Datasets:

kursathalat
/

multilabel_ds

text stringclasses 10 values	label list	label-suggestion sequence	label-suggestion-metadata dict	external_id stringclasses 0 values	metadata stringclasses 1 value
"If you don't wear BROWN AND ORANGE...YOU DON'T MATTER!" We need a tshirt with that on it asap!	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "approval", "neutral" ], "status": "submitted" } ]	[ "annoyance", "neutral" ]	{ "type": "human", "score": null, "agent": null }	null	{}
"What do Scottish people look like?" How I would love to have been there to take a swing at that softball.	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "curiosity", "disapproval" ], "status": "submitted" } ]	[ "curiosity", "love" ]	{ "type": "human", "score": null, "agent": null }	null	{}
### A surprise, to be sure, but a welcome one	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "grief", "surprise" ], "status": "submitted" } ]	[ "surprise" ]	{ "type": "human", "score": null, "agent": null }	null	{}
'Pray, v. To ask that the laws of the universe be annulled in behalf of a single petitioner, confessedly unworthy.' — [NAME]	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "caring", "neutral" ], "status": "submitted" } ]	[ "neutral" ]	{ "type": "human", "score": null, "agent": null }	null	{}
>it'll get invaded by tankie, unfortunately. How apropos.	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "love", "neutral" ], "status": "submitted" } ]	[ "neutral" ]	{ "type": "human", "score": null, "agent": null }	null	{}
And not all children's hospitals need the same stuff, so call and ask what they need. But I like your tip. You're correct.	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "confusion", "neutral" ], "status": "submitted" } ]	[ "approval" ]	{ "type": "human", "score": null, "agent": null }	null	{}
Best number! [NAME], [NAME], [NAME]	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "desire", "neutral" ], "status": "submitted" } ]	[ "admiration" ]	{ "type": "human", "score": null, "agent": null }	null	{}
Calm down and relax are the worst things to say to a woman, ever.	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "disappointment", "relief" ], "status": "submitted" } ]	[ "annoyance" ]	{ "type": "human", "score": null, "agent": null }	null	{}
Change is hard. Find comfort in victory, even the small ones. Let that propel you to become the person you were destined to be.	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "amusement", "neutral" ], "status": "submitted" } ]	[ "neutral" ]	{ "type": "human", "score": null, "agent": null }	null	{}
Don't be so stupid. Terrorism is inherently political, religion is just one of the tools used to carry it out.	[ { "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "annoyance", "gratitude", "grief" ], "status": "submitted" } ]	[ "disapproval" ]	{ "type": "human", "score": null, "agent": null }	null	{}

Dataset Card for multilabel_ds

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.

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 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:

import argilla as rg

ds = rg.FeedbackDataset.from_huggingface("kursathalat/multilabel_ds")

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("kursathalat/multilabel_ds")

Supported Tasks and Leaderboards

This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.

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
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	multi_label_selection	True	N/A	['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']

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 section.

Data Instances

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

{
    "external_id": null,
    "fields": {
        "text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! "
    },
    "metadata": {},
    "responses": [
        {
            "status": "submitted",
            "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4",
            "values": {
                "label": {
                    "value": [
                        "approval",
                        "neutral"
                    ]
                }
            }
        }
    ],
    "suggestions": [
        {
            "agent": null,
            "question_name": "label",
            "score": null,
            "type": "human",
            "value": [
                "annoyance",
                "neutral"
            ]
        }
    ],
    "vectors": {}
}

While the same record in HuggingFace datasets looks as follows:

{
    "external_id": null,
    "label": [
        {
            "status": "submitted",
            "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4",
            "value": [
                "approval",
                "neutral"
            ]
        }
    ],
    "label-suggestion": [
        "annoyance",
        "neutral"
    ],
    "label-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": "human"
    },
    "metadata": "{}",
    "text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! "
}

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.
- text 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.
- label is of type multi_label_selection with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].
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) label-suggestion is of type multi_label_selection with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].

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

This is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection.