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size_categories: n<1K
dataset_info:
features:
- name: instruction
dtype: string
- name: generation
dtype: string
- name: distilabel_metadata
struct:
- name: raw_input_text_generation_0
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_output_text_generation_0
dtype: string
- name: statistics_text_generation_0
struct:
- name: input_tokens
dtype: int64
- name: output_tokens
dtype: int64
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 17686
num_examples: 10
download_size: 22060
dataset_size: 17686
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 distilabel-example
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/vinaybabu/distilabel-example/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/vinaybabu/distilabel-example/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"distilabel_metadata": {
"raw_input_text_generation_0": [
{
"content": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"role": "user"
}
],
"raw_output_text_generation_0": "Let the number of chocolates Robbie has be \\( r \\).\n\nFrom the problem, we know:\n\n1. Danny has 6 chocolates more than Robbie:\n \\[\n d = r + 6\n \\]\n\n2. Arianna has 12 chocolates more than Danny:\n \\[\n a = d + 12\n \\]\n\n3. Arianna has twice as many chocolates as Robbie:\n \\[\n a = 2r\n \\]\n\nWe can now substitute \\( d \\) into the equation for \\( a \\):\n\\[\na = (r + 6) + 12\n\\]\nThis",
"statistics_text_generation_0": {
"input_tokens": 45,
"output_tokens": 128
}
},
"generation": "Let the number of chocolates Robbie has be \\( r \\).\n\nFrom the problem, we know:\n\n1. Danny has 6 chocolates more than Robbie:\n \\[\n d = r + 6\n \\]\n\n2. Arianna has 12 chocolates more than Danny:\n \\[\n a = d + 12\n \\]\n\n3. Arianna has twice as many chocolates as Robbie:\n \\[\n a = 2r\n \\]\n\nWe can now substitute \\( d \\) into the equation for \\( a \\):\n\\[\na = (r + 6) + 12\n\\]\nThis",
"instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"model_name": "gpt-4o-mini"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("vinaybabu/distilabel-example", "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("vinaybabu/distilabel-example")
```
</details>
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