premise
stringlengths 11
296
| hypothesis
stringlengths 11
296
| label
class label 0
classes | idx
int32 0
1.1k
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|---|---|---|---|
The villain is the character who tends to have a negative effect on other characters.
|
The villain is the character who tends to have a negative correlation with other characters.
| -1no label
| 100 |
The villain is the character who tends to have a negative correlation with other characters.
|
The villain is the character who tends to have a negative effect on other characters.
| -1no label
| 101 |
Semantic parsing typically requires using a set of operations to query the knowledge base and process the results.
|
Semantic parsing typically needs a set of operations to query the knowledge base and process the results.
| -1no label
| 102 |
Semantic parsing typically needs a set of operations to query the knowledge base and process the results.
|
Semantic parsing typically requires using a set of operations to query the knowledge base and process the results.
| -1no label
| 103 |
Semantic parsing typically requires using a set of operations to query the knowledge base and process the results.
|
Semantic parsing typically creates a set of operations to query the knowledge base and process the results.
| -1no label
| 104 |
Semantic parsing typically creates a set of operations to query the knowledge base and process the results.
|
Semantic parsing typically requires using a set of operations to query the knowledge base and process the results.
| -1no label
| 105 |
Semantic parsing typically requires using a set of operations to query the knowledge base and process the results.
|
Semantic parsing infrequently creates a set of operations to query the knowledge base and process the results.
| -1no label
| 106 |
Semantic parsing infrequently creates a set of operations to query the knowledge base and process the results.
|
Semantic parsing typically requires using a set of operations to query the knowledge base and process the results.
| -1no label
| 107 |
John broke the window.
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The window was broken by John.
| -1no label
| 108 |
The window was broken by John.
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John broke the window.
| -1no label
| 109 |
John broke the window.
|
The window broke John.
| -1no label
| 110 |
The window broke John.
|
John broke the window.
| -1no label
| 111 |
Mueller’s team asked former senior Justice Department officials for information.
|
Former senior Justice Department officials were asked for information by Mueller's team.
| -1no label
| 112 |
Former senior Justice Department officials were asked for information by Mueller's team.
|
Mueller’s team asked former senior Justice Department officials for information.
| -1no label
| 113 |
Mueller’s team asked former senior Justice Department officials for information.
|
Mueller’s team was asked for information by former senior Justice Department officials.
| -1no label
| 114 |
Mueller’s team was asked for information by former senior Justice Department officials.
|
Mueller’s team asked former senior Justice Department officials for information.
| -1no label
| 115 |
Mueller’s team asked former senior Justice Department officials for information.
|
Mueller’s team was asked for information.
| -1no label
| 116 |
Mueller’s team was asked for information.
|
Mueller’s team asked former senior Justice Department officials for information.
| -1no label
| 117 |
Mueller’s team asked former senior Justice Department officials for information.
|
Former senior Justice Department officials weren't asked for information.
| -1no label
| 118 |
Former senior Justice Department officials weren't asked for information.
|
Mueller’s team asked former senior Justice Department officials for information.
| -1no label
| 119 |
Everyone should be afraid of the part when he asked Congress to allow his cabinet secretaries to terminate whoever they want.
|
Everyone should be afraid of the part when Congress was asked to allow his cabinet secretaries to terminate whoever they want.
| -1no label
| 120 |
Everyone should be afraid of the part when Congress was asked to allow his cabinet secretaries to terminate whoever they want.
|
Everyone should be afraid of the part when he asked Congress to allow his cabinet secretaries to terminate whoever they want.
| -1no label
| 121 |
Everyone should be afraid of the part when he asked Congress to allow his cabinet secretaries to terminate whoever they want.
|
Everyone should be afraid of the part when he was asked to allow his cabinet secretaries to terminate whoever they want.
| -1no label
| 122 |
Everyone should be afraid of the part when he was asked to allow his cabinet secretaries to terminate whoever they want.
|
Everyone should be afraid of the part when he asked Congress to allow his cabinet secretaries to terminate whoever they want.
| -1no label
| 123 |
Cape sparrows eat seeds, along with soft plant parts and insects.
