---
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for Think-SARChat-total







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("xiayang716/Think-SARChat-total", 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("xiayang716/Think-SARChat-total")
```

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 |
| ---------- | ----- | ---- | -------- |
| sar_image | SAR图像 | image | 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| position | 目标方位 | label_selection | True | N/A | ['左上角', '左下角', '右上角', '右下角', '中部'] |
| area_type | 区域类型 | label_selection | True | N/A | ['机场跑道区域', '停机位', '停机坪', '位于廊桥周围'] |
| arrangement | 排列方式 | label_selection | True | N/A | ['稀疏分散排列', '密集排列，目标大小一致', '并排，目标大小有明显差异'] |
| structure | 结构清晰度 | label_selection | True | N/A | ['看出飞机轮廓,有明显结构', '看不出飞机轮廓,无明显结构'] |
| size | 尺寸大小 | label_selection | True | N/A | ['大尺寸', '小尺寸'] |
| aircraft_type | 飞机种类 | label_selection | True | N/A | ['战斗机', '运输机', '加油机'] |
| wing_type | 机翼类型 | label_selection | True | N/A | ['平直翼', '后掠翼'] |
| wing_feature | 机翼特征 | label_selection | True | N/A | ['机翼有明显强散射点', '机翼无明显强散射点'] |
| topology | 拓扑结构 | label_selection | True | N/A | ['Y形状拓扑结构明显', 'T形拓扑结构明显'] |


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### 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

SAR图像目标识别标注

#### 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]