premise
stringlengths 11
296
| hypothesis
stringlengths 11
296
| label
class label 0
classes | idx
int32 0
1.1k
|
|---|---|---|---|
Musk decided to offer up his personal Tesla roadster.
|
Musk decided to offer up his personal yacht.
| -1no label
| 300 |
Musk decided to offer up his personal yacht.
|
Musk decided to offer up his personal Tesla roadster.
| -1no label
| 301 |
TIL David Attenborough and Queen Elizabeth II are roughly the same age.
|
TIL David Attenborough and the Queen of England are roughly the same age.
| -1no label
| 302 |
TIL David Attenborough and the Queen of England are roughly the same age.
|
TIL David Attenborough and Queen Elizabeth II are roughly the same age.
| -1no label
| 303 |
TIL David Attenborough and Queen Elizabeth II are roughly the same age.
|
TIL David Attenborough and the Queen of Denmark are roughly the same age.
| -1no label
| 304 |
TIL David Attenborough and the Queen of Denmark are roughly the same age.
|
TIL David Attenborough and Queen Elizabeth II are roughly the same age.
| -1no label
| 305 |
The Sydney area has been inhabited by indigenous Australians for at least 30,000 years.
|
The Sydney area has been inhabited by Aboriginal people for at least 30,000 years.
| -1no label
| 306 |
The Sydney area has been inhabited by Aboriginal people for at least 30,000 years.
|
The Sydney area has been inhabited by indigenous Australians for at least 30,000 years.
| -1no label
| 307 |
The Sydney area has been inhabited by indigenous Australians for at least 30,000 years.
|
The Sydney area has been inhabited by Europeans for at least 30,000 years.
| -1no label
| 308 |
The Sydney area has been inhabited by Europeans for at least 30,000 years.
|
The Sydney area has been inhabited by indigenous Australians for at least 30,000 years.
| -1no label
| 309 |
Deep neural networks have achieved impressive performance in supervised classification and structured prediction tasks.
|
Deep learning has achieved impressive performance in supervised classification and structured prediction tasks.
| -1no label
| 310 |
Deep learning has achieved impressive performance in supervised classification and structured prediction tasks.
|
Deep neural networks have achieved impressive performance in supervised classification and structured prediction tasks.
| -1no label
| 311 |
Deep neural networks have achieved impressive performance in supervised classification and structured prediction tasks.
|
Decision trees have achieved impressive performance in supervised classification and structured prediction tasks.
| -1no label
| 312 |
Decision trees have achieved impressive performance in supervised classification and structured prediction tasks.
|
Deep neural networks have achieved impressive performance in supervised classification and structured prediction tasks.
| -1no label
| 313 |
Dave jumped into the lake.
|
Dave was wet.
| -1no label
| 314 |
Dave was wet.
|
Dave jumped into the lake.
| -1no label
| 315 |
Dave jumped into the lake.
|
Dave was hungry.
| -1no label
| 316 |
Dave was hungry.
|
Dave jumped into the lake.
| -1no label
| 317 |
Even if the Senate is able to pass a bill, it’s far from certain that the House will move ahead with it.
|
The House might kill the bill anyway.
| -1no label
| 318 |
The House might kill the bill anyway.
|
Even if the Senate is able to pass a bill, it’s far from certain that the House will move ahead with it.
| -1no label
| 319 |
Even if the Senate is able to pass a bill, it’s far from certain that the House will move ahead with it.
|
The House will pass the bill.
| -1no label
| 320 |
The House will pass the bill.
|
Even if the Senate is able to pass a bill, it’s far from certain that the House will move ahead with it.
| -1no label
| 321 |
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
|
Notorious B.I.G. passed away.
| -1no label
| 322 |
Notorious B.I.G. passed away.
|
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
| -1no label
| 323 |
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
|
Notorious B.I.G. hosted his funeral.
| -1no label
| 324 |
Notorious B.I.G. hosted his funeral.
|
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
| -1no label
| 325 |
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
|
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, there was a somber atmosphere part of the time.
| -1no label
| 326 |
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, there was a somber atmosphere part of the time.
|
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
| -1no label
| 327 |
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
|
Notorious B.I.G.'s funeral procession was attended by the general public.
| -1no label
| 328 |
Notorious B.I.G.'s funeral procession was attended by the general public.
