premise
stringlengths 11
296
| hypothesis
stringlengths 11
296
| label
class label 0
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|---|---|---|---|
Bees do not follow the same rules as airplanes.
|
Bees fly using a different mechanism from airplanes.
| -1no label
| 200 |
Bees fly using a different mechanism from airplanes.
|
Bees do not follow the same rules as airplanes.
| -1no label
| 201 |
Bees do not follow the same rules as airplanes.
|
Bees fly using the same mechanism as airplanes.
| -1no label
| 202 |
Bees fly using the same mechanism as airplanes.
|
Bees do not follow the same rules as airplanes.
| -1no label
| 203 |
Bees do not follow the same rules as airplanes.
|
Bees are more energy-efficient flyers than airplanes.
| -1no label
| 204 |
Bees are more energy-efficient flyers than airplanes.
|
Bees do not follow the same rules as airplanes.
| -1no label
| 205 |
Leslie Dixon is married to fellow screenwriter and producer Tom Ropelewski.
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Tom Ropelewski is married to fellow screenwriter and producer Leslie Dixon.
| -1no label
| 206 |
Tom Ropelewski is married to fellow screenwriter and producer Leslie Dixon.
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Leslie Dixon is married to fellow screenwriter and producer Tom Ropelewski.
| -1no label
| 207 |
This means that solving analogy questions with vector arithmetic is mathematically equivalent to seeking a word that is similar to x and y but is different from z.
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This means that seeking a word that is similar to x and y but is different from z is mathematically equivalent to solving analogy questions with vector arithmetic.
| -1no label
| 208 |
This means that seeking a word that is similar to x and y but is different from z is mathematically equivalent to solving analogy questions with vector arithmetic.
|
This means that solving analogy questions with vector arithmetic is mathematically equivalent to seeking a word that is similar to x and y but is different from z.
| -1no label
| 209 |
Bob married Alice.
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Bob and Alice got married.
| -1no label
| 210 |
Bob and Alice got married.
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Bob married Alice.
| -1no label
| 211 |
Tulsi Gabbard disagrees with Bernie Sanders on what is the best way to deal with Bashar al-Assad.
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Tulsi Gabbard and Bernie Sanders disagree on what is the best way to deal with Bashar al-Assad.
| -1no label
| 212 |
Tulsi Gabbard and Bernie Sanders disagree on what is the best way to deal with Bashar al-Assad.
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Tulsi Gabbard disagrees with Bernie Sanders on what is the best way to deal with Bashar al-Assad.
| -1no label
| 213 |
It reminds me of the times I played Super Mario with my little brother.
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It reminds me of the times my little brother and I played Super Mario.
| -1no label
| 214 |
It reminds me of the times my little brother and I played Super Mario.
|
It reminds me of the times I played Super Mario with my little brother.
| -1no label
| 215 |
In this PC game, Shredder fights the turtles in his Manhattan hideout.
|
In this PC game, Shredder and the turtles fight in his Manhattan hideout.
| -1no label
| 216 |
In this PC game, Shredder and the turtles fight in his Manhattan hideout.
|
In this PC game, Shredder fights the turtles in his Manhattan hideout.
| -1no label
| 217 |
If their vectors' cosine similarity is high, we can conclude that "cat" is similar to "dog".
|
If their vectors' cosine similarity is high, we can conclude that "cat" and "dog" are similar.
| -1no label
| 218 |
If their vectors' cosine similarity is high, we can conclude that "cat" and "dog" are similar.
|
If their vectors' cosine similarity is high, we can conclude that "cat" is similar to "dog".
| -1no label
| 219 |
Jane had a party on Sunday.
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On Sunday, Jane had a party.
| -1no label
| 220 |
On Sunday, Jane had a party.
|
Jane had a party on Sunday.
| -1no label
| 221 |
For decades, the FBI has been trusted to investigate corruption inside the government.
|
The FBI has been trusted to investigate corruption inside the government for decades.
| -1no label
| 222 |
The FBI has been trusted to investigate corruption inside the government for decades.
|
For decades, the FBI has been trusted to investigate corruption inside the government.
| -1no label
| 223 |
I was driving through my neighborhood a few weeks ago, and there was a lady holding a child's hand, standing on the side of the road talking to someone parked in a car.
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A few weeks ago, I was driving through my neighborhood, and there was a lady holding a child's hand, standing on the side of the road talking to someone parked in a car.
| -1no label
| 224 |
A few weeks ago, I was driving through my neighborhood, and there was a lady holding a child's hand, standing on the side of the road talking to someone parked in a car.
|
I was driving through my neighborhood a few weeks ago, and there was a lady holding a child's hand, standing on the side of the road talking to someone parked in a car.
| -1no label
| 225 |
Bass River timber was used in construction of the Empire State Building.
|
In construction of the Empire State Building, Bass River timber was used.
| -1no label
| 226 |
In construction of the Empire State Building, Bass River timber was used.
|
Bass River timber was used in construction of the Empire State Building.
| -1no label
| 227 |
In this paper, we present an approach to acquire trivial physical knowledge from unstructured natural language text.
