state stringlengths 0 129k | kind stringclasses 3
values | id stringlengths 22 168 | options listlengths 0 235 | target listlengths 1 235 | question stringlengths 18 11.4k | source stringclasses 668
values | variant stringclasses 6
values | split stringclasses 1
value | group_id stringlengths 22 82 | question_id stringlengths 4 118 | license stringclasses 69
values | license_use stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
You do not look normal. Are you all right?"
I have a headache. | noul | implicatures-9aabab4558:train:0:noul-label-verification | [] | [
0
] | Is "Yes." the correct answer to the question? | implicatures | label_verification | train | implicatures-9aabab4558:train:0 | noul-label-verification | gpl | commercial |
that is not nice what you are doing i'll have you know that some racist | choice | implicit-hate-stg1-b5fb457f0b:train:17 | [
"explicit_hate",
"implicit_hate",
"not_hate"
] | [
0,
0,
1
] | Is the post explicitly hateful, implicitly hateful (coded or indirect), or not hateful? | implicit-hate-stg1 | direct | train | implicit-hate-stg1-b5fb457f0b:train:17 | decision | Request Form (DPI) | non-commercial |
that is not nice what you are doing i'll have you know that some racist | choice | implicit-hate-stg1-b5fb457f0b:train:17:choice-criteria-permutation | [
"not_hate",
"implicit_hate",
"explicit_hate"
] | [
1,
0,
0
] | Is the post explicitly hateful, implicitly hateful (coded or indirect), or not hateful? | implicit-hate-stg1 | criteria_permutation | train | implicit-hate-stg1-b5fb457f0b:train:17 | choice-criteria-permutation | Request Form (DPI) | non-commercial |
How can I send Holi's wishes to all my friends at Quora? | choice | insincere-questions-565322a762:train:2 | [
"insincere question",
"valid question"
] | [
0,
1
] | Select the label that best applies to the state. | insincere-questions | direct | train | insincere-questions-565322a762:train:2 | decision | unspecified | unspecified |
How can I send Holi's wishes to all my friends at Quora? | noul | insincere-questions-565322a762:train:2:noul-label-verification | [] | [
0
] | Is "insincere question" the correct label for this example? | insincere-questions | label_verification | train | insincere-questions-565322a762:train:2 | noul-label-verification | unspecified | unspecified |
Remove File Share Access - [TICKET ID] - [NAME] ([COMPANY]A. [LOCATION]) | choice | it-support-tickets-3a0c274acf:train:2 | [
"Active Directory",
"Computer-Services",
"EOL",
"Fileservice",
"O365",
"Software",
"Support general"
] | [
0,
0,
0,
1,
0,
0,
0
] | Select the label that best applies to the state. | it-support-tickets | direct | train | it-support-tickets-3a0c274acf:train:2 | decision | cc-by-4.0 | commercial |
Remove File Share Access - [TICKET ID] - [NAME] ([COMPANY]A. [LOCATION]) | noul | it-support-tickets-3a0c274acf:train:2:noul-label-verification | [] | [
0
] | Is "EOL" the correct label for this example? | it-support-tickets | label_verification | train | it-support-tickets-3a0c274acf:train:2 | noul-label-verification | cc-by-4.0 | commercial |
"
IRC Chat
Someone, I believe was you, requested information because (s)he had been disconnected to the IRC network. From reading the dialog I believe you were requesting this information:
""The subject now needs to send an email to permissions-commons@wikimedia.org asserting who he is, and giving permission for h... | choice | jigsaw-toxicity-47e658a77f:train:1 | [
"not toxic",
"toxic"
] | [
1,
0
] | Is the text toxic? | jigsaw_toxicity | direct | train | jigsaw-toxicity-47e658a77f:train:1 | decision | apache-2.0 | commercial |
Passage A:
A band that is playing outside for a benefit in New York .
Passage B:
A person improves the benefit . | choice | joci-47f0b47b28:train:360 | [
"impossible",
"technically possible",
"plausible",
"likely",
"very likely"
] | [
0,
0,
1,
0,
0
] | How likely is the hypothesis, given the context? | joci | direct | train | joci-47f0b47b28:train:360 | decision | unspecified | unspecified |
Passage A:
A band that is playing outside for a benefit in New York .
Passage B:
A person improves the benefit . | noul | joci-47f0b47b28:train:360:noul-label-verification | [] | [
0
] | How likely is the hypothesis, given the context? Is "very likely" the correct answer? | joci | label_verification | train | joci-47f0b47b28:train:360 | noul-label-verification | unspecified | unspecified |
Passage A:
A band that is playing outside for a benefit in New York .
Passage B:
A person improves the benefit . | choice | joci-47f0b47b28:train:360:choice-criteria-permutation | [
"impossible",
"technically possible",
"likely",
"very likely",
"plausible"
] | [
0,
0,
0,
0,
1
] | How likely is the hypothesis, given the context? | joci | criteria_permutation | train | joci-47f0b47b28:train:360 | choice-criteria-permutation | unspecified | unspecified |
El pedido nunca llegó y reclamado 1 dia después de pasarse la fecha de entrega, alegan que la empresa de transportes ha perdido varios pedidos entre ellos el mio, además dicen que no tienen más stock hasta pasados unos días, así que decidimos anular el pedido | choice | language-identification-7c3c8a5ee3:train:13 | [
"Arabic",
"Bulgarian",
"Chinese",
"Dutch",
"English",
"French",
"German",
"Greek",
"Hindi",
"Italian",
"Japanese",
"Polish",
"Portuguese",
"Russian",
"Spanish",
"Swahili",
"Thai",
"Turkish",
"Urdu",
"Vietnamese"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0
] | What language is this text in? | language-identification | direct | train | language-identification-7c3c8a5ee3:train:13 | decision | CC BY-NC 4.0 (DPI), CC BY-SA 4.0 (DPI), Custom (DPI) | non-commercial |
RT <user>: There was more to #Brexit than immigration ugghhh | noul | tasksource-lewidi-hs-brexit-320212f38d:train:9332f306765cb41b:lewidi-hs_brexit | [] | [
0
] | What fraction of annotators consider the tweet hate speech? | lewidi/hs_brexit | direct | train | tasksource-lewidi-hs-brexit-320212f38d:train:9332f306765cb41b | lewidi-hs_brexit | other | unspecified |
<user> <user> No way Jose!! | noul | tasksource-lewidi-md-agreement-5e2fda1cbc:train:d6e6ca78d144ea03:lewidi-md_agreement | [] | [
0
] | What fraction of annotators find the tweet offensive? | lewidi/md_agreement | direct | train | tasksource-lewidi-md-agreement-5e2fda1cbc:train:d6e6ca78d144ea03 | lewidi-md_agreement | other | unspecified |
Passage A:
You don't have to hate someone to break up with them. Be firm and honest. This relationship isn't working for you and the two of you are not as good together as you had hoped. They may think you are being a dick, or they may realize you are right, or they may have been thinking the same thing. you can't c... | noul | tasksource-lewidi-mp-ce0ace9aaf:train:620db0aed4b22fbf:lewidi-mp | [] | [
0
] | What fraction of annotators consider the reply ironic? | lewidi/mp | direct | train | tasksource-lewidi-mp-ce0ace9aaf:train:620db0aed4b22fbf | lewidi-mp | other | unspecified |
