larinhaIA/help-code

# 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.

原始信息汇总

数据集卡片：Helper-Jhonny代码导师

数据集详情

数据集描述

Helper-Jhonny是一个精选数据集，旨在支持三种编程语言（Python、JavaScript和SQL）的代码开发辅导。该数据集专注于与编码任务相关的问题解答场景，旨在帮助学习者和开发者提高技能并加深对这些编程语言的理解。

• 策划团队： Helper-Jhonny团队
• 资助者 [可选]： Lara Ayne
• 语言（NLP）： 葡萄牙语（pt）、英语（en）、西班牙语（es）
• 许可证： llama2

数据集用途

直接用途

该数据集适用于学习者和开发者在编码任务中寻求帮助和指导的场景。它可以用于构建提供实时代码辅导和支持的应用程序或平台。

超出范围的用途

该数据集不适用于误用或恶意用途。它可能不适用于与代码无关的问题解答任务。

数据集结构

数据集包含与代码辅导相关的信息，包括Python、JavaScript和SQL的问题及其对应答案。每个条目都标记了编程语言，以便进行特定语言的辅导。

数据集创建

策划理由

该数据集的创建是为了满足对全面代码辅导资源的需求，重点关注三种广泛使用的编程语言。目标是向学习者提供实践指导和支持，帮助他们在编码旅程中取得进步。

源数据

数据收集和处理

数据集包括从各种代码辅导会话中收集的问题和答案。数据选择标准包括与学习者和开发者面临的常见编码挑战和任务的相关性。数据收集过程涉及精选现实世界编码查询及其解决方案。

源数据生产者

源数据生产者是经验丰富的导师和开发者，他们积极参与代码辅导社区。

注释 [可选]

注释过程

注释包括为每个条目标记相应的编程语言，以确保特定语言的辅导。注释者是具有Python、JavaScript和SQL专业知识的经验丰富的导师。

注释者

注释由一组熟练的代码导师完成，他们对Python、JavaScript和SQL有专业知识。

个人信息和敏感信息

数据集不包含个人、敏感或私人信息。

偏见、风险和局限性

数据集可能表现出基于注释者专业知识和观点的偏见。用户应意识到辅导场景可能未涵盖每个边缘案例，数据集并非详尽无遗。

建议

用户应了解数据集的局限性，并鼓励他们通过多样化的资源补充学习。