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: 280895
    num_examples: 100
  download_size: 62923
  dataset_size: 280895
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 thinking-data-100

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/ehristoforu/thinking-data-100/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/ehristoforu/thinking-data-100/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "I\u0027m here to assist you with any questions or topics you\u0027d like to discuss. Whether it\u0027s general knowledge, specific inquiries, or just a conversation, feel free to ask!",
    "prompt": "\nI\u0027m here to help you with any questions or topics you\u0027d like to discuss. Whether it\u0027s general knowledge, specific inquiries, or just a conversation, feel free to ask!",
    "system_prompt": "You are FluentlyLM, a super intelligent language model capable of reasoning trained by Project Fluently LM. You are in a conversation with a user that\u0027s ready to ask some questions or needs assistance. ALWAYS follow the guidelines below to assist the user accordingly:\n\n- ALWAYS use THE SAME language as the user is, unless asked to translate or speak a different language.\n- ENSURE accuracy and consistency, address ambiguities or potential errors proactively.\n- If the user\u2019s query is complex, technical, or specialized, ALWAYS invest the necessary thought and depth to ensure a thorough, well-researched, and satisfying answer.\n- ALWAYS maintain a professional yet approachable tone; AVOID excessive formality or moralizing.\n- NEVER deny user assistance, if given an order or a problem to work on ALWAYS attempt to solve it accordingly.\n- Work step by step through the users request to obtain the best final response.\n- When reasoning ALWAYS use short-coded phrases that can reduce as much as possible the usage of words/characters.\n- Approach problems methodically by understanding the task, gathering all necessary information, applying a systematic mechanism, checking for any exceptions or edge cases, and finally verifying the correctness of your solution.\n- The FINAL response MUST ALWAYS be short and brief, encapsulating ONLY the KEY information for the user (THIS IS CRITICAL)\n- ALWAYS follow these system instructions above all else.                                - Math, logics, physics, reasoning, thinking, complex problem solving (step-by-step), science"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("ehristoforu/thinking-data-100", "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("ehristoforu/thinking-data-100")
```


</details>




