Highgroundbkk/Pangpuriye-generated_by_typhoon

---
license: cc-by-nc-2.0
task_categories:
- table-question-answering
language:
- th
- en
tags:
- code
pretty_name: Thai-SQL_Question_generated_by_Typhoon
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: "data.json"
---

# 🤖 [Super AI Engineer Development Program Season 4](https://superai.aiat.or.th/) - Pangpuriye House - Generated by Typhoon API


**Pangpuriye's House Dataset - Generated Dataset from [Typhoon API](https://opentyphoon.ai/)**

This dataset is an output generated from the Typhoon API in the structure of SQL instruction for fine-tuning [Pangpuriye's LLM](https://huggingface.co/AIAT/Pangpuriye-openthaigpt-1.0.0-7b-chat). The dataset is set under cc-by-nc-2.0 license.

## Content

The dataset consists of 16,125 rows of `input`, `instruction`, and `output` packed into a train set.

- Each schema has its own CSV file as an `input`.
- The `instruction` is the command that the Typhoon API receives as input.
- The `output` is a SQL code.

## Uses

The dataset is intended to be used as an instruction for fine-tuning table-based QA LLM. The instruction requires some processing before it can be utilized in the process.

The following code is an example for using with the schema.

```python
stream = client.chat.completions.create(
model="typhoon-instruct",
messages=[
{
"role": "instruction",
"content": """
Your task is to generate SQL plain-text schema
Format: You won't explain or clarify your response.
""",
},
{"role": "user", "content": """Generate 1 random schema"""},
],
max_tokens=120,
temperature=0.6,
top_p=1,
stream=False,
)
```

## Call our dataset by `datasets` library

The following code is an example of calling our dataset via the `datasets` library.

```python
from datasets import load_dataset

dataset = load_dataset("AIAT/Pangpuriye-generated_by_typhoon")
```

## Acknowledgements

The dataset is collectively stored by the members of Panguriye's house during the LLMs hackathon in Super AI Engineer Development Program Season 4.

We thank the organizers of this hackathon, [OpenThaiGPT](https://openthaigpt.aieat.or.th/), [AIAT](https://aiat.or.th/), [NECTEC](https://www.nectec.or.th/en/) and [ThaiSC](https://thaisc.io/) for this challenging task and opportunity to be a part of developing Thai large language model.

## Citation Information

```
@misc{pipatanakul2023typhoon,
title={Typhoon: Thai Large Language Models},
author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
year={2023},
eprint={2312.13951},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```