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import datasets
import hashlib
import functools
import glob
import os
import json
import openpyxl
_DESCRIPTION = """
"""
_CITATION = """
"""
_VERSION = datasets.Version('1.0.0', "")
_DATASET_ROOT = { # folder
'common_squad': 'AIHub/common',
'paper_summary': 'AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ',
'paper_patent_section': 'AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ',
'paper_patent_total': 'AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ',
'document_summary_law': 'AIHub/๋ฌธ์„œ์š”์•ฝ ํ…์ŠคํŠธ',
'document_summary_editorial': 'AIHub/๋ฌธ์„œ์š”์•ฝ ํ…์ŠคํŠธ',
'emotional_talk': 'AIHub/๊ฐ์„ฑ๋Œ€ํ™”',
}
_PARAGRAPHS_SEQUENCE = datasets.Sequence({ # common_squad
'qas': datasets.Sequence({
'question': datasets.Value("string"),
'answers': datasets.Sequence({
'answer_start': datasets.Value("int32"),
'text': datasets.Value("string"),
}),
'id': datasets.Value("string"),
}),
'context': datasets.Value("string"),
})
_COMMON_SQUAD_FEATURE = datasets.Features({ # common_squad
'id': datasets.Value("int32"),
'paragraphs': _PARAGRAPHS_SEQUENCE,
'title': datasets.Value("string"),
})
_SUMMARY_FEATRUE = datasets.Sequence({ # paper_summary
'orginal_text': datasets.Value("string"),
'summary_text': datasets.Value("string"),
})
_PAPER_SUMMARY_FEATURE = datasets.Features({ # paper_summary
'id': datasets.Value("int32"),
'doc_type': datasets.Value("string"),
'doc_id': datasets.Value("string"),
'title': datasets.Value("string"),
'date': datasets.Value("string"),
'reg_no': datasets.Value("string"),
'ipc': datasets.Value("string"),
'issued_by': datasets.Value("string"),
'author': datasets.Value("string"),
'summary_entire': _SUMMARY_FEATRUE,
'summary_section': _SUMMARY_FEATRUE,
})
_PAPER_PATENT_SECTION_FEATURE = datasets.Features({ # paper_patent_section
'id': datasets.Value("int32"),
'doc_type': datasets.Value("string"),
'doc_id': datasets.Value("string"),
'title': datasets.Value("string"),
'date': datasets.Value("string"),
'reg_no': datasets.Value("string"),
'ipc': datasets.Value("string"),
'author': datasets.Value("string"),
'summary_section': _SUMMARY_FEATRUE,
})
_PAPER_PATENT_TOTAL_FEATURE = datasets.Features({ # paper_patent_total
'id': datasets.Value("int32"),
'doc_type': datasets.Value("string"),
'doc_id': datasets.Value("string"),
'title': datasets.Value("string"),
'date': datasets.Value("string"),
'reg_no': datasets.Value("string"),
'ipc': datasets.Value("string"),
'author': datasets.Value("string"),
'summary_entire': _SUMMARY_FEATRUE,
'summary_section': _SUMMARY_FEATRUE,
})
# document_summary_law, document_summary_editorial, document_summary_newspaper
_TEXT_FEATURE = datasets.Sequence({
'index': datasets.Value("int32"),
'sentence': datasets.Value("string"),
'highlight_indices': datasets.Value("string"),
})
# document_summary_law, document_summary_editorial, document_summary_newspaper
_DOCUMENT_QUALITY_SCORES = datasets.Features({
'readable': datasets.Value("int32"),
'accurate': datasets.Value("int32"),
'informative': datasets.Value("int32"),
'trustworthy': datasets.Value("int32"),
})
_DOCUMENT_SUMMARY_LAW_FEATURE = datasets.Features({ # document_summary_law
'id': datasets.Value("string"),
'category': datasets.Value("string"),
'size': datasets.Value("string"),
'char_count': datasets.Value("int32"),
'publish_date': datasets.Value("string"),
'title': datasets.Value("string"),
'text': _TEXT_FEATURE,
'annotator_id': datasets.Value("int32"),
'document_quality_scores': _DOCUMENT_QUALITY_SCORES,
'extractive': datasets.Sequence(datasets.Value("int32")),
'abstractive': datasets.Sequence(datasets.Value("string")),
})
# document_summary_editorial, document_summary_newspaper
_DOCUMENT_SUMMARY_FEATURE = datasets.Features({
