Dataset Card for consumer-dispute-argilla-samples-likert

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

Using this dataset 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.Dataset.from_hub("Jsoo/consumer-dispute-argilla-samples-likert", settings="auto")

This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.

Using this dataset with datasets

To load the records of 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("Jsoo/consumer-dispute-argilla-samples-likert")

This will only load the records of the dataset, but not the Argilla settings.

Dataset Structure

This dataset repo contains:

• Dataset records in a format compatible with HuggingFace datasets. These records will be loaded automatically when using rg.Dataset.from_hub 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.
• A dataset configuration folder conforming to the Argilla dataset format in .argilla.

The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.

Fields

The fields are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.

Field Name	Title	Type	Required
model_input	model_input	text	False
model_cot	model_cot	text	False
model_answer	model_answer	text	False

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.

Question Name	Title	Type	Required	Description	Values/Labels
rating_0	사건 종합 정보를 정확히 반영하여 판단했나요?	rating	True	사실관계 반영의 정확성	[0, 1, 2, 3, 4]
rating_1	제시된 법령/기준을 올바르게 해석하고 적용했나요?	rating	True	법리 적용의 적절성	[0, 1, 2, 3, 4]
rating_2	판단 과정이 논리적이고 충분한 근거를 제시하고 있나요?	rating	True	판단의 논리성 및 근거	[0, 1, 2, 3, 4]
rating_3	결정사항이 모호하지 않고 명확하며 구체적인가요?	rating	True	결정사항의 명확성/구체성	[0, 1, 2, 3, 4]
rating_4	조정결정서에 적합한 객관적이고 중립적인 어조와 형식을 유지했나요?	rating	True	어조 및 형식의 적절성	[0, 1, 2, 3, 4]

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

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Who are the source language producers?

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Annotations

Annotation guidelines

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

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