Defendants must maintain the status quo as to the possession of the AT documents and may make fair use thereof. However, defendants are prohibited from making additional copies of the AT documents and distributing or transferring the documents. Should the defendants incorporate or attach any of the AT documents to thei... | choice | lex-glue-case-hold-c8db5a9011:train:6 | [
"holding that the transaction must be fair and equitable and in good faith",
"holding that fair use is an affirmative defense",
"holding that the duty of good faith and fair dealing is a contractual duty",
"holding that knowing exploitation of purloined manuscript is not compatible with good faith and fair de... | [
0,
0,
0,
1,
0
] | Which supplied option best answers the question? | lex_glue/case_hold | direct | train | lex-glue-case-hold-c8db5a9011:train:6 | decision | cc-by-4.0 | commercial |
This Agreement may be executed in two or more counterpart copies, each of which shall be deemed an original and all of which, taken together, shall be deemed to constitute one and the same instrument. | choice | lex-glue-ledgar-3f281b23f1:train:27 | [
"Adjustments",
"Agreements",
"Amendments",
"Anti-Corruption Laws",
"Applicable Laws",
"Approvals",
"Arbitration",
"Assignments",
"Assigns",
"Authority",
"Authorizations",
"Base Salary",
"Benefits",
"Binding Effects",
"Books",
"Brokers",
"Capitalization",
"Change In Control",
"Clo... | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | Select the label that best applies to the state. | lex_glue/ledgar | direct | train | lex-glue-ledgar-3f281b23f1:train:27 | decision | cc-by-4.0 | commercial |
This Agreement may be executed in two or more counterpart copies, each of which shall be deemed an original and all of which, taken together, shall be deemed to constitute one and the same instrument. | choice | lex-glue-ledgar-3f281b23f1:train:27:choice-criteria-permutation | [
"Closings",
"Integration",
"Intellectual Property",
"Books",
"Defined Terms",
"Headings",
"Use Of Proceeds",
"Entire Agreements",
"Payments",
"Authority",
"Modifications",
"Insurances",
"Survival",
"Indemnifications",
"No Waivers",
"Tax Withholdings",
"Existence",
"Authorizations",... | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0... | Select the label that best applies to the state. | lex_glue/ledgar | criteria_permutation | train | lex-glue-ledgar-3f281b23f1:train:27 | choice-criteria-permutation | cc-by-4.0 | commercial |
332 U.S. 134
67 S.Ct. 1716
91 L.Ed. 1955
FOSTER et al.v.PEOPLE OF STATE OF ILLINOIS.
No. 540.
Argued May 8, 1947.
Decided June 23, 1947.
Mr. Charles Kaufman, of Chicago, Ill., for petitioners.
Mr. William C. Wines, of Chicago, Ill., for respondent.
Mr. Justice FRANKFURTER delivered the opinionof the Cou rt.
1
This i... | choice | lex-glue-scotus-b41c42ae6a:train:125 | [
"criminal procedure",
"civil rights",
"first amendment",
"due process",
"privacy",
"attorneys",
"unions",
"economic activity",
"judicial power",
"federalism",
"interstate relations",
"federal taxation",
"miscellaneous"
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] | Select the label that best applies to the state. | lex_glue/scotus | direct | train | lex-glue-scotus-b41c42ae6a:train:125 | decision | cc-by-4.0, Custom (DPI) | commercial |
332 U.S. 134
67 S.Ct. 1716
91 L.Ed. 1955
FOSTER et al.v.PEOPLE OF STATE OF ILLINOIS.
No. 540.
Argued May 8, 1947.
Decided June 23, 1947.
Mr. Charles Kaufman, of Chicago, Ill., for petitioners.
Mr. William C. Wines, of Chicago, Ill., for respondent.
Mr. Justice FRANKFURTER delivered the opinionof the Cou rt.
1
This i... | choice | lex-glue-scotus-b41c42ae6a:train:125:choice-instruction-paraphrase | [
"criminal procedure",
"civil rights",
"first amendment",
"due process",
"privacy",
"attorneys",
"unions",
"economic activity",
"judicial power",
"federalism",
"interstate relations",
"federal taxation",
"miscellaneous"
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] | Which of the supplied criteria best matches the state? | lex_glue/scotus | instruction_paraphrase | train | lex-glue-scotus-b41c42ae6a:train:125 | choice-instruction-paraphrase | cc-by-4.0, Custom (DPI) | commercial |
Item A:
if you are not subject to that provision , you agree that you will resolve any claim you have with us relating to , arising out of , or in any way in connection with our terms and policies , us , or our services ( each , a `` dispute '' and together , `` disputes '' ) exclusively in the united states district c... | choice | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836:label-A | [
"Limitation of liability",
"Unilateral termination",
"Unilateral change",
"Content removal",
"Contract by using",
"Choice of law",
"Jurisdiction",
"Arbitration",
"fair clause"
] | [
0,
0,
0,
0,
0,
0,
1,
0,
0
] | Each item answers: "Which kind of unfair term, if any, does this terms-of-service clause contain?"
Choose the criterion that best describes Item A. | lex_glue/unfair_tos | packed_derived | train | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836 | label-A | cc-by-4.0 | commercial |
Item A:
if you are not subject to that provision , you agree that you will resolve any claim you have with us relating to , arising out of , or in any way in connection with our terms and policies , us , or our services ( each , a `` dispute '' and together , `` disputes '' ) exclusively in the united states district c... | noul | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836:in-A-0.1.3.5.7.8 | [] | [
0
] | Each item answers: "Which kind of unfair term, if any, does this terms-of-service clause contain?"
Is the label of Item A one of "Limitation of liability", "Unilateral termination", "Content removal", "Choice of law", "Arbitration", "fair clause"? Possible labels: "Limitation of liability", "Unilateral termination", "U... | lex_glue/unfair_tos | packed_derived | train | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836 | in-A-0.1.3.5.7.8 | cc-by-4.0 | commercial |
Item A:
if you are not subject to that provision , you agree that you will resolve any claim you have with us relating to , arising out of , or in any way in connection with our terms and policies , us , or our services ( each , a `` dispute '' and together , `` disputes '' ) exclusively in the united states district c... | noul | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836:all-same | [] | [
1
] | Each item answers: "Which kind of unfair term, if any, does this terms-of-service clause contain?"
Do all items have the same label? Possible labels: "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration", "fair cl... | lex_glue/unfair_tos | packed_derived | train | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836 | all-same | cc-by-4.0 | commercial |
Item A:
if you are not subject to that provision , you agree that you will resolve any claim you have with us relating to , arising out of , or in any way in connection with our terms and policies , us , or our services ( each , a `` dispute '' and together , `` disputes '' ) exclusively in the united states district c... | score | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836:count-6 | [
"0",
"1",
"2"
] | [
0,
0,
1
] | Each item answers: "Which kind of unfair term, if any, does this terms-of-service clause contain?"