|
Seeds, along with soft plant parts and insects, are eaten by cape sparrows.
| -1no label
| 124 |
Seeds, along with soft plant parts and insects, are eaten by cape sparrows.
|
Cape sparrows eat seeds, along with soft plant parts and insects.
| -1no label
| 125 |
Cape sparrows eat seeds, along with soft plant parts and insects.
|
Cape sparrows are eaten by seeds, along with soft plant parts and insects.
| -1no label
| 126 |
Cape sparrows are eaten by seeds, along with soft plant parts and insects.
|
Cape sparrows eat seeds, along with soft plant parts and insects.
| -1no label
| 127 |
Cape sparrows eat seeds, along with soft plant parts and insects.
|
Cape sparrows are eaten.
| -1no label
| 128 |
Cape sparrows are eaten.
|
Cape sparrows eat seeds, along with soft plant parts and insects.
| -1no label
| 129 |
Cape sparrows eat seeds, along with soft plant parts and insects.
|
Soft plant parts and insects eat seeds.
| -1no label
| 130 |
Soft plant parts and insects eat seeds.
|
Cape sparrows eat seeds, along with soft plant parts and insects.
| -1no label
| 131 |
Cape sparrows eat seeds, along with soft plant parts and insects.
|
Soft plant parts and insects are eaten.
| -1no label
| 132 |
Soft plant parts and insects are eaten.
|
Cape sparrows eat seeds, along with soft plant parts and insects.
| -1no label
| 133 |
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
|
Knowledge bases are populated with facts from unstructured text corpora by relation extraction systems.
| -1no label
| 134 |
Knowledge bases are populated with facts from unstructured text corpora by relation extraction systems.
|
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
| -1no label
| 135 |
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
|
Knowledge bases are populated by relation extraction systems with facts from unstructured text corpora.
| -1no label
| 136 |
Knowledge bases are populated by relation extraction systems with facts from unstructured text corpora.
|
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
| -1no label
| 137 |
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
|
Relation extraction systems are populated by knowledge bases with facts from unstructured text corpora.
| -1no label
| 138 |
Relation extraction systems are populated by knowledge bases with facts from unstructured text corpora.
|
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
| -1no label
| 139 |
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
|
Relation extraction systems are populated with facts from unstructured text corpora by knowledge bases.
| -1no label
| 140 |
Relation extraction systems are populated with facts from unstructured text corpora by knowledge bases.
|
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
| -1no label
| 141 |
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
|
Relation extraction systems populate knowledge bases with assertions from unstructured text corpora.
| -1no label
| 142 |
Relation extraction systems populate knowledge bases with assertions from unstructured text corpora.
|
Relation extraction systems populate knowledge bases with facts from unstructured text corpora.
| -1no label
| 143 |
Wal-Mart's recent move is tied to its continuing efforts to beat back competition against retailers like Amazon.
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The recent move by Wal-Mart is tied to its continuing efforts to beat back competition against retailers like Amazon.
| -1no label
| 144 |
The recent move by Wal-Mart is tied to its continuing efforts to beat back competition against retailers like Amazon.
|
Wal-Mart's recent move is tied to its continuing efforts to beat back competition against retailers like Amazon.
| -1no label
| 145 |
Wal-Mart's recent move is tied to its continuing efforts to beat back competition against retailers like Amazon.
|
The recent move against Wal-Mart is tied to its continuing efforts to beat back competition against retailers like Amazon.
| -1no label
| 146 |
The recent move against Wal-Mart is tied to its continuing efforts to beat back competition against retailers like Amazon.
|
Wal-Mart's recent move is tied to its continuing efforts to beat back competition against retailers like Amazon.
| -1no label
| 147 |
The man gets down on one knee and inspects the bottom of the elephant's foot only to find a large thorn deeply embedded.
|
The man gets down on one knee and inspects the bottom of the foot of the elephant only to find a large thorn deeply embedded.
| -1no label
| 148 |
The man gets down on one knee and inspects the bottom of the foot of the elephant only to find a large thorn deeply embedded.
|
The man gets down on one knee and inspects the bottom of the elephant's foot only to find a large thorn deeply embedded.
