README.md

---
dataset_info:
  features:
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  - name: metadata
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  splits:
  - name: train
    num_bytes: 76240752
    num_examples: 60
  download_size: 0
  dataset_size: 76240752
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Dataset Card for "multi-modal"

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

Argilla supports Markdown within its text fields. This means you can easily add formatting like **bold** and *italic* text, [links](https://www.google.com), and even insert HTML elements like images, audios, videos, and iframes.

A multi-modal dataset can be used to create a dataset with text and different types of media content. It can be useful for different tasks, such as image captioning, video captioning, audio captioning, and so on.

So, this is a multi-modal dataset example that uses three different datasets from Hugging Face:

* **Video**: We use an action recognition dataset, the [ucf101-subset](https://huggingface.co/datasets/sayakpaul/ucf101-subset) from the [UCF101](https://www.crcv.ucf.edu/data/UCF101.php). This dataset contains realistic action videos from YouTube, classified in 101 actions.

* **Audio**: We use an audio classification dataset, the [ccmusic-database/bel_folk](https://huggingface.co/datasets/ccmusic-database/bel_folk). This dataset contains 1 minute audio clips of Chinese folk music, and the genre of the music.

* **Image**: We use an image classification dataset, the [zishuod/pokemon-icons](https://huggingface.co/datasets/zishuod/pokemon-icons). This dataset contains images of Pokemon that need to be classified.

### 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`.

### 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("argilla/multi-modal")
```

### 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("argilla/multi-modal")
```

### Supported Tasks

- Multi-modal classification
- Multi-modal transcription

## Dataset Structure

### Data in Argilla

The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, 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 |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | Label | label_selection | True | N/A | ['World', 'Sports', 'Business', 'Sci/Tech'] |


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".

**✨ NEW** 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`.

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 in "multi-modal" Dataset

* **Fields:** These are the records, each of them is a video, audio or image file encoded in base64.
    
    * **text** is of type `text`.

* **Questions:** These are the questions that should be annotated.
    
    * **TextQuestion** is a feature to describe the content in detail.
    * **RatingQuestion** will allow us to rate the content's quality effectively.
    * **LabelQuestion** is for tagging the content with the most suitable age group.

* **Metadata:** Three metadata properties are added to streamline content management.

    * **groups** is to identify the assigned annotator group.
    * **media** will specify the media source.
    * **source-dataset** will highlight the source dataset of the content in each record.

### Data Splits

The dataset contains a single split, which is `train`.