README.md

metadata

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

Dataset Card for snli-small

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("plaguss/snli-small")

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/snli-small")

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, 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
premise	Premise	TextField	True	False
hypothesis	Hypothesis	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
label	The hypothesis entails the premise, neither entails nor contradict each other, or the hypothesis contradicts the premise?	LabelQuestion	True	N/A	['0', '1', '2']

✨ NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, 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.

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:

{
    "fields": {
        "hypothesis": "A person is training his horse for a competition.",
        "premise": "A person on a horse jumps over a broken down airplane."
    },
    "metadata": {},
    "responses": [
        {
            "status": "submitted",
            "values": {
                "label": {
                    "value": "1"
                }
            }
        }
    ],
    "suggestions": []
}

While the same record in HuggingFace datasets looks as follows:

{
    "external_id": null,
    "hypothesis": "A person is training his horse for a competition.",
    "label": [
        {
            "status": "submitted",
            "user_id": null,
            "value": "1"
        }
    ],
    "label-suggestion": null,
    "label-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "metadata": "{}",
    "premise": "A person on a horse jumps over a broken down airplane."
}

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.

  • premise is of type TextField.
  • hypothesis is of type TextField.

• 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 LabelQuestion with the following allowed values ['0', '1', '2'].

• ✨ NEW 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 label_selection with the following allowed values ['0', '1', '2'].

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

Premise: A string used to determine the truthfulness of the hypothesis, Hypothesis: A string that may be true, false, or whose truth conditions may not be knowable when compared to the premise

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]