'id': datasets.Value("string"),
'category': datasets.Value("string"),
'media_type': datasets.Value("string"),
'media_sub_type': datasets.Value("string"),
'media_name': datasets.Value("string"),
'size': datasets.Value("string"),
'char_count': datasets.Value("string"),
'publish_date': datasets.Value("string"),
'title': datasets.Value("string"),
'text': _TEXT_FEATURE,
'annotator_id': datasets.Value("int32"),
'document_quality_scores': _DOCUMENT_QUALITY_SCORES,
'extractive': datasets.Sequence(datasets.Value("int32")),
'abstractive': datasets.Sequence(datasets.Value("string")),
})
_PERSONA_FEATURE = datasets.Features({ # emotional_talk
'persona-id': datasets.Value("string"),
'human': datasets.Sequence(
datasets.Value("string"),
),
'computer': datasets.Sequence(
datasets.Value("string"),
),
})
_EMOTION_FEATURE = datasets.Features({ # emotional_talk
'emotion-id': datasets.Value("string"),
'type': datasets.Value("string"),
'situation': datasets.Sequence(
datasets.Value("string"),
),
})
_PROFILE_FEATURE = datasets.Features({ # emotional_talk
'persona-id': datasets.Value("string"),
'persona': _PERSONA_FEATURE,
'emotion': _EMOTION_FEATURE,
})
_CONTENT_FEATURE = datasets.Features({ # emotional_talk
'HS01': datasets.Value("string"),
'SS01': datasets.Value("string"),
'HS02': datasets.Value("string"),
'SS02': datasets.Value("string"),
'HS03': datasets.Value("string"),
'SS03': datasets.Value("string"),
})
_TALK_FEATURE = datasets.Features({ # emotional_talk
'id': datasets.Features({
'profile-id': datasets.Value("string"),
'talk-id': datasets.Value("string"),
}),
'content': _CONTENT_FEATURE,
})
_EMOTIONAL_TALK_FEATURE = datasets.Features({ # emotional_talk
'id': datasets.Value("int32"),
'profile': _PROFILE_FEATURE,
'talk': _TALK_FEATURE,
})
def _parsing_common_squad(file_path): # common_squad
with open(file_path, mode='r') as f:
obj = json.loads(f.read())
for id, sample in enumerate(obj['data']):
_id = id
_paragraphs = sample['paragraphs']
_title = sample['title']
yield _id, {
'id': _id,
'paragraphs': _paragraphs,
'title': _title,
}
def _parsing_paper_summary(file_path): # paper_summary
with open(file_path, mode='r') as f:
obj = json.loads(f.read())
for id, sample in enumerate(obj['data']):
_id = id
_doc_type = sample['doc_type']
_doc_id = sample['doc_id']
_title = sample['title']
_date = sample['date']
_reg_no = sample['reg_no']
_ipc = sample['reg_no']
_issued_by = sample['issued_by']
_author = sample['author']
_summary_entire = sample['summary_entire']
_summary_section = sample['summary_section']
yield _id, {
'id': _id,
'doc_type': _doc_type,
'doc_id': _doc_id,
'title': _title,
'date': _date,
'reg_no': _reg_no,
'ipc': _ipc,
'issued_by': _issued_by,
'author': _author,
'summary_entire': _summary_entire,
'summary_section': _summary_section,
}
def _parsing_paper_patent_section(file_path): # paper_patent_section
with open(file_path, mode='r') as f:
obj = json.loads(f.read())
for id, sample in enumerate(obj['data']):
_id = id
_doc_type = sample['doc_type']
_doc_id = sample['doc_id']
_title = sample['title']
_date = sample['date']
_reg_no = sample['reg_no']
_ipc = sample['reg_no']
_author = sample['author']
_summary_section = sample['summary_section']
yield _id, {
'id': _id,
'doc_type': _doc_type,
'doc_id': _doc_id,
'title': _title,
'date': _date,
'reg_no': _reg_no,
'ipc': _ipc,
'author': _author,
'summary_section': _summary_section,
}
def _parsing_paper_patent_total(file_path): # paper_patent_total
with open(file_path, mode='r') as f:
obj = json.loads(f.read())
for id, sample in enumerate(obj['data']):
_id = id
_doc_type = sample['doc_type']
_doc_id = sample['doc_id']
_title = sample['title']
_date = sample['date']
_reg_no = sample['reg_no']