|
We present in this paper an approach to acquire trivial physical knowledge from unstructured natural language text.
| -1no label
| 228 |
We present in this paper an approach to acquire trivial physical knowledge from unstructured natural language text.
|
In this paper, we present an approach to acquire trivial physical knowledge from unstructured natural language text.
| -1no label
| 229 |
In this paper, we present an approach to acquire trivial physical knowledge from unstructured natural language text.
|
We present an approach to acquire trivial physical knowledge from the unstructured natural language text of this paper.
| -1no label
| 230 |
We present an approach to acquire trivial physical knowledge from the unstructured natural language text of this paper.
|
In this paper, we present an approach to acquire trivial physical knowledge from unstructured natural language text.
| -1no label
| 231 |
I ate pizza with friends.
|
I ate pizza.
| -1no label
| 232 |
I ate pizza.
|
I ate pizza with friends.
| -1no label
| 233 |
I ate pizza with some friends.
|
I ate some friends.
| -1no label
| 234 |
I ate some friends.
|
I ate pizza with some friends.
| -1no label
| 235 |
I ate pizza with olives.
|
I ate pizza.
| -1no label
| 236 |
I ate pizza.
|
I ate pizza with olives.
| -1no label
| 237 |
I ate pizza with olives.
|
I ate olives.
| -1no label
| 238 |
I ate olives.
|
I ate pizza with olives.
| -1no label
| 239 |
Mueller received a mandate to investigate possible collusion with Russia.
|
Mueller received a mandate to investigate possible collusion.
| -1no label
| 240 |
Mueller received a mandate to investigate possible collusion.
|
Mueller received a mandate to investigate possible collusion with Russia.
| -1no label
| 241 |
Mueller received a mandate to investigate possible collusion with Russia.
|
Mueller received a mandate to investigate with Russia.
| -1no label
| 242 |
Mueller received a mandate to investigate with Russia.
|
Mueller received a mandate to investigate possible collusion with Russia.
| -1no label
| 243 |
Mueller received a mandate to investigate possible collusion with vast resources.
|
Mueller received a mandate to investigate possible collusion.
| -1no label
| 244 |
Mueller received a mandate to investigate possible collusion.
|
Mueller received a mandate to investigate possible collusion with vast resources.
| -1no label
| 245 |
Mueller received a mandate to investigate possible collusion with vast resources.
|
Mueller received a mandate to investigate with vast resources.
| -1no label
| 246 |
Mueller received a mandate to investigate with vast resources.
|
Mueller received a mandate to investigate possible collusion with vast resources.
| -1no label
| 247 |
They should be attached to the lifting mechanism in the faucet.
|
They should be attached to the lifting mechanism.
| -1no label
| 248 |
They should be attached to the lifting mechanism.
|
They should be attached to the lifting mechanism in the faucet.
| -1no label
| 249 |
They should be attached to the lifting mechanism in the faucet.
|
They should be attached in the faucet.
| -1no label
| 250 |
They should be attached in the faucet.
|
They should be attached to the lifting mechanism in the faucet.
| -1no label
| 251 |
They should be attached to the lifting mechanism in an unbreakable knot.
|
They should be attached to the lifting mechanism.
| -1no label
| 252 |
They should be attached to the lifting mechanism.
|
They should be attached to the lifting mechanism in an unbreakable knot.
| -1no label
| 253 |
They should be attached to the lifting mechanism in an unbreakable knot.
|
They should be attached in an unbreakable knot.
| -1no label
| 254 |
They should be attached in an unbreakable knot.
|
They should be attached to the lifting mechanism in an unbreakable knot.
| -1no label
| 255 |
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with only partial success.
|
Tanks were developed by Britain and France, and were first used with only partial success.
| -1no label
| 256 |
Tanks were developed by Britain and France, and were first used with only partial success.
|
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with only partial success.
| -1no label
| 257 |
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with only partial success.
|
Tanks were developed by Britain and France, and were first used in combat by the British during a battle.
| -1no label
| 258 |
Tanks were developed by Britain and France, and were first used in combat by the British during a battle.
|
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with only partial success.
| -1no label
| 259 |
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with German forces.
|
Tanks were developed by Britain and France, and were first used with German forces.
| -1no label
| 260 |
Tanks were developed by Britain and France, and were first used with German forces.
|
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with German forces.
| -1no label
| 261 |
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with German forces.
|
Tanks were developed by Britain and France, and were first used in combat by the British during a battle.
| -1no label
| 262 |
Tanks were developed by Britain and France, and were first used in combat by the British during a battle.
|
Tanks were developed by Britain and France, and were first used in combat by the British during a battle with German forces.
| -1no label
| 263 |
We propose models of the probability distribution from which the attested vowel inventories have been drawn.
|
We propose models of the probability distribution.
| -1no label
| 264 |
We propose models of the probability distribution.
|
We propose models of the probability distribution from which the attested vowel inventories have been drawn.
| -1no label
| 265 |
We propose models of the probability distribution from which the attested vowel inventories have been drawn.
|
We propose models from which the attested vowel inventories have been drawn.
| -1no label
| 266 |
We propose models from which the attested vowel inventories have been drawn.
