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Dataset: math_qa 🏷
Update math_qa.py on GitHub

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#### Tags

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#### Models trained or fine-tuned on math_qa

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# Dataset Card for "math_qa"

## Dataset Description

### Dataset Summary

Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.

## Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

### Data Instances

#### default

• Size of the generated dataset: 21.90 MB
• Total amount of disk used: 28.87 MB

An example of 'train' looks as follows.

{
"Problem": "a multiple choice test consists of 4 questions , and each question has 5 answer choices . in how many r ways can the test be completed if every question is unanswered ?",
"Rationale": "\"5 choices for each of the 4 questions , thus total r of 5 * 5 * 5 * 5 = 5 ^ 4 = 625 ways to answer all of them . answer : c .\"",
"annotated_formula": "power(5, 4)",
"category": "general",
"correct": "c",
"linear_formula": "power(n1,n0)|",
"options": "a ) 24 , b ) 120 , c ) 625 , d ) 720 , e ) 1024"
}

### Data Fields

The data fields are the same among all splits.

#### default

• Problem: a string feature.
• Rationale: a string feature.
• options: a string feature.
• correct: a string feature.
• annotated_formula: a string feature.
• linear_formula: a string feature.
• category: a string feature.

### Data Splits Sample Size

name train validation test
default 29837 4475 2985