Dataset Preview

Split (3)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 missing columns ({'cross', 'target_np0'})

This happened while the csv dataset builder was generating data using

hf://datasets/Jangyeong/Koglish_GLUE/COLA/en_train.csv (at revision 9fca9b877c71b369b70dd9bcbc7eef1e0bbc84f7)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              sentence: string
              source_np0: string
              label: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 605
              to
              {'sentence': Value(dtype='string', id=None), 'cross': Value(dtype='string', id=None), 'source_np0': Value(dtype='string', id=None), 'target_np0': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1534, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1155, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 missing columns ({'cross', 'target_np0'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Jangyeong/Koglish_GLUE/COLA/en_train.csv (at revision 9fca9b877c71b369b70dd9bcbc7eef1e0bbc84f7)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

sentence string	cross string	source_np0 string	target_np0 string	label int64
That picture of her pleases Jenny .	그녀의 그 사진 pleases Jenny .	That picture of her	그녀의 그 사진	1
The harder it rains , how much faster a flow do you see in the river ?	The harder it rains , how much faster a flow do you see in 강 ?	the river	강	1
Mary is often running the marathon .	Mary is often running 마라톤 .	the marathon	마라톤	1
Teresa bottle fed the baby soy milk .	테레사 병 fed the baby soy milk .	Teresa bottle	테레사 병	1
To delay the march and to go ahead with it have been argued by different people at different times .	To delay 행진 and to go ahead with it have been argued by different people at different times .	the march	행진	1
Copper rods bend easily .	구리 막대 bend easily .	Copper rods	구리 막대	1
Only Glasgow would he travel by train .	글래스고만 would he travel by train .	Only Glasgow	글래스고만	0
The monkey seems despondent .	원숭이 seems despondent .	The monkey	원숭이	1
There is a bench to sit on .	There is 앉을 수 있는 벤치 .	a bench to sit on	앉을 수 있는 벤치	1
Tuesday was slept on by George Washington .	Tuesday was slept on by 조지 워싱턴 .	George Washington	조지 워싱턴	0
John put any carrot from his garden in the salad .	John put 어떤 당근 from his garden in the salad .	any carrot	어떤 당근	1
The spoon ate the ice cream .	숟가락 ate the ice cream .	The spoon	숟가락	0
Amanda carried the package to New York .	Amanda carried 패키지 to New York .	the package	패키지	1
Avoid double negatives .	Avoid 이중 부정 .	double negatives	이중 부정	1
We linguists love to argue	우리는 언어학자 love to argue	We linguists	우리는 언어학자	1
Benjamin said he would give the cloak to Lee and give the cloak he did to Lee .	Benjamin said he would give 망토 to Lee and give the cloak he did to Lee .	the cloak	망토	0
Mary would solve the problem .	Mary would solve 문제 .	the problem	문제	1
I leave for Paris next week .	I leave for Paris 다음 주 .	next week	다음 주	1
Doug removed the scratches out of the drawer .	Doug removed 흠집 out of the drawer .	the scratches	흠집	0
On this table , they put a lamp , and on that table , a radio .	On 이 테이블 , they put a lamp , and on that table , a radio .	this table	이 테이블	1
Mary has more friends than two .	Mary has 둘보다 더 많은 친구 .	more friends than two	둘보다 더 많은 친구	1
He kept company some girls who had been injured in the wreck .	He kept 난파선에서 다친 소녀들과 함께 .	company some girls who had been injured in the wreck	난파선에서 다친 소녀들과 함께	0
John hit the ball with a bat .	John hit 공 with a bat .	the ball	공	1
Mary was solving the problem .	Mary was solving 문제 .	the problem	문제	1
I asked which city which king invaded .	I asked 어느 도시 which king invaded .	which city	어느 도시	1
The book that I said that I ' d never read .	그 책 that I said that I ' d never read .	The book	그 책	1
Is Bill eating his tuna ?	Is Bill eating 그의 참치 ?	his tuna	그의 참치	1
In English , the main verb agrees with the head element of the subject .	In English , 본동사 agrees with the head element of the subject .	the main verb	본동사	1