_ipc = sample['reg_no']
_author = sample['author']
_summary_section = sample['summary_section']
yield _id, {
'id': _id,
'doc_type': _doc_type,
'doc_id': _doc_id,
'title': _title,
'date': _date,
'reg_no': _reg_no,
'ipc': _ipc,
'author': _author,
'summary_entire': _summary_section,
'summary_section': _summary_section,
}
def _parsing_document_summary_law(file_path): # document_summary_law
with open(file_path, mode='r') as f:
obj = json.loads(f.read())
for sample in obj:
_id = sample['id']
_category = sample['category']
_size = sample['size']
_char_count = sample['char_count']
_publish_date = sample['publish_date']
_title = sample['title']
_text = sample['text']
_annotator_id = sample['annotator_id']
_document_quality_scores = sample['document_quality_scores']
_extractive = sample['extractive']
_abstractive = sample['abstractive']
yield _id, {
'id': _id,
'category': _category,
'size': _size,
'char_count': _char_count,
'publish_date': _publish_date,
'title': _title,
'text': _text,
'annotator_id': _annotator_id,
'document_quality_scores': _document_quality_scores,
'extractive': _extractive,
'abstractive': _abstractive,
}
# document_summary_editorial, document_summary_newspaper
def _parsing_document_summary(file_path):
with open(file_path, mode='r') as f:
obj = json.loads(f.read())
for sample in obj:
_id = sample['id']
_category = sample['category']
_media_type = sample['media_type']
_media_sub_type = sample['media_sub_type']
_media_name = sample['media_name']
_size = sample['size']
_char_count = str(sample['char_count'])
_publish_date = sample['publish_date']
_title = sample['title']
_text = sample['text']
_annotator_id = sample['annotator_id']
_document_quality_scores = sample['document_quality_scores']
_extractive = sample['extractive']
_abstractive = sample['abstractive']
yield _id, {
'id': _id,
'category': _category,
'media_type': _media_type,
'media_sub_type': _media_sub_type,
'media_name': _media_name,
'size': _size,
'char_count': _char_count,
'publish_date': _publish_date,
'title': _title,
'text': _text,
'annotator_id': _annotator_id,
'document_quality_scores': _document_quality_scores,
'extractive': _extractive,
'abstractive': _abstractive,
}
def _parsing_emotional_talk(file_path): # emotional talk
with open(file_path, mode='r') as f:
obj = json.loads(f.read())
for id, sample in enumerate(obj):
_id = id
_profile = sample['profile']
_talk = sample['talk']
yield _id, {
'id':_id,
'profile': _profile,
'talk': _talk,
}
def _hash_text(text):
return hashlib.md5(text.encode("utf-8")).hexdigest()
def _filter_fn_hash_id(uid, split_fn):
hash_id = _hash_text(str(uid))
val = int(hash_id, 16)
return split_fn(val)
_DEFAULT_RAW_CORPUS_SPLIT = {
'source': [datasets.Split.TRAIN],
'split': {
datasets.Split.TRAIN: lambda x: x % 1000 > 0,
datasets.Split.VALIDATION: lambda x: x % 1000 == 0,
}}
_DEFAULT_DOWNSTREAMTASK_CORPUS_SPLIT = {
'source': [datasets.Split.TRAIN],
'split': {
datasets.Split.TRAIN: lambda x: x % 10 > 1,
datasets.Split.VALIDATION: lambda x: x % 10 == 0,
datasets.Split.TEST: lambda x: x % 10 == 1,
}}
class AIHubConfig(datasets.BuilderConfig):
def __init__(self,
name,
data_root,
feature,
data_sp_path,
reading_fn,
parsing_fn,
additional_data_root=None,
homepage='https://aihub.or.kr/',
split_fn=None,
metadata=None,
**kwargs):
super(AIHubConfig, self).__init__(
name=name, # error...?
version=_VERSION,
**kwargs
)
self.data_root = data_root
self.feature = feature
self.data_sp_path = data_sp_path
self.reading_fn = reading_fn
self.parsing_fn = parsing_fn
self.additional_data_root = additional_data_root
self.homepage = homepage
self.split_fn = split_fn
self.metadata = metadata
class AIHub(datasets.GeneratorBasedBuilder):
"""DatasetBuilder for AIHub dataset."""
