size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: generations
    sequence: string
  - name: generation_models
    sequence: string
  - name: ratings
    dtype: 'null'
  - name: rationales
    dtype: 'null'
  splits:
  - name: train
    num_bytes: 2399
    num_examples: 1
  download_size: 18794
  dataset_size: 2399
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for synthetic_resume

This dataset has been created with [distilabel](https://distilabel.argilla.io/).

## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/Rohit228/synthetic_resume/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Rohit228/synthetic_resume/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "generation_models": [
        "flamingo"
    ],
    "generations": [
        "**John Doe**\n**Contact Information:**\n\n* Email: [johndoe@email.com](mailto:johndoe@email.com)\n* Phone: (123) 456-7890\n* LinkedIn: linkedin.com/in/johndoe\n* GitHub: github.com/johndoe\n\n**Summary:**\nHighly motivated and detail-oriented software developer with 5+ years of experience in designing, developing, and deploying scalable web applications. Proficient in a range of programming languages, including Java, Python, and JavaScript. Strong understanding of software development life cycles, agile methodologies, and version control systems.\n\n**Technical Skills:**\n\n* Programming Languages: Java, Python, JavaScript\n* Development Frameworks: Spring, Django, React\n* Databases: MySQL, PostgreSQL, MongoDB\n* Operating Systems: Windows, Linux, macOS\n* Version Control: Git, SVN\n* Agile Methodologies: Scrum, Kanban\n\n**Professional Experience:**\n\n**Senior Software Developer, ABC Corporation (2018-Present)**\n\n* Designed and developed multiple web applications using Java and Spring Framework for a leading e-commerce company.\n* Collaborated with cross-functional teams to identify and prioritize project requirements, ensuring timely delivery of high-quality products.\n* Implemented automated testing and deployment scripts using Jenkins and Docker to improve development efficiency.\n* Mentored junior developers, providing guidance on best practices and technical skills.\n\n**Software Developer, DEF Startups (2015-2018)**\n\n* Contributed to the development of a mobile banking app using React Native for a fintech startup.\n* Worked closely with designers to implement responsive UI/UX designs and ensure seamless user experiences.\n* Implemented security measures, including encryption and authentication protocols, to protect sensitive customer data.\n* Participated in code reviews and ensured adherence to coding standards and best practices.\n\n**Education:**\n\n* **Bachelor of Science in Computer Science, XYZ University (2010-2014)**\n\n**Certifications:**\n\n* **Certified Scrum Master (CSM), Scrum Alliance (2016)**\n* **Certified Java Developer, Oracle Corporation (2012)**\n\n**Personal Projects:**\n\n* Developed a machine learning-powered chatbot using Python and TensorFlow for natural language processing.\n* Built a web-based project management tool using Django and React for efficient team collaboration.\n\n**References:**\nAvailable upon request."
    ],
    "instruction": "Software Developer resume",
    "ratings": null,
    "rationales": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Rohit228/synthetic_resume", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("Rohit228/synthetic_resume")
```


</details>
