size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2312.08935 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: answer
    dtype: string
  - name: golden_solution
    sequence: string
  - name: model_name
    dtype: 'null'
  - name: solutions
    dtype: 'null'
  - name: distilabel_metadata
    dtype: 'null'
  - name: completions
    dtype: 'null'
  - name: input
    dtype: 'null'
  - name: label
    dtype: 'null'
  splits:
  - name: train
    num_bytes: 46328
    num_examples: 50
  download_size: 32443
  dataset_size: 46328
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for test_math_shepherd_structured

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/plaguss/test_math_shepherd_structured/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/plaguss/test_math_shepherd_structured/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "answer": "Janet sells 16 - 3 - 4 = \u003c\u003c16-3-4=9\u003e\u003e9 duck eggs a day.\nShe makes 9 * 2 = $\u003c\u003c9*2=18\u003e\u003e18 every day at the farmer\u2019s market.\n#### 18",
    "completions": null,
    "distilabel_metadata": null,
    "golden_solution": [
        "Step 1: Calculate the total number of eggs Janet uses each day for breakfast and baking: 3 (for breakfast) + 4 (for baking) = \u003c\u003c3+4=7\u003e\u003e7 eggs.",
        "Step 2: Determine the number of eggs left for selling: 16 (total eggs) - 7 (eggs used) = \u003c\u003c16-7=9\u003e\u003e9 eggs.",
        "Step 3: Calculate the total revenue from selling the eggs: 9 (eggs) * 2 (dollars per egg) = \u003c\u003c9*2=18\u003e\u003e18 dollars.",
        "The answer is: 18"
    ],
    "input": null,
    "instruction": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers\u0027 market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers\u0027 market?",
    "label": null,
    "model_name": null,
    "solutions": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("plaguss/test_math_shepherd_structured", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("plaguss/test_math_shepherd_structured")
```


</details>





## References


```
@misc{2312.08935,
Author = {Peiyi Wang and Lei Li and Zhihong Shao and R. X. Xu and Damai Dai and Yifei Li and Deli Chen and Y. Wu and Zhifang Sui},
Title = {Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations},
Year = {2023},
Eprint = {arXiv:2312.08935},
}
```


```
@misc{2312.08935,
Author = {Peiyi Wang and Lei Li and Zhihong Shao and R. X. Xu and Damai Dai and Yifei Li and Deli Chen and Y. Wu and Zhifang Sui},
Title = {Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations},
Year = {2023},
Eprint = {arXiv:2312.08935},
}
```


```
@misc{wang2024mathshepherdverifyreinforcellms,
    title={Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations},
    author={Peiyi Wang and Lei Li and Zhihong Shao and R. X. Xu and Damai Dai and Yifei Li and Deli Chen and Y. Wu and Zhifang Sui},
    year={2024},
    eprint={2312.08935},
    archivePrefix={arXiv},
    primaryClass={cs.AI},
    url={https://arxiv.org/abs/2312.08935},
}
```


```
@misc{wang2024mathshepherdverifyreinforcellms,
    title={Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations},
    author={Peiyi Wang and Lei Li and Zhihong Shao and R. X. Xu and Damai Dai and Yifei Li and Deli Chen and Y. Wu and Zhifang Sui},
    year={2024},
    eprint={2312.08935},
    archivePrefix={arXiv},
    primaryClass={cs.AI},
    url={https://arxiv.org/abs/2312.08935},
}
```