RELEASE_NOTES = {
'1.0.0': 'Initial release.',
}
BUILDER_CONFIGS = [
AIHubConfig(
name='common.squad.v1.0',
data_root=_DATASET_ROOT['common_squad'],
feature=_COMMON_SQUAD_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['nia_common_02_squad_์งˆ๋ฌธ, ๋‹ต๋ณ€, ์ œ์‹œ๋ฌธ ๋ง๋ญ‰์น˜/ko_wiki_v1_squad.json']},
reading_fn=_parsing_common_squad,
parsing_fn=lambda x:x,
),
AIHubConfig(
name='common.squad.v1.0.split',
data_root=_DATASET_ROOT['common_squad'],
feature=_COMMON_SQUAD_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['nia_common_02_squad_์งˆ๋ฌธ, ๋‹ต๋ณ€, ์ œ์‹œ๋ฌธ ๋ง๋ญ‰์น˜/ko_wiki_v1_squad.json']},
reading_fn=_parsing_common_squad,
parsing_fn=lambda x:x,
split_fn=_DEFAULT_RAW_CORPUS_SPLIT,
),
AIHubConfig(
name='paper.summary.v1.0.split',
data_root=_DATASET_ROOT['paper_summary'],
feature=_PAPER_SUMMARY_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['Training/training_๋…ผ๋ฌธ/*.json'],
datasets.Split.VALIDATION: ['Validation/validation_๋…ผ๋ฌธ/*.json']},
reading_fn=_parsing_paper_summary,
parsing_fn=lambda x:x,
),
AIHubConfig(
name='paper.patent.section.v1.0.split',
data_root=_DATASET_ROOT['paper_patent_section'],
feature=_PAPER_PATENT_SECTION_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['Training/training_ํŠนํ—ˆ์„น์…˜๋งŒ/*.json'],
datasets.Split.VALIDATION: ['Validation/validation_ํŠนํ—ˆ์„น์…˜๋งŒ/*.json']},
reading_fn=_parsing_paper_patent_section,
parsing_fn=lambda x:x,
),
AIHubConfig(
name='paper.patent.total.v1.0.split',
data_root=_DATASET_ROOT['paper_patent_total'],
feature=_PAPER_PATENT_TOTAL_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['Training/training_ํŠนํ—ˆ์ „์ฒด/*.json'],
datasets.Split.VALIDATION: ['Validation/validation_ํŠนํ—ˆ์ „์ฒด/*.json']},
reading_fn=_parsing_paper_patent_total,
parsing_fn=lambda x:x,
),
AIHubConfig(
name='document.summary.law.v1.0.split',
data_root=_DATASET_ROOT['document_summary_law'],
feature=_DOCUMENT_SUMMARY_LAW_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['Training/train_๋ฒ•๋ฅ _data/๋ฒ•๋ฅ ๋ฌธ์„œ/train_original.json'],
datasets.Split.VALIDATION: ['Validation/valid_๋ฒ•๋ฅ _data/๋ฒ•๋ฅ ๋ฌธ์„œ/dev_original.json']},
reading_fn=_parsing_document_summary_law,
parsing_fn=lambda x:x,
),
AIHubConfig(
name='document.summary.editorial.v1.0.split',
data_root=_DATASET_ROOT['document_summary_editorial'],
feature=_DOCUMENT_SUMMARY_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['Training/train_์‚ฌ์„ค์žก์ง€_data/train_original.json'],
datasets.Split.VALIDATION: ['Validation/valid_์‚ฌ์„ค์žก์ง€_data/dev_original.json']},
reading_fn=_parsing_document_summary,
parsing_fn=lambda x:x,
),
AIHubConfig(
name='emotional.talk.v1.0.split',
data_root=_DATASET_ROOT['emotional_talk'],
feature=_EMOTIONAL_TALK_FEATURE,
data_sp_path={datasets.Split.TRAIN: ['Training/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Training/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Training.json'],
datasets.Split.VALIDATION: ['Validation/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Validation/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Validation.json']},
reading_fn=_parsing_emotional_talk,
parsing_fn=lambda x:x,
),
]
MANUAL_DOWNLOAD_INSTRUCTIONS = """
For the NIKL, you must manually download NIKL data from https://aihub.or.kr/
and extract it under the proper location.
all the data have to located under manual_dir/AIHub.
This is dataset and path pairs. (all the paths are case-sensitive!)
