test-argilla-dataset

# Dataset Card for test-argilla-dataset

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 --pre --upgrade` and then use the following code:

```python
import argilla as rg

ds = rg.Dataset.from_hub("burtenshaw/test-argilla-dataset")
```

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/test-argilla-dataset")
```

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 | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | text | text | True | 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | label | label_selection | True | N/A | ['positive', 'negative'] |
| rating | rating | rating | True | N/A | [1, 2, 3, 4, 5] |
| ranking | ranking | ranking | True | N/A | ['label1', 'label2', 'label3'] |
| comment | comment | text | True | N/A | N/A |
| topics | topics | multi_label_selection | True | N/A | ['topic1', 'topic2', 'topic3'] |
| span | span | span | True | N/A | N/A |

### Metadata

The **metadata** is a dictionary that can be used to provide additional information about the dataset record.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
| comment_score | comment_score | | None - None | True |

### Vectors
The **vectors** contain a vector representation of the record that can be used in search.

| Vector Name | Title | Dimensions |
|-------------|-------|------------|
| vector | vector | [1, 3] |

### Data Instances

An example of a dataset instance in Argilla looks as follows:

```json
{
"_server_id": "8aaf57d2-cb8e-4673-a7ce-2f684b60adf5",
"fields": {
"text": "Hello World, how are you?"
},
"id": "4f56e32b-9582-47de-a2b1-b230732bb07b",
"metadata": {},
"responses": {
"label": [
{
"user_id": "06f7d4c0-e048-43d2-ab3f-06f147616ac6",
"value": "positive"
}
]
},
"suggestions": {
"label": {
"agent": null,
"score": null,
"value": "positive"
},
"topics": {
"agent": null,
"score": [
0.9,
0.8
],
"value": [
"topic1",
"topic2"
]
}
},
"vectors": {}
}
```

While the same record in HuggingFace `datasets` looks as follows:

