Dataset Card for test-argilla-dataset

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 --pre --upgrade and then use the following code:

import argilla as rg

ds = rg.Dataset.from_hub("burtenshaw/test-argilla-dataset")

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("burtenshaw/test-argilla-dataset")

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.

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.

Metadata

The metadata is a dictionary that can be used to provide additional information about the dataset record.

Vectors

The vectors contain a vector representation of the record that can be used in search.

Data Instances

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

{
    "_server_id": "8aaf57d2-cb8e-4673-a7ce-2f684b60adf5",
    "fields": {
        "text": "Hello World, how are you?"
    },
    "id": "4f56e32b-9582-47de-a2b1-b230732bb07b",
    "metadata": {},
    "responses": {
        "label": [
            {
                "user_id": "06f7d4c0-e048-43d2-ab3f-06f147616ac6",
                "value": "positive"
            }
        ]
    },
    "suggestions": {
        "label": {
            "agent": null,
            "score": null,
            "value": "positive"
        },
        "topics": {
            "agent": null,
            "score": [
                0.9,
                0.8
            ],
            "value": [
                "topic1",
                "topic2"
            ]
        }
    },
    "vectors": {}
}

While the same record in HuggingFace datasets looks as follows:

{
    "_server_id": "8aaf57d2-cb8e-4673-a7ce-2f684b60adf5",
    "comment.suggestion": null,
    "comment.suggestion.agent": null,
    "comment.suggestion.score": null,
    "comment_score": null,
    "id": "4f56e32b-9582-47de-a2b1-b230732bb07b",
    "label.responses": [
        "positive"
    ],
    "label.responses.status": [
        "draft"
    ],
    "label.responses.users": [
        "06f7d4c0-e048-43d2-ab3f-06f147616ac6"
    ],
    "label.suggestion": "positive",
    "label.suggestion.agent": null,
    "label.suggestion.score": null,
    "ranking.suggestion": null,
    "ranking.suggestion.agent": null,
    "ranking.suggestion.score": null,
    "rating.suggestion": null,
    "rating.suggestion.agent": null,
    "rating.suggestion.score": null,
    "span.suggestion": null,
    "span.suggestion.agent": null,
    "span.suggestion.score": null,
    "text": "Hello World, how are you?",
    "topics.suggestion": [
        "topic1",
        "topic2"
    ],
    "topics.suggestion.agent": null,
    "topics.suggestion.score": [
        0.9,
        0.8
    ],
    "vector": null
}

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

[More Information Needed]

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

[More Information Needed]