and (2) are irrelevant. Since the specific term (*ex-situ*) is missing, and *in-situ* is incorrect, the most appropriate answer among the given choices is the broader category under which *ex-situ* conservation falls, which is Biodiversity conservation (4). Although imprecise, it's the best fit given the options.\n\n4. **Format the answer:** The correct option number is 4.\n\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 103 |
+
{"question_id": "NEET_2024_T3_103", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 104 |
+
{"question_id": "NEET_2024_T3_104", "subject": "Botany", "ground_truth": [1], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 105 |
+
{"question_id": "NEET_2024_T3_105", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 106 |
+
{"question_id": "NEET_2024_T3_106", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 107 |
+
{"question_id": "NEET_2024_T3_107", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 108 |
+
{"question_id": "NEET_2024_T3_108", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 109 |
+
{"question_id": "NEET_2024_T3_109", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 110 |
+
{"question_id": "NEET_2024_T3_110", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 111 |
+
{"question_id": "NEET_2024_T3_111", "subject": "Botany", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n```xml\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 112 |
+
{"question_id": "NEET_2024_T3_112", "subject": "Botany", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```xml\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 113 |
+
{"question_id": "NEET_2024_T3_113", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 114 |
+
{"question_id": "NEET_2024_T3_114", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 115 |
+
{"question_id": "NEET_2024_T3_115", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 116 |
+
{"question_id": "NEET_2024_T3_116", "subject": "Botany", "ground_truth": [2], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 117 |
+
{"question_id": "NEET_2024_T3_117", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 118 |
+
{"question_id": "NEET_2024_T3_118", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 119 |
+
{"question_id": "NEET_2024_T3_119", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 120 |
+
{"question_id": "NEET_2024_T3_120", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 121 |
+
{"question_id": "NEET_2024_T3_121", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 122 |
+
{"question_id": "NEET_2024_T3_122", "subject": "Botany", "ground_truth": [2], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 123 |
+
{"question_id": "NEET_2024_T3_123", "subject": "Botany", "ground_truth": [3], "predicted_answer": [4], "raw_response": "``` MSON ```\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 124 |
+
{"question_id": "NEET_2024_T3_124", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 125 |
+
{"question_id": "NEET_2024_T3_125", "subject": "Botany", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 126 |
+
{"question_id": "NEET_2024_T3_126", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 127 |
+
{"question_id": "NEET_2024_T3_127", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 128 |
+
{"question_id": "NEET_2024_T3_128", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 129 |
+
{"question_id": "NEET_2024_T3_129", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 130 |
+
{"question_id": "NEET_2024_T3_130", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 131 |
+
{"question_id": "NEET_2024_T3_131", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 132 |
+
{"question_id": "NEET_2024_T3_132", "subject": "Botany", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` MuiTypography::variant=\"body1\" ``````json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 133 |
+
{"question_id": "NEET_2024_T3_133", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 134 |
+
{"question_id": "NEET_2024_T3_134", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` MMATCH FAIL: '\u0410-\u0406\u041f' vs 'A-III' ```\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 135 |
+
{"question_id": "NEET_2024_T3_135", "subject": "Botany", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 136 |
+
{"question_id": "NEET_2024_T3_136", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 137 |
+
{"question_id": "NEET_2024_T3_137", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 138 |
+
{"question_id": "NEET_2024_T3_138", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 139 |
+
{"question_id": "NEET_2024_T3_139", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 140 |
+
{"question_id": "NEET_2024_T3_140", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 141 |
+
{"question_id": "NEET_2024_T3_141", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n[\n {\"box_2d\": [11, 108, 53, 496], \"label\": \"question\"},\n {\"box_2d\": [65, 110, 649, 933], \"label\": \"table\"},\n {\"box_2d\": [670, 110, 752, 972], \"label\": \"question\"},\n {\"box_2d\": [765, 110, 971, 543], \"label\": \"options\"}\n]\n```\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 142 |
+
{"question_id": "NEET_2024_T3_142", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 143 |
+
{"question_id": "NEET_2024_T3_143", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 144 |
+
{"question_id": "NEET_2024_T3_144", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n[\n {\"point\": [94, 216], \"label\": \"list_item\"},\n {\"point\": [96, 305], \"label\": \"list_item\"},\n {\"point\": [101, 427], \"label\": \"list_item\"},\n {\"point\": [94, 535], \"label\": \"list_item\"},\n {\"point\": [424, 216], \"label\": \"list_item\"},\n {\"point\": [424, 305], \"label\": \"list_item\"},\n {\"point\": [424, 427], \"label\": \"list_item\"},\n {\"point\": [424, 535], \"label\": \"list_item\"}\n]\n```\n```json\n[\n {\"box_2d\": [410, 472, 530, 905], \"label\": \"list_item\"},\n {\"box_2d\": [396, 133, 551, 421], \"label\": \"list_item\"},\n {\"box_2d\": [811, 35, 849, 395], \"label\": \"list_item\"},\n {\"box_2d\": [56, 110, 682, 960], \"label\": \"table\"},\n {\"box_2d\": [58, 210, 151, 453], \"label\": \"list_item\"},\n {\"box_2d\": [71, 504, 158, 806], \"label\": \"list_item\"},\n {\"box_2d\": [151, 112, 266, 449], \"label\": \"list_item\"},\n {\"box_2d\": [151, 453, 266, 962], \"label\": \"list_item\"},\n {\"box_2d\": [153, 476, 208, 776], \"label\": \"list_item\"},\n {\"box_2d\": [167, 229, 252, 438], \"label\": \"list_item\"},\n {\"box_2d\": [307, 472, 373, 834], \"label\": \"list_item\"},\n {\"box_2d\": [561, 112, 682, 449], \"label\": \"list_item\"},\n {\"box_2d\": [274, 112, 392, 449], \"label\": \"list_item\"},\n {\"box_2d\": [939, 35, 983, 482], \"label\": \"list_item\"},\n {\"box_2d\": [722, 31, 799, 922], \"label\": \"list\"},\n {\"box_2d\": [274, 453, 392, 958], \"label\": \"list_item\"},\n {\"box_2d\": [896, 35, 939, 478], \"label\": \"list_item\"},\n {\"box_2d\": [561, 451, 682, 958], \"label\": \"list_item\"},\n {\"box_2d\": [290, 131, 330, 410], \"label\": \"list_item\"},\n {\"box_2d\": [854, 35, 892, 399], \"label\": \"list_item\"},\n {\"box_2d\": [578, 468, 660, 937], \"label\": \"list_item\"},\n {\"box_2d\": [64, 453, 151, 960], \"label\": \"list_item\"},\n {\"box_2d\": [396, 451, 554, 958], \"label\": \"list_item\"},\n {\"box_2d\": [575, 131, 665, 371], \"label\": \"list_item\"}\n]\n```\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 145 |
+
{"question_id": "NEET_2024_T3_145", "subject": "Botany", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 146 |
+
{"question_id": "NEET_2024_T3_146", "subject": "Botany", "ground_truth": [1], "predicted_answer": [4], "raw_response": "* **Analyze the question:** The question provides the Net Primary Productivity (NPP) of the first trophic level as 100x (kcal m\u207b\u00b2) yr\u207b\u00b9 and asks for the Gross Primary Productivity (GPP) of the third trophic level.\n* **Key concepts:**\n * GPP (Gross Primary Productivity) is the total rate of photosynthesis by producers (first trophic level).\n * NPP (Net Primary Productivity) is GPP minus the energy used by producers for respiration (NPP = GPP - R). NPP is the energy available to the next trophic level (primary consumers).\n * Energy transfer between trophic levels is typically around 10% (Lindeman's 10% law).\n * GPP and NPP are terms specifically defined for producers (the first trophic level). Higher trophic levels (consumers) acquire energy by consuming other organisms; their energy assimilation and biomass production are referred to as secondary productivity, not GPP or NPP.\n* **Evaluate the question's premise:** Asking for the GPP of the third trophic level is technically incorrect according to standard ecological definitions, as GPP applies only to producers. Consumers (like those in the third trophic level) do not photosynthesize.\n* **Interpret the likely intent:** It is highly probable that the question, despite using incorrect terminology (\"GPP of the third trophic level\"), intends to ask for the amount of energy available or assimilated at the third trophic level, assuming the standard 10% energy transfer efficiency between levels, starting from the NPP of the first level.\n* **Calculate energy transfer:**\n * Energy available at the first trophic level (Producers) as NPP = 100x.\n * Energy transferred to the second trophic level (Primary Consumers) \u2248 10% of 100x = 0.1 * 100x = 10x.\n * Energy transferred to the third trophic level (Secondary Consumers) \u2248 10% of 10x = 0.1 * 10x = x.\n* **Compare with options:**\n * (1) 10x: Energy at the second trophic level.\n * (2) 100x/3x = 100/3: Incorrect calculation.\n * (3) x/10: Energy potentially at the fourth trophic level.\n * (4) x: Energy at the third trophic level based on the 10% rule.\n* **Conclusion:** Based on the likely intent of the question using the 10% energy transfer rule, the energy available at the third trophic level is approximately x.\n\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 147 |
+
{"question_id": "NEET_2024_T3_147", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 148 |
+
{"question_id": "NEET_2024_T3_148", "subject": "Botany", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 149 |
+
{"question_id": "NEET_2024_T3_149", "subject": "Botany", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n{\n\"A\": \"Monoadelphous\",\n\"B\": \"Diadelphous\",\n\"C\": \"Polyadelphous\",\n\"D\": \"Epiphyllous\",\n\"I\": \"Citrus\",\n\"II\": \"Pea\",\n\"III\": \"Lily\",\n\"IV\": \"China-rose\"\n}\n```\n* **(A) Monoadelphous:** Filaments united into one bundle. Example: China-rose. So, A matches IV.\n* **(B) Diadelphous:** Filaments united into two bundles (often 9+1). Example: Pea. So, B matches II.\n* **(C) Polyadelphous:** Filaments united into more than two bundles. Example: Citrus. So, C matches I.\n* **(D) Epiphyllous:** Stamens attached to the perianth (tepals). Example: Lily. So, D matches III.\n\nThe correct matching is A-IV, B-II, C-I, D-III.\n\nLet's check the options:\n(1) A-I, B-II, C-IV, D-III\n(2) A-III, B-I, C-IV, D-II\n(3) A-IV, B-II, C-I, D-III\n(4) A-IV, B-I, C-II, D-III\n\nOption (3) matches our derived pairing.\n\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 150 |
+
{"question_id": "NEET_2024_T3_150", "subject": "Botany", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 151 |
+
{"question_id": "NEET_2024_T3_151", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 152 |
+
{"question_id": "NEET_2024_T3_152", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "``` Mismatch\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 153 |
+
{"question_id": "NEET_2024_T3_153", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```xml\n<answer>1</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 154 |
+
{"question_id": "NEET_2024_T3_154", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 155 |
+
{"question_id": "NEET_2024_T3_155", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 156 |
+
{"question_id": "NEET_2024_T3_156", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 157 |
+
{"question_id": "NEET_2024_T3_157", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 158 |
+
{"question_id": "NEET_2024_T3_158", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 159 |