|
We propose models of the probability distribution from which the attested vowel inventories have been drawn.
| -1no label
| 267 |
We propose models of the probability distribution from a restricted space of linear functions.
|
We propose models of the probability distribution.
| -1no label
| 268 |
We propose models of the probability distribution.
|
We propose models of the probability distribution from a restricted space of linear functions.
| -1no label
| 269 |
We propose models of the probability distribution from a restricted space of linear functions.
|
We propose models from a restricted space of linear functions.
| -1no label
| 270 |
We propose models from a restricted space of linear functions.
|
We propose models of the probability distribution from a restricted space of linear functions.
| -1no label
| 271 |
George went to the lake to catch a fish, but he fell into the water.
|
George fell into the water.
| -1no label
| 272 |
George fell into the water.
|
George went to the lake to catch a fish, but he fell into the water.
| -1no label
| 273 |
George went to the lake to catch a fish, but he fell into the water.
|
A fish fell into the water.
| -1no label
| 274 |
A fish fell into the water.
|
George went to the lake to catch a fish, but he fell into the water.
| -1no label
| 275 |
The Republican party almost universally opposed that bill in 2009, which cost $787 billion over 10 years, on the grounds that it would increase the debt too much.
|
That bill would increase the debt too much.
| -1no label
| 276 |
That bill would increase the debt too much.
|
The Republican party almost universally opposed that bill in 2009, which cost $787 billion over 10 years, on the grounds that it would increase the debt too much.
| -1no label
| 277 |
The Republican party almost universally opposed that bill in 2009, which cost $787 billion over 10 years, on the grounds that it would increase the debt too much.
|
The Republican party would increase the debt too much.
| -1no label
| 278 |
The Republican party would increase the debt too much.
|
The Republican party almost universally opposed that bill in 2009, which cost $787 billion over 10 years, on the grounds that it would increase the debt too much.
| -1no label
| 279 |
The climbing beans choked the corn, and the squash grew so big that it overshadowed several adjacent rows of beans.
|
The squash overshadowed several adjacent rows of beans.
| -1no label
| 280 |
The squash overshadowed several adjacent rows of beans.
|
The climbing beans choked the corn, and the squash grew so big that it overshadowed several adjacent rows of beans.
| -1no label
| 281 |
The climbing beans choked the corn, and the squash grew so big that it overshadowed several adjacent rows of beans.
|
The corn overshadowed several adjacent rows of beans.
| -1no label
| 282 |
The corn overshadowed several adjacent rows of beans.
|
The climbing beans choked the corn, and the squash grew so big that it overshadowed several adjacent rows of beans.
| -1no label
| 283 |
Because Britain still maintained control of Canada's foreign affairs under the Confederation Act, its declaration of war in 1914 automatically brought Canada into World War I.
|
Britain's declaration of war in 1914 automatically brought Canada into World War I.
| -1no label
| 284 |
Britain's declaration of war in 1914 automatically brought Canada into World War I.
|
Because Britain still maintained control of Canada's foreign affairs under the Confederation Act, its declaration of war in 1914 automatically brought Canada into World War I.
| -1no label
| 285 |
Because Britain still maintained control of Canada's foreign affairs under the Confederation Act, its declaration of war in 1914 automatically brought Canada into World War I.
|
Canada's declaration of war in 1914 automatically brought Canada into World War I.
| -1no label
| 286 |
Canada's declaration of war in 1914 automatically brought Canada into World War I.
|
Because Britain still maintained control of Canada's foreign affairs under the Confederation Act, its declaration of war in 1914 automatically brought Canada into World War I.
| -1no label
| 287 |
We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains.
|
Coreference resolvers can hardly generalize to unseen domains.
| -1no label
| 288 |
Coreference resolvers can hardly generalize to unseen domains.
|
We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains.
| -1no label
| 289 |
We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains.
|
Lexical features can hardly generalize to unseen domains.
| -1no label
| 290 |
Lexical features can hardly generalize to unseen domains.
|
We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains.
| -1no label
| 291 |
The sides came to an agreement after their meeting in Stockholm.
|
The sides came to an agreement after their meeting in Sweden.
| -1no label
| 292 |
The sides came to an agreement after their meeting in Sweden.
|
The sides came to an agreement after their meeting in Stockholm.
| -1no label
| 293 |
The sides came to an agreement after their meeting in Stockholm.
|
The sides came to an agreement after their meeting in Europe.
| -1no label
| 294 |
The sides came to an agreement after their meeting in Europe.
|
The sides came to an agreement after their meeting in Stockholm.
| -1no label
| 295 |
The sides came to an agreement after their meeting in Stockholm.
|
The sides came to an agreement after their meeting in Oslo.
| -1no label
| 296 |
The sides came to an agreement after their meeting in Oslo.
|
The sides came to an agreement after their meeting in Stockholm.
| -1no label
| 297 |
Musk decided to offer up his personal Tesla roadster.
|
Musk decided to offer up his personal car.
| -1no label
| 298 |
Musk decided to offer up his personal car.
|
Musk decided to offer up his personal Tesla roadster.
| -1no label
| 299 |
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.