============================================
COMMON_SQUAD(v1.0): manual_dir/AIHub/common/nia_common_02_squad_์งˆ๋ฌธ, ๋‹ต๋ณ€, ์ œ์‹œ๋ฌธ ๋ง๋ญ‰์น˜/ko_wiki_v1_squad.json
PAPER_SUMMARY(v1.0): manual_dir/AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ/Training/training_๋…ผ๋ฌธ/*.json
manual_dir/AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ/Validation/validation_๋…ผ๋ฌธ/*.json
PAPER_PATENT_SECTION(v1.0): manual_dir/AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ/Training/training_ํŠนํ—ˆ์„น์…˜๋งŒ/*.json
manual_dir/AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ/Validation/validation_ํŠนํ—ˆ์„น์…˜๋งŒ/*.json
PAPER_PATENT_TOTAL(v1.0): manual_dir/AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ/Training/training_ํŠนํ—ˆ์ „์ฒด/*.json
manual_dir/AIHub/๋…ผ๋ฌธ์ž๋ฃŒ ์š”์•ฝ/Validation/validation_ํŠนํ—ˆ์ „์ฒด/*.json
DOCUMENT_SUMMARY_LAW(v1.0): manual_dir/AIHub/๋ฌธ์„œ์š”์•ฝ ํ…์ŠคํŠธ/1.Training/train_๋ฒ•๋ฅ _data/๋ฒ•๋ฅ ๋ฌธ์„œ/train_original.json
manual_dir/AIHub/๋ฌธ์„œ์š”์•ฝ ํ…์ŠคํŠธ/2.Validation/valid_๋ฒ•๋ฅ _data/๋ฒ•๋ฅ ๋ฌธ์„œ/dev_original.json
DOCUMENT_SUMMARY_EDITORIAL(v1.0): manual_dir/AIHub/๋ฌธ์„œ์š”์•ฝ ํ…์ŠคํŠธ/Training/train_์‚ฌ์„ค์žก์ง€_data/train_original.json
manual_dir/AIHub/๋ฌธ์„œ์š”์•ฝ ํ…์ŠคํŠธ/Validation/valid_์‚ฌ์„ค์žก์ง€_data/dev_original.json
EMOTIONAL_TALK(v1.0): manual_dir/AIHub/๊ฐ์„ฑ๋Œ€ํ™”/Training/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Training/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Training.json
manual_dir/AIHub/๊ฐ์„ฑ๋Œ€ํ™”/Validation/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Validation/๊ฐ์„ฑ๋Œ€ํ™”๋ง๋ญ‰์น˜(์ตœ์ข…๋ฐ์ดํ„ฐ)_Validation.json
============================================
"""
def _info(self) -> datasets.DatasetInfo:
"""Returns the dataset metadata."""
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=self.config.feature,
homepage=self.config.homepage,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""Returns SplitGenerators."""
path_kv = {}
for k, v in self.config.data_sp_path.items():
path_list = []
for vv in v:
path_list.extend(glob.glob(os.path.join(
dl_manager.manual_dir, self.config.data_root, vv)))
path_kv[k] = path_list
path_list = []
for _, v in path_kv.items():
if len(v) == 0:
raise AssertionError("For the AIHub dataset, you must manually download and extract dataset under {0}/{1}.".format(
dl_manager.manual_dir,
self.config.data_root
))
if self.config.split_fn is not None:
in_files = []
for sp_s_key in self.config.split_fn['source']:
in_files.extend(path_kv[sp_s_key])
split_fn_kv = self.config.split_fn['split']
return [
datasets.SplitGenerator(name=k, gen_kwargs={'path_list': in_files, 'split_fn': v}) for k, v in split_fn_kv.items()
]
return [
datasets.SplitGenerator(name=k, gen_kwargs={'path_list': v}) for k, v in path_kv.items()
]
def _generate_examples(self, path_list, split_fn=None):
"""Yields examples."""
if split_fn is not None:
split_filter = functools.partial(_filter_fn_hash_id, split_fn=split_fn)
_hash_set = set()
for file_path in path_list:
try:
for example in iter(self.config.reading_fn(file_path)):
uid, ex = self.config.parsing_fn(example)
if split_fn is not None:
if not split_filter(str(uid)):
continue
hash_id = _hash_text(str(uid))
if hash_id not in _hash_set:
_hash_set.add(hash_id)
yield uid, ex
except Exception as e:
print(e)