+
{"question_id": "NEET_2024_T3_159", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 160 |
+
{"question_id": "NEET_2024_T3_160", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "``` HBox(children=(FloatProgress(value=0.0, bar_style='info', description='Analyzing Image...', max=1.0, style=Pro\u2026\n[\n {\"box_2d\": [46, 123, 94, 694], \"label\": \"the question\"},\n {\"box_2d\": [130, 208, 181, 528], \"label\": \"the text A. Myasthenia gravis\"},\n {\"box_2d\": [189, 208, 249, 507], \"label\": \"the text B. Rheumatoid arthritis\"},\n {\"box_2d\": [263, 208, 326, 849], \"label\": \"the text C. Gout D. Muscular dystrophy E. Systemic Lupus Erythematosus (SLE) Choose the most appropriate answer from the options given below: (1) B, C & E only (2) C, D & E only (3) A, B & D only (4) A, B & E only\"},\n {\"box_2d\": [360, 208, 411, 537], \"label\": \"the text D. Muscular dystrophy\"},\n {\"box_2d\": [440, 208, 491, 772], \"label\": \"the text E. Systemic Lupus Erythematosus (SLE)\"},\n {\"box_2d\": [520, 129, 654, 990], \"label\": \"the text Choose the most appropriate answer from the options given below:\"},\n {\"box_2d\": [684, 129, 735, 412], \"label\": \"the text (1) B, C & E only\"},\n {\"box_2d\": [758, 129, 809, 410], \"label\": \"the text (2) C, D & E only\"},\n {\"box_2d\": [846, 129, 897, 410], \"label\": \"the text (3) A, B & D only\"},\n {\"box_2d\": [923, 129, 975, 410], \"label\": \"the text (4) A, B & E only\"}\n]\n```\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 161 |
+
{"question_id": "NEET_2024_T3_161", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 162 |
+
{"question_id": "NEET_2024_T3_162", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 163 |
+
{"question_id": "NEET_2024_T3_163", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "``` Mnemonic:\nEC = TV + ERV\nFRC = ERV + RV\nVC = IRV + TV + ERV\nIC = TV + IRV\n\nMatching:\nA. Expiratory capacity (EC) = TV + ERV -> II\nB. Functional residual capacity (FRC) = ERV + RV -> IV\nC. Vital capacity (VC) = IRV + TV + ERV -> I\nD. Inspiratory capacity (IC) = TV + IRV -> III\n\nCombined: A-II, B-IV, C-I, D-III\n\nChecking options:\n(1) A-II, B-I, C-IV, D-III -> Incorrect\n(2) A-I, B-III, C-II, D-IV -> Incorrect\n(3) A-II, B-IV, C-I, D-III -> Correct\n(4) A-III, B-II, C-IV, D-I -> Incorrect\n\nThe correct option number is 3.\n```\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 164 |
+
{"question_id": "NEET_2024_T3_164", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 165 |
+
{"question_id": "NEET_2024_T3_165", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 166 |
+
{"question_id": "NEET_2024_T3_166", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n```json\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 167 |
+
{"question_id": "NEET_2024_T3_167", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```json\n<answer>4</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 168 |
+
{"question_id": "NEET_2024_T3_168", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 169 |
+
{"question_id": "NEET_2024_T3_169", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 170 |
+
{"question_id": "NEET_2024_T3_170", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 171 |
+
{"question_id": "NEET_2024_T3_171", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 172 |
+
{"question_id": "NEET_2024_T3_172", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 173 |
+
{"question_id": "NEET_2024_T3_173", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 174 |
+
{"question_id": "NEET_2024_T3_174", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n{\n \"A\": \"III\",\n \"B\": \"I\",\n \"C\": \"IV\",\n \"D\": \"II\"\n}\n```\n\n```\nA. Fibrous joints -> III. Skull, don't allow any movement\nB. Cartilaginous joints -> I. Adjacent vertebrae, limited movement\nC. Hinge joints -> IV. Knee, help in locomotion (Hinge joints allow movement in one plane, like the knee or elbow)\nD. Ball and socket joints -> II. Humerus and pectoral girdle, rotational movement (Shoulder and hip joints are examples)\n\nMatching:\nA - III\nB - I\nC - IV\nD - II\n\nCheck options:\n(1) A-II, B-III, C-I, D-IV -> Incorrect\n(2) A-III, B-I, C-IV, D-II -> Correct\n(3) A-IV, B-II, C-III, D-I -> Incorrect\n(4) A-I, B-III, C-II, D-IV -> Incorrect\n\nThe correct option is (2).\n```\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 175 |
+
{"question_id": "NEET_2024_T3_175", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 176 |
+
{"question_id": "NEET_2024_T3_176", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 177 |
+
{"question_id": "NEET_2024_T3_177", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 178 |
+
{"question_id": "NEET_2024_T3_178", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 179 |
+
{"question_id": "NEET_2024_T3_179", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 180 |
+
{"question_id": "NEET_2024_T3_180", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 181 |
+
{"question_id": "NEET_2024_T3_181", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 182 |
+
{"question_id": "NEET_2024_T3_182", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 183 |
+
{"question_id": "NEET_2024_T3_183", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 184 |
+
{"question_id": "NEET_2024_T3_184", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 185 |
+
{"question_id": "NEET_2024_T3_185", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n{\n \"A\": \"Down's syndrome\",\n \"B\": \"\u03b1 - Thalassemia\",\n \"C\": \"\u03b2-Thalassemia\",\n \"D\": \"Klinefelter's\",\n \"I\": \"11th chromosome\",\n \"II\": \"'X' chromosome\",\n \"III\": \"21st chromosome\",\n \"IV\": \"16th chromosome\"\n}\n```\n* **A. Down's syndrome:** This is caused by trisomy of the 21st chromosome. So, **A matches III**.\n* **B. \u03b1 - Thalassemia:** This is typically caused by mutations or deletions in the genes located on the 16th chromosome. So, **B matches IV**.\n* **C. \u03b2-Thalassemia:** This is typically caused by mutations in the HBB gene on the 11th chromosome. So, **C matches I**.\n* **D. Klinefelter's syndrome:** This is a sex chromosome aneuploidy in males, characterized by the presence of an extra X chromosome (XXY). So, **D matches II**.\n\nMatching combination: A-III, B-IV, C-I, D-II.\n\nNow check the options:\n(1) A-III, B-IV, C-I, D-II - This matches our findings.\n(2) A-IV, B-I, C-II, D-III - Incorrect.\n(3) A-I, B-II, C-III, D-IV - Incorrect.\n(4) A-II, B-III, C-IV, D-I - Incorrect.\n\nThe correct option is (1).\n\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 186 |
+
{"question_id": "NEET_2024_T3_186", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 187 |
+
{"question_id": "NEET_2024_T3_187", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```json\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 188 |
+
{"question_id": "NEET_2024_T3_188", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n{\n \"reasoning\": \"Statement I states that the cerebral hemispheres are connected by the corpus callosum. This is a well-established fact in neuroanatomy. The corpus callosum is a large, C-shaped nerve fiber bundle beneath the cerebral cortex that stretches across the midline of the brain, connecting the left and right cerebral hemispheres and enabling communication between them. Therefore, Statement I is correct. Statement II states that the brain stem consists of the medulla oblongata, pons, and cerebrum. The brain stem is typically defined as consisting of the midbrain, pons, and medulla oblongata. The cerebrum is the largest part of the brain, responsible for higher cognitive functions, and it is structurally distinct from the brain stem. Therefore, Statement II is incorrect as it incorrectly includes the cerebrum as part of the brain stem. Based on this analysis, Statement I is correct and Statement II is incorrect. Option (1) reflects this conclusion.\",\n \"correct_option_number\": 1\n}\n```\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 189 |
+
{"question_id": "NEET_2024_T3_189", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 190 |
+
{"question_id": "NEET_2024_T3_190", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "```cpp\n<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 191 |
+
{"question_id": "NEET_2024_T3_191", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 2, "evaluation_status": "correct", "marks_awarded": 4}
|
| 192 |
+
{"question_id": "NEET_2024_T3_192", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n{\n \"A\": \"Unicellular glandular epithelium\",\n \"B\": \"Compound epithelium\",\n \"C\": \"Multicellular glandular epithelium\",\n \"D\": \"Endocrine glandular epithelium\",\n \"I\": \"Salivary glands\",\n \"II\": \"Pancreas\",\n \"III\": \"Goblet cells of alimentary canal\",\n \"IV\": \"Moist surface of buccal cavity\"\n}\n```\n\n1. **A. Unicellular glandular epithelium:** Goblet cells are unicellular glands found in the epithelium of the alimentary canal and respiratory tract. They secrete mucus. Therefore, A matches with III.\n2. **B. Compound epithelium:** This epithelium consists of more than one layer of cells. It provides protection against mechanical and chemical stresses. The moist surface of the buccal cavity (lining of the mouth) is lined by stratified squamous non-keratinized epithelium, which is a type of compound epithelium. Therefore, B matches with IV.\n3. **C. Multicellular glandular epithelium:** These glands are formed by clusters of cells. Salivary glands are examples of multicellular exocrine glands. Therefore, C matches with I.\n4. **D. Endocrine glandular epithelium:** Endocrine glands are ductless glands that secrete hormones directly into the bloodstream. The pancreas has both endocrine (Islets of Langerhans secreting insulin, glucagon) and exocrine functions. Thus, the endocrine glandular epithelium is represented by the pancreas (in its endocrine capacity). Therefore, D matches with II.\n\nMatching pairs:\nA - III\nB - IV\nC - I\nD - II\n\nNow let's check the options:\n(1) A-III, B-IV, C-I, D-II - This matches our findings.\n(2) A-II, B-I, C-IV, D-III\n(3) A-II, B-I, C-III, D-IV\n(4) A-IV, B-III, C-I, D-II\n\nThe correct option is (1).\n\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 193 |
+
{"question_id": "NEET_2024_T3_193", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [1], "raw_response": "<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
| 194 |
+
{"question_id": "NEET_2024_T3_194", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 195 |
+
{"question_id": "NEET_2024_T3_195", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [1], "raw_response": "```json\n<answer>1</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 196 |
+
{"question_id": "NEET_2024_T3_196", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "raw_response": "```xml\n<answer>4</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 197 |
+
{"question_id": "NEET_2024_T3_197", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 198 |
+
{"question_id": "NEET_2024_T3_198", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [2], "raw_response": "<answer>2</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 199 |
+
{"question_id": "NEET_2024_T3_199", "subject": "Zoology", "ground_truth": [3], "predicted_answer": [3], "raw_response": "```json\n<answer>3</answer>", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "correct", "marks_awarded": 4}
|
| 200 |
+
{"question_id": "NEET_2024_T3_200", "subject": "Zoology", "ground_truth": [1], "predicted_answer": [3], "raw_response": "```xml\n<answer>3</answer>\n```", "parse_successful": true, "api_call_successful": true, "error": null, "attempt": 1, "evaluation_status": "incorrect", "marks_awarded": -1}
|
results/google_gemini-2.5-pro-preview-03-25_20250505_091752/summary.json
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{
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"model_name": "google/gemini-2.5-pro-preview-03-25",
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| 3 |
+
"timestamp": "20250505_091752",
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| 4 |
+
"total_questions_in_dataset": 200,
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| 5 |
+
"overall_score": 727,
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| 6 |
+
"overall_correct": 185,