How many items have the label "Jurisdiction"? Possible labels: "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitratio... | lex_glue/unfair_tos | packed_derived | train | lex-glue-unfair-tos-e0fa400828:train:pack-e70f74e29836 | count-6 | cc-by-4.0 | commercial |
A: cottage
B: butterfly | choice | lexical-relation-classification-BLESS-2039018563:train:145 | [
"attribute",
"co-hyponym",
"event",
"hypernym",
"meronym",
"unrelated"
] | [
0,
0,
0,
0,
0,
1
] | How is the second word related to the first? | lexical_relation_classification/BLESS | direct | train | lexical-relation-classification-BLESS-2039018563:train:145 | decision | other, CC BY-NC 4.0 (DPI) | non-commercial |
Passage A:
cottage
Passage B:
butterfly | choice | lexical-relation-classification-BLESS-2039018563:train:145:choice-paired-text-format | [
"attribute",
"co-hyponym",
"event",
"hypernym",
"meronym",
"unrelated"
] | [
0,
0,
0,
0,
0,
1
] | How is the second word related to the first? | lexical_relation_classification/BLESS | paired_text_format | train | lexical-relation-classification-BLESS-2039018563:train:145 | choice-paired-text-format | other, CC BY-NC 4.0 (DPI) | non-commercial |
text_A: labor
text_B: work | choice | lexical-relation-classification-CogALexV-d88e30084c:train:76 | [
"antonym relation",
"hypernym relation",
"part-of relation",
"unrelated words",
"synonym relation"
] | [
0,
0,
0,
0,
1
] | How is the second word related to the first? | lexical_relation_classification/CogALexV | direct | train | lexical-relation-classification-CogALexV-d88e30084c:train:76 | decision | other, CC BY-NC 4.0 (DPI) | non-commercial |
text_A: labor
text_B: work | choice | lexical-relation-classification-CogALexV-d88e30084c:train:76:choice-criteria-permutation | [
"part-of relation",
"antonym relation",
"hypernym relation",
"synonym relation",
"unrelated words"
] | [
0,
0,
0,
1,
0
] | How is the second word related to the first? | lexical_relation_classification/CogALexV | criteria_permutation | train | lexical-relation-classification-CogALexV-d88e30084c:train:76 | choice-criteria-permutation | other, CC BY-NC 4.0 (DPI) | non-commercial |
Passage A:
fire
Passage B:
tool | choice | lexical-relation-classification-EVALution-f3aa02c67a:train:52 | [
"Antonym",
"HasA",
"HasProperty",
"IsA",
"MadeOf",
"PartOf",
"Synonym"
] | [
0,
0,
0,
1,
0,
0,
0
] | How is the second word related to the first? | lexical_relation_classification/EVALution | direct | train | lexical-relation-classification-EVALution-f3aa02c67a:train:52 | decision | other, CC BY-NC 4.0 (DPI) | non-commercial |
A: fire
B: tool | choice | lexical-relation-classification-EVALution-f3aa02c67a:train:52:choice-paired-text-format | [
"Antonym",
"HasA",
"HasProperty",
"IsA",
"MadeOf",
"PartOf",
"Synonym"
] | [
0,
0,
0,
1,
0,
0,
0
] | How is the second word related to the first? | lexical_relation_classification/EVALution | paired_text_format | train | lexical-relation-classification-EVALution-f3aa02c67a:train:52 | choice-paired-text-format | other, CC BY-NC 4.0 (DPI) | non-commercial |
Item A:
text_A: car
text_B: grille
Item B:
text_A: car
text_B: bonnet
Item C:
text_A: mollusc
text_B: shell | choice | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6:label-A | [
"co-hyponym",
"hyponym",
"meronym",
"unrelated"
] | [
0,
0,
1,
0
] | Each item answers: "How is the second word related to the first?"
Choose the criterion that best describes Item A. | lexical_relation_classification/K&H+N | packed_derived | train | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6 | label-A | other, CC BY-NC 4.0 (DPI) | non-commercial |
Item A:
text_A: car
text_B: grille
Item B:
text_A: car
text_B: bonnet
Item C:
text_A: mollusc
text_B: shell | noul | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6:same-B-C | [] | [
1
] | Each item answers: "How is the second word related to the first?"
Do Item B and Item C have the same label? Possible labels: "co-hyponym", "hyponym", "meronym", "unrelated". | lexical_relation_classification/K&H+N | packed_derived | train | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6 | same-B-C | other, CC BY-NC 4.0 (DPI) | non-commercial |
Item A:
text_A: car
text_B: grille
Item B:
text_A: car
text_B: bonnet
Item C:
text_A: mollusc
text_B: shell | noul | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6:exists-1 | [] | [
0
] | Each item answers: "How is the second word related to the first?"
Does at least one item have the label "hyponym"? Possible labels: "co-hyponym", "hyponym", "meronym", "unrelated". | lexical_relation_classification/K&H+N | packed_derived | train | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6 | exists-1 | other, CC BY-NC 4.0 (DPI) | non-commercial |
Item A:
text_A: car
text_B: grille
Item B:
text_A: car
text_B: bonnet
Item C:
text_A: mollusc
text_B: shell | score | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6:count-1 | [
"0",
"1",
"2",
"3"
] | [
1,
0,
0,
0
] | Each item answers: "How is the second word related to the first?"
