MPEP_SWAHILI

# Dataset Card for MPEP_SWAHILI

This dataset has been created with [Argilla](https://docs.argilla.io).

As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).

## Dataset Description

- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

This dataset contains:

* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` 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.

### Load 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.FeedbackDataset.from_huggingface("DIBT/MPEP_SWAHILI")
```

### Load with `datasets`

To load 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("DIBT/MPEP_SWAHILI")
```

### Supported Tasks and Leaderboards

This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).

There are no leaderboards associated with this dataset.

### Languages

[More Information Needed]

## Dataset Structure

### Data in Argilla

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

The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| source | Source | text | True | True |

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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| target | Target | text | True | Translate the text. | N/A |

The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".

The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.

| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |

The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.

### Data Instances

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

```json
{
"external_id": "348",
"fields": {
"source": "How would you describe the fur of a swiss mountain dog?"
},
"metadata": {
"evolved_from": null,
"kind": "human",
"source": "OpenAssistant/oasst2"
},
"responses": [
{
"status": "submitted",
"user_id": "d8cfa58c-061c-4c19-8504-741dcbe84cc7",
"values": {
"target": {
"value": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?"
}
}
}
],
"suggestions": [
{
"agent": null,
"question_name": "target",
"score": null,
"type": null,
"value": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?"
}
],
"vectors": {}
}
```

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

```json
{
"external_id": "348",
"metadata": "{\"source\": \"OpenAssistant/oasst2\", \"kind\": \"human\", \"evolved_from\": null}",
"source": "How would you describe the fur of a swiss mountain dog?",
"target": [
{
"status": "submitted",
"user_id": "d8cfa58c-061c-4c19-8504-741dcbe84cc7",
"value": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?"
}
],
"target-suggestion": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?",
"target-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
```

### Data Fields

Among the dataset fields, we differentiate between the following:

* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.

* **source** is of type `text`.

* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.

* **target** is of type `text`, and description "Translate the text.".

* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.

* (optional) **target-suggestion** is of type `text`.

Additionally, we also have two more fields that are optional and are the following:

* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.

### 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

This is a translation dataset that contains texts. Please translate the text in the text field.

#### 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]

# MPEP_SWAHILI 数据集卡片

本数据集基于[Argilla (Argilla)](https://docs.argilla.io)构建。

如下章节所述，本数据集可按照[通过Argilla加载](#load-with-argilla)中的说明导入至Argilla，也可直接通过`datasets (datasets)`库按照[通过datasets加载](#load-with-datasets)中的说明使用。

## 数据集描述

- **主页：** https://argilla.io
- **代码仓库：** https://github.com/argilla-io/argilla
- **相关论文：**
- **排行榜：**
- **联系方式：**

### 数据集摘要

本数据集包含以下内容：
* 符合Argilla数据集格式的数据集配置文件`argilla.yaml`。在Argilla中使用`FeedbackDataset.from_huggingface`方法时，将使用该配置文件对数据集进行配置。
* 兼容HuggingFace `datasets (datasets)`库格式的数据集记录。在使用`FeedbackDataset.from_huggingface`时，这些记录将自动加载，也可通过`datasets (datasets)`库调用`load_dataset`独立加载。
* 用于构建和整理本数据集的[标注指南](#annotation-guidelines)（若已在Argilla中定义）。

### 通过Argilla加载

若要通过Argilla加载本数据集，只需执行`pip install argilla --upgrade`安装Argilla，然后运行以下代码：

python
import argilla as rg

ds = rg.FeedbackDataset.from_huggingface("DIBT/MPEP_SWAHILI")

### 通过datasets加载

若要通过`datasets (datasets)`库加载本数据集，只需执行`pip install datasets --upgrade`安装该库，然后运行以下代码：

python
from datasets import load_dataset

ds = load_dataset("DIBT/MPEP_SWAHILI")

### 支持任务与排行榜

本数据集包含[多字段、多问题及多回复](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset)，因此可根据配置用于多种自然语言处理（NLP）任务。数据集结构详见[数据集结构章节](#dataset-structure)。本数据集暂无关联排行榜。

### 语言

[需补充更多信息]

## 数据集结构

### Argilla中的数据

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）**以及**指南（guidelines）**。

**字段（fields）**即数据集记录本身，目前仅支持文本字段。这些字段将用于接收针对问题的回复。

| 字段名称 | 标题 | 类型 | 是否必填 | 支持Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| source | 源文本 | text | 是 | 是 |

