Datasets:
metadata
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- ko
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: laion2B-multi-korean-subset
size_categories:
- 10M<n<100M
task_categories:
- feature-extraction
laion2B-multi-korean-subset
Dataset Description
- Homepage: laion-5b
- Huggingface: laion/laion2B-multi
About dataset
Data organized by extracting only Korean data from laion/laion2B-multi
Lisence
CC-BY-4.0
Data Structure
Data Instance
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/laion2B-multi-korean-subset")
>>> dataset
DatasetDict({
train: Dataset({
features: ['SAMPLE_ID', 'URL', 'TEXT', 'HEIGHT', 'WIDTH', 'LICENSE', 'LANGUAGE', 'NSFW', 'similarity'],
num_rows: 11376263
})
})
>>> dataset["train"].features
{'SAMPLE_ID': Value(dtype='int64', id=None),
'URL': Value(dtype='string', id=None),
'TEXT': Value(dtype='string', id=None),
'HEIGHT': Value(dtype='int32', id=None),
'WIDTH': Value(dtype='int32', id=None),
'LICENSE': Value(dtype='string', id=None),
'LANGUAGE': Value(dtype='string', id=None),
'NSFW': Value(dtype='string', id=None),
'similarity': Value(dtype='float32', id=None)}
Data Size
download: 1.56 GiB
generated: 2.37 GiB
total: 3.93 GiB
Data Field
- 'SAMPLE_ID':
int
- 'URL':
string
- 'TEXT':
string
- 'HEIGHT':
int
- 'WIDTH':
int
- 'LICENSE':
string
- 'LANGUAGE':
string
- 'NSFW':
string
- 'similarity':
float
Data Splits
train | |
---|---|
# of texts | 11376263 |
Note
Height, Width
μ΄λ―Έμ§μ κ°λ‘κ° HEIGHT
λ‘, μΈλ‘κ° WIDTH
λ‘ λμ΄μλ κ² κ°μ΅λλ€.
>>> dataset["train"][98]
{'SAMPLE_ID': 2937471001780,
'URL': 'https://image.ajunews.com/content/image/2019/04/12/20190412175643597949.png',
'TEXT': 'μΈμ²μκ΅μ‘μ², μΈμ² μꡰꡬλ°μ νμν μμμ§κ³Όμ κ°λ΄ν κ°μ΅',
'HEIGHT': 640,
'WIDTH': 321,
'LICENSE': '?',
'LANGUAGE': 'ko',
'NSFW': 'UNLIKELY',
'similarity': 0.33347243070602417}
Code used to generate
import csv
import re
from datasets import load_dataset
from tqdm import tqdm
pattern = re.compile(r"[κ°-ν£]")
def quote(s: str) -> str:
s = s.replace('"""', "")
return s
def filter_func(example) -> bool:
lang = example.get("LANGUAGE")
text = example.get("TEXT")
if not isinstance(lang, str) or not isinstance(text, str):
return False
return lang == "ko" or pattern.search(text) is not None
file = open("./laion2B-mulit_korean_subset.csv", "w", encoding="utf-8", newline="")
ds = load_dataset("laion/laion2B-multi", split="train", streaming=True)
dsf = ds.filter(filter_func)
header = [
"SAMPLE_ID",
"URL",
"TEXT",
"HEIGHT",
"WIDTH",
"LICENSE",
"LANGUAGE",
"NSFW",
"similarity",
]
writer = csv.DictWriter(file, fieldnames=header)
writer.writeheader()
try:
for data in tqdm(dsf):
data["TEXT"] = quote(data.get("TEXT", ""))
if data["TEXT"]:
writer.writerow(data)
finally:
file.close()
print("Done!")
μ΄νμ HEIGHT
λ WIDTH
κ° NoneμΈ λ°μ΄ν°λ₯Ό μ κ±°νκ³ μ
λ‘λνμμ΅λλ€.
img2dataset
img2datasetμ μ¬μ©νμ¬ URLλ‘λ μ΄λ―Έμ§λ€μ λ°μ΄ν°μ ννλ‘ λ§λ€ μ μμ΅λλ€.