How many items have the label "hyponym"? Possible labels: "co-hyponym", "hyponym", "meronym", "unrelated". | lexical_relation_classification/K&H+N | packed_derived | train | lexical-relation-classification-K-H-N-7adfb9ec3a:train:pack-8ef9da39a4d6 | count-1 | other, CC BY-NC 4.0 (DPI) | non-commercial |
A: coyote
B: tender | choice | lexical-relation-classification-ROOT09-2d7045c12e:train:490 | [
"co-hyponym",
"hypernym",
"unrelated"
] | [
0,
0,
1
] | How is the second word related to the first? | lexical_relation_classification/ROOT09 | direct | train | lexical-relation-classification-ROOT09-2d7045c12e:train:490 | decision | other, CC BY-NC 4.0 (DPI) | non-commercial |
First text:
coyote
Second text:
tender | choice | lexical-relation-classification-ROOT09-2d7045c12e:train:490:choice-paired-text-format | [
"co-hyponym",
"hypernym",
"unrelated"
] | [
0,
0,
1
] | How is the second word related to the first? | lexical_relation_classification/ROOT09 | paired_text_format | train | lexical-relation-classification-ROOT09-2d7045c12e:train:490 | choice-paired-text-format | other, CC BY-NC 4.0 (DPI) | non-commercial |
Says although the unemployment rate seems to be improving, it does not reflect real job creation -- its caused by a shrinking of the actual labor force. | choice | liar-61e652ad7c:train:421 | [
"pants on fire",
"false",
"barely true",
"half true",
"mostly true",
"true"
] | [
0,
0,
0,
0,
1,
0
] | How true is the statement, according to PolitiFact? | liar | direct | train | liar-61e652ad7c:train:421 | decision | unspecified | unspecified |
Says although the unemployment rate seems to be improving, it does not reflect real job creation -- its caused by a shrinking of the actual labor force. | noul | liar-61e652ad7c:train:421:noul-label-verification | [] | [
0
] | How true is the statement, according to PolitiFact? Is "half true" the correct answer? | liar | label_verification | train | liar-61e652ad7c:train:421 | noul-label-verification | unspecified | unspecified |
Says although the unemployment rate seems to be improving, it does not reflect real job creation -- its caused by a shrinking of the actual labor force. | choice | liar-61e652ad7c:train:421:choice-criteria-permutation | [
"true",
"mostly true",
"half true",
"false",
"pants on fire",
"barely true"
] | [
0,
1,
0,
0,
0,
0
] | How true is the statement, according to PolitiFact? | liar | criteria_permutation | train | liar-61e652ad7c:train:421 | choice-criteria-permutation | unspecified | unspecified |
text_A: ::stage Egg:: Fleas lay between four to eight eggs after a meal, with the highest concentrations of laying occurring within the last few days of the female's life. Unlike the eggs of some other parasites, flea eggs are not sticky and usually fall to the ground immediately upon being laid. Flea eggs hatch into l... | choice | lifecycle-entailment-2e88a896f6:train:184 | [
"entailment",
"not_entailment"
] | [
1,
0
] | Does text_A entail text_B? | lifecycle-entailment | direct | train | lifecycle-entailment-2e88a896f6:train:184 | decision | unspecified | unspecified |
text_A: ::stage Egg:: Fleas lay between four to eight eggs after a meal, with the highest concentrations of laying occurring within the last few days of the female's life. Unlike the eggs of some other parasites, flea eggs are not sticky and usually fall to the ground immediately upon being laid. Flea eggs hatch into l... | noul | lifecycle-entailment-2e88a896f6:train:184:noul-label-verification | [] | [
1
] | Does text_A entail text_B? Is "entailment" the correct answer? | lifecycle-entailment | label_verification | train | lifecycle-entailment-2e88a896f6:train:184 | noul-label-verification | unspecified | unspecified |
Passage A:
::stage Egg:: Fleas lay between four to eight eggs after a meal, with the highest concentrations of laying occurring within the last few days of the female's life. Unlike the eggs of some other parasites, flea eggs are not sticky and usually fall to the ground immediately upon being laid. Flea eggs hatch int... | choice | lifecycle-entailment-2e88a896f6:train:184:choice-paired-text-format | [
"entailment",
"not_entailment"
] | [
1,
0
] | Does text_A entail text_B? | lifecycle-entailment | paired_text_format | train | lifecycle-entailment-2e88a896f6:train:184 | choice-paired-text-format | unspecified | unspecified |
Item A:
text_A: The charities, Paris adds, don't endorse the product at all.
text_B: The charities thoroughly endorse all of the products.
Item B:
text_A: Time describes honor killings in Jordan, which comprise a quarter of the Arab nation's homicides.
text_B: The honor killings in Jordan are based on religious belief... | choice | lingnli-36f6080abb:train:pack-dfe03029e28a:label-A | [
"entailment",
"neutral",
"contradiction"
] | [
0,
0,
1
] | Each item answers: "Does text_A entail text_B, contradict it, or neither?"
Choose the criterion that best describes Item A. | lingnli | packed_derived | train | lingnli-36f6080abb:train:pack-dfe03029e28a | label-A | unspecified | unspecified |
Item A:
text_A: The charities, Paris adds, don't endorse the product at all.
text_B: The charities thoroughly endorse all of the products.
Item B:
text_A: Time describes honor killings in Jordan, which comprise a quarter of the Arab nation's homicides.
text_B: The honor killings in Jordan are based on religious belief... | noul | lingnli-36f6080abb:train:pack-dfe03029e28a:same-A-C | [] | [
0
] | Each item answers: "Does text_A entail text_B, contradict it, or neither?"
Do Item A and Item C have the same label? Possible labels: "entailment", "neutral", "contradiction". | lingnli | packed_derived | train | lingnli-36f6080abb:train:pack-dfe03029e28a | same-A-C | unspecified | unspecified |
Item A:
text_A: The charities, Paris adds, don't endorse the product at all.
text_B: The charities thoroughly endorse all of the products.
Item B:
text_A: Time describes honor killings in Jordan, which comprise a quarter of the Arab nation's homicides.
text_B: The honor killings in Jordan are based on religious belief... | noul | lingnli-36f6080abb:train:pack-dfe03029e28a:exists-1 | [] | [
1
] | Each item answers: "Does text_A entail text_B, contradict it, or neither?"
Does at least one item have the label "neutral"? Possible labels: "entailment", "neutral", "contradiction". | lingnli | packed_derived | train | lingnli-36f6080abb:train:pack-dfe03029e28a | exists-1 | unspecified | unspecified |
Item A:
text_A: The charities, Paris adds, don't endorse the product at all.
text_B: The charities thoroughly endorse all of the products.
Item B:
text_A: Time describes honor killings in Jordan, which comprise a quarter of the Arab nation's homicides.
text_B: The honor killings in Jordan are based on religious belief... | score | lingnli-36f6080abb:train:pack-dfe03029e28a:count-1 | [
"0",
"1",
"2",
"3"
] | [
0,
0,
1,
0
] | Each item answers: "Does text_A entail text_B, contradict it, or neither?"
How many items have the label "neutral"? Possible labels: "entailment", "neutral", "contradiction". | lingnli | packed_derived | train | lingnli-36f6080abb:train:pack-dfe03029e28a | count-1 | unspecified | unspecified |
The EOD had already run a full background check on Veronica, and, yes, the woman was as innocent as she looked. | choice | linguisticprobing-bigram-shift-204e05aec7:train:149 | [
"two adjacent words swapped",
"original word order"
] | [
0,
1
] | Were two adjacent words of this sentence swapped? | linguisticprobing/bigram_shift | direct | train | linguisticprobing-bigram-shift-204e05aec7:train:149 | decision | unspecified | unspecified |
Item A:
Money is money, but I had come dangerously close to moving my hand up.
Item B:
Lucy slapped the back of his head, and Damon joked.
Item C:
They tended to Ronald and it wasn 't long before the ambulance showed up. | choice | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325:label-C | [
"inverted clause order",
"original clause order"
] | [
1,
0
] | Each item answers: "Were the two coordinated clauses of this sentence swapped?"
Choose the criterion that best describes Item C. | linguisticprobing/coordination_inversion | packed_derived | train | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325 | label-C | unspecified | unspecified |
Item A:
Money is money, but I had come dangerously close to moving my hand up.
Item B:
Lucy slapped the back of his head, and Damon joked.
Item C:
They tended to Ronald and it wasn 't long before the ambulance showed up. | noul | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325:in-C-1 | [] | [
0
] | Each item answers: "Were the two coordinated clauses of this sentence swapped?"