```json
{
"_server_id": "8aaf57d2-cb8e-4673-a7ce-2f684b60adf5",
"comment.suggestion": null,
"comment.suggestion.agent": null,
"comment.suggestion.score": null,
"comment_score": null,
"id": "4f56e32b-9582-47de-a2b1-b230732bb07b",
"label.responses": [
"positive"
],
"label.responses.status": [
"draft"
],
"label.responses.users": [
"06f7d4c0-e048-43d2-ab3f-06f147616ac6"
],
"label.suggestion": "positive",
"label.suggestion.agent": null,
"label.suggestion.score": null,
"ranking.suggestion": null,
"ranking.suggestion.agent": null,
"ranking.suggestion.score": null,
"rating.suggestion": null,
"rating.suggestion.agent": null,
"rating.suggestion.score": null,
"span.suggestion": null,
"span.suggestion.agent": null,
"span.suggestion.score": null,
"text": "Hello World, how are you?",
"topics.suggestion": [
"topic1",
"topic2"
],
"topics.suggestion.agent": null,
"topics.suggestion.score": [
0.9,
0.8
],
"vector": null
}
```

### 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]

# test-argilla-dataset 数据集卡片

本数据集基于 [Argilla](https://github.com/argilla-io/argilla) 构建。如下文所述，该数据集既可按照[通过Argilla加载](#load-with-argilla)的步骤导入您的Argilla服务器，也可通过[通过datasets库加载](#load-with-datasets)直接配合`datasets`库使用。

## 在Argilla中使用本数据集
若要通过Argilla加载该数据集，只需先执行`pip install argilla --pre --upgrade`安装Argilla，随后运行以下代码：
python
import argilla as rg

ds = rg.Dataset.from_hub("burtenshaw/test-argilla-dataset")

该操作会从数据集仓库加载配置与记录，并推送至您的Argilla服务器，以供探索与标注使用。

## 通过`datasets`库使用本数据集
若要通过`datasets`库加载本数据集的记录，只需先执行`pip install datasets --upgrade`安装`datasets`库，随后运行以下代码：
python
from datasets import load_dataset

ds = load_dataset("burtenshaw/test-argilla-dataset")

该操作仅会加载数据集的记录，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`格式的数据集记录。使用`rg.Dataset.from_hub`时会自动加载此类记录，也可通过`datasets`库的`load_dataset`方法独立加载。
* 构建与整理数据集时使用的[标注指南](#annotation-guidelines)（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中基于以下要素构建：**字段（fields）**、**问题（questions）**、**建议（suggestions）**、**元数据（metadata）**、**向量（vectors）**以及**指南（guidelines）**。

### 字段（Fields）
**字段（fields）**指数据集记录的特征或文本内容。例如，文本分类数据集中的`text`列，或是指令遵循数据集中的`prompt`列。

| 字段名称 | 标题 | 类型 | 是否必填 | 支持Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | 文本 | 文本类型 | 是 | 否 |

### 问题（Questions）
**问题（questions）**指向标注人员提出的标注任务，支持多种类型，包括评分（rating）、文本输入（text）、标签选择（label_selection）、多标签选择（multi_label_selection）以及排序（ranking）。

| 问题名称 | 标题 | 类型 | 是否必填 | 描述 | 取值/标签 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | 标签 | 标签选择（label_selection） | 是 | 无 | ['positive', 'negative'] |
| rating | 评分 | 评分（rating） | 是 | 无 | [1, 2, 3, 4, 5] |
| ranking | 排序 | 排序（ranking） | 是 | 无 | ['label1', 'label2', 'label3'] |
| comment | 评论 | 文本输入（text） | 是 | 无 | 无 |
| topics | 主题 | 多标签选择（multi_label_selection） | 是 | 无 | ['topic1', 'topic2', 'topic3'] |
| span | 片段 | 片段标注（span） | 是 | 无 | 无 |

### 元数据（Metadata）
**元数据（metadata）**是用于提供数据集记录额外信息的字典。

| 元数据名称 | 标题 | 类型 | 取值 | 标注人员可见 |
| ------------- | ----- | ---- | ------ | ---------------------- |
| comment_score | 评论评分 | | 无 - 无 | 是 |

### 向量（Vectors）
**向量（vectors）**包含可用于搜索的记录向量表示。

| 向量名称 | 标题 | 维度 |
|-------------|-------|------------|
| vector | 向量 | [1, 3] |

### 数据实例
本数据集在Argilla中的一条示例记录格式如下：
json
{
"_server_id": "8aaf57d2-cb8e-4673-a7ce-2f684b60adf5",
"fields": {
"text": "Hello World, how are you?"
},
"id": "4f56e32b-9582-47de-a2b1-b230732bb07b",
"metadata": {},
"responses": {
"label": [
{
"user_id": "06f7d4c0-e048-43d2-ab3f-06f147616ac6",
"value": "positive"
}
]
},
"suggestions": {
"label": {
"agent": null,
"score": null,
"value": "positive"
},
"topics": {
"agent": null,
"score": [
0.9,
0.8
],
"value": [
"topic1",
"topic2"
]
}
},
"vectors": {}
}

而该记录在HuggingFace `datasets`库中的格式如下：
json
{
"_server_id": "8aaf57d2-cb8e-4673-a7ce-2f684b60adf5",
"comment.suggestion": null,
"comment.suggestion.agent": null,
"comment.suggestion.score": null,
"comment_score": null,
"id": "4f56e32b-9582-47de-a2b1-b230732bb07b",
"label.responses": [
"positive"
],
"label.responses.status": [
"draft"
],
"label.responses.users": [
"06f7d4c0-e048-43d2-ab3f-06f147616ac6"
],
"label.suggestion": "positive",
"label.suggestion.agent": null,
"label.suggestion.score": null,
"ranking.suggestion": null,
"ranking.suggestion.agent": null,
"ranking.suggestion.score": null,
"rating.suggestion": null,
"rating.suggestion.agent": null,
"rating.suggestion.score": null,
"span.suggestion": null,
"span.suggestion.agent": null,
"span.suggestion.score": null,
"text": "Hello World, how are you?",
"topics.suggestion": [
"topic1",
"topic2"
],
"topics.suggestion.agent": null,
"topics.suggestion.score": [
0.9,
0.8
],
"vector": null
}

### 数据划分
本数据集仅包含一个划分，即`train`（训练集）。

## 数据集创建
### 标注依据
[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]
#### 源语言生产者是谁？
[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]
### 其他已知限制
[需补充更多信息]

## 附加信息
### 数据集整理者
[需补充更多信息]
### 许可信息
[需补充更多信息]
### 引用信息
[需补充更多信息]
### 贡献
[需补充更多信息]