Dataset Card for Helper-Jhonny Code Mentor

Dataset Details

Dataset Description

Helper-Jhonny is a curated dataset designed to support mentoring in code development across three programming languages: Python, JavaScript, and SQL. The dataset is focused on question-answering scenarios related to coding tasks. It aims to assist learners and developers in improving their skills and understanding of these programming languages.

Curated by: Helper-Jhonny Team
Funded by [optional]: Lara Ayne
Language(s) (NLP): Portuguese (pt), English (en), Spanish (es)
License: llama2

Dataset Sources [optional]

Repository: [Link to Repository]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

The dataset is suitable for direct use in scenarios where learners and developers seek assistance and guidance in coding tasks. It can be utilized for building applications or platforms that provide real-time code mentoring and support.

Out-of-Scope Use

The dataset is not intended for misuse or malicious use. It may not work well for non-code related question-answering tasks.

Dataset Structure

The dataset contains information relevant to code mentoring, including questions and corresponding answers for Python, JavaScript, and SQL. Each entry is tagged with the programming language to facilitate language-specific mentoring.

Dataset Creation

Curation Rationale

The dataset was created to address the need for a comprehensive code mentoring resource, focusing on three widely used programming languages. The goal is to provide learners with practical guidance and support in their coding journey.

Source Data

Data Collection and Processing

The dataset comprises questions and answers gathered from various code mentoring sessions. The data selection criteria include relevance to common coding challenges and tasks faced by learners and developers. The data collection process involves curating real-world coding queries and their solutions.

Who are the source data producers?

The source data producers are experienced mentors and developers who actively contribute to the code mentoring community.

Annotations [optional]

Annotation process

Annotations include tagging each entry with the respective programming language to ensure language-specific mentoring. Annotators are experienced mentors with expertise in Python, JavaScript, and SQL.

Who are the annotators?

Annotations are performed by a team of skilled code mentors with proficiency in Python, JavaScript, and SQL.

Personal and Sensitive Information

The dataset does not contain personal, sensitive, or private information.

Bias, Risks, and Limitations

The dataset may exhibit biases based on the expertise and perspectives of the annotators. Users should be aware that mentoring scenarios may not cover every edge case, and the dataset is not exhaustive.

Recommendations

Users should be made aware of the dataset's limitations and encouraged to supplement their learning with diverse resources.