Is the label of Item C "original clause order"? Possible labels: "inverted clause order", "original clause order". | linguisticprobing/coordination_inversion | packed_derived | train | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325 | in-C-1 | unspecified | unspecified |
Item A:
Money is money, but I had come dangerously close to moving my hand up.
Item B:
Lucy slapped the back of his head, and Damon joked.
Item C:
They tended to Ronald and it wasn 't long before the ambulance showed up. | noul | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325:exists-1 | [] | [
0
] | Each item answers: "Were the two coordinated clauses of this sentence swapped?"
Does at least one item have the label "original clause order"? Possible labels: "inverted clause order", "original clause order". | linguisticprobing/coordination_inversion | packed_derived | train | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325 | exists-1 | unspecified | unspecified |
Item A:
Money is money, but I had come dangerously close to moving my hand up.
Item B:
Lucy slapped the back of his head, and Damon joked.
Item C:
They tended to Ronald and it wasn 't long before the ambulance showed up. | score | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325:count-1 | [
"0",
"1",
"2",
"3"
] | [
1,
0,
0,
0
] | Each item answers: "Were the two coordinated clauses of this sentence swapped?"
How many items have the label "original clause order"? Possible labels: "inverted clause order", "original clause order". | linguisticprobing/coordination_inversion | packed_derived | train | linguisticprobing-coordination-inversion-ba00e546f1:train:pack-49dd75221325 | count-1 | unspecified | unspecified |
She forced steady breaths. | choice | linguisticprobing-obj-number-675b5eec4f:train:73 | [
"singular object",
"plural object"
] | [
0,
1
] | Choose the criterion that best describes the state. | linguisticprobing/obj_number | direct | train | linguisticprobing-obj-number-675b5eec4f:train:73 | decision | unspecified | unspecified |
Item A:
Usually locked, with only the stirrup having a key, he somehow found an opportunity to follow the old man.
Item B:
"The water we accumulated . """
Item C:
"Few possess enough injustice to actually earn one . """
Item D:
The fact that I have the midwife doesn 't matter. | choice | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b:label-C | [
"one word replaced",
"original sentence"
] | [
1,
0
] | Each item answers: "Was one word of this sentence replaced by a random word of the same part of speech?"
Choose the criterion that best describes Item C. | linguisticprobing/odd_man_out | packed_derived | train | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b | label-C | unspecified | unspecified |
Item A:
Usually locked, with only the stirrup having a key, he somehow found an opportunity to follow the old man.
Item B:
"The water we accumulated . """
Item C:
"Few possess enough injustice to actually earn one . """
Item D:
The fact that I have the midwife doesn 't matter. | noul | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b:in-B-1 | [] | [
1
] | Each item answers: "Was one word of this sentence replaced by a random word of the same part of speech?"
Is the label of Item B "original sentence"? Possible labels: "one word replaced", "original sentence". | linguisticprobing/odd_man_out | packed_derived | train | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b | in-B-1 | unspecified | unspecified |
Item A:
Usually locked, with only the stirrup having a key, he somehow found an opportunity to follow the old man.
Item B:
"The water we accumulated . """
Item C:
"Few possess enough injustice to actually earn one . """
Item D:
The fact that I have the midwife doesn 't matter. | noul | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b:all-same | [] | [
0
] | Each item answers: "Was one word of this sentence replaced by a random word of the same part of speech?"
Do all items have the same label? Possible labels: "one word replaced", "original sentence". | linguisticprobing/odd_man_out | packed_derived | train | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b | all-same | unspecified | unspecified |
Item A:
Usually locked, with only the stirrup having a key, he somehow found an opportunity to follow the old man.
Item B:
"The water we accumulated . """
Item C:
"Few possess enough injustice to actually earn one . """
Item D:
The fact that I have the midwife doesn 't matter. | score | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b:count-1 | [
"0",
"1",
"2",
"3",
"4"
] | [
0,
1,
0,
0,
0
] | Each item answers: "Was one word of this sentence replaced by a random word of the same part of speech?"
How many items have the label "original sentence"? Possible labels: "one word replaced", "original sentence". | linguisticprobing/odd_man_out | packed_derived | train | linguisticprobing-odd-man-out-1ad753291d:train:pack-d626dd97c38b | count-1 | unspecified | unspecified |
Serena stands, studying the wolf's reaction. | choice | linguisticprobing-past-present-c2e9fd9324:train:42 | [
"past tense",
"present tense"
] | [
0,
1
] | Select the label that best applies to the state. | linguisticprobing/past_present | direct | train | linguisticprobing-past-present-c2e9fd9324:train:42 | decision | unspecified | unspecified |
This is what I remember next: a sound screeching through my mind, bleary voices. | choice | linguisticprobing-sentence-length-650cae4236:train:6148 | [
"5-8 words",
"9-12 words",
"13-16 words",
"17-20 words",
"21-25 words",
"26-28 words"
] | [
0,
0,
0,
1,
0,
0
] | How many words does the sentence have? | linguisticprobing/sentence_length | direct | train | linguisticprobing-sentence-length-650cae4236:train:6148 | decision | unspecified | unspecified |
This is what I remember next: a sound screeching through my mind, bleary voices. | choice | linguisticprobing-sentence-length-650cae4236:train:6148:choice-criteria-permutation | [
"5-8 words",
"21-25 words",
"9-12 words",
"17-20 words",
"13-16 words",
"26-28 words"
] | [
0,
0,
0,
1,
0,
0
] | How many words does the sentence have? | linguisticprobing/sentence_length | criteria_permutation | train | linguisticprobing-sentence-length-650cae4236:train:6148 | choice-criteria-permutation | unspecified | unspecified |
"That vow, and the book, has cursed our family for generations . """ | choice | linguisticprobing-subj-number-eadb064979:train:115 | [
"singular subject",
"plural subject"
] | [
1,
0
] | Choose the most appropriate category for the state. | linguisticprobing/subj_number | direct | train | linguisticprobing-subj-number-eadb064979:train:115 | decision | unspecified | unspecified |
Remember those soft hands are attached to a professional woman and a damned sharp officer. | choice | linguisticprobing-top-constituents-b42ea75375:train:286 | [
"constituents ADVP NP VP .",
"constituents CC ADVP NP VP .",
"constituents CC NP VP .",
"constituents IN NP VP .",
"constituents NP ADVP VP .",
"constituents NP NP VP .",
"constituents NP PP .",
"constituents NP VP .",
"other constituents",
"constituents PP NP VP .",
"constituents RB NP VP .",
... | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
] | What is the sequence of top-level constituents in the sentence's parse tree? | linguisticprobing/top_constituents | direct | train | linguisticprobing-top-constituents-b42ea75375:train:286 | decision | unspecified | unspecified |
Remember those soft hands are attached to a professional woman and a damned sharp officer. | choice | linguisticprobing-top-constituents-b42ea75375:train:286:choice-criteria-permutation | [
"other constituents",
"constituents NP PP .",
"constituents NP NP VP .",
"constituents PP NP VP .",
"constituents VBD NP VP .",
"constituents WHADVP SQ .",
"constituents IN NP VP .",
"constituents S VP .",
"constituents NP ADVP VP .",
"constituents WHNP SQ .",
"constituents CC ADVP NP VP .",
"... | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0
] | What is the sequence of top-level constituents in the sentence's parse tree? | linguisticprobing/top_constituents | criteria_permutation | train | linguisticprobing-top-constituents-b42ea75375:train:286 | choice-criteria-permutation | unspecified | unspecified |
He stopped suddenly and turned to face me. | choice | linguisticprobing-tree-depth-96c2fbbdae:train:142 | [
"parse tree depth 5",
"parse tree depth 6",
"parse tree depth 7",
"parse tree depth 8",
"parse tree depth 9",
"parse tree depth 10",
"parse tree depth 11"
] | [
0,
0,
0,
0,
1,
0,
0
] | What is the depth of the sentence's constituency parse tree? | linguisticprobing/tree_depth | direct | train | linguisticprobing-tree-depth-96c2fbbdae:train:142 | decision | unspecified | unspecified |
Item A:
text_A: (y&((y>(~(x)|~((j&y))))&(j&~((u&j)))))
text_B: ((j&u)>~(~(v)))
Item B:
text_A: (~((s|(r&q)))&m)
text_B: (x>~((q&(c|c)))) | choice | logical-entailment-5fde239036:train:pack-98d7a485359a:label-A | [
"entailment",
"not entailment"
] | [
1,
0
] | Each item answers: "Does propositional formula text_A logically entail formula text_B?"
