cusomer-assitant

# Dataset Card for cusomer-assitant

This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).

## Using this dataset with Argilla

To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:

```python
import argilla as rg

ds = rg.Dataset.from_hub("burtenshaw/cusomer-assitant", settings="auto")
```

This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.

## Using this dataset with `datasets`

To load the records of this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:

```python
from datasets import load_dataset

ds = load_dataset("burtenshaw/cusomer-assitant")
```

This will only load the records of the dataset, but not the Argilla settings.

## Dataset Structure

This dataset repo contains:

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
* A dataset configuration folder conforming to the Argilla dataset format in `.argilla`.

The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.

### Fields

The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.

| Field Name | Title | Type | Required |
| ---------- | ----- | ---- | -------- |
| messages | messages | chat | False |

### Questions

The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| rating_0 | rating_0 | rating | True | N/A | [0, 1, 2, 3, 4, 5, 6, 7] |

<!-- check length of metadata properties -->

### Data Splits

The dataset contains a single split, which is `train`.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation guidelines

[More Information Needed]

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]

# 客户助手（cusomer-assitant）数据集卡片

本数据集由 [Argilla](https://github.com/argilla-io/argilla) 开发。如下文所述，您可以按照[使用Argilla加载](#使用Argilla加载)中的说明将其加载到Argilla服务器中，也可以通过[使用datasets库加载](#使用datasets库加载)直接结合`datasets`库使用。

## 结合Argilla使用本数据集

若要使用Argilla加载该数据集，只需执行`pip install argilla --upgrade`升级安装Argilla，随后运行如下代码：

python
import argilla as rg

ds = rg.Dataset.from_hub("burtenshaw/cusomer-assitant", settings="auto")

该代码将从数据集仓库加载配置与记录，并推送至您的Argilla服务器，以供探索与标注。

## 结合datasets库使用本数据集

若要通过`datasets`库加载该数据集的记录，只需执行`pip install datasets --upgrade`升级安装`datasets`库，随后运行如下代码：

python
from datasets import load_dataset

ds = load_dataset("burtenshaw/cusomer-assitant")

该代码仅会加载数据集的记录，而不会加载Argilla相关配置。

## 数据集结构

本数据集仓库包含以下内容：
* 兼容HuggingFace `datasets`库格式的数据集记录。使用`rg.Dataset.from_hub`时会自动加载这些记录，也可通过`datasets`库的`load_dataset`函数独立加载。
* 用于构建与整理数据集的[标注指南](#标注指南)（若已在Argilla中定义）。
* 符合Argilla数据集格式的`.argilla`数据集配置文件夹。

本数据集在Argilla中通过以下元素构建：**字段（fields）**、**问题（questions）**、**建议（suggestions）**、**元数据（metadata）**、**向量（vectors）**与**指南（guidelines）**。

### 字段

**字段**即数据集记录的特征或文本内容。例如，文本分类数据集的`text`列，或指令跟随数据集的`prompt`列。

| 字段名称 | 标题 | 类型 | 是否必填 |
| ---------- | ----- | ---- | -------- |
| messages | messages | 对话（chat） | 否 |

### 标注问题

**标注问题**即向标注人员提出的问题，可分为评分、文本、标签选择、多标签选择或排序等多种类型。

| 问题名称 | 标题 | 类型 | 是否必填 | 描述 | 可选值/标签 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| rating_0 | rating_0 | 评分（rating） | 是 | 无可用信息 | [0, 1, 2, 3, 4, 5, 6, 7] |

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### 数据划分

本数据集仅包含一个划分，即`train`（训练集）。

## 数据集构建

### 整理依据

[需补充更多信息]

### 源数据

#### 初始数据收集与标准化

[需补充更多信息]

#### 源文本创作者是谁？

[需补充更多信息]

### 标注信息

#### 标注指南

[需补充更多信息]

#### 标注流程

[需补充更多信息]

#### 标注人员是谁？

[需补充更多信息]

### 个人与敏感信息

[需补充更多信息]

## 数据使用注意事项

### 数据集的社会影响

[需补充更多信息]

### 偏差讨论

[需补充更多信息]

### 其他已知局限性

[需补充更多信息]

## 补充信息

### 数据集整理者

[需补充更多信息]

### 授权信息

[需补充更多信息]

### 引用信息

[需补充更多信息]

### 贡献者

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