README.md

metadata

size_categories: n<1K
tags:
  - rlfh
  - argilla
  - human-feedback
dataset_info:
  features:
    - name: metadata
      dtype: string
    - name: text
      dtype: string
      id: field
    - name: label
      dtype: string
      id: field
    - name: question-1
      sequence:
        - name: user_id
          dtype: string
        - name: value
          dtype: string
        - name: status
          dtype: string
      id: question
    - name: question-2
      sequence:
        - name: user_id
          dtype: string
        - name: value
          dtype: int32
        - name: status
          dtype: string
      id: question
    - name: external_id
      dtype: string
      id: external_id
  splits:
    - name: train
      num_bytes: 148
      num_examples: 1
  download_size: 0
  dataset_size: 148

Dataset Card for testargilla

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.cfg. 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("vegeta/testargilla")

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("vegeta/testargilla")

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, and guidelines.

The fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.

Field Name	Title	Type	Required	Markdown
text	Text	TextField	True	False
label	Label	TextField	True	False

The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.

Question Name	Title	Type	Required	Description	Values/Labels
question-1	Question-1	TextQuestion	True	This is the first question	N/A
question-2	Question-2	RatingQuestion	True	This is the second question	[1, 2, 3, 4, 5]

Finally, the guidelines 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": "entry-1",
    "fields": {
        "label": "positive",
        "text": "This is the first record"
    },
    "metadata": null,
    "responses": [
        {
            "status": "submitted",
            "user_id": null,
            "values": {
                "question-1": {
                    "value": "This is the first answer"
                },
                "question-2": {
                    "value": 5
                }
            }
        }
    ]
}

While the same record in HuggingFace datasets looks as follows:

{
    "external_id": "entry-1",
    "label": "positive",
    "metadata": null,
    "question-1": {
        "status": [
            "submitted"
        ],
        "user_id": [
            null
        ],
        "value": [
            "This is the first answer"
        ]
    },
    "question-2": {
        "status": [
            "submitted"
        ],
        "user_id": [
            null
        ],
        "value": [
            5
        ]
    },
    "text": "This is the first record"
}

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 suppported. These are the ones that will be used to provide responses to the questions.

  • text is of type TextField.
  • label is of type TextField.

• Questions: These are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.

  • question-1 is of type TextQuestion, and description "This is the first question".
  • question-2 is of type RatingQuestion with the following allowed values [1, 2, 3, 4, 5], and description "This is the second question".

Additionally, we also have one more field which is optional and is the following:

• 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

These are the annotation guidelines.

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]