metadata

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

Dataset Card for stackoverflow_feedback_demo

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("frascuchon/stackoverflow_feedback_demo")

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("frascuchon/stackoverflow_feedback_demo")

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
title	Title	text	True	False
question	Question	text	True	True
answer	Answer	text	True	True

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
title_question_fit	Does the title match the question?	label_selection	True	N/A	['yes', 'no']
tags	What are the topics mentioned in this question?	multi_label_selection	True	N/A	['python', 'django', 'python-2.7', 'list', 'python-3.x', 'numpy', 'pandas', 'regex', 'dictionary', 'string', 'matplotlib', 'arrays', 'google-app-engine', 'csv', 'tkinter', 'flask', 'json', 'linux', 'mysql', 'html', 'function', 'file', 'class', 'algorithm', 'windows', 'scipy', 'loops', 'multithreading', 'beautifulsoup', 'django-models', 'for-loop', 'javascript', 'xml', 'sqlalchemy', 'parsing', 'performance', 'datetime', 'osx', 'sorting', 'unicode', 'c++', 'dataframe', 'selenium', 'subprocess', 'pygame', 'java', 'pyqt', 'pip', 'tuples', 'scrapy']
answer_quality	Rate the quality of the answer:	rating	True	N/A	[1, 2, 3, 4, 5]
new_answer	If needed, correct the answer	text	False	N/A	N/A

✨ 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:

{
    "external_id": null,
    "fields": {
        "answer": "\u003cp\u003eUnfortunately the only API that isn\u0027t deprecated is located in the ApplicationServices framework, which doesn\u0027t have a bridge support file, and thus isn\u0027t available in the bridge. If you\u0027re wanting to use ctypes, you can use ATSFontGetFileReference after looking up the ATSFontRef.\u003c/p\u003e\r\n\r\n\u003cp\u003eCocoa doesn\u0027t have any native support, at least as of 10.5, for getting the location of a font.\u003c/p\u003e",
        "question": "\u003cp\u003eI am using the Photoshop\u0027s javascript API to find the fonts in a given PSD.\u003c/p\u003e\n\n\u003cp\u003eGiven a font name returned by the API, I want to find the actual physical font file that that font name corresponds to on the disc.\u003c/p\u003e\n\n\u003cp\u003eThis is all happening in a python program running on OSX so I guess I\u0027m looking for one of:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003eSome Photoshop javascript\u003c/li\u003e\n\u003cli\u003eA Python function\u003c/li\u003e\n\u003cli\u003eAn OSX API that I can call from python\u003c/li\u003e\n\u003c/ul\u003e\n",
        "title": "How can I find the full path to a font from its display name on a Mac?"
    },
    "metadata": {},
    "responses": [
        {
            "status": "submitted",
            "user_id": "5a053951-24cd-4c9d-9e0c-8a054b95b812",
            "values": {
                "answer_quality": {
                    "value": 1
                },
                "new_answer": {
                    "value": "Sample answer"
                },
                "tags": {
                    "value": [
                        "tkinter"
                    ]
                },
                "title_question_fit": {
                    "value": "yes"
                }
            }
        }
    ],
    "suggestions": []
}

While the same record in HuggingFace datasets looks as follows:

