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
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value | group_id stringlengths 22 82 | question_id stringlengths 4 118 | license stringclasses 69
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
Item A:
text_A: Jeff went back to the bedroom. Fred grabbed the milk there. Fred went back to the kitchen. Fred put down the milk. Jeff went back to the garden. Mary picked up the football there. Fred went to the bedroom. Mary left the football there. Fred went back to the hallway. Bill went back to the office. Jeff tr... | score | babi-nli-three-arg-relations-9af4adf52c:train:pack-120da4f6f6ba:count-1 | [
"0",
"1",
"2"
] | [
1,
0,
0
] | Each item answers: "Does text_A entail text_B?"
How many items have the label "entailed"? Possible labels: "not-entailed", "entailed". | babi_nli/three-arg-relations | packed_derived | train | babi-nli-three-arg-relations-9af4adf52c:train:pack-120da4f6f6ba | count-1 | bsd | commercial |
text_A: Mary moved to the bedroom. Mary travelled to the office. Mary picked up the football. Sandra travelled to the kitchen. Mary moved to the hallway. Sandra journeyed to the bathroom. Mary went to the bathroom. Mary put down the football.
text_B: The football before the bathroom was in the bathroom. | choice | babi-nli-three-supporting-facts-f6540ced19:train:166 | [
"not-entailed",
"entailed"
] | [
1,
0
] | Does text_A entail text_B? | babi_nli/three-supporting-facts | direct | train | babi-nli-three-supporting-facts-f6540ced19:train:166 | decision | bsd | commercial |
text_A: Mary moved to the bedroom. Mary travelled to the office. Mary picked up the football. Sandra travelled to the kitchen. Mary moved to the hallway. Sandra journeyed to the bathroom. Mary went to the bathroom. Mary put down the football.
text_B: The football before the bathroom was in the bathroom. | choice | babi-nli-three-supporting-facts-f6540ced19:train:166:choice-criteria-permutation | [
"entailed",
"not-entailed"
] | [
0,
1
] | Does text_A entail text_B? | babi_nli/three-supporting-facts | criteria_permutation | train | babi-nli-three-supporting-facts-f6540ced19:train:166 | choice-criteria-permutation | bsd | commercial |
A: Mary moved to the bedroom. Mary travelled to the office. Mary picked up the football. Sandra travelled to the kitchen. Mary moved to the hallway. Sandra journeyed to the bathroom. Mary went to the bathroom. Mary put down the football.
B: The football before the bathroom was in the bathroom. | choice | babi-nli-three-supporting-facts-f6540ced19:train:166:choice-paired-text-format | [
"not-entailed",
"entailed"
] | [
1,
0
] | Does text_A entail text_B? | babi_nli/three-supporting-facts | paired_text_format | train | babi-nli-three-supporting-facts-f6540ced19:train:166 | choice-paired-text-format | bsd | commercial |
text_A: Yesterday Fred travelled to the kitchen. This afternoon Julie moved to the cinema. Julie moved to the park yesterday. This morning Julie journeyed to the kitchen. This morning Fred went to the school. Yesterday Mary went to the bedroom. Yesterday Bill travelled to the office. This morning Mary went to the cinem... | choice | babi-nli-time-reasoning-f89e7fc8a2:train:42 | [
"not-entailed",
"entailed"
] | [
1,
0
] | Does text_A entail text_B? | babi_nli/time-reasoning | direct | train | babi-nli-time-reasoning-f89e7fc8a2:train:42 | decision | bsd | commercial |
text_A: Yesterday Fred travelled to the kitchen. This afternoon Julie moved to the cinema. Julie moved to the park yesterday. This morning Julie journeyed to the kitchen. This morning Fred went to the school. Yesterday Mary went to the bedroom. Yesterday Bill travelled to the office. This morning Mary went to the cinem... | noul | babi-nli-time-reasoning-f89e7fc8a2:train:42:noul-label-verification | [] | [
1
] | Does text_A entail text_B? Is "not-entailed" the correct answer? | babi_nli/time-reasoning | label_verification | train | babi-nli-time-reasoning-f89e7fc8a2:train:42 | noul-label-verification | bsd | commercial |
text_A: The bathroom is south of the office. The office is south of the garden.
text_B: The garden north of is garden. | choice | babi-nli-two-arg-relations-9fc54432c3:train:36 | [
"not-entailed",
"entailed"
] | [
1,
0
] | Does text_A entail text_B? | babi_nli/two-arg-relations | direct | train | babi-nli-two-arg-relations-9fc54432c3:train:36 | decision | bsd | commercial |
Passage A:
The bathroom is south of the office. The office is south of the garden.
Passage B:
The garden north of is garden. | choice | babi-nli-two-arg-relations-9fc54432c3:train:36:choice-paired-text-format | [
"not-entailed",
"entailed"
] | [
1,
0
] | Does text_A entail text_B? | babi_nli/two-arg-relations | paired_text_format | train | babi-nli-two-arg-relations-9fc54432c3:train:36 | choice-paired-text-format | bsd | commercial |
text_A: Daniel got the football there. Mary moved to the bathroom. Daniel dropped the football. Daniel picked up the apple there. Daniel journeyed to the office. Mary went back to the hallway.
text_B: The apple is in the hallway. | choice | babi-nli-two-supporting-facts-1756bf4c34:train:319 | [
"not-entailed",
"entailed"
] | [
1,
0
] | Does text_A entail text_B? | babi_nli/two-supporting-facts | direct | train | babi-nli-two-supporting-facts-1756bf4c34:train:319 | decision | bsd | commercial |
text_A: Daniel got the football there. Mary moved to the bathroom. Daniel dropped the football. Daniel picked up the apple there. Daniel journeyed to the office. Mary went back to the hallway.
text_B: The apple is in the hallway. | choice | babi-nli-two-supporting-facts-1756bf4c34:train:319:choice-criteria-permutation | [
"entailed",
"not-entailed"
] | [
0,
1
] | Does text_A entail text_B? | babi_nli/two-supporting-facts | criteria_permutation | train | babi-nli-two-supporting-facts-1756bf4c34:train:319 | choice-criteria-permutation | bsd | commercial |
text_A: John took the apple there. Sandra moved to the bedroom. Mary went to the office. John discarded the apple.
text_B: Sandra is in the bedroom. | choice | babi-nli-yes-no-questions-53d08980f1:train:131 | [
"not-entailed",
"entailed"
] | [
0,
1
] | Does text_A entail text_B? | babi_nli/yes-no-questions | direct | train | babi-nli-yes-no-questions-53d08980f1:train:131 | decision | bsd | commercial |
A: John took the apple there. Sandra moved to the bedroom. Mary went to the office. John discarded the apple.