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+
"overall_incorrect": 13,
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"overall_skipped": 2,
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+
"overall_api_parse_failures": 0,
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+
"total_questions_processed": 200,
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| 11 |
+
"unmapped_section_questions": 0,
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| 12 |
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"section_breakdown": {
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| 13 |
+
"Physics_A": {
|
| 14 |
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"score": 130,
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"correct": 33,
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| 16 |
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"incorrect": 2,
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"skipped": 0
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| 18 |
+
},
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| 19 |
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"Physics_B": {
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| 20 |
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"score": 47,
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"correct": 12,
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"incorrect": 1,
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"skipped": 2
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| 24 |
+
},
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| 25 |
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"Chemistry_A": {
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| 26 |
+
"score": 140,
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| 27 |
+
"correct": 35,
|
| 28 |
+
"incorrect": 0,
|
| 29 |
+
"skipped": 0
|
| 30 |
+
},
|
| 31 |
+
"Chemistry_B": {
|
| 32 |
+
"score": 55,
|
| 33 |
+
"correct": 14,
|
| 34 |
+
"incorrect": 1,
|
| 35 |
+
"skipped": 0
|
| 36 |
+
},
|
| 37 |
+
"Botany_A": {
|
| 38 |
+
"score": 120,
|
| 39 |
+
"correct": 31,
|
| 40 |
+
"incorrect": 4,
|
| 41 |
+
"skipped": 0
|
| 42 |
+
},
|
| 43 |
+
"Botany_B": {
|
| 44 |
+
"score": 55,
|
| 45 |
+
"correct": 14,
|
| 46 |
+
"incorrect": 1,
|
| 47 |
+
"skipped": 0
|
| 48 |
+
},
|
| 49 |
+
"Zoology_A": {
|
| 50 |
+
"score": 130,
|
| 51 |
+
"correct": 33,
|
| 52 |
+
"incorrect": 2,
|
| 53 |
+
"skipped": 0
|
| 54 |
+
},
|
| 55 |
+
"Zoology_B": {
|
| 56 |
+
"score": 50,
|
| 57 |
+
"correct": 13,
|
| 58 |
+
"incorrect": 2,
|
| 59 |
+
"skipped": 0
|
| 60 |
+
}
|
| 61 |
+
}
|
| 62 |
+
}
|
results/google_gemini-2.5-pro-preview-03-25_20250505_091752/summary.md
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Benchmark Results: google/gemini-2.5-pro-preview-03-25
|
| 2 |
+
**Timestamp:** 20250505_091752
|
| 3 |
+
**Total Questions in Dataset:** 200
|
| 4 |
+
|
| 5 |
+
---
|
| 6 |
+
|
| 7 |
+
## NEET Scoring Results
|
| 8 |
+
**Overall Score:** **727 / 800**
|
| 9 |
+
- **Correct Answers (+4):** 185
|
| 10 |
+
- **Incorrect Answers (-1):** 13
|
| 11 |
+
- **Skipped Questions (0):** 2
|
| 12 |
+
- **API/Parse Failures (-1):** 0
|
| 13 |
+
- **Total Questions Processed:** 200
|
| 14 |
+
|
| 15 |
+
### Section Breakdown
|
| 16 |
+
| Section | Score | Correct | Incorrect | Skipped |
|
| 17 |
+
|---------------|-------|---------|-----------|---------|
|
| 18 |
+
| Physics A | 130 | 33 | 2 | 0 |
|
| 19 |
+
| Physics B | 47 | 12 | 1 | 2 |
|
| 20 |
+
| Chemistry A | 140 | 35 | 0 | 0 |
|
| 21 |
+
| Chemistry B | 55 | 14 | 1 | 0 |
|
| 22 |
+
| Botany A | 120 | 31 | 4 | 0 |
|
| 23 |
+
| Botany B | 55 | 14 | 1 | 0 |
|
| 24 |
+
| Zoology A | 130 | 33 | 2 | 0 |
|
| 25 |
+
| Zoology B | 50 | 13 | 2 | 0 |
|
src/benchmark_runner.py
CHANGED
|
@@ -1,8 +1,12 @@
|
|
| 1 |
import argparse
|
| 2 |
import yaml
|
|
|
|
|
|
|
| 3 |
import os
|
| 4 |
import json
|
| 5 |
import logging
|
|
|
|
|
|
|
| 6 |
from datasets import load_dataset, Image as HFImage # Import Image feature type
|
| 7 |
from tqdm import tqdm
|
| 8 |
from PIL import Image as PILImage # Import PIL for type hinting
|
|
@@ -10,7 +14,8 @@ from PIL import Image as PILImage # Import PIL for type hinting
|
|
| 10 |
# Import local modules
|
| 11 |
from utils import load_api_key
|
| 12 |
from llm_interface import get_openrouter_prediction
|
| 13 |
-
|
|
|
|
| 14 |
|
| 15 |
# Configure logging
|
| 16 |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
|
@@ -29,36 +34,108 @@ def load_config(config_path: str) -> dict:
|
|
| 29 |
logging.error(f"Error parsing configuration file {config_path}: {e}")
|
| 30 |
raise
|
| 31 |
|
| 32 |
-
def
|
| 33 |
-
"""
|
| 34 |
-
# Sanitize model name for directory/filename usage
|
| 35 |
-
safe_model_name = model_name.replace('/', '_')
|
| 36 |
-
model_output_dir = os.path.join(output_dir, safe_model_name)
|
| 37 |
-
os.makedirs(model_output_dir, exist_ok=True)
|
| 38 |
-
|
| 39 |
-
# Save detailed predictions
|
| 40 |
-
predictions_path = os.path.join(model_output_dir, "predictions.jsonl")
|
| 41 |
try:
|
| 42 |
-
with open(
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
f.write('\n')
|
| 46 |
-
logging.info(f"Detailed predictions saved to {predictions_path}")
|
| 47 |
except IOError as e:
|
| 48 |
-
logging.error(f"Failed to
|
|
|
|
|
|
|
| 49 |
|
| 50 |
-
|
| 51 |
-
|
|
|
|
| 52 |
try:
|
| 53 |
-
with open(
|
| 54 |
json.dump(summary, f, indent=4)
|
| 55 |
-
logging.info(f"Summary results saved to {
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 56 |
except IOError as e:
|
| 57 |
-
logging.error(f"Failed to save summary
|
|
|
|
|
|
|
| 58 |
|
| 59 |
|
| 60 |
def run_benchmark(config: dict, api_key: str, models_override: list[str] | None = None, output_dir_override: str | None = None):
|
| 61 |
-
"""Runs the benchmark evaluation loop."""
|
| 62 |
|
| 63 |
# Determine models to run
|
| 64 |
models_to_run = models_override if models_override else config.get("openrouter_models", [])
|
|
@@ -66,11 +143,11 @@ def run_benchmark(config: dict, api_key: str, models_override: list[str] | None
|
|
| 66 |
logging.error("No models specified in config or via --models argument.")
|
| 67 |
return
|
| 68 |
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
os.makedirs(
|
| 72 |
|
| 73 |
-
|
| 74 |
dataset_path = config.get("dataset_path", ".") # Default to current dir if not specified
|
| 75 |
try:
|
| 76 |
# Load the dataset using the loading script from the specified path
|
|
@@ -88,98 +165,312 @@ def run_benchmark(config: dict, api_key: str, models_override: list[str] | None
|
|
| 88 |
|
| 89 |
total_questions = len(dataset)
|
| 90 |
|
| 91 |
-
|
| 92 |
for model_id in models_to_run:
|
| 93 |
logging.info(f"--- Starting benchmark for model: {model_id} ---")
|
| 94 |
-
model_results = []
|
| 95 |
-
predictions = []
|
| 96 |
-
ground_truths = []
|
| 97 |
-
api_failures = 0
|
| 98 |
|
| 99 |
-
#
|
| 100 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 101 |
question_id = example["question_id"]
|
| 102 |
-
|
|
|
|
| 103 |
truth = example["correct_answer"]
|
| 104 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 105 |
|
| 106 |
try:
|
| 107 |
-
#
|
| 108 |
parsed_answer, raw_response = get_openrouter_prediction(
|
| 109 |
-
image=image,
|
| 110 |
model_identifier=model_id,
|
| 111 |
api_key=api_key,
|
| 112 |
-
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
#
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 121 |
else:
|
| 122 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 123 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 124 |
|
| 125 |
-
# Store detailed result for this question
|
| 126 |
-
model_results.append({
|
| 127 |
-
"question_id": question_id,
|
| 128 |
-
"ground_truth": truth,
|
| 129 |
-
"predicted_answer": parsed_answer,
|
| 130 |
-
"raw_response": raw_response,
|
| 131 |
-
"parse_successful": parsed_answer is not None,
|
| 132 |
-
"api_call_successful": True
|
| 133 |
-
})
|
| 134 |
|
| 135 |
except Exception as e:
|
| 136 |
-
# Catch potential failures from get_openrouter_prediction after retries
|
| 137 |
-
logging.error(f"API call failed
|
| 138 |
-
|
| 139 |
-
|
| 140 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 141 |
"question_id": question_id,
|
|
|
|
| 142 |
"ground_truth": truth,
|
| 143 |
"predicted_answer": None,
|
| 144 |
"raw_response": None,
|
| 145 |
"parse_successful": False,
|
| 146 |
-
"api_call_successful": False,
|
| 147 |
-
"error":
|
| 148 |
-
|
| 149 |
-
|
| 150 |
-
logging.info(f"Question {question_id}: API call failed")
|
| 151 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 152 |
|
| 153 |
-
|
| 154 |
-
|
| 155 |
-
|
| 156 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 157 |
|
| 158 |
-
if not valid_predictions:
|
| 159 |
-
logging.warning(f"No valid predictions were generated for model {model_id}. Skipping accuracy calculation.")
|
| 160 |
-
accuracy = 0.0
|
| 161 |
else:
|
| 162 |
-
|
| 163 |
-
|
| 164 |
-
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
|
| 169 |
-
|
| 170 |
-
|
| 171 |
-
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
|
| 177 |
-
|
| 178 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 179 |
logging.info("-------------------------------------")
|
| 180 |
|
| 181 |
-
#
|
| 182 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 183 |
|
| 184 |
logging.info("Benchmark run completed.")
|
| 185 |
|
|
|
|
| 1 |
import argparse
|
| 2 |
import yaml
|
| 3 |
+
import argparse
|
| 4 |
+
import yaml
|
| 5 |
import os
|
| 6 |
import json
|
| 7 |
import logging
|
| 8 |
+
import datetime # Added for timestamp
|
| 9 |
+
from typing import Dict, Any # Added for type hinting
|
| 10 |
from datasets import load_dataset, Image as HFImage # Import Image feature type
|
| 11 |
from tqdm import tqdm
|
| 12 |
from PIL import Image as PILImage # Import PIL for type hinting
|
|
|
|
| 14 |
# Import local modules
|
| 15 |
from utils import load_api_key
|
| 16 |
from llm_interface import get_openrouter_prediction
|
| 17 |
+
# Import both evaluation functions
|
| 18 |
+
from evaluation import calculate_accuracy, calculate_neet_scores
|
| 19 |
|
| 20 |
# Configure logging
|
| 21 |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
|
|
|
| 34 |
logging.error(f"Error parsing configuration file {config_path}: {e}")
|
| 35 |
raise
|
| 36 |
|
| 37 |
+
def append_prediction(result: Dict[str, Any], filepath: str):
|
| 38 |
+
"""Appends a single prediction result to a JSONL file."""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 39 |
try:
|
| 40 |
+
with open(filepath, 'a') as f:
|
| 41 |
+
json.dump(result, f)
|
| 42 |
+
f.write('\n')
|
|
|
|
|
|
|
| 43 |
except IOError as e:
|
| 44 |
+
logging.error(f"Failed to append prediction to {filepath}: {e}")
|
| 45 |
+
except Exception as e:
|
| 46 |
+
logging.error(f"Unexpected error appending prediction to {filepath}: {e}")
|
| 47 |
|
| 48 |
+
|
| 49 |
+
def save_summary(summary: Dict[str, Any], filepath: str):
|
| 50 |
+
"""Saves the final summary dictionary to a JSON file."""
|
| 51 |
try:
|
| 52 |
+
with open(filepath, 'w') as f:
|
| 53 |
json.dump(summary, f, indent=4)
|
| 54 |
+
logging.info(f"Summary results saved to {filepath}")
|
| 55 |
+
except IOError as e:
|
| 56 |
+
logging.error(f"Failed to save summary results to {filepath}: {e}")
|
| 57 |
+
except Exception as e:
|
| 58 |
+
logging.error(f"Unexpected error saving summary to {filepath}: {e}")
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
def generate_markdown_summary(summary: Dict[str, Any], filepath: str):
|
| 62 |
+
"""Generates a human-readable Markdown summary from the results dictionary."""
|
| 63 |
+
try:
|
| 64 |
+
md_content = []
|
| 65 |
+
model_name = summary.get("model_name", "N/A")
|
| 66 |
+
timestamp = summary.get("timestamp", "N/A")
|
| 67 |
+
total_questions = summary.get("total_questions_in_dataset", "N/A")
|
| 68 |
+
|
| 69 |
+
md_content.append(f"# Benchmark Results: {model_name}")
|
| 70 |
+
md_content.append(f"**Timestamp:** {timestamp}")
|
| 71 |
+
md_content.append(f"**Total Questions in Dataset:** {total_questions}")
|
| 72 |
+
md_content.append("\n---\n")
|
| 73 |
+
|
| 74 |
+
# Check if NEET results are present
|
| 75 |
+
if "overall_score" in summary and "section_breakdown" in summary:
|
| 76 |
+
total_processed = summary.get("total_questions_processed", 0)
