Aayushi-Shah/puree

---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for puree

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("Aayushi-Shah/puree")
```

### 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("Aayushi-Shah/puree")
```

### 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 | False |
| chosen | Option 1 | text | True | False |
| rejected | Option 2 | text | True | False |

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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| win | Choose the best response: | rating | True | Choose the most helpful, harmless, and truthful response. Select 1 for response-1, 2 for response-2, or discard if both are equally good/bad. | [1, 2] |

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": null,
"fields": {
"chosen": "Jeju always feels new no matter when you visit. New attractions quickly become famous by word of mouth in the blink of an eye. Especially on the west side of Jeju, there are places with an exotic atmosphere and trendy museums that attract the attention of travelers. We have gathered recently popular hot places that you must visit while traveling on the west side of Jeju, organized by theme.",
"rejected": "Jeju feels new every time you visit. In the blink of an eye, new attractions become famous through word of mouth. In particular, the western part of Jeju attracts the attention of travelers as it is home to places with an exotic atmosphere and sensuous museums. We\u0026#39;ve compiled a collection of recently emerging hot places by theme that you must visit while traveling to the west of Jeju.",
"source": "\uc5b8\uc81c \ubc29\ubb38\ud574\ub3c4 \uc0c8\ub86d\uac8c \ub290\uaef4\uc9c0\ub294 \uc81c\uc8fc. \ub208 \uae5c\uc9dd\ud560 \uc0c8\uc5d0 \uc0c8\ub85c\uc6b4 \uba85\uc18c\ub4e4\uc774 \uc785\uc18c\ubb38\uc744 \ud0c0\uace0 \uc720\uba85\ud574\uc9c0\uace4 \ud55c\ub2e4. \ud2b9\ud788 \uc81c\uc8fc \uc11c\ucabd\uc5d0\ub294 \uc774\uad6d\uc801\uc778 \ubd84\uc704\uae30\ub97c \ud48d\uae30\ub294 \uc7a5\uc18c\uc640 \uac10\uac01\uc801\uc778 \ubba4\uc9c0\uc5c4\ub4e4\uc774 \uc704\uce58\ud574\uc788\uc5b4 \uc5ec\ud589\uc790\ub4e4\uc758 \uc774\ubaa9\uc744 \ub048\ub2e4. \uc81c\uc8fc \uc11c\ucabd\uc744 \uc5ec\ud589\ud558\uba74\uc11c \uaf2d \uac00\ubd10\uc57c \ud560, \ucd5c\uadfc \ub5a0\uc624\ub978 \ud56b\ud50c\ub808\uc774\uc2a4\ub97c \ud14c\ub9c8\ubcc4\ub85c \ubaa8\uc544\ubd24\ub2e4."
},
"metadata": {},
"responses": [],
"suggestions": [],
"vectors": {}
}
```

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

```json
{
"chosen": "Jeju always feels new no matter when you visit. New attractions quickly become famous by word of mouth in the blink of an eye. Especially on the west side of Jeju, there are places with an exotic atmosphere and trendy museums that attract the attention of travelers. We have gathered recently popular hot places that you must visit while traveling on the west side of Jeju, organized by theme.",
"external_id": null,
"metadata": "{}",
"rejected": "Jeju feels new every time you visit. In the blink of an eye, new attractions become famous through word of mouth. In particular, the western part of Jeju attracts the attention of travelers as it is home to places with an exotic atmosphere and sensuous museums. We\u0026#39;ve compiled a collection of recently emerging hot places by theme that you must visit while traveling to the west of Jeju.",
"source": "\uc5b8\uc81c \ubc29\ubb38\ud574\ub3c4 \uc0c8\ub86d\uac8c \ub290\uaef4\uc9c0\ub294 \uc81c\uc8fc. \ub208 \uae5c\uc9dd\ud560 \uc0c8\uc5d0 \uc0c8\ub85c\uc6b4 \uba85\uc18c\ub4e4\uc774 \uc785\uc18c\ubb38\uc744 \ud0c0\uace0 \uc720\uba85\ud574\uc9c0\uace4 \ud55c\ub2e4. \ud2b9\ud788 \uc81c\uc8fc \uc11c\ucabd\uc5d0\ub294 \uc774\uad6d\uc801\uc778 \ubd84\uc704\uae30\ub97c \ud48d\uae30\ub294 \uc7a5\uc18c\uc640 \uac10\uac01\uc801\uc778 \ubba4\uc9c0\uc5c4\ub4e4\uc774 \uc704\uce58\ud574\uc788\uc5b4 \uc5ec\ud589\uc790\ub4e4\uc758 \uc774\ubaa9\uc744 \ub048\ub2e4. \uc81c\uc8fc \uc11c\ucabd\uc744 \uc5ec\ud589\ud558\uba74\uc11c \uaf2d \uac00\ubd10\uc57c \ud560, \ucd5c\uadfc \ub5a0\uc624\ub978 \ud56b\ud50c\ub808\uc774\uc2a4\ub97c \ud14c\ub9c8\ubcc4\ub85c \ubaa8\uc544\ubd24\ub2e4.",
"win": [],
"win-suggestion": null,
"win-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`.
* **chosen** is of type `text`.
* **rejected** 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`.

* **win** is of type `rating` with the following allowed values [1, 2], and description "Choose the most helpful, harmless, and truthful response. Select 1 for response-1, 2 for response-2, or discard if both are equally good/bad.".

* **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) **win-suggestion** is of type `rating` with the following allowed values [1, 2].

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

[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]

原始信息汇总

数据集概述

名称: puree

大小: 1K<n<10K

标签:

• rlfh
• argilla
• human-feedback

创建工具: Argilla

数据集内容

• 配置文件: argilla.yaml，符合Argilla数据集格式。
• 数据记录: 兼容HuggingFace datasets的格式。
• 标注指南: 如在Argilla中定义，提供标注指南。

数据集加载

• 使用Argilla加载: python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("Aayushi-Shah/puree")

• 使用datasets库加载: python from datasets import load_dataset ds = load_dataset("Aayushi-Shah/puree")

数据集结构

• 字段:

  • source (文本类型)
  • chosen (文本类型)
  • rejected (文本类型)

• 问题:

  • win (评分类型，允许值[1, 2])

• 建议:

  • win-suggestion (可选，评分类型，允许值[1, 2])

• 元数据:

  • 提供额外信息，如来源链接、作者等。

• 外部ID:

  • 提供外部资源的链接。

数据集使用

• 支持任务: 可用于多种NLP任务，具体取决于配置。
• 语言: 待补充信息。

数据实例

• 示例包括文本字段和元数据，用于标注和分析。

数据分割

• 分割: 仅包含train分割。