B: Sandra is in the bedroom. | choice | babi-nli-yes-no-questions-53d08980f1:train:131:choice-paired-text-format | [
"not-entailed",
"entailed"
] | [
0,
1
] | Does text_A entail text_B? | babi_nli/yes-no-questions | paired_text_format | train | babi-nli-yes-no-questions-53d08980f1:train:131 | choice-paired-text-format | bsd | commercial |
The parents received a birth certificate. What was the cause of this? | choice | balanced-copa-90d823fa83:train:0 | [
"The parents shook a rattle in front of the baby.",
"The parents picked out a name for the baby."
] | [
0,
1
] | Which supplied option best answers the question? | balanced-copa | direct | train | balanced-copa-90d823fa83:train:0 | decision | cc-by-4.0, BSD 2-Clause License (DPI) | commercial |
The parents received a birth certificate. What was the cause of this? | noul | balanced-copa-90d823fa83:train:0:noul-label-verification | [] | [
1
] | Is "The parents picked out a name for the baby." the correct answer to the question? | balanced-copa | label_verification | train | balanced-copa-90d823fa83:train:0 | noul-label-verification | cc-by-4.0, BSD 2-Clause License (DPI) | commercial |
There's a problem - I bought something about a week or two ago but just today the payment somehow came back into my account! I already received the item though, what's going on here? | choice | banking77-8ef8a39243:train:0 | [
"activate_my_card",
"age_limit",
"apple_pay_or_google_pay",
"atm_support",
"automatic_top_up",
"balance_not_updated_after_bank_transfer",
"balance_not_updated_after_cheque_or_cash_deposit",
"beneficiary_not_allowed",
"cancel_transfer",
"card_about_to_expire",
"card_acceptance",
"card_arrival",... | [
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,
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0,
0,
0,
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0,
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0,
0,
0,
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0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | Choose the most appropriate category for the state. | banking77 | direct | train | banking77-8ef8a39243:train:0 | decision | cc-by-4.0, CC BY 4.0 (DPI) | commercial |
Item A:
text_A: Vitamin D is important for bone health and immune regulation, and has been shown to be low in multiple sclerosis (MS). We sought to determine the effect of over the counter low dose cholecalciferol (LDC) and high dose ergocalciferol (HDE) on the vitamin D levels in MS patients. We retrospectively evalua... | choice | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21:label-B | [
"entailment",
"not-entailment"
] | [
1,
0
] | Each item answers: "Does text_A entail text_B?"
Choose the criterion that best describes Item B. | biosift-nli | packed_derived | train | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21 | label-B | unspecified | unspecified |
Item A:
text_A: Vitamin D is important for bone health and immune regulation, and has been shown to be low in multiple sclerosis (MS). We sought to determine the effect of over the counter low dose cholecalciferol (LDC) and high dose ergocalciferol (HDE) on the vitamin D levels in MS patients. We retrospectively evalua... | noul | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21:same-A-B | [] | [
0
] | Each item answers: "Does text_A entail text_B?"
Do Item A and Item B have the same label? Possible labels: "entailment", "not-entailment". | biosift-nli | packed_derived | train | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21 | same-A-B | unspecified | unspecified |
Item A:
text_A: Vitamin D is important for bone health and immune regulation, and has been shown to be low in multiple sclerosis (MS). We sought to determine the effect of over the counter low dose cholecalciferol (LDC) and high dose ergocalciferol (HDE) on the vitamin D levels in MS patients. We retrospectively evalua... | noul | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21:exists-0 | [] | [
1
] | Each item answers: "Does text_A entail text_B?"
Does at least one item have the label "entailment"? Possible labels: "entailment", "not-entailment". | biosift-nli | packed_derived | train | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21 | exists-0 | unspecified | unspecified |
Item A:
text_A: Vitamin D is important for bone health and immune regulation, and has been shown to be low in multiple sclerosis (MS). We sought to determine the effect of over the counter low dose cholecalciferol (LDC) and high dose ergocalciferol (HDE) on the vitamin D levels in MS patients. We retrospectively evalua... | score | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21:count-0 | [
"0",
"1",
"2"
] | [
0,
1,
0
] | Each item answers: "Does text_A entail text_B?"
How many items have the label "entailment"? Possible labels: "entailment", "not-entailment". | biosift-nli | packed_derived | train | biosift-nli-9ea8641dce:train:pack-6a26dc3f9a21 | count-0 | unspecified | unspecified |
Hey, LCX, looks like they take PayPal... | choice | blog-authorship-corpus-age-8880d04019:train:5 | [
"13-17",
"23-27",
"33-48"
] | [
0,
1,
0
] | What is the blogger's age group? | blog_authorship_corpus/age | direct | train | blog-authorship-corpus-age-8880d04019:train:5 | decision | apache-2.0 | commercial |
Item A:
Well I joined you guys here too. I was looking at the AP English Barron's book and it seems that in order to do well on the test we need a lot of just practice of taking the answers and knowing what kind of questions are on the test and how to interpret, it's kind of like SATs or ACTs, you do better ... | choice | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e:label-A | [
"female",
"male"
] | [
0,
1
] | Each item answers: "What is the blogger's gender?"
Choose the criterion that best describes Item A. | blog_authorship_corpus/gender | packed_derived | train | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e | label-A | apache-2.0, Custom (DPI) | commercial |
Item A:
Well I joined you guys here too. I was looking at the AP English Barron's book and it seems that in order to do well on the test we need a lot of just practice of taking the answers and knowing what kind of questions are on the test and how to interpret, it's kind of like SATs or ACTs, you do better ... | noul | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e:same-A-B | [] | [
0
] | Each item answers: "What is the blogger's gender?"
