agentlans/lime-nlp-difficulty

---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- math
- difficulty
- qwen
---
# lime-nlp Difficulty Estimation Math Datasets collection

Unofficial reformatted version of [lime-nlp/difficulty-estimation-math-datasets](https://huggingface.co/collections/lime-nlp/difficulty-estimation-math-datasets),
which contains math problems and the [Qwen 2.5 7B MATH model's](https://huggingface.co/Qwen/Qwen2.5-Math-7B) success rates at solving those problems.

The combined dataset has been split into 80% training and 20% testing data.

Fields:

- `row_id`: the row number of each dataset entry, starting at 0
- `input`: the math question from the dataset
- `output`: the correct answer (ground truth)
- `solved_proportion`: the fraction of times the Qwen 2.5 7B MATH model answered correctly (percentage from the original dataset divided by 100%)
- `source`: the name of the `lime-nlp` dataset from which the question originates

Example row:
```json
{
"row_id": 23199,
"input": "For the system of equations \\(x^{2} + x^{2} y^{2} + x^{2} y^{4} = 525\\) and \\(x + x y + x y^{2} = 35\\), find the sum of the real y values that satisfy the equations.",
"output": "\frac{5}{2}",
"solved_percentage": 3.125,
"source": "lime-nlp/DeepScaleR_Difficulty"
}
```

## Licence

MIT like the original dataset

Be sure to cite the original authors:
```bibtex
@misc{shi2025efficientreinforcementfinetuningadaptive,
title={Efficient Reinforcement Finetuning via Adaptive Curriculum Learning},
author={Taiwei Shi and Yiyang Wu and Linxin Song and Tianyi Zhou and Jieyu Zhao},
year={2025},
eprint={2504.05520},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05520},
}
```