John taught English Syntax to new students .	John taught 영어 구문 to new students .	English Syntax	영어 구문	1
A wonderful opportunity presented itself yesterday .	멋진 기회 presented itself yesterday .	A wonderful opportunity	멋진 기회	1
Bill ate the peaches , but Harry the grapes .	Bill ate the peaches , but 포도 해리 .	Harry the grapes	포도 해리	0
John has two sisters , who became lawyers .	John has 변호사가 된 두 자매 .	two sisters , who became lawyers	변호사가 된 두 자매	1
Mary dates exactly two of the men who know a producer I like .	Mary dates 내가 좋아하는 프로듀서를 아는 정확히 두 남자 .	exactly two of the men who know a producer I like	내가 좋아하는 프로듀서를 아는 정확히 두 남자	1
I am removing the shovel from the shed .	I am removing 삽 from the shed .	the shovel	삽	1
John suddenly put off the customers .	John suddenly put off 고객 .	the customers	고객	1
He has books by several Greek authors .	He has 여러 그리스 작가의 책 .	books by several Greek authors	여러 그리스 작가의 책	1
We consider the men all totally crazy .	We consider 남자들 all totally crazy .	the men	남자들	1
Mary talked to any actual student .	Mary talked to 실제 학생 .	any actual student	실제 학생	0
I broke twigs off those branches .	I broke twigs off 그 가지들 .	those branches	그 가지들	1
No doubt that he was forced to leave his family against his will .	No doubt that he was forced to leave 그의 가족 against his will .	his family	그의 가족	1
The birds give the worm a tug .	새들 give the worm a tug .	The birds	새들	1
Waldo didn ' t report the possibility that anyone had left .	Waldo didn ' t report 누군가가 떠났을 가능성 .	the possibility that anyone had left	누군가가 떠났을 가능성	0
The offer made Smith admire the administrators .	제안 made Smith admire the administrators .	The offer	제안	1
Where did John put the books ?	Where did John put 책 ?	the books	책	1
Gilgamesh is not reading the cuneiform tablets .	Gilgamesh is not reading 설형 문자 정제 .	the cuneiform tablets	설형 문자 정제	1
Calvin did not do a back flip .	Calvin did not do 백플립 .	a back flip	백플립	1
As it rains harder , how much faster a flow that appears in the river ?	As it rains harder , how much faster 강에 나타나는 흐름 ?	a flow that appears in the river	강에 나타나는 흐름	0
That the Dalai Lama claims Tibet independence disturbs the Chinese government .	That 달라이 라마 claims Tibet independence disturbs the Chinese government .	the Dalai Lama	달라이 라마	1
While Holly didn ' t discuss a report about every boy , she did every girl .	While Holly didn ' t discuss a report about every boy , she did 모든 소녀들 .	every girl	모든 소녀들	0
He was hoping for more than we offered .	He was hoping for 우리가 제안한 것보다 더 .	more than we offered	우리가 제안한 것보다 더	1
They skated along the canals .	They skated along 운하 .	the canals	운하	1
Tony bent at the rod .	Tony bent at 막대 .	the rod	막대	0
Tony broke the window with a hammer .	Tony broke 창 with a hammer .	the window	창	1
Most columnists claim that a senior White House official has been briefing them , but none will reveal which one .	대부분의 칼럼니스트 claim that a senior White House official has been briefing them , but none will reveal which one .	Most columnists	대부분의 칼럼니스트	1
Most columnists claim that a senior White House official has been briefing them , and the newspaper today reveals which one .	대부분의 칼럼니스트 claim that a senior White House official has been briefing them , and the newspaper today reveals which one .	Most columnists	대부분의 칼럼니스트	1
The son took care of his parents .	아들 took care of his parents .	The son	아들	1
Anna ' s tickling him drove Frank crazy .	안나가 그를 간지럽히고 있다 drove Frank crazy .	Anna ' s tickling him	안나가 그를 간지럽히고 있다	1
The book to whom did you give .	그 책 to whom did you give .	The book	그 책	0
The fence hit .	울타리 hit .	The fence	울타리	0
The cat was having eaten .	고양이 was having eaten .	The cat	고양이	0
Susan whispered Rachel the news .	Susan whispered Rachel 뉴스 .	the news	뉴스	0
Bill bought a red house , and Max bought one too .	Bill bought 빨간 집 , and Max bought one too .	a red house	빨간 집	1
The doctor arrived a new actor .	의사 arrived a new actor .	The doctor	의사	0