|
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
| -1no label
| 329 |
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
|
Only members of the public attended B.I.G.'s funeral procession was restricted to an exclusive club of associates.
| -1no label
| 330 |
Only members of the public attended B.I.G.'s funeral procession was restricted to an exclusive club of associates.
|
During Notorious B.I.G.'s funeral procession through the streets of Brooklyn, someone interrupted the somber atmosphere by playing "Hyponotize" at full volume, which prompted the public to dance and sing along.
| -1no label
| 331 |
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
|
Poor Irish people could not get food because it was too expensive.
| -1no label
| 332 |
Poor Irish people could not get food because it was too expensive.
|
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
| -1no label
| 333 |
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
|
The poor in Ireland starved.
| -1no label
| 334 |
The poor in Ireland starved.
|
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
| -1no label
| 335 |
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
|
The poor in Ireland had plentiful food.
| -1no label
| 336 |
The poor in Ireland had plentiful food.
|
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
| -1no label
| 337 |
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
|
The problem in Ireland was the price of food.
| -1no label
| 338 |
The problem in Ireland was the price of food.
|
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
| -1no label
| 339 |
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
|
The problem in Ireland was lack of food.
| -1no label
| 340 |
The problem in Ireland was lack of food.
|
The problem in Ireland was not lack of food, which was plentiful, but the price of it, which was beyond the reach of the poor.
| -1no label
| 341 |
This dataset is two orders of magnitude larger than all other existing resources.
|
Every other existing resource is smaller than this dataset.
| -1no label
| 342 |
Every other existing resource is smaller than this dataset.
|
This dataset is two orders of magnitude larger than all other existing resources.
| -1no label
| 343 |
This dataset is two orders of magnitude larger than all other existing resources.
|
This dataset is very big.
| -1no label
| 344 |
This dataset is very big.
|
This dataset is two orders of magnitude larger than all other existing resources.
| -1no label
| 345 |
I have failed my resolutions every year since 1997, and it's now 2008.
|
I failed my resolutions in 2004.
| -1no label
| 346 |
I failed my resolutions in 2004.
|
I have failed my resolutions every year since 1997, and it's now 2008.
| -1no label
| 347 |
I have failed my resolutions every year since 1997, and it's now 2008.
|
I failed my resolutions in 1995.
| -1no label
| 348 |
I failed my resolutions in 1995.
|
I have failed my resolutions every year since 1997, and it's now 2008.
| -1no label
| 349 |
I have failed my resolutions every year since 1997, and it's now 2008.
|
I did not fail my resolutions in 2004.
| -1no label
| 350 |
I did not fail my resolutions in 2004.
|
I have failed my resolutions every year since 1997, and it's now 2008.
| -1no label
| 351 |
I have failed my resolutions every year since 1997, and it's now 2008.
|
I did not fail my resolutions in 1995.
| -1no label
| 352 |
I did not fail my resolutions in 1995.
|
I have failed my resolutions every year since 1997, and it's now 2008.
| -1no label
| 353 |
Both doctor and patient bear some responsibility for successful care.
|
The doctor bears some responsibility for successful care.
| -1no label
| 354 |
The doctor bears some responsibility for successful care.
|
Both doctor and patient bear some responsibility for successful care.
| -1no label
| 355 |
Both doctor and patient bear some responsibility for successful care.
|
The doctor does not bear responsibility for successful care.
| -1no label
| 356 |
The doctor does not bear responsibility for successful care.
|
Both doctor and patient bear some responsibility for successful care.
| -1no label
| 357 |
Both doctor and patient bear some responsibility for successful care.
|
The patient bears some responsibility for successful care.
| -1no label
| 358 |
The patient bears some responsibility for successful care.
|
Both doctor and patient bear some responsibility for successful care.
| -1no label
| 359 |
Both doctor and patient bear some responsibility for successful care.
|
The patient does not bear responsibility for successful care.
| -1no label
| 360 |
The patient does not bear responsibility for successful care.
|
Both doctor and patient bear some responsibility for successful care.
| -1no label
| 361 |
Both doctor and patient bear some responsibility for successful care.
|
The attorney bears some responsibility for successful care.
| -1no label
| 362 |
The attorney bears some responsibility for successful care.
|
Both doctor and patient bear some responsibility for successful care.
| -1no label
| 363 |
Both doctor and patient bear some responsibility for successful care.
