huggingartists/armin-van-buuren
收藏资源简介：
该数据集包含从Genius解析的歌词数据，旨在与HuggingArtists一起生成歌词。数据集的生成大小为0.358063 MB，语言为英语。数据集结构包括一个名为text的字段，所有分割的数据字段相同。数据分割包括546个训练样本，没有验证和测试样本，但可以通过代码进行分割。
This dataset contains lyric data parsed from Genius, and is designed for lyric generation in conjunction with HuggingArtists. The dataset has a size of 0.358063 MB and is in English. The dataset structure includes a field named `text`, which is consistent across all data splits. The data splits comprise 546 training samples, with no dedicated validation or test splits provided, yet custom code can be used to create such splits.
Dataset Card for "huggingartists/armin-van-buuren"
Dataset Description
- Size of the generated dataset: 0.358063 MB
Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Supported Tasks and Leaderboards
Languages
en
How to use
How to load this dataset directly with the datasets library:
python from datasets import load_dataset
dataset = load_dataset("huggingartists/armin-van-buuren")
Dataset Structure
Data Fields
The data fields are the same among all splits.
text: astringfeature.
Data Splits
| train | validation | test |
|---|---|---|
| 546 | - | - |
Train can be easily divided into train & validation & test with few lines of code:
python from datasets import load_dataset, Dataset, DatasetDict import numpy as np
datasets = load_dataset("huggingartists/armin-van-buuren")
train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03
train, validation, test = np.split(datasets[train][text], [int(len(datasets[train][text])train_percentage), int(len(datasets[train][text])(train_percentage + validation_percentage))])
datasets = DatasetDict( { train: Dataset.from_dict({text: list(train)}), validation: Dataset.from_dict({text: list(validation)}), test: Dataset.from_dict({text: list(test)}) } )
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 }
