task_categories:text-generation task_categories:text2text-generation task_categories:question-answering size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
- question-answering
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: system_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 30153
    num_examples: 10
  download_size: 19802
  dataset_size: 30153
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<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 my-distiset-a094d3c0

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/amrelfeqy/my-distiset-a094d3c0/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/amrelfeqy/my-distiset-a094d3c0/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "To write a Boolean query about COVID-19 and anxiety, you can use the following syntax:\n\n**General Query:**\n\n`(COVID-19 OR COVID OR Coronavirus) AND anxiety`\n\n**Including Specific Words:**\n\n`(COVID-19 OR COVID OR Coronavirus) AND (anxiety OR stress OR pandemic fatigue)`\n\n**Excluding Irrelevant Results:**\n\n`(COVID-19 OR COVID OR Coronavirus) AND anxiety - (-symptom OR treatment OR vaccine)`\n\nExplanation:\n\n- **OR** is used to include multiple keywords, allowing the search to return results that contain any of the specified words.\n- **AND** is used to narrow down the search results to include only posts that contain all the specified words.\n- **-** (minus sign) is used to exclude results that contain the specified words.\n\n**Tips:**\n\n* Use quotes to search for exact phrases: `\"COVID-19 pandemic anxiety\"`\n* Use hashtags to search for trending topics: `#COVID-19 AND #anxiety`\n* Use the \"site:\" operator to search within a specific website or domain: `site:mayoclinic.org COVID-19 anxiety`\n\n**Tracking COVID-19 Updates:**\n\nYou can also track COVID-19 updates and trending topics using real-time queries like:\n\n```\n(Materials:COVID-19 OR(Materials:COVID(Materials:CoronavirusTracking COVID-19 updates(MaterialTracking COVID-19 updates(Materials(Materials(MaterialTracking COVID-19(MaterialTracking(MaterialTracking Mater(MatTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking Mater(MTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(Material(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(Material(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(Material(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(Material(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking Mater(MTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking MaterTracking(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(Material(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking MaterTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking(MaterialTracking",
    "prompt": "How to write a Boolean query about COVID-19 and anxiety? \n\n",
    "system_prompt": "You are a social media assistant specializing in crafting effective Boolean search queries to help users find specific information, track trends, and analyze data on various social media platforms. Your purpose is to assist users in refining their search terms, selecting relevant keywords, and structuring their queries to retrieve accurate and relevant results. Provide clear explanations, offer tips on Boolean logic, and guide users through the process of creating complex search queries, including using operators, filters, and syntax for each social media platform, such as Twitter, Facebook, Instagram, LinkedIn, and more. User questions are direct and concise."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("amrelfeqy/my-distiset-a094d3c0", "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("amrelfeqy/my-distiset-a094d3c0")
```


</details>