Choose the criterion that best describes Item A. | logical-entailment | packed_derived | train | logical-entailment-5fde239036:train:pack-98d7a485359a | label-A | apache-2.0 | commercial |
Item A:
text_A: (y&((y>(~(x)|~((j&y))))&(j&~((u&j)))))
text_B: ((j&u)>~(~(v)))
Item B:
text_A: (~((s|(r&q)))&m)
text_B: (x>~((q&(c|c)))) | noul | logical-entailment-5fde239036:train:pack-98d7a485359a:in-B-0 | [] | [
0
] | Each item answers: "Does propositional formula text_A logically entail formula text_B?"
Is the label of Item B "entailment"? Possible labels: "entailment", "not entailment". | logical-entailment | packed_derived | train | logical-entailment-5fde239036:train:pack-98d7a485359a | in-B-0 | apache-2.0 | commercial |
Item A:
text_A: (y&((y>(~(x)|~((j&y))))&(j&~((u&j)))))
text_B: ((j&u)>~(~(v)))
Item B:
text_A: (~((s|(r&q)))&m)
text_B: (x>~((q&(c|c)))) | noul | logical-entailment-5fde239036:train:pack-98d7a485359a:all-same | [] | [
0
] | Each item answers: "Does propositional formula text_A logically entail formula text_B?"
Do all items have the same label? Possible labels: "entailment", "not entailment". | logical-entailment | packed_derived | train | logical-entailment-5fde239036:train:pack-98d7a485359a | all-same | apache-2.0 | commercial |
Item A:
text_A: (y&((y>(~(x)|~((j&y))))&(j&~((u&j)))))
text_B: ((j&u)>~(~(v)))
Item B:
text_A: (~((s|(r&q)))&m)
text_B: (x>~((q&(c|c)))) | score | logical-entailment-5fde239036:train:pack-98d7a485359a:count-1 | [
"0",
"1",
"2"
] | [
0,
1,
0
] | Each item answers: "Does propositional formula text_A logically entail formula text_B?"
How many items have the label "not entailment"? Possible labels: "entailment", "not entailment". | logical-entailment | packed_derived | train | logical-entailment-5fde239036:train:pack-98d7a485359a | count-1 | apache-2.0 | commercial |
Martin: All white people are not racists.
Charlie: Yes they are. You just believe that because you are white.
| choice | logical-fallacy-49ee8e577f:train:20 | [
"ad hominem",
"ad populum",
"appeal to emotion",
"circular reasoning",
"equivocation",
"fallacy of credibility",
"fallacy of extension",
"fallacy of logic",
"fallacy of relevance",
"false causality",
"false dilemma",
"faulty generalization",
"intentional"
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] | Choose the criterion that best describes the state. | logical-fallacy | direct | train | logical-fallacy-49ee8e577f:train:20 | decision | unspecified | unspecified |
Martin: All white people are not racists.
Charlie: Yes they are. You just believe that because you are white.
| choice | logical-fallacy-49ee8e577f:train:20:choice-instruction-paraphrase | [
"ad hominem",
"ad populum",
"appeal to emotion",
"circular reasoning",
"equivocation",
"fallacy of credibility",
"fallacy of extension",
"fallacy of logic",
"fallacy of relevance",
"false causality",
"false dilemma",
"faulty generalization",
"intentional"
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] | Choose the most appropriate category for the state. | logical-fallacy | instruction_paraphrase | train | logical-fallacy-49ee8e577f:train:20 | choice-instruction-paraphrase | unspecified | unspecified |
Bribery: Refers to the behavior of state personnel to take advantage of their position to solicit others 'property, or illegally accept others' property to seek benefits for others. Is the following behavior a bribery? | choice | logiqa-519764b05f:train:45 | [
"Yang is the deputy head of a county and is in charge of foreign trade.Yang is greedy by nature and believes that everyone below should pay him tribute.However, he was afraid of getting his hands dirty, so he instructed his wife to go to the county's jurisdiction.The foreign trade companies called and asked them to... | [
1,
0,
0,
0
] | Choose the criterion that best answers the question. | logiqa | direct | train | logiqa-519764b05f:train:45 | decision | unspecified | unspecified |
Bribery: Refers to the behavior of state personnel to take advantage of their position to solicit others 'property, or illegally accept others' property to seek benefits for others. Is the following behavior a bribery? | noul | logiqa-519764b05f:train:45:noul-label-verification | [] | [
0
] | Is "Li, a salesman of a large advertising company, often uses his identity to collect benefits and rebates paid by the other party in his business dealings with other companies." the correct answer to the question? | logiqa | label_verification | train | logiqa-519764b05f:train:45 | noul-label-verification | unspecified | unspecified |
text_A: All foreign students from China live on campus; All students living on campus must participate in the sports meeting; Some Chinese students have joined the student union; Some students majoring in psychology have also joined the student union; None of the psychology majors took part in the sports meeting
text_B... | choice | logiqa-2-0-nli-cf23683f05:train:5 | [
"entailment",
"not-entailment"
] | [
1,
0
] | Does text_A entail text_B? | logiqa-2.0-nli | direct | train | logiqa-2-0-nli-cf23683f05:train:5 | decision | cc, CC BY-NC-SA 4.0 (DPI) | non-commercial |
First text:
All foreign students from China live on campus; All students living on campus must participate in the sports meeting; Some Chinese students have joined the student union; Some students majoring in psychology have also joined the student union; None of the psychology majors took part in the sports meeting
S... | choice | logiqa-2-0-nli-cf23683f05:train:5:choice-paired-text-format | [
"entailment",
"not-entailment"
] | [
1,
0
] | Does text_A entail text_B? | logiqa-2.0-nli | paired_text_format | train | logiqa-2-0-nli-cf23683f05:train:5 | choice-paired-text-format | cc, CC BY-NC-SA 4.0 (DPI) | non-commercial |
text_A: Joseph has 9 dollars. He received 7 more dollars.