Do Item A and Item B have the same label? Possible labels: "female", "male". | blog_authorship_corpus/gender | packed_derived | train | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e | same-A-B | apache-2.0, Custom (DPI) | commercial |
Item A:
Well I joined you guys here too. I was looking at the AP English Barron's book and it seems that in order to do well on the test we need a lot of just practice of taking the answers and knowing what kind of questions are on the test and how to interpret, it's kind of like SATs or ACTs, you do better ... | noul | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e:exists-0 | [] | [
1
] | Each item answers: "What is the blogger's gender?"
Does at least one item have the label "female"? Possible labels: "female", "male". | blog_authorship_corpus/gender | packed_derived | train | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e | exists-0 | apache-2.0, Custom (DPI) | commercial |
Item A:
Well I joined you guys here too. I was looking at the AP English Barron's book and it seems that in order to do well on the test we need a lot of just practice of taking the answers and knowing what kind of questions are on the test and how to interpret, it's kind of like SATs or ACTs, you do better ... | score | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e:count-0 | [
"0",
"1",
"2",
"3"
] | [
0,
1,
0,
0
] | Each item answers: "What is the blogger's gender?"
How many items have the label "female"? Possible labels: "female", "male". | blog_authorship_corpus/gender | packed_derived | train | blog-authorship-corpus-gender-5fe8900d6d:train:pack-4d08512a3a7e | count-0 | apache-2.0, Custom (DPI) | commercial |
Your True Nature by urlLink llScorpiusll Username The quality that most appeals to you: Beauty In a survival situation, you: Act crazy as a diversion Your hidden talent is: Spiritual wisdom Your gift is: Physical beauty In groups, you: Feel uncomfortable... | choice | blog-authorship-corpus-job-b565e07828:train:20 | [
"Accounting",
"Advertising",
"Agriculture",
"Architecture",
"Arts",
"Automotive",
"Banking",
"Biotech",
"BusinessServices",
"Chemicals",
"Communications-Media",
"Construction",
"Consulting",
"Education",
"Engineering",
"Environment",
"Fashion",
"Government",
"HumanResources",
"... | [
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
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0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] | In which industry does the blogger work? | blog_authorship_corpus/job | direct | train | blog-authorship-corpus-job-b565e07828:train:20 | decision | apache-2.0, Custom (DPI) | commercial |
Your True Nature by urlLink llScorpiusll Username The quality that most appeals to you: Beauty In a survival situation, you: Act crazy as a diversion Your hidden talent is: Spiritual wisdom Your gift is: Physical beauty In groups, you: Feel uncomfortable... | noul | blog-authorship-corpus-job-b565e07828:train:20:noul-label-verification | [] | [
1
] | In which industry does the blogger work? Is "BusinessServices" the correct answer? | blog_authorship_corpus/job | label_verification | train | blog-authorship-corpus-job-b565e07828:train:20 | noul-label-verification | apache-2.0, Custom (DPI) | commercial |
Item A:
is the 4th season of the flash a spin-off?
Item B:
can i not ever buy alcohol in south carolina on sunday? | choice | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651:label-A | [
"False",
"True"
] | [
0,
1
] | Choose the criterion that best describes Item A. | boolq-natural-perturbations | packed_derived | train | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651 | label-A | unspecified | unspecified |
Item A:
is the 4th season of the flash a spin-off?
Item B:
can i not ever buy alcohol in south carolina on sunday? | noul | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651:in-A-1 | [] | [
1
] | Is the label of Item A "True"? Possible labels: "False", "True". | boolq-natural-perturbations | packed_derived | train | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651 | in-A-1 | unspecified | unspecified |
Item A:
is the 4th season of the flash a spin-off?
Item B:
can i not ever buy alcohol in south carolina on sunday? | noul | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651:all-same | [] | [
1
] | Do all items have the same label? Possible labels: "False", "True". | boolq-natural-perturbations | packed_derived | train | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651 | all-same | unspecified | unspecified |
Item A:
is the 4th season of the flash a spin-off?
Item B:
can i not ever buy alcohol in south carolina on sunday? | choice | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651:most-common | [
"False",
"True"
] | [
0,
1
] | Which label is shared by the most items? | boolq-natural-perturbations | packed_derived | train | boolq-natural-perturbations-508f5ac0f9:train:pack-66a997284651 | most-common | unspecified | unspecified |
I am an odd number, but removing only one letter makes me even. Can you figure out what my number is? | choice | brainteasers-SP-a00f6024c3:train:0 | [
"None of the other options.",
"Five.",
"Seven",
"Eleven."
] | [
0,
0,
1,
0
] | Choose the criterion that best answers the question. | brainteasers/SP | direct | train | brainteasers-SP-a00f6024c3:train:0 | decision | unspecified | unspecified |
Which word in the English language becomes shorter when it is lengthened? | choice | brainteasers-WP-b360da8d57:train:3 | [
"None of the other options.",
"Short.",
"Truncation.",
"Abbreviation."
] | [
0,
1,
0,
0
] | Select the option that best answers the question. | brainteasers/WP | direct | train | brainteasers-WP-b360da8d57:train:3 | decision | unspecified | unspecified |
Which word in the English language becomes shorter when it is lengthened? | noul | brainteasers-WP-b360da8d57:train:3:noul-label-verification | [] | [
1
] | Is "Short." the correct answer to the question? | brainteasers/WP | label_verification | train | brainteasers-WP-b360da8d57:train:3 | noul-label-verification | unspecified | unspecified |
text_A: A young man is wearing a three piece suit.
text_B: A young man is wearing a 9 piece suit. | choice | breaking-nli-9bb3f705a7:train:19 | [
"entailment",
"neutral",
"contradiction"
] | [
0,
0,
1
] | Does text_A entail text_B, contradict it, or neither? | breaking_nli | direct | train | breaking-nli-9bb3f705a7:train:19 | decision | CC BY-SA 4.0 (DPI) | commercial |
First text:
A young man is wearing a three piece suit.
Second text:
A young man is wearing a 9 piece suit. | choice | breaking-nli-9bb3f705a7:train:19:choice-paired-text-format | [
"entailment",
"neutral",
"contradiction"
] | [
0,
0,
1
] | Does text_A entail text_B, contradict it, or neither? | breaking_nli | paired_text_format | train | breaking-nli-9bb3f705a7:train:19 | choice-paired-text-format | CC BY-SA 4.0 (DPI) | commercial |
First text:
For the upcoming world championships in microhockey, a super-vaccine was to be developed, which would be administered to all participants and audience members.
