alvarobartt/prometheus-abs-eval-distilabel

---
size_categories: n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: generation
dtype: string
- name: model_name
dtype: string
- name: feedback
dtype: string
- name: score
dtype: int64
splits:
- name: train
num_bytes: 384
num_examples: 1
download_size: 4953
dataset_size: 384
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 prometheus-abs-eval-distilabel

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/alvarobartt/prometheus-abs-eval-distilabel/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/alvarobartt/prometheus-abs-eval-distilabel/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

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

```json
{
"feedback": "The response directly and correctly answers the simple mathematical question posed, showing clear logical reasoning in arriving at the answer without any error or inconsistency. Therefore, it aligns with the criteria for demonstrating advanced reasoning abilities in relation to the complexity of the question asked.",
"generation": "The answer is 4",
"instruction": "What\u0027s 2+2?",
"model_name": "gpt-4-0125-preview",
"score": 5
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("alvarobartt/prometheus-abs-eval-distilabel", "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("alvarobartt/prometheus-abs-eval-distilabel")
```

</details>

原始信息汇总

数据集概述

数据集基本信息

• 数据集大小: 小于1KB
• 下载大小: 4953字节
• 数据集大小: 384字节

配置信息

• 默认配置:
  • 数据文件路径: data/train-*

数据集特征

• 特征名称: instruction, generation, model_name, feedback, score
• 数据类型: 均为字符串类型，除了score为int64类型

数据集分割

• 训练集:
  • 字节数: 384字节
  • 示例数量: 1个

数据集标签

• 标签: synthetic, distilabel, rlaif

数据集加载示例

python from datasets import load_dataset

ds = load_dataset("alvarobartt/prometheus-abs-eval-distilabel")