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: system_prompt
    dtype: string
  - name: generation
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_output_text_generation
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 23662
    num_examples: 9
  download_size: 28992
  dataset_size: 23662
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 step-one-output-dataset

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/jamal-khan/step-one-output-dataset/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/jamal-khan/step-one-output-dataset/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "distilabel_metadata": {
        "raw_output_text_generation": " Large Language Models (LLMs), like GPT series developed by Microsoft, have revolutionized how we approach various aspects of artificial intelligence in relation to human languages. These sophisticated systems excel at producing versatile outputs across numerous applications within Natural Language Processing (NLP). They accomplish this through their unique architecture that enables them not only to generate new sentences but also perform complex NLP functions including categorization based on learned patterns extracted from extensive datasets. \n\nThe core strength of LLMs lies in their capacity to understand linguistic structures via deep neural networks which undergo rigorous pretraining phases involving both un"
    },
    "generation": " Large Language Models (LLMs), like GPT series developed by Microsoft, have revolutionized how we approach various aspects of artificial intelligence in relation to human languages. These sophisticated systems excel at producing versatile outputs across numerous applications within Natural Language Processing (NLP). They accomplish this through their unique architecture that enables them not only to generate new sentences but also perform complex NLP functions including categorization based on learned patterns extracted from extensive datasets. \n\nThe core strength of LLMs lies in their capacity to understand linguistic structures via deep neural networks which undergo rigorous pretraining phases involving both un",
    "instruction": "A large language model (LLM) is a computational model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. Based on language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a computationally intensive self-supervised and semi-supervised training process. LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.",
    "model_name": "microsoft/Phi-3-mini-4k-instruct",
    "system_prompt": "You are an AI assistant designed to help transform raw paragraphs into structured text suitable for creating open-domain questions.Your task is to convert the provided raw text into a clear, coherent, and structured passage while retaining the original meaning and context. This transformation should simplify the creation of instructions tailored to specific objectives.\nGuidelines for Transformation:\n1. Clarity and Coherence: Ensure the text is easy to read and logically structured.\n2. Retain Meaning: Preserve the original message and context of the raw paragraph.\n3. Expand and Detail: Add relevant details to enhance the depth and richness of the content.\n4. Avoid Redundancy: Ensure the text is concise and free from unnecessary repetition.\nPurpose of Transformation:\nWe are preparing the text for generating open-domain questions. The transformed passage will serve as the basis for creating diverse and meaningful questions.\n\n"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("jamal-khan/step-one-output-dataset", "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("jamal-khan/step-one-output-dataset")
```


</details>
