premise
stringlengths 11
296
| hypothesis
stringlengths 11
296
| label
class label 0
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int32 0
1.1k
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|---|---|---|---|
Bees do not follow the same rules as airplanes.
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Bees fly using a different mechanism from airplanes.
| -1no label
| 200
|
Bees fly using a different mechanism from airplanes.
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Bees do not follow the same rules as airplanes.
| -1no label
| 201
|
Bees do not follow the same rules as airplanes.
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Bees fly using the same mechanism as airplanes.
| -1no label
| 202
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Bees fly using the same mechanism as airplanes.
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Bees do not follow the same rules as airplanes.
| -1no label
| 203
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Bees do not follow the same rules as airplanes.
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Bees are more energy-efficient flyers than airplanes.
| -1no label
| 204
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Bees are more energy-efficient flyers than airplanes.
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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
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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
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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.
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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
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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
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It reminds me of the times my little brother and I played Super Mario.
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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.
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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.
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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".
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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.
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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.
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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.
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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.
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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.
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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.
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I ate some friends.
| -1no label
| 234
|
I ate some friends.
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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.
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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
SQL Console for nyu-mll/glue
Performs basic filtering to find training examples where the hypothesis text contains 'tennis', providing limited analytical value beyond simple keyword search.
MNLI Validation Label Counts
Counts the number of entries in each label category, providing basic insights into the distribution of labels in the dataset.