Second text:
A super-vaccine was being developed to keep people attending the world championships from falling ill with the disease. | choice | tasksource-chaos-mnli-ambiguity-e7d44e91ea:train:f787d5ea111ff2c3:chaos-mnli-ambiguity-votes | [
"entailment",
"neutral",
"contradiction"
] | [
0.82,
0.18,
0
] | How would annotators label the relation of the hypothesis to the premise? | chaos-mnli-ambiguity/votes | direct | train | tasksource-chaos-mnli-ambiguity-e7d44e91ea:train:f787d5ea111ff2c3 | chaos-mnli-ambiguity-votes | unspecified | unspecified |
taking the gravity and the common sense, answer:
Bob is in the living room.
He walks to the kitchen, carrying a cup.
He puts a ball in the cup and carries the cup to the bedroom.
He turns the cup upside down, then walks to the garden.
He puts the cup down in the garden, then walks to the garage.
Where is the ball... | choice | chatbot-arena-conversations-93d787d507:train:2 | [
"Okay, let's think through this step-by-step:\n\n1. Bob is in the living room to start. The ball is not mentioned, so we don't know where it is.\n\n2. Bob walks to the kitchen carrying a cup. The ball is still not mentioned, so we still don't know where it is. \n\n3. Bob puts a ball in the cup in the kitchen. So no... | [
1,
0
] | Which assistant did the user prefer? | chatbot_arena_conversations | direct | train | chatbot-arena-conversations-93d787d507:train:2 | decision | cc, CC BY 4.0 (DPI), CC BY-NC 4.0 (DPI), OpenAI (DPI) | non-commercial |
Item A:
text_A: Development of a cell-based high-throughput peroxisome proliferator-activated receptors (PPARs) screening model and its application for evaluation of the extracts from Rhizoma Coptis.
To date, peroxisome proliferator-activated receptors (PPARs) are becoming the new therapeutic targets for the treatment ... | choice | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f:label-A | [
"CPR:0",
"CPR:1",
"CPR:10",
"CPR:2",
"CPR:3",
"CPR:4",
"CPR:5",
"CPR:6",
"CPR:7",
"CPR:8",
"CPR:9"
] | [
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
] | Each item answers: "Which ChemProt relation group (CPR) holds between the chemical and the protein in text_B?"
Choose the criterion that best describes Item A. | chemprot/chemprot_full_source | packed_derived | train | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f | label-A | other | unspecified |
Item A:
text_A: Development of a cell-based high-throughput peroxisome proliferator-activated receptors (PPARs) screening model and its application for evaluation of the extracts from Rhizoma Coptis.
To date, peroxisome proliferator-activated receptors (PPARs) are becoming the new therapeutic targets for the treatment ... | noul | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f:in-B-1.3.6.7 | [] | [
0
] | Each item answers: "Which ChemProt relation group (CPR) holds between the chemical and the protein in text_B?"
Is the label of Item B one of "CPR:1", "CPR:2", "CPR:5", "CPR:6"? Possible labels: "CPR:0", "CPR:1", "CPR:10", "CPR:2", "CPR:3", "CPR:4", "CPR:5", "CPR:6", "CPR:7", "CPR:8", "CPR:9". | chemprot/chemprot_full_source | packed_derived | train | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f | in-B-1.3.6.7 | other | unspecified |
Item A:
text_A: Development of a cell-based high-throughput peroxisome proliferator-activated receptors (PPARs) screening model and its application for evaluation of the extracts from Rhizoma Coptis.
To date, peroxisome proliferator-activated receptors (PPARs) are becoming the new therapeutic targets for the treatment ... | noul | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f:exists-10 | [] | [
0
] | Each item answers: "Which ChemProt relation group (CPR) holds between the chemical and the protein in text_B?"
Does at least one item have the label "CPR:9"? Possible labels: "CPR:0", "CPR:1", "CPR:10", "CPR:2", "CPR:3", "CPR:4", "CPR:5", "CPR:6", "CPR:7", "CPR:8", "CPR:9". | chemprot/chemprot_full_source | packed_derived | train | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f | exists-10 | other | unspecified |
Item A:
text_A: Development of a cell-based high-throughput peroxisome proliferator-activated receptors (PPARs) screening model and its application for evaluation of the extracts from Rhizoma Coptis.
To date, peroxisome proliferator-activated receptors (PPARs) are becoming the new therapeutic targets for the treatment ... | score | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f:count-3 | [
"0",
"1",
"2"
] | [
1,
0,
0
] | Each item answers: "Which ChemProt relation group (CPR) holds between the chemical and the protein in text_B?"
How many items have the label "CPR:2"? Possible labels: "CPR:0", "CPR:1", "CPR:10", "CPR:2", "CPR:3", "CPR:4", "CPR:5", "CPR:6", "CPR:7", "CPR:8", "CPR:9". | chemprot/chemprot_full_source | packed_derived | train | chemprot-chemprot-full-source-ea2ca5027c:train:pack-b9ec02c0186f | count-3 | other | unspecified |
A: I think it's high time we had lunch .
B: Of course . I can eat a horse now .
A: I am sorry for that . I was so attracted by the beautiful scenery .
B: Where shall we go now ? A Chinese restaurant or a local one ?
A: I suppose the local one .
Target utterance: Of course . I can eat a horse now .
What is or could be ... | choice | cicero-03b1d98b47:train:2 | [
"The speaker has no need for food.",
"The speaker is badly craving for food.",
"The speaker is having a wonderful time.",
"The speaker has just eaten a lot of food.",
"The speaker is a happy eater."
] | [
0,
1,
0,
0,
0
] | Choose the criterion that best answers the question. | cicero | direct | train | cicero-03b1d98b47:train:2 | decision | mit | commercial |
A: X wants to know what sorts of books Y likes to read. Are you interested in history or historical novels?
B: I wouldn't say so | choice | circa-9b8c5093f3:train:39 | [
"Yes",
"No",
"In the middle, neither yes nor no",
"Yes, subject to some conditions",
"Other"
] | [
0,
1,
0,
0,
0
] | Choose the most appropriate category for the state. | circa | direct | train | circa-9b8c5093f3:train:39 | decision | cc-by-4.0 | commercial |
A: X wants to know what sorts of books Y likes to read. Are you interested in history or historical novels?