|
| 77 |
+
# Assuming standard NEET: 200 questions, 4 marks each. Adjust if needed.
|
| 78 |
+
max_score = 200 * 4 # Calculate max possible score based on standard NEET
|
| 79 |
+
overall_score = summary.get('overall_score', 'N/A')
|
| 80 |
+
correct_count = summary.get('overall_correct', 'N/A')
|
| 81 |
+
incorrect_count = summary.get('overall_incorrect', 'N/A')
|
| 82 |
+
skipped_count = summary.get('overall_skipped', 'N/A')
|
| 83 |
+
failures_count = summary.get('overall_api_parse_failures', 'N/A')
|
| 84 |
+
unmapped_count = summary.get('unmapped_section_questions', 'N/A')
|
| 85 |
+
|
| 86 |
+
md_content.append("## NEET Scoring Results")
|
| 87 |
+
md_content.append(f"**Overall Score:** **{overall_score} / {max_score}**")
|
| 88 |
+
md_content.append(f"- **Correct Answers (+4):** {correct_count}")
|
| 89 |
+
md_content.append(f"- **Incorrect Answers (-1):** {incorrect_count}")
|
| 90 |
+
md_content.append(f"- **Skipped Questions (0):** {skipped_count}")
|
| 91 |
+
md_content.append(f"- **API/Parse Failures (-1):** {failures_count}")
|
| 92 |
+
md_content.append(f"- **Total Questions Processed:** {total_processed}")
|
| 93 |
+
if unmapped_count > 0:
|
| 94 |
+
md_content.append(f"- **Unmapped Section Questions:** {unmapped_count} *(These were not included in section breakdown)*")
|
| 95 |
+
|
| 96 |
+
md_content.append("\n### Section Breakdown")
|
| 97 |
+
md_content.append("| Section | Score | Correct | Incorrect | Skipped |")
|
| 98 |
+
md_content.append("|---------------|-------|---------|-----------|---------|")
|
| 99 |
+
section_breakdown = summary.get("section_breakdown", {})
|
| 100 |
+
sections = [
|
| 101 |
+
"Physics_A", "Physics_B", "Chemistry_A", "Chemistry_B",
|
| 102 |
+
"Botany_A", "Botany_B", "Zoology_A", "Zoology_B"
|
| 103 |
+
]
|
| 104 |
+
for section_name in sections:
|
| 105 |
+
stats = section_breakdown.get(section_name, {})
|
| 106 |
+
score = stats.get('score', 'N/A')
|
| 107 |
+
correct = stats.get('correct', 'N/A')
|
| 108 |
+
incorrect = stats.get('incorrect', 'N/A')
|
| 109 |
+
skipped = stats.get('skipped', 'N/A')
|
| 110 |
+
md_content.append(f"| {section_name.replace('_', ' '):<13} | {score:<5} | {correct:<7} | {incorrect:<9} | {skipped:<7} |")
|
| 111 |
+
|
| 112 |
+
# Fallback or alternative for simple accuracy
|
| 113 |
+
elif "accuracy_on_parsed" in summary:
|
| 114 |
+
md_content.append("## Simple Accuracy Results")
|
| 115 |
+
md_content.append(f"- **Accuracy (on successfully parsed non-skipped):** {summary.get('accuracy_on_parsed', 'N/A'):.4f}")
|
| 116 |
+
md_content.append(f"- **Total Processed Attempts:** {summary.get('total_processed_attempts', 'N/A')}")
|
| 117 |
+
md_content.append(f"- **Successful API Calls:** {summary.get('successful_api_calls', 'N/A')}")
|
| 118 |
+
md_content.append(f"- **Successful Parses:** {summary.get('successful_parses', 'N/A')}")
|
| 119 |
+
else:
|
| 120 |
+
md_content.append("## Summary")
|
| 121 |
+
md_content.append("*(No specific NEET or Accuracy metrics found in summary)*")
|
| 122 |
+
# Optionally print raw summary keys/values
|
| 123 |
+
# for key, value in summary.items():
|
| 124 |
+
# md_content.append(f"- **{key}:** {value}")
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
with open(filepath, 'w') as f:
|
| 128 |
+
f.write("\n".join(md_content))
|
| 129 |
+
logging.info(f"Markdown summary saved to {filepath}")
|
| 130 |
+
|
| 131 |
except IOError as e:
|
| 132 |
+
logging.error(f"Failed to save markdown summary to {filepath}: {e}")
|
| 133 |
+
except Exception as e:
|
| 134 |
+
logging.error(f"Unexpected error generating or saving markdown summary to {filepath}: {e}")
|
| 135 |
|
| 136 |
|
| 137 |
def run_benchmark(config: dict, api_key: str, models_override: list[str] | None = None, output_dir_override: str | None = None):
|
| 138 |
+
"""Runs the benchmark evaluation loop with incremental saving and retries."""
|
| 139 |
|
| 140 |
# Determine models to run
|
| 141 |
models_to_run = models_override if models_override else config.get("openrouter_models", [])
|
|
|
|
| 143 |
logging.error("No models specified in config or via --models argument.")
|
| 144 |
return
|
| 145 |
|
| 146 |
+
# Determine base output directory
|
| 147 |
+
base_output_dir = output_dir_override if output_dir_override else config.get("results_base_dir", "results")
|
| 148 |
+
os.makedirs(base_output_dir, exist_ok=True)
|
| 149 |
|
| 150 |
+
# Load dataset
|
| 151 |
dataset_path = config.get("dataset_path", ".") # Default to current dir if not specified
|
| 152 |
try:
|
| 153 |
# Load the dataset using the loading script from the specified path
|
|
|
|
| 165 |
|
| 166 |
total_questions = len(dataset)
|
| 167 |
|
| 168 |
+
# --- Main Loop: Iterate through models ---
|
| 169 |
for model_id in models_to_run:
|
| 170 |
logging.info(f"--- Starting benchmark for model: {model_id} ---")
|
|
|
|
|
|
|
|
|
|
|
|
|
| 171 |
|
| 172 |
+
# Create timestamped output directory for this model run
|
| 173 |
+
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
| 174 |
+
safe_model_name = model_id.replace('/', '_')
|
| 175 |
+
model_output_dir = os.path.join(base_output_dir, f"{safe_model_name}_{timestamp}")
|
| 176 |
+
os.makedirs(model_output_dir, exist_ok=True)
|
| 177 |
+
predictions_path = os.path.join(model_output_dir, "predictions.jsonl")
|
| 178 |
+
summary_path = os.path.join(model_output_dir, "summary.json")
|
| 179 |
+
markdown_summary_path = os.path.join(model_output_dir, "summary.md") # Define path for MD summary
|
| 180 |
+
logging.info(f"Results for {model_id} will be saved to: {model_output_dir}")
|
| 181 |
+
|
| 182 |
+
model_results = [] # Stores results in memory for final calculation
|
| 183 |
+
failed_questions_data = [] # Stores data needed to retry failed questions
|
| 184 |
+
|
| 185 |
+
# --- Initial Pass: Iterate through questions ---
|
| 186 |
+
for example in tqdm(dataset, desc=f"Processing {model_id} (Initial Pass)", total=total_questions):
|
| 187 |
question_id = example["question_id"]
|
| 188 |
+
subject = example["subject"] # Get subject for evaluation
|
| 189 |
+
image: PILImage.Image = example["image"]
|
| 190 |
truth = example["correct_answer"]
|
| 191 |
+
|
| 192 |
+
result_data = {
|
| 193 |
+
"question_id": question_id,
|
| 194 |
+
"subject": subject,
|
| 195 |
+
"ground_truth": truth,
|
| 196 |
+
"predicted_answer": None,
|
| 197 |
+
"raw_response": None,
|
| 198 |
+
"parse_successful": False,
|
| 199 |
+
"api_call_successful": False, # Assume failure initially
|
| 200 |
+
"error": None,
|
| 201 |
+
"attempt": 1 # Mark as first attempt
|
| 202 |
+
}
|
| 203 |
|
| 204 |
try:
|
| 205 |
+
# --- Initial API Call ---
|
| 206 |
parsed_answer, raw_response = get_openrouter_prediction(
|
|
|
|
| 207 |
model_identifier=model_id,
|
| 208 |
api_key=api_key,
|
| 209 |
+
image=image, # Pass image for initial call
|
| 210 |
+
max_tokens=config.get("max_tokens", 100),
|
| 211 |
+
request_timeout=config.get("request_timeout", 60)
|
| 212 |
+
)
|
| 213 |
+
|
| 214 |
+
# Initial attempt results
|
| 215 |
+
api_success_attempt1 = True
|
| 216 |
+
parse_success_attempt1 = parsed_answer is not None # Includes SKIP
|
| 217 |
+
raw_response_attempt1 = raw_response
|
| 218 |
+
|
| 219 |
+
# --- Re-prompt Logic ---
|
| 220 |
+
if api_success_attempt1 and not parse_success_attempt1:
|
| 221 |
+
logging.warning(f"Question {question_id}: Initial parse failed. Attempting re-prompt.")
|
| 222 |
+
try:
|
| 223 |
+
# Second API call (re-prompt)
|
| 224 |
+
parsed_answer_rp, raw_response_rp = get_openrouter_prediction(
|
| 225 |
+
model_identifier=model_id,
|
| 226 |
+
api_key=api_key,
|
| 227 |
+
previous_raw_response=raw_response_attempt1, # Pass previous response
|
| 228 |
+
max_tokens=config.get("max_tokens", 100),
|
| 229 |
+
request_timeout=config.get("request_timeout", 60)
|
| 230 |
+
)
|
| 231 |
+
# Update results with re-prompt outcome
|
| 232 |
+
result_data.update({
|
| 233 |
+
"predicted_answer": parsed_answer_rp,
|
| 234 |
+
"raw_response": raw_response_rp, # Store the re-prompt response
|
| 235 |
+
"parse_successful": parsed_answer_rp is not None, # Includes SKIP
|
| 236 |
+
"api_call_successful": True, # API call was successful
|
| 237 |
+
"attempt": 2 # Mark as second attempt due to re-prompt
|
| 238 |
+
})
|
| 239 |
+
logging.info(f"Question {question_id}: Re-prompt {'succeeded' if result_data['parse_successful'] else 'failed to parse'}.")
|
| 240 |
+
|
| 241 |
+
except Exception as e_rp:
|
| 242 |
+
# Handle failure during the re-prompt API call itself
|
| 243 |
+
logging.error(f"Re-prompt API call failed for question {question_id}: {e_rp}")
|
| 244 |
+
# Keep initial attempt data, but mark as failed parse and add re-prompt error
|
| 245 |
+
result_data.update({
|
| 246 |
+
"predicted_answer": None, # Failed parse overall
|
| 247 |
+
"raw_response": raw_response_attempt1, # Keep original raw response
|
| 248 |
+
"parse_successful": False,
|
| 249 |
+
"api_call_successful": True, # Initial API call succeeded
|
| 250 |
+
"error": f"Initial parse failed. Re-prompt API call failed: {str(e_rp)}",
|
| 251 |
+
"attempt": 1 # Revert attempt count as re-prompt failed
|
| 252 |
+
})
|
| 253 |
else:
|
| 254 |
+
# Initial API call was successful and parsed correctly, or API failed initially
|
| 255 |
+
result_data.update({
|
| 256 |
+
"predicted_answer": parsed_answer,
|
| 257 |
+
"raw_response": raw_response_attempt1,
|
| 258 |
+
"parse_successful": parse_success_attempt1,
|
| 259 |
+
"api_call_successful": True, # API call was successful
|
| 260 |
+
"attempt": 1
|
| 261 |
+
})
|
| 262 |
|
| 263 |
+
# Append final result (from initial success or re-prompt attempt)
|
| 264 |
+
model_results.append(result_data)
|
| 265 |
+
append_prediction(result_data, predictions_path)
|
| 266 |
+
|
| 267 |
+
# Provide live feedback based on final result_data
|
| 268 |
+
final_parsed_answer = result_data["predicted_answer"]
|
| 269 |
+
if result_data["parse_successful"]:
|
| 270 |
+
if final_parsed_answer == "SKIP":
|
| 271 |
+
logging.info(f"Question {question_id}: Skipped (Attempt {result_data['attempt']})")
|
| 272 |
+
else:
|
| 273 |
+
is_correct = isinstance(final_parsed_answer, list) and sorted(final_parsed_answer) == sorted(truth)
|
| 274 |
+
logging.info(f"Question {question_id}: {'Correct' if is_correct else 'Incorrect'} (Attempt {result_data['attempt']})")
|
| 275 |
+
else: # Parse failed even after potential re-prompt
|
| 276 |
+
logging.info(f"Question {question_id}: Failed to parse answer (Attempt {result_data['attempt']})")
|
| 277 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 278 |
|
| 279 |
except Exception as e:
|
| 280 |
+
# Catch potential failures from the *initial* get_openrouter_prediction call (after its internal retries)
|
| 281 |
+
logging.error(f"Initial API call failed for question {question_id} (Attempt 1): {e}")
|
| 282 |
+
result_data["error"] = str(e)
|
| 283 |
+
result_data["api_call_successful"] = False # Explicitly mark API failure
|
| 284 |
+
# Store data needed for the separate API retry pass
|
| 285 |
+
failed_questions_data.append(example) # Store original example data
|
| 286 |
+
# Do not append to model_results or predictions file yet for initial API failures
|
| 287 |
+
|
| 288 |
+
|
| 289 |
+
# --- Retry Pass: Iterate through questions where the *initial API call* failed ---
|
| 290 |
+
if failed_questions_data:
|
| 291 |
+
logging.info(f"--- Retrying {len(failed_questions_data)} questions with initial API failures for model: {model_id} ---")
|
| 292 |
+
for example in tqdm(failed_questions_data, desc=f"Processing {model_id} (API Retry Pass)", total=len(failed_questions_data)):
|
| 293 |
+
question_id = example["question_id"]
|
| 294 |
+
subject = example["subject"]
|
| 295 |
+
image: PILImage.Image = example["image"]
|
| 296 |
+
truth = example["correct_answer"]
|
| 297 |
+
|
| 298 |
+
# Initialize result data for the API retry attempt
|
| 299 |
+
result_data = {
|
| 300 |
"question_id": question_id,
|
| 301 |
+
"subject": subject, # Get subject for evaluation
|
| 302 |
"ground_truth": truth,
|
| 303 |
"predicted_answer": None,
|
| 304 |
"raw_response": None,
|
| 305 |
"parse_successful": False,
|
| 306 |
+
"api_call_successful": False, # Assume failure initially
|
| 307 |
+
"error": None,
|
| 308 |
+
"attempt": 2 # Mark as second attempt (due to API retry)
|
| 309 |
+
}
|
|
|
|
| 310 |
|
| 311 |
+
try:
|
| 312 |
+
# Retry getting prediction (initial call logic again)
|
| 313 |
+
parsed_answer, raw_response = get_openrouter_prediction(
|
| 314 |
+
model_identifier=model_id,
|
| 315 |
+
api_key=api_key,
|
| 316 |
+
image=image, # Pass image for this retry
|
| 317 |
+
max_tokens=config.get("max_tokens", 100),
|
| 318 |
+
request_timeout=config.get("request_timeout", 60)
|
| 319 |
+
)
|
| 320 |
+
# API Retry succeeded, now check parsing
|
| 321 |
+
api_success_attempt2 = True
|
| 322 |
+
parse_success_attempt2 = parsed_answer is not None # Includes SKIP
|
| 323 |
+
raw_response_attempt2 = raw_response
|
| 324 |
|
| 325 |
+
# --- Re-prompt Logic (within API Retry Pass) ---
|
| 326 |
+
if api_success_attempt2 and not parse_success_attempt2:
|
| 327 |
+
logging.warning(f"Question {question_id}: API Retry succeeded, but parse failed. Attempting re-prompt.")
