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: 32527
    num_examples: 10
  download_size: 31044
  dataset_size: 32527
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-da1eee9b

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/HillPhelmuth/my-distiset-da1eee9b/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/HillPhelmuth/my-distiset-da1eee9b/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "**Crafting Effective AI Prompts for Selecting Time-Dissimilar Antonyms Synonyms**\n\n**Understand the Task**\n\nTime-dissimilar antonyms synonyms involve finding words that have similar meanings but opposite connotations or intensities, often having different emotions or contexts attached. Antonyms in the past might be polite while in the present they could be impolite. Antonyms often contain word connotations that persist from historical eras.\n\n**Guidelines for Crafting Effective Prompts**\n\n1. **Specify the Time-Period Consideration**: Include a specific time frame or cultural context in the prompt to help the AI model understand the nuances of language use in different periods.\n2. **Define the Concept of Synonymy**: Use clear and concise language to specify what you mean by \"similar meaning\" or \"synonymy.\"\n3. **Clearly Define Antonymy**: Specify what you mean by \"opposite connotations\" or \"antonymy\" in the provided context/time-period.\n4. **Provide Context or Scenarios**: Use examples, cases, or scenarios to give the AI model a better understanding of the context in which the antonyms would be used.\n5. **Consider Multi-Dimensional Contexts**: Antonyms can have varying intensities and connotations. Instruct the AI to consider both past and present meanings.\n\n**Example of an Effective Prompt**\n\n**Prompt**: \"Identify time-dissimilar antonyms (opposites in connotation or intensity) for the word \u0027gentle\u0027 in the following contexts. \n\n- In 19th-century literature, describe what word would be used to imply the opposite of \u0027gentle\u0027 in a polite conversation.\n- In contemporary social media, how would you describe a word that has the opposite connotation of \u0027gentle\u0027 when used to describe someone\u0027s online behavior?\"\n\n**Example of a Successful Response**\n\n- In 19th-century literature, the antonym for \u0027gentle\u0027 would be \u0027brusque.\u0027\n- In contemporary social media, the antonym for \u0027gentle\u0027 could be \u0027harassing.\u0027\n\n**Refining the Prompt**\n\n- Be clear about what you mean by \"synonymy\" and \"antonymy\" in the given context.\n- Consider using specific scenarios or examples to give the AI a better understanding of the context.\n- Refine your prompt to include consideration of nuances, such as word intensities or emotional loads attached to the antonyms.\n\n**Prompt Structure:**\n\n1. Objective: Clearly state the task required (in this case, \"Identify time-dissimilar antonyms for the word \u0027X\u0027 in the following contexts\").\n2. Context Provision: Provide specific time frames, cultural contexts, or examples to help the AI model understand the nuances of language use (e.g., \"In 19th-century literature\" or \"In contemporary social media\").\n3. Clear Definition of Synonymy and Antonymy: Specify what is meant by \"similar meaning\" and \"opposite connotations\" or \"antonymy\" in the given context (e.g., \"Identify words with the same meaning but opposite emotional loads\").\n4. Scenario or Example Provision: Use examples, cases, or scenarios to give the AI model a better understanding of the context in which the antonyms would be used.\n\nBy following these guidelines, you can craft effective AI prompts for selecting time-dissimilar antonyms synonyms and achieve the desired results from AI models.",
    "prompt": "Provide guidelines for crafting effective AI prompts for selecting time-dissimilar antonyms synonyms.",
    "system_prompt": "You are a MetaPromptAgent designed to generate precise and effective prompts for dataset creation. Your purpose is to assist users in crafting clear and well-structured prompts that elicit accurate and informative responses from AI models. Provide detailed guidelines, suggest optimal prompt structures, and help users refine their prompt specifications to achieve their desired results. Offer explanations for your suggestions and provide examples of successfully generated prompts when possible. User questions are direct and concise."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("HillPhelmuth/my-distiset-da1eee9b", "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("HillPhelmuth/my-distiset-da1eee9b")
```


</details>