B: I wouldn't say so | choice | circa-9b8c5093f3:train:39:choice-criteria-permutation | [
"Other",
"In the middle, neither yes nor no",
"Yes",
"Yes, subject to some conditions",
"No"
] | [
0,
0,
0,
0,
1
] | Choose the most appropriate category for the state. | circa | criteria_permutation | train | circa-9b8c5093f3:train:39 | choice-criteria-permutation | cc-by-4.0 | commercial |
Thus , over the past few years , along with advances in the use of learning and statistical methods for acquisition of full parsers ( Collins , 1997 ; Charniak , 1997a ; Charniak , 1997b ; Ratnaparkhi , 1997 ) , significant progress has been made on the use of statistical learning methods to recognize shallow parsing p... | choice | citation-intent-d614a94399:train:0 | [
"Background",
"CompareOrContrast",
"Extends",
"Future",
"Motivation",
"Uses"
] | [
1,
0,
0,
0,
0,
0
] | Which of the supplied criteria best matches the state? | citation_intent | direct | train | citation-intent-d614a94399:train:0 | decision | unspecified | unspecified |
That's justice, not liberty. | noul | google-civil-comments-22ba067369:train:ca18e10247b8a56b:civil_comments-identity_attack_share | [] | [
0
] | What fraction of annotators rated the comment as an identity attack? | civil_comments/identity_attack_share | direct | train | google-civil-comments-22ba067369:train:ca18e10247b8a56b | civil_comments-identity_attack_share | cc0-1.0 | commercial |
That's justice, not liberty. | noul | google-civil-comments-22ba067369:train:ca18e10247b8a56b:civil_comments-insult_share | [] | [
0
] | What fraction of annotators rated the comment as insulting? | civil_comments/insult_share | direct | train | google-civil-comments-22ba067369:train:ca18e10247b8a56b | civil_comments-insult_share | cc0-1.0 | commercial |
That's justice, not liberty. | noul | google-civil-comments-22ba067369:train:ca18e10247b8a56b:civil_comments-obscene_share | [] | [
0
] | What fraction of annotators rated the comment as obscene? | civil_comments/obscene_share | direct | train | google-civil-comments-22ba067369:train:ca18e10247b8a56b | civil_comments-obscene_share | cc0-1.0 | commercial |
That's justice, not liberty. | noul | google-civil-comments-22ba067369:train:ca18e10247b8a56b:civil_comments-severe_toxicity_share | [] | [
0
] | What fraction of annotators rated the comment as severely toxic? | civil_comments/severe_toxicity_share | direct | train | google-civil-comments-22ba067369:train:ca18e10247b8a56b | civil_comments-severe_toxicity_share | cc0-1.0 | commercial |
That's justice, not liberty. | noul | google-civil-comments-22ba067369:train:ca18e10247b8a56b:civil_comments-sexual_explicit_share | [] | [
0
] | What fraction of annotators rated the comment as sexually explicit? | civil_comments/sexual_explicit_share | direct | train | google-civil-comments-22ba067369:train:ca18e10247b8a56b | civil_comments-sexual_explicit_share | cc0-1.0 | commercial |
That's justice, not liberty. | noul | google-civil-comments-22ba067369:train:ca18e10247b8a56b:civil_comments-threat_share | [] | [
0
] | What fraction of annotators rated the comment as threatening? | civil_comments/threat_share | direct | train | google-civil-comments-22ba067369:train:ca18e10247b8a56b | civil_comments-threat_share | cc0-1.0 | commercial |
That's justice, not liberty. | noul | google-civil-comments-22ba067369:train:ca18e10247b8a56b:civil_comments-toxicity_share | [] | [
0
] | What fraction of annotators rated the comment as toxic? | civil_comments/toxicity_share | direct | train | google-civil-comments-22ba067369:train:ca18e10247b8a56b | civil_comments-toxicity_share | cc0-1.0 | commercial |
Physical violence is wrong for either side. Unless the reporter actually put hands on the candidate I can't envision a scenario where this can be defended. That said, it is the eleventh hour, no way to get another candidate. The Republicans don't have much choice but to condemn the act but keep the candidate. | noul | google-civil-comments-22ba067369:train:ef73ec27a7fc511b:civil_comments-identity_attack_share | [] | [
0
] | What fraction of annotators rated the comment as an identity attack? | civil_comments/identity_attack_share | direct | train | google-civil-comments-22ba067369:train:ef73ec27a7fc511b | civil_comments-identity_attack_share | cc0-1.0 | commercial |
Physical violence is wrong for either side. Unless the reporter actually put hands on the candidate I can't envision a scenario where this can be defended. That said, it is the eleventh hour, no way to get another candidate. The Republicans don't have much choice but to condemn the act but keep the candidate. | noul | google-civil-comments-22ba067369:train:ef73ec27a7fc511b:civil_comments-insult_share | [] | [
0
] | What fraction of annotators rated the comment as insulting? | civil_comments/insult_share | direct | train | google-civil-comments-22ba067369:train:ef73ec27a7fc511b | civil_comments-insult_share | cc0-1.0 | commercial |
Physical violence is wrong for either side. Unless the reporter actually put hands on the candidate I can't envision a scenario where this can be defended. That said, it is the eleventh hour, no way to get another candidate. The Republicans don't have much choice but to condemn the act but keep the candidate. | noul | google-civil-comments-22ba067369:train:ef73ec27a7fc511b:civil_comments-obscene_share | [] | [
0
] | What fraction of annotators rated the comment as obscene? | civil_comments/obscene_share | direct | train | google-civil-comments-22ba067369:train:ef73ec27a7fc511b | civil_comments-obscene_share | cc0-1.0 | commercial |
Physical violence is wrong for either side. Unless the reporter actually put hands on the candidate I can't envision a scenario where this can be defended. That said, it is the eleventh hour, no way to get another candidate. The Republicans don't have much choice but to condemn the act but keep the candidate. | noul | google-civil-comments-22ba067369:train:ef73ec27a7fc511b:civil_comments-severe_toxicity_share | [] | [
0
] | What fraction of annotators rated the comment as severely toxic? | civil_comments/severe_toxicity_share | direct | train | google-civil-comments-22ba067369:train:ef73ec27a7fc511b | civil_comments-severe_toxicity_share | cc0-1.0 | commercial |
Physical violence is wrong for either side. Unless the reporter actually put hands on the candidate I can't envision a scenario where this can be defended. That said, it is the eleventh hour, no way to get another candidate. The Republicans don't have much choice but to condemn the act but keep the candidate. | noul | google-civil-comments-22ba067369:train:ef73ec27a7fc511b:civil_comments-sexual_explicit_share | [] | [
0
] | What fraction of annotators rated the comment as sexually explicit? | civil_comments/sexual_explicit_share | direct | train | google-civil-comments-22ba067369:train:ef73ec27a7fc511b | civil_comments-sexual_explicit_share | cc0-1.0 | commercial |
