metadata

annotations_creators:
  - no-annotation
language_creators:
  - crowdsourced
language:
  - ko
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
pretty_name: KcBERT Pre-Training Corpus (Korean News Comments)
size_categories:
  - 10M<n<100M
source_datasets:
  - original
task_categories:
  - fill-mask
  - text-generation
task_ids:
  - masked-language-modeling
  - language-modeling

KcBERT Pre-Training Corpus (Korean News Comments)

Dataset Description

Homepage: KcBERT Pre-Training Corpus
Repository: Beomi/KcBERT
Paper: [Needs More Information]
Leaderboard: [Needs More Information]
Point of Contact: [Needs More Information]

KcBERT

beomi/kcbert-base

Github KcBERT Repo: https://github.com/Beomi/KcBERT
KcBERT is Korean Comments BERT pretrained on this Corpus set.
(You can use it via Huggingface's Transformers library!)

This Kaggle Dataset contains CLEANED dataset preprocessed with the code below.

import re
import emoji
from soynlp.normalizer import repeat_normalize

emojis = ''.join(emoji.UNICODE_EMOJI.keys())
pattern = re.compile(f'[^ .,?!/@$%~％·∼()\x00-\x7Fㄱ-힣{emojis}]+')
url_pattern = re.compile(
    r'https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)')

def clean(x):
    x = pattern.sub(' ', x)
    x = url_pattern.sub('', x)
    x = x.strip()
    x = repeat_normalize(x, num_repeats=2)
    return x

License

CC BY-SA 4.0

Dataset Structure

Data Instance

>>> from datasets import load_dataset

>>> dataset = load_dataset("Bingsu/KcBERT_Pre-Training_Corpus")
>>> dataset
DatasetDict({
    train: Dataset({
        features: ['text'],
        num_rows: 86246285
    })
})

Data Size

download: 7.90 GiB
generated: 11.86 GiB
total: 19.76 GiB

※ You can download this dataset from kaggle, and it's 5 GiB. (12.48 GiB when uncompressed)

Data Fields

text: string

Data Splits

	train
# of texts	86246285