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 |
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.