Physical violence is wrong for either side. Unless the reporter actually put hands on the candidate I can't envision a scenario where this can be defended. That said, it is the eleventh hour, no way to get another candidate. The Republicans don't have much choice but to condemn the act but keep the candidate. | noul | google-civil-comments-22ba067369:train:ef73ec27a7fc511b:civil_comments-threat_share | [] | [
0
] | What fraction of annotators rated the comment as threatening? | civil_comments/threat_share | direct | train | google-civil-comments-22ba067369:train:ef73ec27a7fc511b | civil_comments-threat_share | cc0-1.0 | commercial |
Physical violence is wrong for either side. Unless the reporter actually put hands on the candidate I can't envision a scenario where this can be defended. That said, it is the eleventh hour, no way to get another candidate. The Republicans don't have much choice but to condemn the act but keep the candidate. | noul | google-civil-comments-22ba067369:train:ef73ec27a7fc511b:civil_comments-toxicity_share | [] | [
0
] | What fraction of annotators rated the comment as toxic? | civil_comments/toxicity_share | direct | train | google-civil-comments-22ba067369:train:ef73ec27a7fc511b | civil_comments-toxicity_share | cc0-1.0 | commercial |
First text:
For those who are not tijv, the probability of gyzp is 12%. For those who are tijv, the probability of gyzp is 12%. For those who are not tijv, the probability of xevo is 27%. For those who are tijv, the probability of xevo is 35%.
Second text:
Will xevo decrease the chance of gyzp? | choice | cladder-55e0a301db:train:4 | [
"no",
"yes"
] | [
1,
0
] | Which of the supplied criteria best matches the state? | cladder | direct | train | cladder-55e0a301db:train:4 | decision | mit | commercial |
First text:
For those who are not tijv, the probability of gyzp is 12%. For those who are tijv, the probability of gyzp is 12%. For those who are not tijv, the probability of xevo is 27%. For those who are tijv, the probability of xevo is 35%.
Second text:
Will xevo decrease the chance of gyzp? | choice | cladder-55e0a301db:train:4:choice-instruction-paraphrase | [
"no",
"yes"
] | [
1,
0
] | Select the label that best applies to the state. | cladder | instruction_paraphrase | train | cladder-55e0a301db:train:4 | choice-instruction-paraphrase | mit | commercial |
Item A:
text_A: Everyone has visited China, The Bahamas, Trinidad & Tobago, Bolivia, Dominica, Papua New Guinea, Egypt, Cabo, Turkey, Finland, Montenegro, Philippines, Uzbekistan, Saint Vincent & the Grenadines, Hungary and Senegal
text_B: Clinton didn't visit Papua New Guinea
Item B:
text_A: Perry is the person that ... | choice | clcd-english-ee91b0e324:train:pack-bc463e6e7065:label-B | [
"contradiction",
"not_contradiction"
] | [
0,
1
] | Choose the criterion that best describes Item B. | clcd-english | packed_derived | train | clcd-english-ee91b0e324:train:pack-bc463e6e7065 | label-B | apache-2.0 | commercial |
Item A:
text_A: Everyone has visited China, The Bahamas, Trinidad & Tobago, Bolivia, Dominica, Papua New Guinea, Egypt, Cabo, Turkey, Finland, Montenegro, Philippines, Uzbekistan, Saint Vincent & the Grenadines, Hungary and Senegal
text_B: Clinton didn't visit Papua New Guinea
Item B:
text_A: Perry is the person that ... | noul | clcd-english-ee91b0e324:train:pack-bc463e6e7065:same-A-B | [] | [
0
] | Do Item A and Item B have the same label? Possible labels: "contradiction", "not_contradiction". | clcd-english | packed_derived | train | clcd-english-ee91b0e324:train:pack-bc463e6e7065 | same-A-B | apache-2.0 | commercial |
Item A:
text_A: Everyone has visited China, The Bahamas, Trinidad & Tobago, Bolivia, Dominica, Papua New Guinea, Egypt, Cabo, Turkey, Finland, Montenegro, Philippines, Uzbekistan, Saint Vincent & the Grenadines, Hungary and Senegal
text_B: Clinton didn't visit Papua New Guinea
Item B:
text_A: Perry is the person that ... | noul | clcd-english-ee91b0e324:train:pack-bc463e6e7065:exists-0 | [] | [
1
] | Does at least one item have the label "contradiction"? Possible labels: "contradiction", "not_contradiction". | clcd-english | packed_derived | train | clcd-english-ee91b0e324:train:pack-bc463e6e7065 | exists-0 | apache-2.0 | commercial |
Item A:
text_A: Everyone has visited China, The Bahamas, Trinidad & Tobago, Bolivia, Dominica, Papua New Guinea, Egypt, Cabo, Turkey, Finland, Montenegro, Philippines, Uzbekistan, Saint Vincent & the Grenadines, Hungary and Senegal
text_B: Clinton didn't visit Papua New Guinea
Item B:
text_A: Perry is the person that ... | score | clcd-english-ee91b0e324:train:pack-bc463e6e7065:count-0 | [
"0",
"1",
"2"
] | [
0,
1,
0
] | How many items have the label "contradiction"? Possible labels: "contradiction", "not_contradiction". | clcd-english | packed_derived | train | clcd-english-ee91b0e324:train:pack-bc463e6e7065 | count-0 | apache-2.0 | commercial |
what is the equivalent of, 'life is good' in french | choice | clinc-oos-plus-1b9b3d1a5a:train:2 | [
"restaurant_reviews",
"nutrition_info",
"account_blocked",
"oil_change_how",
"time",
"weather",
"redeem_rewards",
"interest_rate",
"gas_type",
"accept_reservations",
"smart_home",
"user_name",
"report_lost_card",
"repeat",
"whisper_mode",
"what_are_your_hobbies",
"order",
"jump_sta... | [
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,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0... | Select the label that best applies to the state. | clinc_oos/plus | direct | train | clinc-oos-plus-1b9b3d1a5a:train:2 | decision | cc-by-3.0 | commercial |
what is the equivalent of, 'life is good' in french | choice | clinc-oos-plus-1b9b3d1a5a:train:2:choice-instruction-paraphrase | [
"restaurant_reviews",
"nutrition_info",
"account_blocked",
"oil_change_how",
"time",
"weather",
"redeem_rewards",
"interest_rate",
"gas_type",
"accept_reservations",
"smart_home",
"user_name",
"report_lost_card",
"repeat",
"whisper_mode",
"what_are_your_hobbies",
"order",
"jump_sta... | [
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,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0... | Select the label that best applies to the state. | clinc_oos/plus | instruction_paraphrase | train | clinc-oos-plus-1b9b3d1a5a:train:2 | choice-instruction-paraphrase | cc-by-3.0 | commercial |
He would [MASK] the basket on purpose so I wouldn't lose against him. | choice | cloth-a8d3866ed4:train:4 | [
"miss",
"hit",
"catch",
"get"
] | [
1,
0,
0,
0
] | Choose the most appropriate answer from the supplied options. | cloth | direct | train | cloth-a8d3866ed4:train:4 | decision | mit | commercial |
He would [MASK] the basket on purpose so I wouldn't lose against him. | noul | cloth-a8d3866ed4:train:4:noul-label-verification | [] | [
1
] | Is "miss" the correct answer to the question? | cloth | label_verification | train | cloth-a8d3866ed4:train:4 | noul-label-verification | mit | commercial |
A: Dale and his sister Nancy are decorating for a party. Nancy's daughter Louise thinks the party will be fun.
