metadata
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
- text-retrieval
- question-answering
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 12762
num_examples: 18
download_size: 7057
dataset_size: 12762
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
Dataset Card for Hactal_Repo
This dataset has been created with distilabel.
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:
distilabel pipeline run --config "https://huggingface.co/datasets/samuelsuen/Hactal_Repo/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config "https://huggingface.co/datasets/samuelsuen/Hactal_Repo/raw/main/pipeline.yaml"
Dataset structure
The examples have the following structure per configuration:
Configuration: default
{
"context": "Hong Kong Air Cargo Terminals Limited (Chinese: \u9999\u6e2f\u7a7a\u904b\u8ca8\u7ad9\u6709\u9650\u516c\u53f8), commonly known as Hactl, is one of the leading air cargo terminal operators in the world. Located at Hong Kong International Airport, it handled a total throughput of 1.65 million tonnes of cargo in 2018.",
"question": null,
"response": "The document does not mention anything about Hong Kong Air Cargo Terminals Limited (Hactl) handling a specific amount of cargo in 2018. It only mentions that it is one of the leading air cargo terminal operators in the world, but provides no further details on its throughput or any other metrics."
}
This subset can be loaded as:
from datasets import load_dataset
ds = load_dataset("samuelsuen/Hactal_Repo", "default")
Or simply as it follows, since there's only one configuration and is named default:
from datasets import load_dataset
ds = load_dataset("samuelsuen/Hactal_Repo")