Datasets:
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---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
dataset_info:
features:
- name: problem
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
- name: answer
dtype: string
- name: unique_id
dtype: int64
splits:
- name: train
num_bytes: 9548394
num_examples: 12000
download_size: 4670838
dataset_size: 9548394
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# MATH (minus MATH-500)
This dataset is derived from the original MATH dataset by Hendrycks et al.
([qwedsacf/competition_math](https://huggingface.co/datasets/qwedsacf/competition_math)) with all problems from the MATH-500 benchmark set removed.
## Construction
- Source: 12,500 problems from the MATH dataset by Hendrycks et al. ([qwedsacf/competition_math](https://huggingface.co/datasets/qwedsacf/competition_math))
- Benchmark held out: 500 problems from the MATH-500 dataset ([HuggingFaceH4/MATH-500](https://huggingface.co/datasets/HuggingFaceH4/MATH-500))
- Matching criterion: exact match on the `problem` field (see https://github.com/rasbt/math_full_minus_math500 for code to prepare the dataset)
- Remaining size: 12,000 problems
- Additionally, an `"answer"` field was added to the entries in the MATH dataset that contains the short answer similar to MATH-500
## Fields
Each example contains:
- `problem`: math problem statement
- `solution`: full worked solution
- `answer`: extracted final answer using `extract_final_candidate` function from the [reasoning-from-scratch](https://github.com/rasbt/reasoning-from-scratch) Python package (matches those in the MATH-500 dataset)
- `subject`: math subject
- `level`: difficulty level
- `unique_id`: original problem identifier
## Intended use
This dataset is intended for training only.
Evaluation should be performed on MATH-500, which is excluded.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset(
"rasbt/math_full_minus_math500",
split="train"
)
print(len(dataset))
print(dataset[0].keys())
```
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