B: How is Louise related to Dale? | choice | clutrr-effc7ced7a:train:2 | [
"aunt",
"brother",
"daughter",
"daughter-in-law",
"father",
"father-in-law",
"granddaughter",
"grandfather",
"grandmother",
"grandson",
"mother",
"mother-in-law",
"nephew",
"niece",
"sister",
"son",
"son-in-law",
"uncle"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
] | Choose the most appropriate category for the state. | clutrr | direct | train | clutrr-effc7ced7a:train:2 | decision | unspecified | unspecified |
A: Dale and his sister Nancy are decorating for a party. Nancy's daughter Louise thinks the party will be fun.
B: How is Louise related to Dale? | choice | clutrr-effc7ced7a:train:2:choice-instruction-paraphrase | [
"aunt",
"brother",
"daughter",
"daughter-in-law",
"father",
"father-in-law",
"granddaughter",
"grandfather",
"grandmother",
"grandson",
"mother",
"mother-in-law",
"nephew",
"niece",
"sister",
"son",
"son-in-law",
"uncle"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
] | Which of the supplied criteria best matches the state? | clutrr | instruction_paraphrase | train | clutrr-effc7ced7a:train:2 | choice-instruction-paraphrase | unspecified | unspecified |
text_A: A bald man with glasses is cutting into a turkey on the dinner table.
text_B: A man cuts into some poultry. | choice | cnli-14f2bf6434:train:0 | [
"entailment",
"neutral",
"contradiction"
] | [
1,
0,
0
] | Does text_A entail text_B, contradict it, or neither? | cnli | direct | train | cnli-14f2bf6434:train:0 | decision | unspecified | unspecified |
text_A: A bald man with glasses is cutting into a turkey on the dinner table.
text_B: A man cuts into some poultry. | choice | cnli-14f2bf6434:train:0:choice-criteria-permutation | [
"neutral",
"contradiction",
"entailment"
] | [
0,
0,
1
] | Does text_A entail text_B, contradict it, or neither? | cnli | criteria_permutation | train | cnli-14f2bf6434:train:0 | choice-criteria-permutation | unspecified | unspecified |
John is a horrible programmer. He | choice | codah-codah-29ecccdde1:train:0 | [
"drinks soup.",
"codes like a professional.",
"is amazing at coding.",
"does not know how to code."
] | [
0,
0,
0,
1
] | Choose the criterion that best answers the question. | codah/codah | direct | train | codah-codah-29ecccdde1:train:0 | decision | odc-by | commercial |
void kvm_s390_io_interrupt(S390CPU *cpu, uint16_t subchannel_id,
uint16_t subchannel_nr, uint32_t io_int_parm,
uint32_t io_int_word)
{
uint32_t type;
if (io_int_word & IO_INT_WORD_AI) {
type = KVM_S390_INT_IO(1, 0, 0, 0);
} else {
... | choice | code-x-glue-cc-defect-detection-1c76c57e72:train:137 | [
"defect",
"no defect"
] | [
0,
1
] | Does this C function contain a defect, such as a vulnerability or a memory bug? | code_x_glue_cc_defect_detection | direct | train | code-x-glue-cc-defect-detection-1c76c57e72:train:137 | decision | c-uda | unspecified |
void kvm_s390_io_interrupt(S390CPU *cpu, uint16_t subchannel_id,
uint16_t subchannel_nr, uint32_t io_int_parm,
uint32_t io_int_word)
{
uint32_t type;
if (io_int_word & IO_INT_WORD_AI) {
type = KVM_S390_INT_IO(1, 0, 0, 0);
} else {
... | noul | code-x-glue-cc-defect-detection-1c76c57e72:train:137:noul-label-verification | [] | [
0
] | Does this C function contain a defect, such as a vulnerability or a memory bug? Is "defect" the correct answer? | code_x_glue_cc_defect_detection | label_verification | train | code-x-glue-cc-defect-detection-1c76c57e72:train:137 | noul-label-verification | c-uda | unspecified |
As the weather was very cold he put on his jacket to protect himself. | choice | com2sense-cbe923accf:train:2 | [
"False",
"True"
] | [
0,
1
] | Which of the supplied criteria best matches the state? | com2sense | direct | train | com2sense-cbe923accf:train:2 | decision | unspecified | unspecified |
As the weather was very cold he put on his jacket to protect himself. | choice | com2sense-cbe923accf:train:2:choice-instruction-paraphrase | [
"False",
"True"
] | [
0,
1
] | Select the label that best applies to the state. | com2sense | instruction_paraphrase | train | com2sense-cbe923accf:train:2 | choice-instruction-paraphrase | unspecified | unspecified |
What do people use to absorb extra ink from a fountain pen? | choice | commonsense-qa-89cea5128c:train:8 | [
"calligrapher's hand",
"blotter",
"desk drawer",
"inkwell",
"shirt pocket"
] | [
0,
1,
0,
0,
0
] | Select the option that best answers the question. | commonsense_qa | direct | train | commonsense-qa-89cea5128c:train:8 | decision | mit | commercial |
What do people use to absorb extra ink from a fountain pen? | noul | commonsense-qa-89cea5128c:train:8:noul-label-verification | [] | [
0
] | Is "shirt pocket" the correct answer to the question? | commonsense_qa | label_verification | train | commonsense-qa-89cea5128c:train:8 | noul-label-verification | mit | commercial |
Unity has a lot to do with family. | choice | commonsense-qa-2-0-fe3c76eb16:train:2 | [
"no",
"yes"
] | [
0,
1
] | Choose the most appropriate category for the state. | commonsense_qa_2.0 | direct | train | commonsense-qa-2-0-fe3c76eb16:train:2 | decision | cc-by-4.0 | commercial |
