adarshjha3026/final_ouput

---
size_categories: n<1K
dataset_info:
features:
- name: instruction
dtype: string
- name: generations
sequence: string
- name: generation_models
sequence: string
- name: ratings
sequence: int64
- name: rationales
sequence: string
- name: ultrafeedback_model
dtype: string
splits:
- name: train
num_bytes: 14947
num_examples: 10
download_size: 16489
dataset_size: 14947
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 final_ouput

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/adarshjha3026/final_ouput/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/adarshjha3026/final_ouput/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

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

```json
{
"generation_models": [
"gpt-3.5-turbo-0125",
"gpt-3.5-turbo-1106"
],
"generations": [
"Yes! We can simplify the expression step by step:\n\n1. Combine like terms:\n(3x^2 - 5xy + 7y^2) + (-5x^2 + 2xy - 4y^2)\n= 3x^2 - 5xy + 7y^2 - 5x^2 + 2xy - 4y^2\n\n2. Combine like terms together:\n= (3x^2 - 5x^2) + (-5xy + 2xy) + (7y^2 - 4y^2)\n= -2",
"Step 1: Combine like terms within the parentheses:\n\n(3x^2 - 5xy + 7y^2) + (-5x^2 + 2xy - 4y^2) \n= 3x^2 - 5xy + 7y^2 - 5x^2 + 2xy - 4y^2 \n\nStep 2: Combine like terms outside the parentheses:\n\n3x^2 - 5xy + 7y^2 - 5x^2 + 2xy - 4y^2\n= (3x^2 - "
],
"instruction": "Can you simplify the algebraic expression (3x^2 - 5xy + 7y^2) + (-5x^2 + 2xy - 4y^2) step by step?",
"ratings": [
2,
3
],
"rationales": [
"The output correctly identifies the need to combine like terms but makes a severe error in simplification. The final result given (\"= -2\") is incorrect and indicates a fundamental misunderstanding. Additionally, the steps are not clearly separated or accurately executed.",
"This output explains the combining of like terms correctly but is cut off before completing the final step. It doesn\u0027t reach an incorrect conclusion and shows a better understanding of the process than Text 1. However, the incomplete response significantly affects its overall quality and informat"
],
"ultrafeedback_model": "gpt-4o"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("adarshjha3026/final_ouput", "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("adarshjha3026/final_ouput")
```

</details>

原始信息汇总

数据集概述

数据集基本信息

• 大小分类: n<1K
• 下载大小: 16489字节
• 数据集大小: 14947字节

数据集特征

• instruction: 字符串类型
• generations: 字符串序列
• generation_models: 字符串序列
• ratings: 整数序列（int64）
• rationales: 字符串序列
• ultrafeedback_model: 字符串类型

数据集分割

• 训练集:
  • 大小: 14947字节
  • 样本数: 10

配置信息

• 配置名称: default
• 数据文件:
  • 分割: train
  • 路径: data/train-*

数据集标签

• 标签:
  • synthetic
  • distilabel
  • rlaif