|
| 328 |
+
try:
|
| 329 |
+
# Third API call (re-prompt after API retry)
|
| 330 |
+
parsed_answer_rp2, raw_response_rp2 = get_openrouter_prediction(
|
| 331 |
+
model_identifier=model_id,
|
| 332 |
+
api_key=api_key,
|
| 333 |
+
previous_raw_response=raw_response_attempt2, # Pass API retry response
|
| 334 |
+
max_tokens=config.get("max_tokens", 100),
|
| 335 |
+
request_timeout=config.get("request_timeout", 60)
|
| 336 |
+
)
|
| 337 |
+
# Update results with re-prompt outcome
|
| 338 |
+
result_data.update({
|
| 339 |
+
"predicted_answer": parsed_answer_rp2,
|
| 340 |
+
"raw_response": raw_response_rp2,
|
| 341 |
+
"parse_successful": parsed_answer_rp2 is not None,
|
| 342 |
+
"api_call_successful": True,
|
| 343 |
+
"attempt": 3 # Mark as third attempt (API retry + re-prompt)
|
| 344 |
+
})
|
| 345 |
+
logging.info(f"Question {question_id}: API Retry + Re-prompt {'succeeded' if result_data['parse_successful'] else 'failed to parse'}.")
|
| 346 |
+
|
| 347 |
+
except Exception as e_rp2:
|
| 348 |
+
# Handle failure during the re-prompt API call itself
|
| 349 |
+
logging.error(f"Re-prompt API call failed for question {question_id} after API retry: {e_rp2}")
|
| 350 |
+
result_data.update({
|
| 351 |
+
"predicted_answer": None, # Failed parse overall
|
| 352 |
+
"raw_response": raw_response_attempt2, # Keep API retry raw response
|
| 353 |
+
"parse_successful": False,
|
| 354 |
+
"api_call_successful": True, # API retry call succeeded
|
| 355 |
+
"error": f"API retry succeeded, but parse failed. Re-prompt failed: {str(e_rp2)}",
|
| 356 |
+
"attempt": 2 # Revert attempt count
|
| 357 |
+
})
|
| 358 |
+
else:
|
| 359 |
+
# API retry succeeded and parsed correctly
|
| 360 |
+
result_data.update({
|
| 361 |
+
"predicted_answer": parsed_answer,
|
| 362 |
+
"raw_response": raw_response_attempt2,
|
| 363 |
+
"parse_successful": parse_success_attempt2,
|
| 364 |
+
"api_call_successful": True,
|
| 365 |
+
"attempt": 2
|
| 366 |
+
})
|
| 367 |
+
|
| 368 |
+
except Exception as e:
|
| 369 |
+
# Final API failure after the API retry pass
|
| 370 |
+
logging.error(f"API call failed permanently for question {question_id} (Attempt 2 API Retry): {e}")
|
| 371 |
+
result_data["error"] = str(e)
|
| 372 |
+
result_data["api_call_successful"] = False # Remains False
|
| 373 |
+
result_data["attempt"] = 2 # Still attempt 2
|
| 374 |
+
|
| 375 |
+
# Append the final result (success or failure) from the API retry pass (including potential re-prompt)
|
| 376 |
+
model_results.append(result_data)
|
| 377 |
+
append_prediction(result_data, predictions_path)
|
| 378 |
+
|
| 379 |
+
|
| 380 |
+
# --- Final Evaluation for the current model ---
|
| 381 |
+
logging.info(f"--- Calculating final results for model: {model_id} ---")
|
| 382 |
+
|
| 383 |
+
# Use the new NEET scoring function
|
| 384 |
+
# Check if the dataset is NEET before applying NEET scoring
|
| 385 |
+
# We infer this based on the presence of subjects like Botany/Zoology or question ID format
|
| 386 |
+
is_neet_dataset = any(res.get("subject") in ["Botany", "Zoology"] for res in model_results) or \
|
| 387 |
+
(model_results and model_results[0].get("question_id", "").startswith("NEET"))
|
| 388 |
+
|
| 389 |
+
if is_neet_dataset:
|
| 390 |
+
logging.info("Detected NEET dataset. Applying NEET scoring.")
|
| 391 |
+
evaluation_summary = calculate_neet_scores(model_results)
|
| 392 |
+
summary = {
|
| 393 |
+
"model_name": model_id,
|
| 394 |
+
"timestamp": timestamp,
|
| 395 |
+
"total_questions_in_dataset": total_questions,
|
| 396 |
+
**evaluation_summary # Merge NEET score details
|
| 397 |
+
}
|
| 398 |
+
logging.info(f"NEET Score: {summary.get('overall_score')}")
|
| 399 |
+
logging.info(f"Correct: {summary.get('overall_correct')}, Incorrect: {summary.get('overall_incorrect')}, Skipped: {summary.get('overall_skipped')}, API/Parse Failures: {summary.get('overall_api_parse_failures')}")
|
| 400 |
|
|
|
|
|
|
|
|
|
|
| 401 |
else:
|
| 402 |
+
# Fallback to simple accuracy for non-NEET datasets (like JEE)
|
| 403 |
+
logging.info("Non-NEET dataset detected (or could not determine). Applying simple accuracy scoring.")
|
| 404 |
+
# Extract predictions and truths for accuracy calculation
|
| 405 |
+
predictions_for_acc = [res.get("predicted_answer") for res in model_results if isinstance(res.get("predicted_answer"), list)] # Only use list predictions
|
| 406 |
+
truths_for_acc = [res.get("ground_truth") for res in model_results if isinstance(res.get("predicted_answer"), list)]
|
| 407 |
+
# Handle cases where no valid list predictions exist
|
| 408 |
+
accuracy = 0.0
|
| 409 |
+
if predictions_for_acc:
|
| 410 |
+
try:
|
| 411 |
+
# Note: calculate_accuracy expects Optional[List[int]], but we filtered Nones/SKIPs
|
| 412 |
+
# We need to align the lists properly if we want to use the original function
|
| 413 |
+
# For simplicity here, let's recalculate based on the final model_results
|
| 414 |
+
correct_count = 0
|
| 415 |
+
valid_attempts = 0
|
| 416 |
+
api_parse_failures = 0
|
| 417 |
+
for res in model_results:
|
| 418 |
+
pred = res.get("predicted_answer")
|
| 419 |
+
truth = res.get("ground_truth")
|
| 420 |
+
api_success = res.get("api_call_successful")
|
| 421 |
+
parse_success = res.get("parse_successful")
|
| 422 |
+
|
| 423 |
+
if not api_success or not parse_success:
|
| 424 |
+
api_parse_failures += 1
|
| 425 |
+
continue # Count as failure, not incorrect for accuracy
|
| 426 |
+
|
| 427 |
+
valid_attempts += 1
|
| 428 |
+
if isinstance(pred, list) and sorted(pred) == sorted(truth):
|
| 429 |
+
correct_count += 1
|
| 430 |
+
|
| 431 |
+
accuracy = correct_count / valid_attempts if valid_attempts > 0 else 0.0
|
| 432 |
+
|
| 433 |
+
except ValueError as e:
|
| 434 |
+
logging.error(f"Error calculating accuracy for model {model_id}: {e}")
|
| 435 |
+
accuracy = 0.0
|
| 436 |
+
else:
|
| 437 |
+
logging.warning(f"No valid list predictions were generated for model {model_id} for accuracy calculation.")
|
| 438 |
+
|
| 439 |
+
|
| 440 |
+
summary = {
|
| 441 |
+
"model_name": model_id,
|
| 442 |
+
"timestamp": timestamp,
|
| 443 |
+
"total_questions_in_dataset": total_questions,
|
| 444 |
+
"total_processed_attempts": len(model_results), # Includes retries
|
| 445 |
+
"successful_api_calls": sum(1 for res in model_results if res.get("api_call_successful")),
|
| 446 |
+
"successful_parses": sum(1 for res in model_results if res.get("parse_successful")),
|
| 447 |
+
"accuracy_on_parsed": accuracy # Accuracy based only on successfully parsed non-skipped answers
|
| 448 |
+
}
|
| 449 |
+
logging.info(f"Accuracy (on successfully parsed non-skipped): {accuracy:.4f}")
|
| 450 |
+
|
| 451 |
+
logging.info(f"--- Results Summary for model: {model_id} ---")
|
| 452 |
+
logging.info(json.dumps(summary, indent=2))
|
| 453 |
logging.info("-------------------------------------")
|
| 454 |
|
| 455 |
+
# --- Overwrite predictions file with final evaluated results ---
|
| 456 |
+
# The model_results list was modified in-place by calculate_neet_scores (if applicable)
|
| 457 |
+
# to include evaluation_status and marks_awarded.
|
| 458 |
+
logging.info(f"Overwriting {predictions_path} with final evaluated results...")
|
| 459 |
+
try:
|
| 460 |
+
with open(predictions_path, 'w') as f:
|
| 461 |
+
for final_result in model_results:
|
| 462 |
+
json.dump(final_result, f)
|
| 463 |
+
f.write('\n')
|
| 464 |
+
logging.info(f"Successfully updated {predictions_path} with evaluation details.")
|
| 465 |
+
except IOError as e:
|
| 466 |
+
logging.error(f"Failed to overwrite predictions file {predictions_path}: {e}")
|
| 467 |
+
except Exception as e:
|
| 468 |
+
logging.error(f"Unexpected error overwriting predictions file {predictions_path}: {e}")
|
| 469 |
+
|
| 470 |
+
|
| 471 |
+
# Save final summary (JSON and Markdown) for the current model
|
| 472 |
+
save_summary(summary, summary_path)
|
| 473 |
+
generate_markdown_summary(summary, markdown_summary_path) # Call the new function
|
| 474 |
|
| 475 |
logging.info("Benchmark run completed.")
