README.md

---
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())
```