metadata

size_categories: n<1K
tags:
  - rlfh
  - argilla
  - human-feedback

Dataset Card for MPEP_SPANISH

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 Description

Homepage: https://argilla.io
Repository: https://github.com/argilla-io/argilla
Paper:
Leaderboard:
Point of Contact:

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("DIBT/MPEP_SPANISH")

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("DIBT/MPEP_SPANISH")

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
source	Source	text	True	True

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
target	Target	text	True	Translate the text.	N/A

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": "165",
    "fields": {
        "source": "Given the text: An experienced and enthusiastic innovator...you want on your team.\nMargaret Hines is the founder and Principal Consultant of Inspire Marketing, LLC, investing in local businesses, serving the community with business brokerage and marketing consulting. She has an undergraduate degree from Washington University in St. Louis, MO, and an MBA from the University of Wisconsin-Milwaukee.\nMargaret offers consulting in marketing, business sales and turnarounds and franchising. She is also an investor in local businesses.\nPrior to founding Inspire Marketing in 2003, Margaret gained her business acumen, sales and marketing expertise while working at respected Fortune 1000 companies.\nSummarize the background and expertise of Margaret Hines, the founder of Inspire Marketing."
    },
    "metadata": {
        "evolved_from": null,
        "kind": "synthetic",
        "source": "ultrachat"
    },
    "responses": [
        {
            "status": "submitted",
            "user_id": "8581ce44-b17e-40a8-81a0-e20b63074c9d",
            "values": {
                "target": {
                    "value": "Dado el texto: Una innovadora experimentada y entusiasta... que quieres en tu equipo.\nMargaret Hines es la fundadora y Consultora Principal de Inspire Marketing, LLC, que invierte en negocios locales, sirviendo a la comunidad con consultor\u00eda de negocios y marketing. Ella tiene un t\u00edtulo universitario de la Universidad de Washington en St. Louis, MO, y un MBA de la Universidad de Wisconsin-Milwaukee.\nMargaret ofrece consultor\u00eda en marketing, ventas de negocios, transformaciones de negocios y franquicias. Tambi\u00e9n es inversora en negocios locales.\nAntes de fundar Inspire Marketing en 2003, Margaret adquiri\u00f3 su habilidad para los negocios, experiencia en ventas y marketing mientras trabajaba en respetadas empresas de Fortune 1000.\nResume la formaci\u00f3n y experiencia de Margaret Hines, la fundadora de Inspire Marketing."
                }
            }
        }
    ],
    "suggestions": [
        {
            "agent": null,
            "question_name": "target",
            "score": null,
            "type": null,
            "value": "Dado el texto: Una innovadora experimentada y entusiasta... que quieres en tu equipo.\nMargaret Hines es la fundadora y Consultora Principal de Inspire Marketing, LLC, invirtiendo en negocios locales, sirviendo a la comunidad con consultor\u00eda de negocios y marketing. Ella tiene un t\u00edtulo universitario de la Universidad de Washington en St. Louis, MO, y un MBA de la Universidad de Wisconsin-Milwaukee.\nMargaret ofrece consultor\u00eda en marketing, ventas de negocios, transformaciones de negocios y franquicias. Tambi\u00e9n es inversora en negocios locales.\nAntes de fundar Inspire Marketing en 2003, Margaret adquiri\u00f3 su habilidad para los negocios, experiencia en ventas y marketing mientras trabajaba en respetadas empresas Fortune 1000.\nResumen de la formaci\u00f3n y experiencia de Margaret Hines, la fundadora de Inspire Marketing."
        }
    ],
    "vectors": {}
}

While the same record in HuggingFace datasets looks as follows:

{
    "external_id": "165",
    "metadata": "{\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"evolved_from\": null}",
    "source": "Given the text: An experienced and enthusiastic innovator...you want on your team.\nMargaret Hines is the founder and Principal Consultant of Inspire Marketing, LLC, investing in local businesses, serving the community with business brokerage and marketing consulting. She has an undergraduate degree from Washington University in St. Louis, MO, and an MBA from the University of Wisconsin-Milwaukee.\nMargaret offers consulting in marketing, business sales and turnarounds and franchising. She is also an investor in local businesses.\nPrior to founding Inspire Marketing in 2003, Margaret gained her business acumen, sales and marketing expertise while working at respected Fortune 1000 companies.\nSummarize the background and expertise of Margaret Hines, the founder of Inspire Marketing.",
    "target": [
        {
            "status": "submitted",
            "user_id": "8581ce44-b17e-40a8-81a0-e20b63074c9d",
            "value": "Dado el texto: Una innovadora experimentada y entusiasta... que quieres en tu equipo.\nMargaret Hines es la fundadora y Consultora Principal de Inspire Marketing, LLC, que invierte en negocios locales, sirviendo a la comunidad con consultor\u00eda de negocios y marketing. Ella tiene un t\u00edtulo universitario de la Universidad de Washington en St. Louis, MO, y un MBA de la Universidad de Wisconsin-Milwaukee.\nMargaret ofrece consultor\u00eda en marketing, ventas de negocios, transformaciones de negocios y franquicias. Tambi\u00e9n es inversora en negocios locales.\nAntes de fundar Inspire Marketing en 2003, Margaret adquiri\u00f3 su habilidad para los negocios, experiencia en ventas y marketing mientras trabajaba en respetadas empresas de Fortune 1000.\nResume la formaci\u00f3n y experiencia de Margaret Hines, la fundadora de Inspire Marketing."
        }
    ],
    "target-suggestion": "Dado el texto: Una innovadora experimentada y entusiasta... que quieres en tu equipo.\nMargaret Hines es la fundadora y Consultora Principal de Inspire Marketing, LLC, invirtiendo en negocios locales, sirviendo a la comunidad con consultor\u00eda de negocios y marketing. Ella tiene un t\u00edtulo universitario de la Universidad de Washington en St. Louis, MO, y un MBA de la Universidad de Wisconsin-Milwaukee.\nMargaret ofrece consultor\u00eda en marketing, ventas de negocios, transformaciones de negocios y franquicias. Tambi\u00e9n es inversora en negocios locales.\nAntes de fundar Inspire Marketing en 2003, Margaret adquiri\u00f3 su habilidad para los negocios, experiencia en ventas y marketing mientras trabajaba en respetadas empresas Fortune 1000.\nResumen de la formaci\u00f3n y experiencia de Margaret Hines, la fundadora de Inspire Marketing.",
    "target-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    }
}

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.
- source 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.
- target is of type text, and description "Translate the text.".
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) target-suggestion is of type text.

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 translation dataset that contains texts. Please translate the text in the text field.

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]

Dataset Card for MPEP_SPANISH

Dataset Description

Dataset Summary

Load with Argilla

Load with datasets

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data in Argilla

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation guidelines

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions

Load with `datasets`