|
| 476 |
|
src/evaluation.py
CHANGED
|
@@ -1,5 +1,6 @@
|
|
| 1 |
import logging
|
| 2 |
-
|
|
|
|
| 3 |
|
| 4 |
# Configure logging
|
| 5 |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
|
@@ -44,17 +45,174 @@ def calculate_accuracy(predictions: List[Optional[List[int]]], ground_truths: Li
|
|
| 44 |
if sorted_pred is not None and sorted_pred == sorted_truth:
|
| 45 |
correct_count += 1
|
| 46 |
else:
|
| 47 |
-
|
|
|
|
|
|
|
|
|
|
| 48 |
|
| 49 |
|
| 50 |
accuracy = correct_count / len(ground_truths)
|
| 51 |
logging.info(f"Accuracy calculated: {correct_count}/{len(ground_truths)} = {accuracy:.4f}")
|
| 52 |
return accuracy
|
| 53 |
|
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|
|
|
|
|
| 54 |
# Example Usage (for testing)
|
| 55 |
if __name__ == '__main__':
|
| 56 |
print("Running evaluation tests...")
|
| 57 |
|
|
|
|
|
|
|
| 58 |
# Test case 1: Perfect match
|
| 59 |
preds1 = [[1], [2], [1, 3]]
|
| 60 |
truths1 = [[1], [2], [3, 1]]
|
|
@@ -99,4 +257,61 @@ if __name__ == '__main__':
|
|
| 99 |
print(f"Test Case 6: Preds={preds6}, Truths={truths6} -> Accuracy: {acc6} (Expected: ~0.6667)")
|
| 100 |
assert abs(acc6 - 2/3) < 1e-6
|
| 101 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
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|
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|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 102 |
print("\nEvaluation tests completed.")
|
|
|
|
| 1 |
import logging
|
| 2 |
+
import re
|
| 3 |
+
from typing import List, Optional, Union, Dict, Any
|
| 4 |
|
| 5 |
# Configure logging
|
| 6 |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
|
|
|
| 45 |
if sorted_pred is not None and sorted_pred == sorted_truth:
|
| 46 |
correct_count += 1
|
| 47 |
else:
|
| 48 |
+
# Log incorrect only if prediction was not None (parsing succeeded but answer wrong)
|
| 49 |
+
if sorted_pred is not None:
|
| 50 |
+
logging.debug(f"Incorrect prediction for index {i}: Pred={sorted_pred}, Truth={sorted_truth}")
|
| 51 |
+
# If sorted_pred is None, it means parsing failed or API failed, already counted as incorrect implicitly
|
| 52 |
|
| 53 |
|
| 54 |
accuracy = correct_count / len(ground_truths)
|
| 55 |
logging.info(f"Accuracy calculated: {correct_count}/{len(ground_truths)} = {accuracy:.4f}")
|
| 56 |
return accuracy
|
| 57 |
|
| 58 |
+
|
| 59 |
+
def get_neet_section(subject: str, question_num: int) -> Optional[str]:
|
| 60 |
+
"""Determines the NEET section based on subject and question number."""
|
| 61 |
+
if subject == "Physics":
|
| 62 |
+
if 1 <= question_num <= 35: return "Physics_A"
|
| 63 |
+
if 36 <= question_num <= 50: return "Physics_B"
|
| 64 |
+
elif subject == "Chemistry":
|
| 65 |
+
if 51 <= question_num <= 85: return "Chemistry_A"
|
| 66 |
+
if 86 <= question_num <= 100: return "Chemistry_B"
|
| 67 |
+
elif subject == "Botany":
|
| 68 |
+
if 101 <= question_num <= 135: return "Botany_A"
|
| 69 |
+
if 136 <= question_num <= 150: return "Botany_B"
|
| 70 |
+
elif subject == "Zoology":
|
| 71 |
+
if 151 <= question_num <= 185: return "Zoology_A"
|
| 72 |
+
if 186 <= question_num <= 200: return "Zoology_B"
|
| 73 |
+
return None # Not a standard NEET question number/subject
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
def calculate_neet_scores(results: List[Dict[str, Any]]) -> Dict[str, Any]:
|
| 77 |
+
"""
|
| 78 |
+
Calculates NEET scores (+4 / -1 / 0) and provides section-wise breakdown.
|
| 79 |
+
|
| 80 |
+
Args:
|
| 81 |
+
results (List[Dict[str, Any]]): A list of result dictionaries. Each dict must contain:
|
| 82 |
+
'question_id' (str): e.g., "NEET_2024_T3_045"
|
| 83 |
+
'subject' (str): e.g., "Physics"
|
| 84 |
+
'ground_truth' (List[int]): Correct answer(s)
|
| 85 |
+
'predicted_answer' (List[int] | str | None): Model's prediction ("SKIP", list, or None)
|
| 86 |
+
'api_call_successful' (bool): Whether the API call succeeded.
|
| 87 |
+
This list will be modified in-place to add 'evaluation_status' and 'marks_awarded'.
|
| 88 |
+
|
| 89 |
+
Returns:
|
| 90 |
+
Dict[str, Any]: A dictionary containing overall and section-wise scores and counts.
|
| 91 |
+
"""
|
| 92 |
+
if not results:
|
| 93 |
+
return {"error": "No results provided."}
|
| 94 |
+
|
| 95 |
+
# Initialize overall and section counters
|
| 96 |
+
overall_stats = {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0, "api_parse_failures": 0}
|
| 97 |
+
section_stats = {
|
| 98 |
+
"Physics_A": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 99 |
+
"Physics_B": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 100 |
+
"Chemistry_A": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 101 |
+
"Chemistry_B": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 102 |
+
"Botany_A": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 103 |
+
"Botany_B": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 104 |
+
"Zoology_A": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 105 |
+
"Zoology_B": {"score": 0, "correct": 0, "incorrect": 0, "skipped": 0},
|
| 106 |
+
}
|
| 107 |
+
unmapped_questions = 0
|
| 108 |
+
|
| 109 |
+
for result in results:
|
| 110 |
+
question_id = result.get("question_id")
|
| 111 |
+
subject = result.get("subject")
|
| 112 |
+
pred = result.get("predicted_answer")
|
| 113 |
+
truth = result.get("ground_truth")
|
| 114 |
+
api_success = result.get("api_call_successful", False) # Default to False if missing
|
| 115 |
+
|
| 116 |
+
# Determine section
|
| 117 |
+
section = None
|
| 118 |
+
question_num = -1
|
| 119 |
+
if question_id and subject:
|
| 120 |
+
match = re.search(r'_(\d+)$', question_id) # Extract number at the end
|
| 121 |
+
if match:
|
| 122 |
+
try:
|
| 123 |
+
question_num = int(match.group(1))
|
| 124 |
+
section = get_neet_section(subject, question_num)
|
| 125 |
+
except ValueError:
|
| 126 |
+
logging.warning(f"Could not parse number from question_id: {question_id}")
|
| 127 |
+
else:
|
| 128 |
+
logging.warning(f"Could not extract number from question_id format: {question_id}")
|
| 129 |
+
else:
|
| 130 |
+
logging.warning(f"Missing question_id or subject for a result: {result}")
|
| 131 |
+
|
| 132 |
+
if section is None:
|
| 133 |
+
unmapped_questions += 1
|
| 134 |
+
logging.warning(f"Could not map question to NEET section: ID={question_id}, Subject={subject}, Num={question_num}")
|
| 135 |
+
# Decide how to handle unmapped questions - here we just count them and don't score them section-wise
|
| 136 |
+
# They will still contribute to overall failure counts if applicable
|
| 137 |
+
|
| 138 |
+
# --- Scoring Logic ---
|
| 139 |
+
current_score_change = 0
|
| 140 |
+
is_correct = False
|
| 141 |
+
is_incorrect = False
|
| 142 |
+
is_skipped = False
|
| 143 |
+
is_failure = False
|
| 144 |
+
evaluation_status = "unknown" # Initialize status
|
| 145 |
+
|
| 146 |
+
if not api_success or pred is None:
|
| 147 |
+
# API call failed OR parsing failed (but wasn't a deliberate SKIP)
|
| 148 |
+
evaluation_status = "failure" # API or Parse Failure
|
| 149 |
+
is_incorrect = True
|
| 150 |
+
is_failure = True
|
| 151 |
+
current_score_change = -1
|
| 152 |
+
elif pred == "SKIP":
|
| 153 |
+
is_skipped = True
|
| 154 |
+
current_score_change = 0
|
| 155 |
+
evaluation_status = "skipped"
|
| 156 |
+
elif isinstance(pred, list):
|
| 157 |
+
# API and parsing succeeded, compare answers
|
| 158 |
+
sorted_pred = sorted(pred)
|
| 159 |
+
sorted_truth = sorted(truth)
|
| 160 |
+
if sorted_pred == sorted_truth:
|
| 161 |
+
is_correct = True
|
| 162 |
+
current_score_change = 4
|
| 163 |
+
evaluation_status = "correct"
|
| 164 |
+
else:
|
| 165 |
+
is_incorrect = True
|
| 166 |
+
current_score_change = -1
|
| 167 |
+
evaluation_status = "incorrect" # Wrong answer
|
| 168 |
+
else:
|
| 169 |
+
# Should not happen if parsing logic is correct, but handle defensively
|
| 170 |
+
logging.error(f"Unexpected prediction type for {question_id}: {pred}")
|
| 171 |
+
is_incorrect = True
|
| 172 |
+
is_failure = True # Treat unexpected type as failure
|
| 173 |
+
current_score_change = -1
|
| 174 |
+
evaluation_status = "failure" # Unexpected type
|
| 175 |
+
|
| 176 |
+
# Add evaluation details to the result dictionary
|
| 177 |
+
result['evaluation_status'] = evaluation_status
|
| 178 |
+
result['marks_awarded'] = current_score_change
|
| 179 |
+
|
| 180 |
+
# Update overall stats
|
| 181 |
+
overall_stats["score"] += current_score_change
|
| 182 |
+
if is_correct: overall_stats["correct"] += 1
|
| 183 |
+
if is_incorrect: overall_stats["incorrect"] += 1
|
| 184 |
+
if is_skipped: overall_stats["skipped"] += 1
|
| 185 |
+
if is_failure: overall_stats["api_parse_failures"] += 1
|
| 186 |
+
|
| 187 |
+
# Update section stats if section was determined
|
| 188 |
+
if section:
|
| 189 |
+
section_stats[section]["score"] += current_score_change
|
| 190 |
+
if is_correct: section_stats[section]["correct"] += 1
|
| 191 |
+
if is_incorrect: section_stats[section]["incorrect"] += 1
|
| 192 |
+
if is_skipped: section_stats[section]["skipped"] += 1
|
| 193 |
+
|
| 194 |
+
logging.info(f"NEET Score Calculation Complete. Overall Score: {overall_stats['score']}")
|
| 195 |
+
if unmapped_questions > 0:
|
| 196 |
+
logging.warning(f"{unmapped_questions} questions could not be mapped to a NEET section.")
|
| 197 |
+
|
| 198 |
+
return {
|
| 199 |
+
"overall_score": overall_stats["score"],
|
| 200 |
+
"overall_correct": overall_stats["correct"],
|
| 201 |
+
"overall_incorrect": overall_stats["incorrect"],
|
| 202 |
+
"overall_skipped": overall_stats["skipped"],
|
| 203 |
+
"overall_api_parse_failures": overall_stats["api_parse_failures"],
|
| 204 |
+
"total_questions_processed": len(results),
|
| 205 |
+
"unmapped_section_questions": unmapped_questions,
|
| 206 |
+
"section_breakdown": section_stats
|
| 207 |
+
}
|
| 208 |
+
|
| 209 |
+
|
| 210 |
# Example Usage (for testing)
|
| 211 |
if __name__ == '__main__':
|
| 212 |
print("Running evaluation tests...")