**问题（questions）**即向标注人员提出的查询，支持多种类型，包括评分、文本、单标签选择、多标签选择以及排序任务。

| 问题名称 | 标题 | 类型 | 是否必填 | 描述 | 可选值/标签 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| target | 目标文本 | text | 是 | "请翻译该文本。" | 无 |

**建议（suggestions）**是为辅助标注人员完成标注流程，针对每个问题生成的人工或机器推荐结果。建议始终与对应问题绑定，命名方式为在问题名称后追加`-suggestion`（存储建议内容）与`-suggestion-metadata`（存储建议元数据）。建议的可选值与上述表格一致，仅字段名称追加对应后缀。

**元数据（metadata）**是用于存储数据集记录附加信息的字典结构，可为标注人员提供额外上下文，或补充数据集记录本身的相关信息。例如，可用于存储数据集记录的原始来源链接，或记录作者、创建日期、来源等信息。元数据为可选字段，可与`argilla.yaml`数据集配置文件中定义的`metadata_properties`关联。

| 元数据名称 | 标题 | 类型 | 可选值 | 对标注人员可见 |
| ------------- | ----- | ---- | ------ | ---------------------- |

**指南（guidelines）**同样为可选字段，是用于向标注人员提供操作说明的纯文本字符串，详见[标注指南](#annotation-guidelines)章节。

### 数据实例

以下为Argilla中一条数据集实例的示例：

json
{
"external_id": "348",
"fields": {
"source": "How would you describe the fur of a swiss mountain dog?"
},
"metadata": {
"evolved_from": null,
"kind": "human",
"source": "OpenAssistant/oasst2"
},
"responses": [
{
"status": "submitted",
"user_id": "d8cfa58c-061c-4c19-8504-741dcbe84cc7",
"values": {
"target": {
"value": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?"
}
}
}
],
"suggestions": [
{
"agent": null,
"question_name": "target",
"score": null,
"type": null,
"value": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?"
}
],
"vectors": {}
}

该记录在HuggingFace `datasets (datasets)`中的格式如下：

json
{
"external_id": "348",
"metadata": "{"source": "OpenAssistant/oasst2", "kind": "human", "evolved_from": null}",
"source": "How would you describe the fur of a swiss mountain dog?",
"target": [
{
"status": "submitted",
"user_id": "d8cfa58c-061c-4c19-8504-741dcbe84cc7",
"value": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?"
}
],
"target-suggestion": "Ungefafanuaje manyoya ya mbwa wa mlima wa Uswisi?",
"target-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}

### 数据字段

本数据集的字段可分为以下几类：
* **字段（Fields）**：即数据集记录本身，目前仅支持文本字段，用于接收针对问题的回复。
* `source`：类型为`text`（文本）。
* **问题（Questions）**：向标注人员提出的查询，支持`RatingQuestion`（评分问题）、`TextQuestion`（文本问题）、`LabelQuestion`（单标签选择问题）、`MultiLabelQuestion`（多标签选择问题）以及`RankingQuestion`（排序问题）等多种类型。
* `target`：类型为`text`，描述为"请翻译该文本。"
* **建议（Suggestions）**：自Argilla 1.13.0版本起引入，用于在标注流程中为标注人员提供辅助参考。建议与对应问题绑定，为可选字段，不仅包含建议内容，还可关联对应元数据（若有）。
* （可选）`target-suggestion`：类型为`text`。

此外，本数据集还包含以下两个可选字段：
* **metadata（元数据）**：用于存储数据集记录的附加信息，可为标注人员提供额外上下文，或补充记录本身的相关细节（如原始来源链接、作者、创建日期等）。元数据为可选字段，可与`argilla.yaml`配置文件中定义的`metadata_properties`关联。
* **external_id（外部ID）**：用于为数据集记录分配外部标识，方便将记录与外部资源（如数据库或文件）关联，为可选字段。

### 数据划分

本数据集仅包含一个划分：`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]

#### 源语言生产者是谁？

[需补充更多信息]

### 标注

#### 标注指南

本数据集为文本翻译数据集，请将文本字段中的内容进行翻译。

#### 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]

## 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]

### 贡献者

[需补充更多信息]