| -1no label
| 149 |
The Cape sparrow's population has not decreased significantly, and is not seriously threatened by human activities.
|
The population of the Cape sparrow has not decreased significantly, and is not seriously threatened by human activities.
| -1no label
| 150 |
The population of the Cape sparrow has not decreased significantly, and is not seriously threatened by human activities.
|
The Cape sparrow's population has not decreased significantly, and is not seriously threatened by human activities.
| -1no label
| 151 |
The Cape sparrow's population has not decreased significantly, and is not seriously threatened by human activities.
|
The population of the Cape sparrow has decreased significantly, and is seriously threatened by human activities.
| -1no label
| 152 |
The population of the Cape sparrow has decreased significantly, and is seriously threatened by human activities.
|
The Cape sparrow's population has not decreased significantly, and is not seriously threatened by human activities.
| -1no label
| 153 |
This paper presents an approach for understanding the contents of these message vectors by translating them into natural language.
|
This paper presents an approach for understanding these message vectors' content by translating them into natural language.
| -1no label
| 154 |
This paper presents an approach for understanding these message vectors' content by translating them into natural language.
|
This paper presents an approach for understanding the contents of these message vectors by translating them into natural language.
| -1no label
| 155 |
This paper presents an approach for understanding the contents of these message vectors by translating them into natural language.
|
This paper presents an approach for understanding the contents of these message vectors by translating them into foreign language.
| -1no label
| 156 |
This paper presents an approach for understanding the contents of these message vectors by translating them into foreign language.
|
This paper presents an approach for understanding the contents of these message vectors by translating them into natural language.
| -1no label
| 157 |
She is skilled at violin.
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She is a skilled violinist.
| -1no label
| 158 |
She is a skilled violinist.
|
She is skilled at violin.
| -1no label
| 159 |
She is skilled.
|
She is a skilled violinist.
| -1no label
| 160 |
She is a skilled violinist.
|
She is skilled.
| -1no label
| 161 |
She is a surgeon and skilled violinist.
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She is a skilled surgeon.
| -1no label
| 162 |
She is a skilled surgeon.
|
She is a surgeon and skilled violinist.
| -1no label
| 163 |
The combination of mentos and diet coke caused the explosion.
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Combining mentos and diet coke caused the explosion.
| -1no label
| 164 |
Combining mentos and diet coke caused the explosion.
|
The combination of mentos and diet coke caused the explosion.
| -1no label
| 165 |
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general to submit a letter of resignation, according to The New York Times.
|
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general's submission of a letter of resignation, according to The New York Times.
| -1no label
| 166 |
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general's submission of a letter of resignation, according to The New York Times.
|
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general to submit a letter of resignation, according to The New York Times.
| -1no label
| 167 |
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general to submit a letter of resignation, according to The New York Times.
|
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general's withholding of a letter of resignation, according to The New York Times.
| -1no label
| 168 |
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general's withholding of a letter of resignation, according to The New York Times.
|
In an Oval Office meeting after Mueller's appointment, Trump told Sessions he should resign, prompting the attorney general to submit a letter of resignation, according to The New York Times.
| -1no label
| 169 |
Thought this was super cool, and a really important step in preserving all the physical books.
|
Thought this was super cool, and a really important step in the preservation of all the physical books.
| -1no label
| 170 |
Thought this was super cool, and a really important step in the preservation of all the physical books.
|
Thought this was super cool, and a really important step in preserving all the physical books.
| -1no label
| 171 |
Thought this was super cool, and a really important step in preserving all the physical books.
|
Thought this was super cool, and a really important step in the destruction of all the physical books.
| -1no label
| 172 |
Thought this was super cool, and a really important step in the destruction of all the physical books.
|
Thought this was super cool, and a really important step in preserving all the physical books.
| -1no label
| 173 |
Thought this was super cool, and a really important step in preserving all the physical books.
|
Thought this was super cool, and a really important step in the processing of all the physical books.
| -1no label
| 174 |
Thought this was super cool, and a really important step in the processing of all the physical books.
|
Thought this was super cool, and a really important step in preserving all the physical books.
| -1no label
| 175 |
Thought this was super cool, and a really important step in preserving all the physical books.