|
The attorney does not bear responsibility for successful care.
| -1no label
| 364 |
The attorney does not bear responsibility for successful care.
|
Both doctor and patient bear some responsibility for successful care.
| -1no label
| 365 |
Either he has a blind trust or he has a conflict of interest.
|
He has a conflict of interest.
| -1no label
| 366 |
He has a conflict of interest.
|
Either he has a blind trust or he has a conflict of interest.
| -1no label
| 367 |
Either he has a blind trust or he has a conflict of interest.
|
He does not have a conflict of interest.
| -1no label
| 368 |
He does not have a conflict of interest.
|
Either he has a blind trust or he has a conflict of interest.
| -1no label
| 369 |
Either he has a blind trust or he has a conflict of interest.
|
He has a blind trust.
| -1no label
| 370 |
He has a blind trust.
|
Either he has a blind trust or he has a conflict of interest.
| -1no label
| 371 |
Either he has a blind trust or he has a conflict of interest.
|
He does not have a blind trust.
| -1no label
| 372 |
He does not have a blind trust.
|
Either he has a blind trust or he has a conflict of interest.
| -1no label
| 373 |
Everyone has a set of principles to live by.
|
Someone has a set of principles to live by.
| -1no label
| 374 |
Someone has a set of principles to live by.
|
Everyone has a set of principles to live by.
| -1no label
| 375 |
Everyone has a set of principles to live by.
|
No one has a set of principles to live by.
| -1no label
| 376 |
No one has a set of principles to live by.
|
Everyone has a set of principles to live by.
| -1no label
| 377 |
Everyone has a set of principles to live by.
|
Susan doesn't have a set of principles to live by.
| -1no label
| 378 |
Susan doesn't have a set of principles to live by.
|
Everyone has a set of principles to live by.
| -1no label
| 379 |
Susan knows how turtles reproduce.
|
Someone knows how turtles reproduce.
| -1no label
| 380 |
Someone knows how turtles reproduce.
|
Susan knows how turtles reproduce.
| -1no label
| 381 |
Susan knows how turtles reproduce.
|
No one knows how turtles reproduce.
| -1no label
| 382 |
No one knows how turtles reproduce.
|
Susan knows how turtles reproduce.
| -1no label
| 383 |
Susan knows how turtles reproduce.
|
Cedric doesn't know how turtles reproduce.
| -1no label
| 384 |
Cedric doesn't know how turtles reproduce.
|
Susan knows how turtles reproduce.
| -1no label
| 385 |
Either there is no bathroom in this house, or it is in a funny place.
|
If there is a bathroom in this house, it is in a funny place.
| -1no label
| 386 |
If there is a bathroom in this house, it is in a funny place.
|
Either there is no bathroom in this house, or it is in a funny place.
| -1no label
| 387 |
Either there is no bathroom in this house, or it is in a funny place.
|
The bathroom in this house is in a funny place.
| -1no label
| 388 |
The bathroom in this house is in a funny place.
|
Either there is no bathroom in this house, or it is in a funny place.
| -1no label
| 389 |
Joan believes that all speech is political speech.
|
All speech is political speech.
| -1no label
| 390 |
All speech is political speech.
|
Joan believes that all speech is political speech.
| -1no label
| 391 |
Joan doubts that all speech is political speech.
|
All speech is political speech.
| -1no label
| 392 |
All speech is political speech.
|
Joan doubts that all speech is political speech.
| -1no label
| 393 |
Joan knows that all speech is political speech.
|
All speech is political speech.
| -1no label
| 394 |
All speech is political speech.
|
Joan knows that all speech is political speech.
| -1no label
| 395 |
Joan knows that all speech is political speech.
|
No speech is political speech.
| -1no label
| 396 |
No speech is political speech.
|
Joan knows that all speech is political speech.
| -1no label
| 397 |
Grisham hoped to win the popular vote.
|
Grisham won the popular vote.
| -1no label
| 398 |
Grisham won the popular vote.
|
Grisham hoped to win the popular vote.
| -1no label
| 399 |
Subsets and Splits
MNLI Train: Tennis Hypotheses
Retrieves 100 samples from the mnli_train dataset where the hypothesis contains the word 'tennis', providing a basic filter of the data.
MNLI Validation Label Counts
Counts the number of entries in each label category, providing basic insights into the distribution of labels in the dataset.