kimnt93/vietdish

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 16035063 num_examples: 6652 download_size: 6802487 dataset_size: 16035063 configs: - config_name: default data_files: - split: train path: data/train-* --- **Dataset Name:** vietdish **Description:** The vietdish dataset is generated by collecting instructions and input from Cooky (https://www.cooky.vn/) and generating output using GPT-3.5. It contains Vietnamese cooking instructions and corresponding generated responses. **Source:** [vietdish on Hugging Face Datasets](https://huggingface.co/datasets/kimnt93/vietdish) **Instruction and Input Source:** [Cooky](https://www.cooky.vn/) **Method:** Instructions and input were collected from Cooky, and GPT-3.5 was used to generate responses based on the collected instructions and input. **License:** Please refer to the license information provided by the original source. --- **Python Script to Download the Dataset:** ```python from datasets import load_dataset # Load the vietdish dataset dataset = load_dataset("kimnt93/vietdish") # Print some basic information about the dataset print("Dataset Name:", dataset.name) print("Number of Samples:", len(dataset)) # Example usage: accessing a sample from the dataset sample = dataset[0] print("Example Sample:", sample) ``` This Python script uses the `datasets` library from Hugging Face to download and access the vietdish dataset. You can run this script in your Python environment to download the dataset and print some basic information about it. Ensure you have the `datasets` library installed (`pip install datasets`) before running the script. Let me know if you need further assistance!

原始信息汇总

数据集概述

数据集名称

vietdish

数据集描述

该数据集通过收集Cooky网站的烹饪指令和输入，并使用GPT-3.5生成相应的输出。数据集包含越南烹饪指令及其对应的生成响应。

数据集特征

• instruction：字符串类型
• input：字符串类型
• output：字符串类型

数据集拆分

• train：
  • 样本数量：6652
  • 数据大小：16035063字节

数据集大小

• 下载大小：6802487字节
• 数据集大小：16035063字节

配置

• config_name: default
• data_files:
  • split: train
  • path: data/train-*