|
| 213 |
|
| 214 |
+
# --- Test calculate_accuracy ---
|
| 215 |
+
print("\n--- Testing calculate_accuracy ---")
|
| 216 |
# Test case 1: Perfect match
|
| 217 |
preds1 = [[1], [2], [1, 3]]
|
| 218 |
truths1 = [[1], [2], [3, 1]]
|
|
|
|
| 257 |
print(f"Test Case 6: Preds={preds6}, Truths={truths6} -> Accuracy: {acc6} (Expected: ~0.6667)")
|
| 258 |
assert abs(acc6 - 2/3) < 1e-6
|
| 259 |
|
| 260 |
+
# --- Test calculate_neet_scores ---
|
| 261 |
+
print("\n--- Testing calculate_neet_scores ---")
|
| 262 |
+
test_results = [
|
| 263 |
+
# Physics A - Correct
|
| 264 |
+
{"question_id": "NEET_2024_T3_001", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "api_call_successful": True},
|
| 265 |
+
# Physics B - Incorrect
|
| 266 |
+
{"question_id": "NEET_2024_T3_040", "subject": "Physics", "ground_truth": [4], "predicted_answer": [2], "api_call_successful": True},
|
| 267 |
+
# Chemistry A - Skipped
|
| 268 |
+
{"question_id": "NEET_2024_T3_055", "subject": "Chemistry", "ground_truth": [4], "predicted_answer": "SKIP", "api_call_successful": True},
|
| 269 |
+
# Chemistry B - API Fail
|
| 270 |
+
{"question_id": "NEET_2024_T3_090", "subject": "Chemistry", "ground_truth": [3], "predicted_answer": None, "api_call_successful": False},
|
| 271 |
+
# Botany A - Parse Fail (None)
|
| 272 |
+
{"question_id": "NEET_2024_T3_110", "subject": "Botany", "ground_truth": [4], "predicted_answer": None, "api_call_successful": True},
|
| 273 |
+
# Botany B - Correct (multi)
|
| 274 |
+
{"question_id": "NEET_2024_T3_145", "subject": "Botany", "ground_truth": [2, 4], "predicted_answer": [4, 2], "api_call_successful": True}, # Assuming multi-correct allowed
|
| 275 |
+
# Zoology A - Correct
|
| 276 |
+
{"question_id": "NEET_2024_T3_160", "subject": "Zoology", "ground_truth": [4], "predicted_answer": [4], "api_call_successful": True},
|
| 277 |
+
# Zoology B - Incorrect
|
| 278 |
+
{"question_id": "NEET_2024_T3_190", "subject": "Zoology", "ground_truth": [2], "predicted_answer": [1], "api_call_successful": True},
|
| 279 |
+
# Unmapped ID
|
| 280 |
+
{"question_id": "JEE_2023_Q1", "subject": "Physics", "ground_truth": [1], "predicted_answer": [1], "api_call_successful": True},
|
| 281 |
+
# Missing Subject
|
| 282 |
+
{"question_id": "NEET_2024_T3_002", "subject": None, "ground_truth": [3], "predicted_answer": [3], "api_call_successful": True},
|
| 283 |
+
]
|
| 284 |
+
|
| 285 |
+
neet_summary = calculate_neet_scores(test_results)
|
| 286 |
+
print("\nNEET Score Summary:")
|
| 287 |
+
import json
|
| 288 |
+
print(json.dumps(neet_summary, indent=2))
|
| 289 |
+
|
| 290 |
+
# Expected:
|
| 291 |
+
# Overall: Score=4-1+0-1-1+4+4-1 = 8. Correct=3, Incorrect=4, Skipped=1, Failures=2, Unmapped=2
|
| 292 |
+
# Phys A: Score=4, Correct=1, Incorrect=0, Skipped=0
|
| 293 |
+
# Phys B: Score=-1, Correct=0, Incorrect=1, Skipped=0
|
| 294 |
+
# Chem A: Score=0, Correct=0, Incorrect=0, Skipped=1
|
| 295 |
+
# Chem B: Score=-1, Correct=0, Incorrect=1, Skipped=0 (API Fail counts as incorrect)
|
| 296 |
+
# Bot A: Score=-1, Correct=0, Incorrect=1, Skipped=0 (Parse Fail counts as incorrect)
|
| 297 |
+
# Bot B: Score=4, Correct=1, Incorrect=0, Skipped=0
|
| 298 |
+
# Zoo A: Score=4, Correct=1, Incorrect=0, Skipped=0
|
| 299 |
+
# Zoo B: Score=-1, Correct=0, Incorrect=1, Skipped=0
|
| 300 |
+
|
| 301 |
+
assert neet_summary["overall_score"] == 8
|
| 302 |
+
assert neet_summary["overall_correct"] == 3
|
| 303 |
+
assert neet_summary["overall_incorrect"] == 4
|
| 304 |
+
assert neet_summary["overall_skipped"] == 1
|
| 305 |
+
assert neet_summary["overall_api_parse_failures"] == 2
|
| 306 |
+
assert neet_summary["unmapped_section_questions"] == 2
|
| 307 |
+
assert neet_summary["section_breakdown"]["Physics_A"]["score"] == 4
|
| 308 |
+
assert neet_summary["section_breakdown"]["Physics_B"]["score"] == -1
|
| 309 |
+
assert neet_summary["section_breakdown"]["Chemistry_A"]["score"] == 0
|
| 310 |
+
assert neet_summary["section_breakdown"]["Chemistry_B"]["score"] == -1
|
| 311 |
+
assert neet_summary["section_breakdown"]["Botany_A"]["score"] == -1
|
| 312 |
+
assert neet_summary["section_breakdown"]["Botany_B"]["score"] == 4
|
| 313 |
+
assert neet_summary["section_breakdown"]["Zoology_A"]["score"] == 4
|
| 314 |
+
assert neet_summary["section_breakdown"]["Zoology_B"]["score"] == -1
|
| 315 |
+
|
| 316 |
+
|
| 317 |
print("\nEvaluation tests completed.")
|
src/llm_interface.py
CHANGED
|
@@ -46,21 +46,43 @@ def encode_image_to_base64(image: Image.Image) -> str:
|
|
| 46 |
img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
|
| 47 |
return img_str
|
| 48 |
|
| 49 |
-
def
|
| 50 |
-
"""Constructs the message list for
|
| 51 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 52 |
prompt_text = """You are an expert at analyzing exam questions and extracting the correct answer option(s).
|
| 53 |
|
| 54 |
Examine the provided image of a multiple-choice question carefully.
|
| 55 |
1. Analyze the question and the provided options.
|
| 56 |
-
2. Determine the correct integer number
|
| 57 |
-
3. Format your final answer by enclosing ONLY the integer number
|
| 58 |
|
| 59 |
Examples:
|
| 60 |
- If the correct option is 2: <answer>2</answer>
|
| 61 |
-
- If
|
| 62 |
|
| 63 |
-
It is crucial that your response contains ONLY the <answer>
|
| 64 |
|
| 65 |
messages = [
|
| 66 |
{
|
|
@@ -79,27 +101,51 @@ It is crucial that your response contains ONLY the <answer> tags with the correc
|
|
| 79 |
return messages
|
| 80 |
|
| 81 |
@retry(**retry_config)
|
| 82 |
-
def get_openrouter_prediction(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 83 |
"""
|
| 84 |
-
Gets a prediction from an OpenRouter
|
| 85 |
|
| 86 |
Args:
|
| 87 |
-
|
|
|
|
|
|
|
|
|
|
| 88 |
model_identifier (str): The OpenRouter model identifier (e.g., "openai/gpt-4o").
|
| 89 |
api_key (str): The OpenRouter API key.
|
| 90 |
max_tokens (int): Max tokens for the response.
|
| 91 |
request_timeout (int): Timeout for the API request in seconds.
|
| 92 |
|
| 93 |
Returns:
|
| 94 |
-
tuple[list[int] | None, str | None]: A tuple containing:
|
| 95 |
-
- The parsed answer as a list of integers
|
| 96 |
- The raw response text from the LLM (or None if API call failed).
|
|
|
|
|
|
|
|
|
|
|
|
|
| 97 |
"""
|
| 98 |
logging.info(f"Requesting prediction from model: {model_identifier}")
|
| 99 |
-
|
|
|
|
|
|
|
|
|
|
| 100 |
base64_image = encode_image_to_base64(image)
|
| 101 |
-
messages =
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 102 |
|
|
|
|
| 103 |
headers = {
|
| 104 |
"Authorization": f"Bearer {api_key}",
|
| 105 |
"Content-Type": "application/json"
|
|
@@ -143,7 +189,7 @@ def get_openrouter_prediction(image: Image.Image, model_identifier: str, api_key
|
|
| 143 |
parsed_answer = parse_llm_answer(raw_response_text)
|
| 144 |
|
| 145 |
if parsed_answer is None:
|
| 146 |
-
logging.warning(f"Failed to parse answer from model {model_identifier}.
|
| 147 |
|
| 148 |
return parsed_answer, raw_response_text
|
| 149 |
|
|
|
|
| 46 |
img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
|
| 47 |
return img_str
|
| 48 |
|
| 49 |
+
def construct_reprompt_prompt(previous_raw_response: str) -> list:
|
| 50 |
+
"""Constructs the message list for a re-prompt API call."""
|
| 51 |
+
prompt_text = f"""You previously provided the following response to an exam question:
|
| 52 |
+
--- PREVIOUS RESPONSE START ---
|
| 53 |
+
{previous_raw_response}
|
| 54 |
+
--- PREVIOUS RESPONSE END ---
|
| 55 |
+
|
| 56 |
+
Your previous response did not correctly format the final answer within <answer> tags, or it contained multiple answers.
|
| 57 |
+
|
| 58 |
+
For NEET exam questions, only a single answer option is correct.
|
| 59 |
+
|
| 60 |
+
Please re-examine your previous reasoning and provide ONLY the single integer corresponding to the correct answer choice, enclosed in <answer> tags.
|
| 61 |
+
|
| 62 |
+
Example:
|
| 63 |
+
- If the correct option is 2: <answer>2</answer>
|
| 64 |
+
- If you are unsure or cannot determine the answer: <answer>SKIP</answer>
|
| 65 |
+
|
| 66 |
+
It is crucial that your response contains ONLY the <answer> tag with the single correct integer number OR the word SKIP inside. Do not include any other text, explanation, or formatting."""
|
| 67 |
+
messages = [{"role": "user", "content": prompt_text}]
|
| 68 |
+
return messages
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
def construct_initial_prompt(base64_image: str) -> list:
|
| 72 |
+
"""Constructs the initial message list with image for the OpenRouter API call."""
|
| 73 |
+
# Original prompt for the first attempt
|
| 74 |
prompt_text = """You are an expert at analyzing exam questions and extracting the correct answer option(s).
|
| 75 |
|
| 76 |
Examine the provided image of a multiple-choice question carefully.
|
| 77 |
1. Analyze the question and the provided options.
|
| 78 |
+
2. Determine the single correct integer number corresponding to the correct option.
|
| 79 |
+
3. Format your final answer by enclosing ONLY the single integer number within <answer> tags.
|
| 80 |
|
| 81 |
Examples:
|
| 82 |
- If the correct option is 2: <answer>2</answer>
|
| 83 |
+
- If you are unsure or cannot determine the answer: <answer>SKIP</answer>
|
| 84 |
|
| 85 |
+
It is crucial that your response contains ONLY the <answer> tag with the single correct integer number OR the word SKIP inside. Do not include any other text, explanation, or formatting."""
|
| 86 |
|
| 87 |
messages = [
|
| 88 |
{
|
|
|
|
| 101 |
return messages
|
| 102 |
|
| 103 |
@retry(**retry_config)
|
| 104 |
+
def get_openrouter_prediction(
|
| 105 |
+
model_identifier: str,
|
| 106 |
+
api_key: str,
|
| 107 |
+
image: Image.Image | None = None, # Image is now optional
|
| 108 |
+
previous_raw_response: str | None = None, # Added for re-prompting
|
| 109 |
+
max_tokens: int = 100,
|
| 110 |
+
request_timeout: int = 60
|
| 111 |
+
) -> tuple[list[int] | str | None, str | None]: # Allow predicted_answer to be "SKIP"
|
| 112 |
"""
|
| 113 |
+
Gets a prediction from an OpenRouter model. Handles initial image prompts and text-only re-prompts.
|
| 114 |
|
| 115 |
Args:
|
| 116 |
+
model_identifier (str): The OpenRouter model identifier (e.g., "openai/gpt-4o").
|
| 117 |
+
api_key (str): The OpenRouter API key.
|
| 118 |
+
image (Image.Image | None): The question image (for initial prompt). Default None.
|
| 119 |
+
previous_raw_response (str | None): The raw response from a previous failed parse attempt (for re-prompt). Default None.
|
| 120 |
model_identifier (str): The OpenRouter model identifier (e.g., "openai/gpt-4o").
|
| 121 |
api_key (str): The OpenRouter API key.
|
| 122 |
max_tokens (int): Max tokens for the response.
|
| 123 |
request_timeout (int): Timeout for the API request in seconds.
|
| 124 |
|
| 125 |
Returns:
|
| 126 |
+
tuple[list[int] | str | None, str | None]: A tuple containing:
|
| 127 |
+
- The parsed answer as a list of integers, the string "SKIP", or None if failed.
|
| 128 |
- The raw response text from the LLM (or None if API call failed).
