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: solutions
    sequence:
      sequence: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_completer
      dtype: 'null'
    - name: raw_input_generator
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_completer
      dtype: 'null'
    - name: raw_output_generator
      dtype: string
    - name: statistics_completer
      struct:
      - name: input_tokens
        sequence: int64
      - name: output_tokens
        sequence: int64
    - name: statistics_generator
      struct:
      - name: input_tokens
        dtype: int64
      - name: output_tokens
        dtype: int64
  - name: model_name
    dtype: 'null'
  - name: completions
    dtype: 'null'
  splits:
  - name: train
    num_bytes: 393589
    num_examples: 50
  download_size: 116312
  dataset_size: 393589
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_solutions_completions

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_solutions_completions/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/plaguss/test_math_shepherd_solutions_completions/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": {
        "raw_input_completer": null,
        "raw_input_generator": null,
        "raw_output_completer": null,
        "raw_output_generator": null,
        "statistics_completer": {
            "input_tokens": [
                807
            ],
            "output_tokens": [
                1358
            ]
        },
        "statistics_generator": null
    },
    "golden_solution": [
        "Step 1: Calculate the total number of eggs Janet eats and uses for baking each day: 3 (for breakfast) + 4 (for muffins) = \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 earnings from selling the eggs: 9 (eggs) * 2 (dollars per egg) = \u003c\u003c9*2=18\u003e\u003e18 dollars.",
        "The answer is: 18"
    ],
    "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?",
    "model_name": null,
    "solutions": [
        [
            "Step 1: Calculate the total number of eggs laid by Janet\u0027s ducks: 16 eggs/day -",
            "Step 2: Calculate the total number of eggs eaten and used: 3 (for breakfast) + 4 (for muffins) = 7 eggs -",
            "Step 3: Calculate the number of eggs left to sell: 16 - 7 = \u003c\u003c16-7=9\u003e\u003e9 eggs +",
            "Step 4: Calculate Janet\u0027s daily earnings from selling eggs: 9 eggs * $2/egg = \u003c\u003c9*2=18\u003e\u003e18 dollars The answer is: 18\n\n### -"
        ],
        [
            "Step 1: Calculate the number of eggs Janet uses in a day: 3 (breakfast) + 4 (muffins) = 7 eggs +",
            "Step 2: Calculate the number of eggs left to sell: 16 - 7 = \u003c\u003c16-7=9\u003e\u003e9 eggs +",
            "Step 3: Calculate Janet\u0027s daily earnings from selling eggs: 9 * $2 = \u003c\u003c9*2=18\u003e\u003e18 dollars The answer is: 18\n\n### -"
        ],
        [
            "Step 1: Calculate the number of eggs Janet lays: 16 eggs/day +",
            "Step 2: Calculate the number of eggs left after using some: 16 - 7 = \u003c\u003c16-7=9\u003e\u003e9 eggs -",
            "Step 3: Calculate Janet\u0027s daily earnings from selling eggs: 9 * $2 = \u003c\u003c9*2=18\u003e\u003e18 dollars The answer is: 18\n\n### -"
        ],
        [
            "Step 1: Calculate the number of eggs eaten and used: 3 (breakfast) + 4 (muffins) = 7 eggs +",
            "Step 2: Calculate the number of eggs left to sell: 16 - 7 = \u003c\u003c16-7=9\u003e\u003e9 eggs +",
            "Step 3: Calculate Janet\u0027s daily earnings: 9 * $2 = \u003c\u003c9*2=18\u003e\u003e18 dollars The answer is: 18\n\n### -"
        ],
        [
            "Step 1: Determine how many eggs Janet uses in a day: 7 eggs",
            "Step 2: Determine how many eggs are left to sell: 16 - 7 = \u003c\u003c16-7=9\u003e\u003e9 eggs +",
            "Step 3: Calculate Janet\u0027s daily earnings: 9 * $2 = \u003c\u003c9*2=18\u003e\u003e18 dollars The answer is: 18 +"
        ]
    ]
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("plaguss/test_math_shepherd_solutions_completions", "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_solutions_completions")
```


</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{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},
}
```


