size_categories:n<1K library:distilabel arxiv:2406.08464 region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 30127
    num_examples: 10
  download_size: 21776
  dataset_size: 30127
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<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 my-distiset

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


The pipeline script was uploaded to easily reproduce the dataset:
[app.py](https://huggingface.co/datasets/asoria/my-distiset/raw/main/app.py).

It can be run directly using the CLI:

```console
distilabel pipeline run --script "https://huggingface.co/datasets/asoria/my-distiset/raw/main/app.py"
```


## 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/asoria/my-distiset/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/asoria/my-distiset/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "## Domain of the Composition of Two Functions\n\nTo find the domain of the composition of functions f(x) and g(x), we first need to find the composite function h(x) = f(g(x)). Then, we\u0027ll determine the domain of the composite function.\n\n### Step 1: Define the Functions f(x) and g(x)\n\n```python\nimport sympy as sp\n\n# Define the variable\nx = sp.symbols(\u0027x\u0027)\n\n# Define the functions f(x) and g(x)\nf = 3*x**2 + 2*x - 5\ng = 2*x**2 - x - 1\n```\n\n### Step 2: Find the Composite Function h(x) = f(g(x))\n\nTo find the composite function h(x) = f(g(x)), we\u0027ll substitute g(x) into f(x) in place of x.\n\n```python\n# Find the composite function h(x) = f(g(x))\nh = sp.simplify(f.subs(x, g))\nprint(\"Composite function h(x) =\", h)\n```\n\n### Step 3: Determine the Domain of the Composite Function\n\nThe domain of the composite function h(x) will be all real numbers unless the expression inside the square root is negative, since we cannot take the square root of a negative number.\n\n```python\n# Define the inequality to find the domain\ninequality = h.as_numer_denom()[1] \u003e 0\n\n# Solve the inequality\ndomain = sp.solve_univariate_inequality(inequality, x, relational=False)\n\nprint(\"Domain of the composite function h(x):\", domain)\n```\n\n### Complete Code\n\nHere\u0027s the complete code:\n\n```python\nimport sympy as sp\n\ndef find_domain():\n    # Define the variable\n    x = sp.symbols(\u0027x\u0027)\n\n    # Define the functions f(x) and g(x)\n    f = 3*x**2 + 2*x - 5\n    g = 2*x**2 - x - 1\n\n    # Find the composite function h(x) = f(g(x))\n    h = sp.simplify(f.subs(x, g))\n\n    # Define the inequality to find the domain\n    inequality = h.as_numer_denom()[1] \u003e 0\n\n    # Solve the inequality\n    domain = sp.solve_univariate_inequality(inequality, x, relational=False)\n\n    return domain\n\n# Execute the function\ndomain = find_domain()\nprint(domain)\n```\n\nPlease note that the output may vary based on the specific implementation of the library and the version of SymPy you are using.",
    "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "prompt": "Given two complex functions f(x) = 3x^2 + 2x - 5 and g(x) = 2x^2 - x - 1, find the domain of the composition of these functions."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("asoria/my-distiset", "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("asoria/my-distiset")
```


</details>





## References


```
@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}
```


