image_preferences_results

# Dataset Card for image_preferences_results

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("DIBT/image_preferences_results")
```

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("DIBT/image_preferences_results")
```

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 |
| ---------- | ----- | ---- | -------- | -------- |
| images | images | custom | True | |

### 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| preference | preference | label_selection | True | Which image do you prefer given the prompt? | ['image_1', 'image_2', 'both_good', 'both_bad'] |

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

### Data Instances

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

```json
{
"_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda",
"fields": {
"images": {
"image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance"
}
},
"id": "f5224be1-2e1b-428e-94b1-9c0f397092fa",
"metadata": {
"category": "Animation",
"evolution": "quality",
"model_1": "schnell",
"model_2": "dev",
"sub_category": "Pixel Art"
},
"responses": {
"preference": [
{
"user_id": "c53e62ab-d792-4854-98f6-593b2ffb55bc",
"value": "image_2"
},
{
"user_id": "b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3",
"value": "image_2"
},
{
"user_id": "da3e5871-920c-44da-8c44-1e94260c581e",
"value": "both_good"
},
{
"user_id": "b31dd1ed-78b6-4d50-8f11-7ce32ba17d64",
"value": "image_2"
},
{
"user_id": "6b984f66-86b3-421e-a32c-cd3592ee27a1",
"value": "both_bad"
}
]
},
"status": "completed",
"suggestions": {},
"vectors": {}
}
```

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

```json
{
"_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda",
"category": "Animation",
"evolution": "quality",
"id": "f5224be1-2e1b-428e-94b1-9c0f397092fa",
"images": {
"image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance"
},
"model_1": "schnell",
"model_2": "dev",
"preference.responses": [
"image_2",
"image_2",
"both_good",
"image_2",
"both_bad"
],
"preference.responses.status": [
"submitted",
"submitted",
"submitted",
"submitted",
"submitted"
],
"preference.responses.users": [
"c53e62ab-d792-4854-98f6-593b2ffb55bc",
"b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3",
"da3e5871-920c-44da-8c44-1e94260c581e",
"b31dd1ed-78b6-4d50-8f11-7ce32ba17d64",
"6b984f66-86b3-421e-a32c-cd3592ee27a1"
],
"status": "completed",
"sub_category": "Pixel Art"
}
```

### 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]

# 图像偏好结果数据集卡片

本数据集基于[Argilla](https://github.com/argilla-io/argilla)构建。如下章节所述，本数据集可按照[通过Argilla加载](#load-with-argilla)中的说明加载至您的Argilla服务器，也可通过[通过`datasets`库加载](#load-with-datasets)直接结合`datasets`库使用。

## 在Argilla中使用本数据集

若要通过Argilla加载本数据集，仅需执行`pip install argilla --upgrade`安装Argilla，随后运行以下代码：
python
import argilla as rg

ds = rg.Dataset.from_hub("DIBT/image_preferences_results")

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

## 在`datasets`库中使用本数据集

若要通过`datasets`库加载本数据集的记录，仅需执行`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("DIBT/image_preferences_results")

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`库格式的数据集记录。使用`rg.Dataset.from_hub`时将自动加载此类记录，也可通过`datasets`库的`load_dataset`方法独立加载。
* 若在Argilla中已定义，则包含用于构建与整理本数据集的[标注指南](#annotation-guidelines)。
* 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）
**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，或指令跟随数据集中的`prompt`列。

| 字段名称 | 标题 | 类型 | 必填项 | Markdown支持 |
| ---------- | ----- | ---- | -------- | -------- |
| images | images | 自定义（custom） | 是 | |

### 问题（Questions）
**问题（questions）**指向标注者提出的查询内容，支持多种类型，如评分、文本、标签选择、多标签选择或排序。

| 问题名称 | 标题 | 类型 | 必填项 | 描述 | 可选值/标签 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| preference | preference | 标签选择（label_selection） | 是 | 给定提示词后，您更偏好哪张图像？ | ['image_1', 'image_2', 'both_good', 'both_bad'] |

### 数据实例
Argilla中的数据集实例示例如下：
json
{
"_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda",
"fields": {
"images": {
"image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance"
}
},
"id": "f5224be1-2e1b-428e-94b1-9c0f397092fa",
"metadata": {
"category": "Animation",
"evolution": "quality",
"model_1": "schnell",
"model_2": "dev",
"sub_category": "Pixel Art"
},
"responses": {
"preference": [
{
"user_id": "c53e62ab-d792-4854-98f6-593b2ffb55bc",
"value": "image_2"
},
{
"user_id": "b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3",
"value": "image_2"
},
{
"user_id": "da3e5871-920c-44da-8c44-1e94260c581e",
"value": "both_good"
},
{
"user_id": "b31dd1ed-78b6-4d50-8f11-7ce32ba17d64",
"value": "image_2"
},
{
"user_id": "6b984f66-86b3-421e-a32c-cd3592ee27a1",
"value": "both_bad"
}
]
},
"status": "completed",
"suggestions": {},
"vectors": {}
}

而该记录在HuggingFace `datasets`中的格式如下：
json
{
"_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda",
"category": "Animation",
"evolution": "quality",
"id": "f5224be1-2e1b-428e-94b1-9c0f397092fa",
"images": {
"image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg",
"prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance"
},
"model_1": "schnell",
"model_2": "dev",
"preference.responses": [
"image_2",
"image_2",
"both_good",
"image_2",
"both_bad"
],
"preference.responses.status": [
"submitted",
"submitted",
"submitted",
"submitted",
"submitted"
],
"preference.responses.users": [
"c53e62ab-d792-4854-98f6-593b2ffb55bc",
"b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3",
"da3e5871-920c-44da-8c44-1e94260c581e",
"b31dd1ed-78b6-4d50-8f11-7ce32ba17d64",
"6b984f66-86b3-421e-a32c-cd3592ee27a1"
],
"status": "completed",
"sub_category": "Pixel Art"
}

### 数据拆分
本数据集仅包含一个拆分，即`train`。

## 数据集构建
### 构建初衷
[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]

#### 源语言生成者是谁？
[需补充更多信息]

### 标注
#### 标注指南
[需补充更多信息]

#### 标注流程
[需补充更多信息]

#### 标注者是谁？
[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
### 数据集社会影响
[需补充更多信息]

### 偏差讨论
[需补充更多信息]

### 已知其他限制
[需补充更多信息]

## 附加信息
### 数据集构建者
[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]