| premise
				 stringlengths 11 296 | hypothesis
				 stringlengths 11 296 | label
				 class label 0
				classes | idx
				 int32 0 1.1k | 
|---|---|---|---|
| 
	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. | 
	The window was broken by John. | -1no label
 | 108 | 
| 
	The window was broken by John. | 
	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. | 
	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. | 
	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. | 
	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. | 
	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 | 
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