|
| 129 |
+
|
| 130 |
+
Raises:
|
| 131 |
+
ValueError: If neither image nor previous_raw_response is provided.
|
| 132 |
+
requests.exceptions.RequestException: If the API call fails after retries.
|
| 133 |
"""
|
| 134 |
logging.info(f"Requesting prediction from model: {model_identifier}")
|
| 135 |
+
|
| 136 |
+
if image is not None and previous_raw_response is None:
|
| 137 |
+
# Initial prompt with image
|
| 138 |
+
logging.debug("Constructing initial prompt with image.")
|
| 139 |
base64_image = encode_image_to_base64(image)
|
| 140 |
+
messages = construct_initial_prompt(base64_image)
|
| 141 |
+
elif image is None and previous_raw_response is not None:
|
| 142 |
+
# Re-prompt based on previous response
|
| 143 |
+
logging.debug("Constructing re-prompt based on previous response.")
|
| 144 |
+
messages = construct_reprompt_prompt(previous_raw_response)
|
| 145 |
+
else:
|
| 146 |
+
raise ValueError("Either 'image' (for initial call) or 'previous_raw_response' (for re-prompt) must be provided, but not both.")
|
| 147 |
|
| 148 |
+
try:
|
| 149 |
headers = {
|
| 150 |
"Authorization": f"Bearer {api_key}",
|
| 151 |
"Content-Type": "application/json"
|
|
|
|
| 189 |
parsed_answer = parse_llm_answer(raw_response_text)
|
| 190 |
|
| 191 |
if parsed_answer is None:
|
| 192 |
+
logging.warning(f"Failed to parse answer from model {model_identifier}.") # Simplified log
|
| 193 |
|
| 194 |
return parsed_answer, raw_response_text
|
| 195 |
|
src/utils.py
CHANGED
|
@@ -19,33 +19,50 @@ def load_api_key(key_name="OPENROUTER_API_KEY"):
|
|
| 19 |
Raises:
|
| 20 |
ValueError: If the API key environment variable is not set.
|
| 21 |
"""
|
| 22 |
-
load_dotenv()
|
| 23 |
api_key = os.getenv(key_name)
|
| 24 |
if not api_key:
|
| 25 |
-
raise ValueError(
|
|
|
|
|
|
|
| 26 |
return api_key
|
| 27 |
|
| 28 |
-
|
|
|
|
| 29 |
"""
|
| 30 |
-
Parses the LLM response text to extract
|
| 31 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 32 |
|
| 33 |
Args:
|
| 34 |
response_text (str): The raw text response from the LLM.
|
| 35 |
|
| 36 |
Returns:
|
| 37 |
-
list[int] | None:
|
| 38 |
-
|
|
|
|
|
|
|
| 39 |
"""
|
| 40 |
if not response_text:
|
| 41 |
return None
|
| 42 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 43 |
# Regex to find content within <answer>...</answer>, allowing for newlines (re.DOTALL)
|
| 44 |
# It captures the content inside the tags.
|
| 45 |
-
match = re.search(r
|
| 46 |
|
| 47 |
if match:
|
| 48 |
extracted_content = match.group(1).strip()
|
|
|
|
| 49 |
if not extracted_content:
|
| 50 |
logging.warning(f"Found <answer> tag but content is empty.")
|
| 51 |
return None # Empty content within tags
|
|
@@ -58,57 +75,53 @@ def parse_llm_answer(response_text: str) -> list[int] | None:
|
|
| 58 |
for ans_str in potential_answers:
|
| 59 |
if not ans_str: continue # Skip empty strings resulting from trailing commas etc.
|
| 60 |
try:
|
|
|
|
| 61 |
# Attempt to convert to integer
|
| 62 |
parsed_answers.append(int(ans_str))
|
| 63 |
except ValueError:
|
| 64 |
-
|
|
|
|
| 65 |
valid = False
|
| 66 |
break # Stop parsing this tag content if any part is invalid
|
| 67 |
|
| 68 |
-
if valid and parsed_answers:
|
| 69 |
-
# Return
|
| 70 |
-
return
|
|
|
|
|
|
|
|
|
|
| 71 |
else:
|
| 72 |
-
# Parsing failed or resulted in empty list after validation
|
|
|
|
| 73 |
return None
|
| 74 |
else:
|
| 75 |
-
#
|
| 76 |
-
logging.warning(f"Could not find <answer> tag in response
|
| 77 |
-
#
|
| 78 |
-
# This is less precise and might pick up unintended numbers.
|
| 79 |
-
numbers = re.findall(r'\b\d+\b', response_text)
|
| 80 |
-
if numbers:
|
| 81 |
-
try:
|
| 82 |
-
parsed_answers = sorted([int(n) for n in numbers])
|
| 83 |
-
logging.info(f"Fallback parsing extracted: {parsed_answers}")
|
| 84 |
-
return parsed_answers
|
| 85 |
-
except ValueError:
|
| 86 |
-
logging.warning(f"Fallback parsing failed to convert found numbers: {numbers}")
|
| 87 |
-
return None # Found digits but couldn't convert all
|
| 88 |
-
else:
|
| 89 |
-
logging.warning(f"Fallback parsing found no digits.")
|
| 90 |
-
return None # Tag not found and no digits found
|
| 91 |
|
| 92 |
# Example Usage (for testing)
|
| 93 |
if __name__ == '__main__':
|
| 94 |
test_responses = [
|
| 95 |
"Some text before <answer>2</answer> and after.",
|
| 96 |
"Blah blah <answer> 1 </answer> blah",
|
| 97 |
-
"<answer>1,3</answer>",
|
| 98 |
-
"<answer> 4 , 2 </answer> end",
|
| 99 |
"<answer>\n 3 \n</answer>",
|
| 100 |
-
"<answer>\n 1,\n 4 \n</answer>",
|
| 101 |
-
"No answer tag here.",
|
| 102 |
"<answer></answer>",
|
| 103 |
"<answer> </answer>",
|
| 104 |
"<answer>abc</answer>",
|
| 105 |
"<answer>1, abc</answer>",
|
| 106 |
-
"<answer>1, </answer>",
|
| 107 |
-
"<answer>,2</answer>",
|
| 108 |
None,
|
| 109 |
-
""
|
|
|
|
|
|
|
| 110 |
]
|
| 111 |
|
|
|
|
| 112 |
for resp in test_responses:
|
| 113 |
parsed = parse_llm_answer(resp)
|
| 114 |
print(f"Response: '{str(resp)[:50]}...' -> Parsed: {parsed}")
|
|
|
|
| 19 |
Raises:
|
| 20 |
ValueError: If the API key environment variable is not set.
|
| 21 |
"""
|
| 22 |
+
load_dotenv() # Load environment variables from .env file if it exists
|
| 23 |
api_key = os.getenv(key_name)
|
| 24 |
if not api_key:
|
| 25 |
+
raise ValueError(
|
| 26 |
+
f"'{key_name}' environment variable not set. Please create a .env file or set the variable."
|
| 27 |
+
)
|
| 28 |
return api_key
|
| 29 |
|
| 30 |
+
|
| 31 |
+
def parse_llm_answer(response_text: str) -> list[int] | str | None:
|
| 32 |
"""
|
| 33 |
+
Parses the LLM response text to extract answers within <answer> tags.
|
| 34 |
+
Requires exactly one integer answer for NEET context.
|
| 35 |
+
|
| 36 |
+
Handles:
|
| 37 |
+
- Single integer answers.
|
| 38 |
+
- The specific string "SKIP" for skipped questions.
|
| 39 |
+
- Potential newlines within the tags.
|
| 40 |
|
| 41 |
Args:
|
| 42 |
response_text (str): The raw text response from the LLM.
|
| 43 |
|
| 44 |
Returns:
|
| 45 |
+
list[int] | str | None:
|
| 46 |
+
- A list containing the single integer answer if found and valid.
|
| 47 |
+
- The string "SKIP" if the response indicates a skip.
|
| 48 |
+
- None if parsing fails (no tag, invalid content, multiple answers, etc.).
|
| 49 |
"""
|
| 50 |
if not response_text:
|
| 51 |
return None
|
| 52 |
|
| 53 |
+
# Check for exact SKIP response first (case-insensitive)
|
| 54 |
+
# Use strip() to handle potential leading/trailing whitespace around the tag itself
|
| 55 |
+
if response_text.strip().upper() == "<ANSWER>SKIP</ANSWER>":
|
| 56 |
+
logging.info("Parsed answer as SKIP.")
|
| 57 |
+
return "SKIP"
|
| 58 |
+
|
| 59 |
# Regex to find content within <answer>...</answer>, allowing for newlines (re.DOTALL)
|
| 60 |
# It captures the content inside the tags.
|
| 61 |
+
match = re.search(r"<answer>(.*?)</answer>", response_text, re.DOTALL | re.IGNORECASE)
|
| 62 |
|
| 63 |
if match:
|
| 64 |
extracted_content = match.group(1).strip()
|
| 65 |
+
logging.warning(f"DEBUG_PARSE: Raw extracted content: {match.group(1)!r}, Stripped: {extracted_content!r}") # Added minimal log
|
| 66 |
if not extracted_content:
|
| 67 |
logging.warning(f"Found <answer> tag but content is empty.")
|
| 68 |
return None # Empty content within tags
|
|
|
|
| 75 |
for ans_str in potential_answers:
|
| 76 |
if not ans_str: continue # Skip empty strings resulting from trailing commas etc.
|
| 77 |
try:
|
| 78 |
+
logging.warning(f"DEBUG_PARSE: Attempting int conversion on: {ans_str!r}") # Added minimal log
|
| 79 |
# Attempt to convert to integer
|
| 80 |
parsed_answers.append(int(ans_str))
|
| 81 |
except ValueError:
|
| 82 |
+
# Log only the problematic part, not the whole content
|
| 83 |
+
logging.warning(f"Could not parse '{ans_str}' as an integer within <answer> tag.")
|
| 84 |
valid = False
|
| 85 |
break # Stop parsing this tag content if any part is invalid
|
| 86 |
|
| 87 |
+
if valid and len(parsed_answers) == 1:
|
| 88 |
+
# Return list with the single integer answer
|
| 89 |
+
return parsed_answers # No need to sort a single-element list
|
| 90 |
+
elif valid and len(parsed_answers) > 1:
|
| 91 |
+
logging.warning(f"Found multiple answers ({parsed_answers}) within <answer> tag. Treating as parse failure for single-answer context.")
|
| 92 |
+
return None # Treat multiple answers as failure
|
| 93 |
else:
|
| 94 |
+
# Parsing failed (invalid content) or resulted in empty list after validation
|
| 95 |
+
# No need to log here, handled by the ValueError log above if applicable
|
| 96 |
return None
|
| 97 |
else:
|
| 98 |
+
# Tag not found
|
| 99 |
+
logging.warning(f"Could not find <answer> tag in response.") # Simplified log
|
| 100 |
+
return None # Return None if tag is missing, fallback removed
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 101 |
|
| 102 |
# Example Usage (for testing)
|
| 103 |
if __name__ == '__main__':
|
| 104 |
test_responses = [
|
| 105 |
"Some text before <answer>2</answer> and after.",
|
| 106 |
"Blah blah <answer> 1 </answer> blah",
|
| 107 |
+
"<answer>1,3</answer>", # Should fail now
|
| 108 |
+
"<answer> 4 , 2 </answer> end", # Should fail now
|
| 109 |
"<answer>\n 3 \n</answer>",
|
| 110 |
+
"<answer>\n 1,\n 4 \n</answer>", # Should fail now
|
| 111 |
+
"No answer tag here.", # Should fail now
|
| 112 |
"<answer></answer>",
|
| 113 |
"<answer> </answer>",
|
| 114 |
"<answer>abc</answer>",
|
| 115 |
"<answer>1, abc</answer>",
|
| 116 |
+
"<answer>1, </answer>", # Should fail now
|
| 117 |
+
"<answer>,2</answer>", # Should fail now
|
| 118 |
None,
|
| 119 |
+
"",
|
| 120 |
+
"<ANSWER>SKIP</ANSWER>",
|
| 121 |
+
" <ANSWER>SKIP</ANSWER> "
|
| 122 |
]
|
| 123 |
|
| 124 |
+
print("\n--- Testing parse_llm_answer (single answer required) ---")
|
| 125 |
for resp in test_responses:
|
| 126 |
parsed = parse_llm_answer(resp)
|
| 127 |
print(f"Response: '{str(resp)[:50]}...' -> Parsed: {parsed}")
|