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--- |
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language: |
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- en |
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pretty_name: Math Notebooks |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Math Notebooks |
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This repository contains mathematically informative ipython notebooks that were collated from OpenWebMath, RedPajama, and the Algebraic Stack in the [AutoMathText](https://huggingface.co/datasets/math-ai/AutoMathText) effort. Zhang et. al. used Qwen 72B to score text with the following prompt: |
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``` |
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<system> |
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You are ChatGPT, equipped with extensive expertise in mathematics and coding, and skilled |
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in complex reasoning and problem-solving. In the following task, I will present a text excerpt |
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from a website. Your role is to evaluate whether this text exhibits mathematical intelligence |
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and if it is suitable for educational purposes in mathematics. Please respond with only YES |
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or NO |
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</system> |
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User: { |
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“url”: “{url}”, |
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“text”: “{text}” |
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} |
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1. Does the text exhibit elements of mathematical intelligence? Respond with YES or NO |
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2. Is the text suitable for educational purposes for YOURSELF in the field of mathematics? Respond with YES or NO |
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``` |
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The responses to these questions were each scored with the function: |
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$$LM–Score(\cdot) = \frac{exp(logit('YES'))}{exp(logit('YES')) + exp(logit('NO'))}$$ |
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|
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These scores are found in the `meta.lm_q1_score` and `meta.lm_q2_score` columns. A total score (`meta.lm_q1q2_score`) is achieved by taking the product of the two scores. |
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$$ LM–Score(Q_1, Q_2) = LM–Score(Q_1) \cdot LM–Score(Q_2) $$ |