{
    "answer": "\u003cp\u003eUnfortunately the only API that isn\u0027t deprecated is located in the ApplicationServices framework, which doesn\u0027t have a bridge support file, and thus isn\u0027t available in the bridge. If you\u0027re wanting to use ctypes, you can use ATSFontGetFileReference after looking up the ATSFontRef.\u003c/p\u003e\r\n\r\n\u003cp\u003eCocoa doesn\u0027t have any native support, at least as of 10.5, for getting the location of a font.\u003c/p\u003e",
    "answer_quality": [
        {
            "status": "submitted",
            "user_id": "5a053951-24cd-4c9d-9e0c-8a054b95b812",
            "value": 1
        }
    ],
    "answer_quality-suggestion": null,
    "answer_quality-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "external_id": null,
    "metadata": "{}",
    "new_answer": [
        {
            "status": "submitted",
            "user_id": "5a053951-24cd-4c9d-9e0c-8a054b95b812",
            "value": "Sample answer"
        }
    ],
    "new_answer-suggestion": null,
    "new_answer-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "question": "\u003cp\u003eI am using the Photoshop\u0027s javascript API to find the fonts in a given PSD.\u003c/p\u003e\n\n\u003cp\u003eGiven a font name returned by the API, I want to find the actual physical font file that that font name corresponds to on the disc.\u003c/p\u003e\n\n\u003cp\u003eThis is all happening in a python program running on OSX so I guess I\u0027m looking for one of:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003eSome Photoshop javascript\u003c/li\u003e\n\u003cli\u003eA Python function\u003c/li\u003e\n\u003cli\u003eAn OSX API that I can call from python\u003c/li\u003e\n\u003c/ul\u003e\n",
    "tags": [
        {
            "status": "submitted",
            "user_id": "5a053951-24cd-4c9d-9e0c-8a054b95b812",
            "value": [
                "tkinter"
            ]
        }
    ],
    "tags-suggestion": null,
    "tags-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "title": "How can I find the full path to a font from its display name on a Mac?",
    "title_question_fit": [
        {
            "status": "submitted",
            "user_id": "5a053951-24cd-4c9d-9e0c-8a054b95b812",
            "value": "yes"
        }
    ],
    "title_question_fit-suggestion": null,
    "title_question_fit-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    }
}

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.
- title is of type text.
- question is of type text.
- answer is of type text.
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.
- title_question_fit is of type label_selection with the following allowed values ['yes', 'no'].
- tags is of type multi_label_selection with the following allowed values ['python', 'django', 'python-2.7', 'list', 'python-3.x', 'numpy', 'pandas', 'regex', 'dictionary', 'string', 'matplotlib', 'arrays', 'google-app-engine', 'csv', 'tkinter', 'flask', 'json', 'linux', 'mysql', 'html', 'function', 'file', 'class', 'algorithm', 'windows', 'scipy', 'loops', 'multithreading', 'beautifulsoup', 'django-models', 'for-loop', 'javascript', 'xml', 'sqlalchemy', 'parsing', 'performance', 'datetime', 'osx', 'sorting', 'unicode', 'c++', 'dataframe', 'selenium', 'subprocess', 'pygame', 'java', 'pyqt', 'pip', 'tuples', 'scrapy'].
- answer_quality is of type rating with the following allowed values [1, 2, 3, 4, 5].
- (optional) new_answer is of type text.
✨ 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) title_question_fit-suggestion is of type label_selection with the following allowed values ['yes', 'no'].
- (optional) tags-suggestion is of type multi_label_selection with the following allowed values ['python', 'django', 'python-2.7', 'list', 'python-3.x', 'numpy', 'pandas', 'regex', 'dictionary', 'string', 'matplotlib', 'arrays', 'google-app-engine', 'csv', 'tkinter', 'flask', 'json', 'linux', 'mysql', 'html', 'function', 'file', 'class', 'algorithm', 'windows', 'scipy', 'loops', 'multithreading', 'beautifulsoup', 'django-models', 'for-loop', 'javascript', 'xml', 'sqlalchemy', 'parsing', 'performance', 'datetime', 'osx', 'sorting', 'unicode', 'c++', 'dataframe', 'selenium', 'subprocess', 'pygame', 'java', 'pyqt', 'pip', 'tuples', 'scrapy'].
- (optional) answer_quality-suggestion is of type rating with the following allowed values [1, 2, 3, 4, 5].
- (optional) new_answer-suggestion is of type text.

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

[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

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

[More Information Needed]

Dataset Card for stackoverflow_feedback_demo

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

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`