Dataset Card for "multi-modal"

This dataset has been created with Argilla.

As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla or used directly with the datasets library in Load with datasets.

Argilla supports Markdown within its text fields. This means you can easily add formatting like bold and italic text, links, 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 from the UCF101. This dataset contains realistic action videos from YouTube, classified in 101 actions.

• Audio: We use an audio classification dataset, the 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. 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:

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:

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 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.