task_categories:text-generation task_categories:text2text-generation size_categories:10K<n<100K language:en license:mit general knowledge GK reasoning facts alpaca region:us
---
license: mit
task_categories:
- text-generation
- text2text-generation
language:
- en
tags:
- general knowledge
- GK
- reasoning
- facts
- alpaca
pretty_name: General knowledge dataset
size_categories:
- 10K<n<100K
---

# Dataset Card for Dataset Name

### Dataset Summary

The dataset is a collection of questions and answers themed on general facts and reasoning. The dataset is divided into two features - 'Question' and 'Answer'. 
It is meant to be used for training a model to be good at general knowledge and reasoning. This dataset is inspired from the Alpaca dataset, and infact contains a subset of the alpaca dataset in itself.

### Distribution
  The distribution of the MuskumPillerum/General-Knowledge dataset is:
```
Total (non alpaca): 6315
- Facts - 80.8 %
    - Nature - 16.5 % 
    - AI, Computer science, Robotics - 7.3 %
    - Physics, Chemistry - 16.3 %
    - Geography, History - 11.2 %
    - People - 16 %
    - Sports - 13.5 %
- Recommendation, Reasoning, Dilemma - 17.8 %
- Others - 1.4 %
```
### Format
```
{'Question': 'What is the largest species of shark',
 'Answer': 'The whale shark is considered the largest species of shark, with adults reaching lengths of up to 40 feet or more and weighing several tons.'}
```

### Languages

English


### Source Data

This dataset is inspired from Stanfords alpaca dataset: tatsu-lab/alpaca
```
@misc{alpaca,
  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
  title = {Stanford Alpaca: An Instruction-following LLaMA model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```



### Licensing Information

This uses MIT licence

### Citation Information

Will be added soon
