
## Dataset Description
- **Repository:** https://reasonwithpal.com/
- **Paper:** [PaL: Program-Aided Language Model](https://arxiv.org/abs/2211.10435)

### Dataset Summary
This is the harder version of gsm8k math reasoning dataset (https://huggingface.co/datasets/gsm8k).
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.


### Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning

### Languages
English - Numbers

## Dataset Structure
```python
dataset = load_dataset("reasoning-machines/gsm-hard")
DatasetDict({
    train: Dataset({
        features: ['input', 'code', 'target'],
        num_rows: 1319
    })
})
```

### Data Fields
train/dev/test:
- input: The question
- code: The corresponding code solution to the question
- target: The answer

### Citation Information

```
@article{gao2022pal,
  title={PAL: Program-aided Language Models},
  author={Gao, Luyu and Madaan, Aman and Zhou, Shuyan and Alon, Uri and Liu, Pengfei and Yang, Yiming and Callan, Jamie and Neubig, Graham},
  journal={arXiv preprint arXiv:2211.10435},
  year={2022}
}
```
task_categories:text2text-generation language_creators:crowdsourced language_creators:expert-generated multilinguality:monolingual size_categories:unknown source_datasets:gsm8k (https://huggingface.co/datasets/gsm8k) language:code license:mit math_reasoning symbolic_reasoning arxiv:2211.10435 region:us
{"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["gsm8k (https://huggingface.co/datasets/gsm8k)"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "gsm-hard", "tags": ["math_reasoning", "symbolic_reasoning"]}
TAGS
#task_categories-text2text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-gsm8k (https-//huggingface.co/datasets/gsm8k) #language-code #license-mit #math_reasoning #symbolic_reasoning #arxiv-2211.10435 #region-us 


## Dataset Description
- Repository: URL
- Paper: PaL: Program-Aided Language Model

### Dataset Summary
This is the harder version of gsm8k math reasoning dataset (URL
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.


### Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning

### Languages
English - Numbers

## Dataset Structure


### Data Fields
train/dev/test:
- input: The question
- code: The corresponding code solution to the question
- target: The answer




## Dataset Description
- Repository: URL
- Paper: PaL: Program-Aided Language Model ### Dataset Summary
This is the harder version of gsm8k math reasoning dataset (URL
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.
 ### Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning ### Languages
English - Numbers ## Dataset Structure ### Data Fields
train/dev/test:
- input: The question
- code: The corresponding code solution to the question
- target: The answer
TAGS
#task_categories-text2text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-gsm8k (https-//huggingface.co/datasets/gsm8k) #language-code #license-mit #math_reasoning #symbolic_reasoning #arxiv-2211.10435 #region-us 
 ## Dataset Description
- Repository: URL
- Paper: PaL: Program-Aided Language Model ### Dataset Summary
This is the harder version of gsm8k math reasoning dataset (URL
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.
 ### Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning ### Languages
English - Numbers ## Dataset Structure ### Data Fields
train/dev/test:
- input: The question
- code: The corresponding code solution to the question
- target: The answer