|
Thought this was super cool, and a really important step in all the physical books' preservation.
| -1no label
| 176 |
Thought this was super cool, and a really important step in all the physical books' preservation.
|
Thought this was super cool, and a really important step in preserving all the physical books.
| -1no label
| 177 |
During World War II, the five remaining Greek boats were sunk by Axis aircraft during the German invasion of Greece in April 1941.
|
During World War II, the five remaining Greek boats were sunk by Axis aircraft when the Germans invaded Greece in April 1941.
| -1no label
| 178 |
During World War II, the five remaining Greek boats were sunk by Axis aircraft when the Germans invaded Greece in April 1941.
|
During World War II, the five remaining Greek boats were sunk by Axis aircraft during the German invasion of Greece in April 1941.
| -1no label
| 179 |
During World War II, the five remaining Greek boats were sunk by Axis aircraft during the German invasion of Greece in April 1941.
|
During World War II, the five remaining Greek boats were sunk by Axis aircraft during the Greek invasion of Germany in April 1941.
| -1no label
| 180 |
During World War II, the five remaining Greek boats were sunk by Axis aircraft during the Greek invasion of Germany in April 1941.
|
During World War II, the five remaining Greek boats were sunk by Axis aircraft during the German invasion of Greece in April 1941.
| -1no label
| 181 |
Often, the first step in building statistical NLP models involves feature extraction.
|
Often, the first step in building statistical NLP models involves extracting features.
| -1no label
| 182 |
Often, the first step in building statistical NLP models involves extracting features.
|
Often, the first step in building statistical NLP models involves feature extraction.
| -1no label
| 183 |
Bob likes Alice.
|
Alice likes Bob.
| -1no label
| 184 |
Alice likes Bob.
|
Bob likes Alice.
| -1no label
| 185 |
Bob is Alice's parent.
|
Alice is Bob's parent.
| -1no label
| 186 |
Alice is Bob's parent.
|
Bob is Alice's parent.
| -1no label
| 187 |
President Trump will ask Republican lawmakers to use a controversial White House framework as the baseline for a coming Senate debate on immigration policy.
|
Republican lawmakers will ask President Trump to use a controversial White House framework as the baseline for a coming Senate debate on immigration policy.
| -1no label
| 188 |
Republican lawmakers will ask President Trump to use a controversial White House framework as the baseline for a coming Senate debate on immigration policy.
|
President Trump will ask Republican lawmakers to use a controversial White House framework as the baseline for a coming Senate debate on immigration policy.
| -1no label
| 189 |
Mythbusters proved that bulls react to movement and not color.
|
Bulls proved that mythbusters react to movement and not color.
| -1no label
| 190 |
Bulls proved that mythbusters react to movement and not color.
|
Mythbusters proved that bulls react to movement and not color.
| -1no label
| 191 |
The models generate translations with their constituency tree and their attention-derived alignments.
|
The translations generate models with their constituency tree and their attention-derived alignments.
| -1no label
| 192 |
The translations generate models with their constituency tree and their attention-derived alignments.
|
The models generate translations with their constituency tree and their attention-derived alignments.
| -1no label
| 193 |
Bob married Alice.
|
Alice married Bob.
| -1no label
| 194 |
Alice married Bob.
|
Bob married Alice.
| -1no label
| 195 |
The head of Russia's foreign spy service met with top U.S. intelligence officials, despite existing sanctions.
|
Top U.S. intelligence officials met with the head of Russia's foreign spy service, despite existing sanctions.
| -1no label
| 196 |
Top U.S. intelligence officials met with the head of Russia's foreign spy service, despite existing sanctions.
|
The head of Russia's foreign spy service met with top U.S. intelligence officials, despite existing sanctions.
| -1no label
| 197 |
Bees do not follow the same rules as airplanes.
|
Airplanes do not follow the same rules as bees.
| -1no label
| 198 |
Airplanes do not follow the same rules as bees.
|
Bees do not follow the same rules as airplanes.
| -1no label
| 199 |
Subsets and Splits
MNLI Validation Label Counts
Counts the number of entries in each label category, providing basic insights into the distribution of labels in the dataset.