Dataset Card for go_emotions_raw

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("plaguss/go_emotions_raw")

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("plaguss/go_emotions_raw")

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.

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.

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.

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": "00000000-0000-0000-0000-000000000001",
            "values": {
                "label": {
                    "value": [
                        "neutral"
                    ]
                }
            }
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000016",
            "values": {
                "label": {
                    "value": [
                        "anger",
                        "annoyance",
                        "optimism"
                    ]
                }
            }
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000028",
            "values": {
                "label": {
                    "value": [
                        "approval"
                    ]
                }
            }
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000039",
            "values": {
                "label": {
                    "value": [
                        "neutral"
                    ]
                }
            }
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000048",
            "values": {
                "label": {
                    "value": [
                        "annoyance"
                    ]
                }
            }
        }
    ],
    "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": "00000000-0000-0000-0000-000000000001",
            "value": [
                "neutral"
            ]
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000016",
            "value": [
                "anger",
                "annoyance",
                "optimism"
            ]
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000028",
            "value": [
                "approval"
            ]
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000039",
            "value": [
                "neutral"
            ]
        },
        {
            "status": "submitted",
            "user_id": "00000000-0000-0000-0000-000000000048",
            "value": [
                "annoyance"
            ]
        }
    ],
    "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'], and description "Classify the text by selecting the correct label from the given list of labels.".

• 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

Script used for the generation

import argilla as rg
from datasets import load_dataset
import uuid
from datasets import concatenate_datasets

ds = load_dataset("go_emotions", "raw", split="train")
ds_prepared = load_dataset("go_emotions")

_CLASS_NAMES = [
    "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",
]
label_to_id = {label: i for i, label in enumerate(_CLASS_NAMES)}
id_to_label = {i: label for i, label in enumerate(_CLASS_NAMES)}

# Concatenate the datasets and transform to pd.DataFrame

ds_prepared = concatenate_datasets([ds_prepared["train"], ds_prepared["validation"], ds_prepared["test"]])
df_prepared = ds_prepared.to_pandas()

# Obtain the final labels as a dict, to later include these as suggestions

labels_prepared = {}
for idx in df_prepared.index:
    labels = [id_to_label[label_id] for label_id in df_prepared['labels'][idx]]
    labels_prepared[df_prepared['id'][idx]] = labels

# Add labels to the dataset and keep only the relevant columns

def add_labels(ex):
    labels = []
    for label in _CLASS_NAMES:
        if ex[label] == 1:
            labels.append(label)
    ex["labels"] = labels
    
    return ex
    
ds = ds.map(add_labels)
df = ds.select_columns(["text", "labels", "rater_id", "id"]).to_pandas()

# Create a FeedbackDataset for text classification

feedback_dataset = rg.FeedbackDataset.for_text_classification(labels=_CLASS_NAMES, multi_label=True)

# Create the records with the original responses, and use as suggestions
# the final labels in the "simplified" go_emotions dataset.

records = []
for text, df_text in df.groupby("text"):
    responses = []
    for rater_id, df_raters in df_text.groupby("rater_id"):
        responses.append(
            {
                "values": {"label": {"value": df_raters["labels"].iloc[0].tolist()}},
                "status": "submitted",
                "user_id": uuid.UUID(int=rater_id),
            }
        )
    suggested_labels = labels_prepared.get(df_raters["id"].iloc[0], None)
    if not suggested_labels:
        continue
    suggestion = [
        {
            "question_name": "label",
            "value": suggested_labels,
            "type": "human",
        }
    ]
    records.append(
        rg.FeedbackRecord(
            fields={"text": df_raters["text"].iloc[0]},
            responses=responses,
            suggestions=suggestion
        )
    )


feedback_dataset.add_records(records)

# Push to the hub
feedback_dataset.push_to_huggingface("plaguss/go_emotions_raw")

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.

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

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Citation Information

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Contributions

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