He ' ll bring me one if he sees a hot dog .	He ' ll bring me one if he sees 핫도그 .	a hot dog	핫도그	0
Students who fail the final exam will be executed or students who do not do the reading will be executed .	기말고사 불합격자 will be executed or students who do not do the reading will be executed .	Students who fail the final exam	기말고사 불합격자	1
If Mary listens to the Grateful Dead , she gets depressed .	If Mary listens to 고마운 죽음 , she gets depressed .	the Grateful Dead	고마운 죽음	1
Jennifer craned his neck .	Jennifer craned 그의 목 .	his neck	그의 목	0
That pea soup tasted delicious to me .	그 완두콩 수프 tasted delicious to me .	That pea soup	그 완두콩 수프	1
Two ships appeared , arrived , remained , emerged .	두 척의 배 appeared , arrived , remained , emerged .	Two ships	두 척의 배	1
Not until I was perhaps twenty - five was when I read them and enjoyed them .	Not until I was perhaps 이십오 was when I read them and enjoyed them .	twenty - five	이십오	0
Catherine did her homework .	Catherine did 그녀의 숙제 .	her homework	그녀의 숙제	1
Some student from Australia speaks Chinese .	호주에서 온 어떤 학생 speaks Chinese .	Some student from Australia	호주에서 온 어떤 학생	1
He put in the washing machine .	He put in 세탁기 .	the washing machine	세탁기	1
We have someone in the living room .	We have 거실에 누군가 .	someone in the living room	거실에 누군가	1
I lifted onto the table .	I lifted onto 탁자 .	the table	탁자	0
Nora brought Pamela the book .	Nora brought Pamela 그 책 .	the book	그 책	1
He announced he had eaten the asparagus , but we didn ' t know what .	He announced he had eaten 아스파라거스 , but we didn ' t know what .	the asparagus	아스파라거스	0
Some sentences can go on and on and on .	일부 문장 can go on and on and on .	Some sentences	일부 문장	1
John put his gold under the bathtub .	John put 그의 금 under the bathtub .	his gold	그의 금	1
The teacher hated the pupils angry .	선생님 hated the pupils angry .	The teacher	선생님	0
Paul has interviewed every student who was at the scene of the crime and Kate has interviewed them too .	Paul has interviewed 범죄 현장에 있던 모든 학생 and Kate has interviewed them too .	every student who was at the scene of the crime	범죄 현장에 있던 모든 학생	1
I said that never in my life had I seen a place like Bangor .	I said that never in 내 인생 had I seen a place like Bangor .	my life	내 인생	1
Carrie touched that cat .	Carrie touched 그 고양이 .	that cat	그 고양이	1
He ' s a so reliable man .	He ' s 너무 믿음직한 남자 .	a so reliable man	너무 믿음직한 남자	0
The tube was escaped by gas .	튜브 was escaped by gas .	The tube	튜브	0
They believe that Charles Darwin ' s theory of evolution is just a scientific theory .	They believe that 찰스 다윈의 진화론 is just a scientific theory .	Charles Darwin ' s theory of evolution	찰스 다윈의 진화론	1
That dog is so ferocious , it even tried to bite itself .	그 개 is so ferocious , it even tried to bite itself .	That dog	그 개	1
There aren ' t many linguistics students here .	There aren ' t 많은 언어학 학생 here .	t many linguistics students	t 많은 언어학 학생	1
The fence hit with a stick .	울타리 hit with a stick .	The fence	울타리	0
Lee solved the puzzle how Kim solved it .	Lee solved 퍼즐 how Kim solved it .	the puzzle	퍼즐	0
Lora buttered at the toast .	Lora buttered at 토스트 .	the toast	토스트	0
Which pictures of himself did Chris like ?	어떤 사진 of himself did Chris like ?	Which pictures	어떤 사진	1
Ida hunted for deer in the woods .	Ida hunted for deer in 숲 .	the woods	숲	1
The soundly and furry cat slept .	건전하고 털이 많은 고양이 slept .	The soundly and furry cat	건전하고 털이 많은 고양이	0
The worker will have a job .	노동자 will have a job .	The worker	노동자	1
The piano was bought for Jane by Frank .	피아노 was bought for Jane by Frank .	The piano	피아노	1
The men were struck by the idea as nonsense .	남자들 were struck by the idea as nonsense .	The men	남자들	0
Cotton clothes dry easily .	면 옷 dry easily .	Cotton clothes	면 옷	1
We forgot our invitations .	We forgot 우리의 초대장 .	our invitations	우리의 초대장	1
Because never had Sir Thomas been so offended , even Mr Yates left .	Because never had Sir Thomas been so offended , 심지어 미스터 예이츠 left .	even Mr Yates	심지어 미스터 예이츠	0

End of preview.

No dataset card yet

Downloads last month: 12