size_categories:100K<n<1M format:parquet modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: chosen
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: rejected
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: score_chosen
    dtype: float64
  - name: score_rejected
    dtype: float64
  splits:
  - name: train
    num_bytes: 95779989
    num_examples: 180209
  download_size: 12723299
  dataset_size: 95779989
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-dataset2

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/xiao-zhuo/test-dataset2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/xiao-zhuo/test-dataset2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "chosen": [
        {
            "content": "\u6a31\u6843\u5f00\u529e\u4e86\u4e00\u5bb6\u5feb\u9012\u670d\u52a1\u3002\u5979\u5bf9\u4e8e3-5\u516c\u65a4\u7684\u8d27\u7269\u6536\u53d62.50\u7f8e\u5143\uff0c\u5bf9\u4e8e6-8\u516c\u65a4\u7684\u8d27\u7269\u6536\u53d64\u7f8e\u5143\u3002\u5982\u679c\u5979\u6bcf\u5929\u9001\u51fa\u56db\u4e2a5\u516c\u65a4\u7684\u8d27\u7269\u548c\u4e24\u4e2a8\u516c\u65a4\u7684\u8d27\u7269\uff0c\u4e00\u5468\u5979\u80fd\u8d5a\u591a\u5c11\u94b1\uff1f",
            "role": "user"
        },
        {
            "content": "\u8ba1\u7b97\u6bcf\u5929\u56db\u4e2a5\u516c\u65a4\u8d27\u7269\u7684\u603b\u6536\u5165\uff1a2.50\u7f8e\u5143 \u00d7 4 = 10\u7f8e\u5143\u3002",
            "role": "assistant"
        }
    ],
    "rejected": [
        {
            "content": "\u6a31\u6843\u5f00\u529e\u4e86\u4e00\u5bb6\u5feb\u9012\u670d\u52a1\u3002\u5979\u5bf9\u4e8e3-5\u516c\u65a4\u7684\u8d27\u7269\u6536\u53d62.50\u7f8e\u5143\uff0c\u5bf9\u4e8e6-8\u516c\u65a4\u7684\u8d27\u7269\u6536\u53d64\u7f8e\u5143\u3002\u5982\u679c\u5979\u6bcf\u5929\u9001\u51fa\u56db\u4e2a5\u516c\u65a4\u7684\u8d27\u7269\u548c\u4e24\u4e2a8\u516c\u65a4\u7684\u8d27\u7269\uff0c\u4e00\u5468\u5979\u80fd\u8d5a\u591a\u5c11\u94b1\uff1f",
            "role": "user"
        },
        {
            "content": "\u8ba1\u7b97\u6bcf\u4e2a5\u516c\u65a4\u8d27\u7269\u7684\u6536\u5165\uff1a5\u516c\u65a4\u5c5e\u4e8e3-5\u516c\u65a4\u8303\u56f4\uff0c\u6536\u53d62.50\u7f8e\u5143\u3002",
            "role": "assistant"
        }
    ],
    "score_chosen": 1.0,
    "score_rejected": 0.9
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("xiao-zhuo/test-dataset2", "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("xiao-zhuo/test-dataset2")
```


</details>




