metadata

size_categories: 10K<n<100K
tags:
  - rlfh
  - argilla
  - human-feedback

Dataset Card for dolly_tutorial

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("nataliaElv/dolly_tutorial")

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("nataliaElv/dolly_tutorial")

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
category	Task category	TextField	True	False
instruction	Instruction	TextField	True	False
context	Input	TextField	True	False
response	Response	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
final-instruction	Final Instruction:	TextQuestion	True	Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.	N/A
final-context	Final Input:	TextQuestion	True	Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing.	N/A
final-response	Final Response:	TextQuestion	True	Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here. Make sure that the grammar and orthography are correct.	N/A

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": "0",
    "fields": {
        "category": "closed_qa",
        "context": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
        "instruction": "When did Virgin Australia start operating?",
        "response": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route."
    },
    "metadata": null,
    "responses": [
        {
            "status": "submitted",
            "user_id": "dc9c373f-c589-4845-b7e8-890520ca7d43",
            "values": {
                "final-context": {
                    "value": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney."
                },
                "final-instruction": {
                    "value": "When did Virgin Australia start operating?"
                },
                "final-response": {
                    "value": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue."
                }
            }
        }
    ]
}

While the same record in HuggingFace datasets looks as follows:

{
    "category": "closed_qa",
    "context": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
    "external_id": "0",
    "final-context": {
        "status": [
            "submitted"
        ],
        "user_id": [
            "dc9c373f-c589-4845-b7e8-890520ca7d43"
        ],
        "value": [
            "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney."
        ]
    },
    "final-instruction": {
        "status": [
            "submitted"
        ],
        "user_id": [
            "dc9c373f-c589-4845-b7e8-890520ca7d43"
        ],
        "value": [
            "When did Virgin Australia start operating?"
        ]
    },
    "final-response": {
        "status": [
            "submitted"
        ],
        "user_id": [
            "dc9c373f-c589-4845-b7e8-890520ca7d43"
        ],
        "value": [
            "Virgin Australia commenced services on 31 August 2000 as Virgin Blue."
        ]
    },
    "instruction": "When did Virgin Australia start operating?",
    "metadata": null,
    "response": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route."
}

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.
- category is of type TextField.
- instruction is of type TextField.
- (optional) context is of type TextField.
- response 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.
- final-instruction is of type TextQuestion, and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
- (optional) final-context is of type TextQuestion, and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing.".
- final-response is of type TextQuestion, and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here. Make sure that the grammar and orthography are correct.".

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

Introduction

In this dataset, you will find a collection of records that show a task category, an instruction, an input and a response. The aim of the project is to correct the instructions, inputs and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information.

Task categories

Instructions are classified according to 7 possible task categories. Please, read and understand these categories because they will change the way the instruction and input are formulated. The definitions are based on those made by Databricks.

Open / General Q&A

Here you will find an open question, for instance, “Why do people like comedy movies?” or “What is the capital of France?”. In some cases, there’s not a correct answer, and in others, it requires drawing on knowledge of the world at large. This type of task shouldn’t have an input.

Closed Q&A

These are questions that can be answered using only the information contained in a passage of reference text. For instance, given a paragraph from Wikipedia on the atom, one might ask, “What is the ratio between protons and neutrons in the nucleus?”. In this case, the task should have an instruction (the question), an input (the reference text) and a response.

Information extraction

In this task, the instruction will ask to extract entities or other factual information from a passage. In this case, the task should have an instruction (the question), an input (the reference text) and a response.

Summarization

Instructions of this kind of task will ask to summarize a passage. The passage should be in the input. The response should be a summarized version of the passage.

Brainstorming

Brainstorming instructions should ask for open-ended ideation and an associated list of possible options. For instance, “What are some fun activities I can do with my friends this weekend?”.

Classification

Instructions of this type should ask to make judgments about class membership (e.g. are the items in a list animals, minerals or vegetables) or to judge the properties of a short passage of text, such as the sentiment of a movie review. The item(s) to be classified should appear in the input field.

Creative writing

Instructions of this class include things like writing a poem or a love letter.

Questionnaire

To curate the dataset, you will need to provide and answer to the questions below. Please, follow the pointers below to answer each question accordingly.

If you are not sure about a record and you prefer not to provide a response, click Discard.

1. Final instruction:

The final version of the instruction field. You may copy it using the copy icon in the instruction field.
Leave it as it is if it's ok or apply any necessary corrections.
Remember to change the instruction if it doesn't represent well the task category of the record.
Instructions can contain grammar and orthography errors as long as they are clear.

2. Final input:

The final version of the instruction field. You may copy it using the copy icon in the input field.
Leave it as it is if it's ok or apply any necessary corrections.
Remember to add an input to the tasks that need one: closed Q&A, information extraction, summarization and classification.
This question should be blank whenever the task doesn’t need one: open / general Q&A, brainstorming, creative writing.
Inputs can contain grammar and orthography errors as long as they are clear.

3. Final response:

The final version of the response field.
You may copy it using the copy icon in the response field.
Leave it as it is if it's ok or apply any necessary corrections.
Check that the response makes sense given all the fields above and that it is as complete and concise as possible.
Responses should have their grammar and orthography checked and correct at all times.

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 dolly_tutorial

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

Introduction

Task categories

Open / General Q&A

Closed Q&A

Information extraction

Summarization

Brainstorming

Classification

Creative writing

Questionnaire

1. Final instruction:

2. Final input:

3. Final response:

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`