BrokenSoul/distillama3-prompts10k

---
size_categories: n<1K
dataset_info:
- config_name: keep_columns
features:
- name: instruction
dtype: string
- name: generations
sequence: 'null'
- name: generation_models
sequence: string
- name: ratings
sequence: 'null'
- name: rationales
sequence: 'null'
splits:
- name: train
num_bytes: 59971
num_examples: 100
download_size: 34961
dataset_size: 59971
- config_name: push_to_argilla
features:
- name: instruction
dtype: string
- name: quality
list:
- name: status
dtype: string
- name: user_id
dtype: string
- name: value
dtype: string
- name: metadata
dtype: string
- name: avg_rating
dtype: float64
- name: num_responses
dtype: int64
- name: agreement_ratio
dtype: float64
- name: raw_responses
sequence: int64
- name: kind
dtype: string
- name: cluster_description
dtype: string
- name: topic
dtype: string
- name: generation_models
sequence: string
- name: generations
sequence: 'null'
- name: model_name
dtype: string
- name: ratings
sequence: 'null'
- name: rationales
sequence: 'null'
splits:
- name: train
num_bytes: 100695
num_examples: 100
download_size: 49102
dataset_size: 100695
configs:
- config_name: keep_columns
data_files:
- split: train
path: keep_columns/train-*
- config_name: push_to_argilla
data_files:
- split: train
path: push_to_argilla/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 distillama3-prompts10k

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/BrokenSoul/distillama3-prompts10k/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/BrokenSoul/distillama3-prompts10k/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

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

```json
{
"generation_models": [
"meta-llama/Meta-Llama-3-8B-Instruct",
"meta-llama/Meta-Llama-3-8B-Instruct"
],
"generations": [
null,
null
],
"instruction": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.",
"ratings": [
null,
null
],
"rationales": [
null,
null
]
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("BrokenSoul/distillama3-prompts10k", "keep_columns")
```

</details>

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

```json
{
"agreement_ratio": 1.0,
"avg_rating": 5.0,
"cluster_description": "Sustainable Packaging \u0026 Skin Care Products",
"generation_models": [
"meta-llama/Meta-Llama-3-8B-Instruct",
"meta-llama/Meta-Llama-3-8B-Instruct"
],
"generations": [
null,
null
],
"instruction": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.",
"kind": "synthetic",
"metadata": "{\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"evolved_from\": null}",
"model_name": "meta-llama/Meta-Llama-3-8B-Instruct",
"num_responses": 2,
"quality": [
{
"status": "submitted",
"user_id": "d23b12c2-b601-490e-b5b3-2040eb393a00",
"value": "4"
},
{
"status": "submitted",
"user_id": "e2bdd868-f28e-46fc-9254-a6ec1e291889",
"value": "4"
}
],
"ratings": [
null,
null
],
"rationales": [
null,
null
],
"raw_responses": [
5,
5
],
"topic": "Environmental Issues"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("BrokenSoul/distillama3-prompts10k", "push_to_argilla")
```

</details>

原始信息汇总

数据集概述

数据集大小

• 总大小: 小于1K

数据集配置

1. 配置名称: keep_columns

   • 特征:
     • instruction: 字符串
     • generations: 序列（空）
     • generation_models: 序列（字符串）
     • ratings: 序列（空）
     • rationales: 序列（空）
   • 分割:
     • train: 100个样本，59971字节
   • 下载大小: 34961字节
   • 数据集大小: 59971字节

2. 配置名称: push_to_argilla

   • 特征:
     • instruction: 字符串
     • quality: 列表（包含status, user_id, value，均为字符串）
     • metadata: 字符串
     • avg_rating: 浮点数
     • num_responses: 整数
     • agreement_ratio: 浮点数
     • raw_responses: 序列（整数）
     • kind: 字符串
     • cluster_description: 字符串
     • topic: 字符串
     • generation_models: 序列（字符串）
     • generations: 序列（空）
     • model_name: 字符串
     • ratings: 序列（空）
     • rationales: 序列（空）
   • 分割:
     • train: 100个样本，100695字节
   • 下载大小: 49102字节
   • 数据集大小: 100695字节

数据集标签

• synthetic
• distilabel
• rlaif