text_B: Joseph now has 2 dollars. | choice | lonli-c26dcc495d:train:61 | [
"entailment",
"neutral",
"contradiction"
] | [
0,
0,
1
] | Does text_A entail text_B, contradict it, or neither? | lonli | direct | train | lonli-c26dcc495d:train:61 | decision | mit | commercial |
text_A: Joseph has 9 dollars. He received 7 more dollars.
text_B: Joseph now has 2 dollars. | choice | lonli-c26dcc495d:train:61:choice-criteria-permutation | [
"entailment",
"contradiction",
"neutral"
] | [
0,
1,
0
] | Does text_A entail text_B, contradict it, or neither? | lonli | criteria_permutation | train | lonli-c26dcc495d:train:61 | choice-criteria-permutation | mit | commercial |
A small software firm has four offices, numbered 1, 2, 3, and 4. Each of its offices has exactly one computer and exactly one printer. Each of these eight machines was bought in either 1987, 1988, or 1989. The eight machines were bought in a manner consistent with the following conditions: The computer in each office w... | choice | lsat-ar-eb3f507971:train:9 | [
"3",
"1",
"0",
"2",
"4"
] | [
0,
0,
1,
0,
0
] | Choose the most appropriate answer from the supplied options. | lsat-ar | direct | train | lsat-ar-eb3f507971:train:9 | decision | unspecified | unspecified |
A small software firm has four offices, numbered 1, 2, 3, and 4. Each of its offices has exactly one computer and exactly one printer. Each of these eight machines was bought in either 1987, 1988, or 1989. The eight machines were bought in a manner consistent with the following conditions: The computer in each office w... | choice | lsat-ar-eb3f507971:train:9:choice-instruction-paraphrase | [
"3",
"1",
"0",
"2",
"4"
] | [
0,
0,
1,
0,
0
] | Select the option that best answers the question. | lsat-ar | instruction_paraphrase | train | lsat-ar-eb3f507971:train:9 | choice-instruction-paraphrase | unspecified | unspecified |
One type of violation of the antitrust laws is the abuse of monopoly power. Monopoly power is the ability of a firm to raise its prices above the competitive level-that is, above the level that would exist naturally if several firms had to compete-without driving away so many customers as to make the price increase unp... | choice | lsat-rc-094f659543:train:72 | [
"Yes, unless the firm using leverage is charging competitive prices.",
"Yes, because leverage is a characteristic of monopoly power.",
"No, because leverage involves a nonmonopolized market.",
"Yes, because leverage is used to eliminate competition in a related market.",
"No, unless the leverage involves a ... | [
0,
0,
0,
1,
0
] | Select the option that best answers the question. | lsat-rc | direct | train | lsat-rc-094f659543:train:72 | decision | unspecified | unspecified |
One type of violation of the antitrust laws is the abuse of monopoly power. Monopoly power is the ability of a firm to raise its prices above the competitive level-that is, above the level that would exist naturally if several firms had to compete-without driving away so many customers as to make the price increase unp... | noul | lsat-rc-094f659543:train:72:noul-label-verification | [] | [
1
] | Is "Yes, because leverage is used to eliminate competition in a related market." the correct answer to the question? | lsat-rc | label_verification | train | lsat-rc-094f659543:train:72 | noul-label-verification | unspecified | unspecified |
One type of violation of the antitrust laws is the abuse of monopoly power. Monopoly power is the ability of a firm to raise its prices above the competitive level-that is, above the level that would exist naturally if several firms had to compete-without driving away so many customers as to make the price increase unp... | choice | lsat-rc-094f659543:train:72:choice-criteria-permutation | [
"Yes, because leverage is a characteristic of monopoly power.",
"No, unless the leverage involves a tying arrangement.",
"No, because leverage involves a nonmonopolized market.",
"Yes, because leverage is used to eliminate competition in a related market.",
"Yes, unless the firm using leverage is charging c... | [
0,
0,
0,
1,
0
] | Select the option that best answers the question. | lsat-rc | criteria_permutation | train | lsat-rc-094f659543:train:72 | choice-criteria-permutation | unspecified | unspecified |
A panel of five scientists will be formed. The panelists will be selected from among three botanists—F, G, and H—three chemists—K, L, and M—and three zoologists—P, Q, and R. Selection is governed by the following conditions: The panel must include at least one scientist of each of the three types. If more than one bota... | choice | lsat-qa-all-05417eda9d:train:0 | [
"G and H are both selected.",
"F and G are both selected.",
"F, G, and H are all selected.",
"P, Q, and R are all selected.",
"H and P are both selected."
] | [
0,
0,
0,
1,
0
] | Choose the criterion that best answers the question. | lsat_qa/all | direct | train | lsat-qa-all-05417eda9d:train:0 | decision | unspecified | unspecified |
because he ’ s taxed by his home planet , mork pays a tax rate of 40 % on his income , while mindy pays a rate of only 30 % on hers . if mindy earned 3 times as much as mork did , what was their combined tax rate ? | choice | math-qa-1c90874154:train:34 | [
"35 %",
"37.5 %",
"36 %",
"34 %",
"32.5 %"
] | [
0,
0,
0,
0,
1
] | Select the option that best answers the question. | math_qa | direct | train | math-qa-1c90874154:train:34 | decision | apache-2.0 | commercial |
because he ’ s taxed by his home planet , mork pays a tax rate of 40 % on his income , while mindy pays a rate of only 30 % on hers . if mindy earned 3 times as much as mork did , what was their combined tax rate ? | noul | math-qa-1c90874154:train:34:noul-label-verification | [] | [
1
] | Is "32.5 %" the correct answer to the question? | math_qa | label_verification | train | math-qa-1c90874154:train:34 | noul-label-verification | apache-2.0 | commercial |
A: She explained that Frank 's father was an alcoholic and that his mother worked as a toll booth operator .
How long has Frank's father been an alcoholic?
B: 5 hours | choice | mc-taco-632cdc8c5b:train:175 | [
"no",
"yes"
] | [
1,
0
] | Is this answer plausible? | mc_taco | direct | train | mc-taco-632cdc8c5b:train:175 | decision | unspecified | unspecified |
First text:
She explained that Frank 's father was an alcoholic and that his mother worked as a toll booth operator .
How long has Frank's father been an alcoholic?
Second text:
5 hours | choice | mc-taco-632cdc8c5b:train:175:choice-paired-text-format | [
"no",
"yes"
] | [
1,
0
] | Is this answer plausible? | mc_taco | paired_text_format | train | mc-taco-632cdc8c5b:train:175 | choice-paired-text-format | unspecified | unspecified |
A: Tom had to fix some things around the house. He had to fix the door. He had to fix the window. But before he did anything he had to fix the toilet. Tom called over his best friend Jim to help him. Jim brought with him his friends Molly and Holly. Tom thought that Jim was going to bring Dolly with him but he didn't. ... | choice | mctest-nli-2379e69ea7:train:0 | [
"entailment",
"non_entailment"
] | [
0,
1
] | Which of the supplied criteria best matches the state? | mctest-nli | direct | train | mctest-nli-2379e69ea7:train:0 | decision | unspecified | unspecified |
A: Tom had to fix some things around the house. He had to fix the door. He had to fix the window. But before he did anything he had to fix the toilet. Tom called over his best friend Jim to help him. Jim brought with him his friends Molly and Holly. Tom thought that Jim was going to bring Dolly with him but he didn't. ... | noul | mctest-nli-2379e69ea7:train:0:noul-label-verification | [] | [
0
] | Is "entailment" the correct label for this example? | mctest-nli | label_verification | train | mctest-nli-2379e69ea7:train:0 | noul-label-verification | unspecified | unspecified |
A: I have hypermobile joints and am experiencing right arm, elbow, shoulder and muscle pain. What can I do?
B: My joint have always been hypermobile and I have pain over right arm, elbow, shoulder and muscles, that got worse recently. Any ideas on what I can do for relief? | choice | medical-questions-pairs-c9bca85350:train:21 | [
"not similar",
"similar"
] | [
0,
1
] | Do the two medical questions ask the same thing? | medical_questions_pairs | direct | train | medical-questions-pairs-c9bca85350:train:21 | decision | unspecified | unspecified |
First text:
I have hypermobile joints and am experiencing right arm, elbow, shoulder and muscle pain. What can I do?
Second text:
My joint have always been hypermobile and I have pain over right arm, elbow, shoulder and muscles, that got worse recently. Any ideas on what I can do for relief? | choice | medical-questions-pairs-c9bca85350:train:21:choice-paired-text-format | [
"not similar",
"similar"
] | [
0,
1
] | Do the two medical questions ask the same thing? | medical_questions_pairs | paired_text_format | train | medical-questions-pairs-c9bca85350:train:21 | choice-paired-text-format | unspecified | unspecified |
Which of the following enzyme does not catalyse the irreversible step in glycolysis? | choice | medmcqa-d10217a116:train:67 | [
"Phosphoglycero kinase",
"Phosphofructokinase",
"Pyruvate kinase",
"Hexokinase"
] | [
1,
0,
0,
0
] | Choose the most appropriate answer from the supplied options. | medmcqa | direct | train | medmcqa-d10217a116:train:67 | decision | apache-2.0 | commercial |
Which of the following enzyme does not catalyse the irreversible step in glycolysis? | noul | medmcqa-d10217a116:train:67:noul-label-verification | [] | [
0
] | Is "Pyruvate kinase" the correct answer to the question? | medmcqa | label_verification | train | medmcqa-d10217a116:train:67 | noul-label-verification | apache-2.0 | commercial |
Which of the following enzyme does not catalyse the irreversible step in glycolysis? | choice | medmcqa-d10217a116:train:67:choice-criteria-permutation | [
"Phosphoglycero kinase",
"Hexokinase",
"Pyruvate kinase",
"Phosphofructokinase"
] | [
1,
0,
0,
0
] | Choose the most appropriate answer from the supplied options. | medmcqa | criteria_permutation | train | medmcqa-d10217a116:train:67 | choice-criteria-permutation | apache-2.0 | commercial |
text_A: There are four persons. Everyone is visible to others. It is publicly announced that someone is thirsty. It is publicly announced that Stephanie knows whether or not nobody is thirsty. It is publicly announced that Stephanie does not know whether Tracey is thirsty.
text_B: Stephanie can now know whether Vickie ... | choice | mindgames-dfe4fff9e3:train:96 | [
"entailment",
"not_entailment"
] | [
1,
0
] | Does text_A entail text_B? | mindgames | direct | train | mindgames-dfe4fff9e3:train:96 | decision | apache-2.0, Apache License 2.0 (DPI) | commercial |
First text:
There are four persons. Everyone is visible to others. It is publicly announced that someone is thirsty. It is publicly announced that Stephanie knows whether or not nobody is thirsty. It is publicly announced that Stephanie does not know whether Tracey is thirsty.
Second text:
Stephanie can now know wheth... | choice | mindgames-dfe4fff9e3:train:96:choice-paired-text-format | [
"entailment",
"not_entailment"
] | [
1,
0
] | Does text_A entail text_B? | mindgames | paired_text_format | train | mindgames-dfe4fff9e3:train:96 | choice-paired-text-format | apache-2.0, Apache License 2.0 (DPI) | commercial |
11, 7, 15, 1, 13, 2, 14, 3, 4, 8, 16, 10, 5, 6, 0, 9. Is "12" in the previous list? | choice | missing-item-prediction-contrastive-6be2dc5c8a:train:44 | [
"no",
"yes"
] | [
1,
0
] | Choose the criterion that best describes the state. | missing-item-prediction/contrastive | direct | train | missing-item-prediction-contrastive-6be2dc5c8a:train:44 | decision | unspecified | unspecified |
11, 7, 15, 1, 13, 2, 14, 3, 4, 8, 16, 10, 5, 6, 0, 9. Is "12" in the previous list? | noul | missing-item-prediction-contrastive-6be2dc5c8a:train:44:noul-label-verification | [] | [
1
] | Is "no" the correct label for this example? | missing-item-prediction/contrastive | label_verification | train | missing-item-prediction-contrastive-6be2dc5c8a:train:44 | noul-label-verification | unspecified | unspecified |
text_A: The girl here is not wearing any necklaces at all.
text_B: The girl here is not wearing any jewelry at all. | choice | monli-e20b1da17a:train:38 | [
"entailment",
"neutral"
] | [
0,
1
] | Does text_A entail text_B? | monli | direct | train | monli-e20b1da17a:train:38 | decision | unspecified | unspecified |
A: The girl here is not wearing any necklaces at all.
B: The girl here is not wearing any jewelry at all. | choice | monli-e20b1da17a:train:38:choice-paired-text-format | [
"entailment",
"neutral"
] | [
0,
1
] | Does text_A entail text_B? | monli | paired_text_format | train | monli-e20b1da17a:train:38 | choice-paired-text-format | unspecified | unspecified |
text_A: a typhoon simoron is approaching japan.
text_B: a typhoon is approaching japan. | choice | monotonicity-entailment-5b266929a5:train:30 | [
"entailment",
"neutral"
] | [
1,
0
] | Does text_A entail text_B? | monotonicity-entailment | direct | train | monotonicity-entailment-5b266929a5:train:30 | decision | apache-2.0 | commercial |
text_A: a typhoon simoron is approaching japan.
text_B: a typhoon is approaching japan. | noul | monotonicity-entailment-5b266929a5:train:30:noul-label-verification | [] | [
1
] | Does text_A entail text_B? Is "entailment" the correct answer? | monotonicity-entailment | label_verification | train | monotonicity-entailment-5b266929a5:train:30 | noul-label-verification | apache-2.0 | commercial |
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