text_A: School is in the vicinity of town. Backpack is capable of carry load. Dry fruit is not in the vicinity of patient. Diner is in the vicinity of town. Dry fruit is in the vicinity of backpack. Rutabaga is in the vicinity of valley. Dry fruit is not in the vicinity of bulletin board. Backpack is in the vicinity of... | choice | conceptrules-v2-83f331d8b8:train:91 | [
"False",
"True"
] | [
0,
1
] | Is the statement true given the context? | conceptrules_v2 | direct | train | conceptrules-v2-83f331d8b8:train:91 | decision | mit, Custom (DPI) | commercial |
text_A: School is in the vicinity of town. Backpack is capable of carry load. Dry fruit is not in the vicinity of patient. Diner is in the vicinity of town. Dry fruit is in the vicinity of backpack. Rutabaga is in the vicinity of valley. Dry fruit is not in the vicinity of bulletin board. Backpack is in the vicinity of... | choice | conceptrules-v2-83f331d8b8:train:91:choice-criteria-permutation | [
"True",
"False"
] | [
1,
0
] | Is the statement true given the context? | conceptrules_v2 | criteria_permutation | train | conceptrules-v2-83f331d8b8:train:91 | choice-criteria-permutation | mit, Custom (DPI) | commercial |
text_A: The chairs appear to be in a diner or restaurant of some sort.)
text_B: The chairs appear to be in a diner of some sort. | choice | conj-nli-0f0ab95726:train:129 | [
"entailment",
"neutral",
"contradiction"
] | [
1,
0,
0
] | Does text_A entail text_B, contradict it, or neither? | conj_nli | direct | train | conj-nli-0f0ab95726:train:129 | decision | unspecified | unspecified |
Passage A:
The chairs appear to be in a diner or restaurant of some sort.)
Passage B:
The chairs appear to be in a diner of some sort. | choice | conj-nli-0f0ab95726:train:129:choice-paired-text-format | [
"entailment",
"neutral",
"contradiction"
] | [
1,
0,
0
] | Does text_A entail text_B, contradict it, or neither? | conj_nli | paired_text_format | train | conj-nli-0f0ab95726:train:129 | choice-paired-text-format | unspecified | unspecified |
Sentence: EU rejects German call to boycott British lamb .
Target token at position 0: EU
Marked sentence: [TARGET: EU] rejects German call to boycott British lamb . | choice | conll2003-ner-tags-be686b5302:train:0:token-0 | [
"outside any named entity",
"beginning of a person entity",
"inside a person entity",
"beginning of an organization entity",
"inside an organization entity",
"beginning of a location entity",
"inside a location entity",
"beginning of a miscellaneous entity",
"inside a miscellaneous entity"
] | [
0,
0,
0,
1,
0,
0,
0,
0,
0
] | Choose the criterion that best labels the target token. | conll2003/ner_tags | direct | train | conll2003-ner-tags-be686b5302:train:0 | token-0 | other, Academic Research Purposes Only (DPI), Request Form (DPI) | non-commercial |
Sentence: EU rejects German call to boycott British lamb .
Target token at position 4: to
Marked sentence: EU rejects German call [TARGET: to] boycott British lamb . | choice | conll2003-ner-tags-be686b5302:train:0:token-4 | [
"outside any named entity",
"beginning of a person entity",
"inside a person entity",
"beginning of an organization entity",
"inside an organization entity",
"beginning of a location entity",
"inside a location entity",
"beginning of a miscellaneous entity",
"inside a miscellaneous entity"
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0
] | Choose the criterion that best labels the target token. | conll2003/ner_tags | direct | train | conll2003-ner-tags-be686b5302:train:0 | token-4 | other, Academic Research Purposes Only (DPI), Request Form (DPI) | non-commercial |
Sentence: EU rejects German call to boycott British lamb .
Target token at position 4: to
Marked sentence: EU rejects German call [TARGET: to] boycott British lamb . | choice | conll2003-ner-tags-be686b5302:train:0:token-4:choice-criteria-permutation | [
"outside any named entity",
"inside an organization entity",
"inside a location entity",
"inside a person entity",
"beginning of a person entity",
"beginning of a miscellaneous entity",
"beginning of an organization entity",
"inside a miscellaneous entity",
"beginning of a location entity"
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0
] | Choose the criterion that best labels the target token. | conll2003/ner_tags | criteria_permutation | train | conll2003-ner-tags-be686b5302:train:0 | token-4:choice-criteria-permutation | other, Academic Research Purposes Only (DPI), Request Form (DPI) | non-commercial |
text_A: 4. Nothing in this Agreement is to be construed as granting the Recipient, by implication or otherwise, any right whatsoever with respect to the Confidential Information or part thereof.
text_B: Agreement shall not grant Receiving Party any right to Confidential Information. | choice | contract-nli-contractnli-a-seg-c5d5a7346a:train:2 | [
"contradiction",
"entailment",
"neutral"
] | [
0,
1,
0
] | Does text_A entail text_B, contradict it, or neither? | contract-nli/contractnli_a/seg | direct | train | contract-nli-contractnli-a-seg-c5d5a7346a:train:2 | decision | cc-by-nc-sa-4.0 | non-commercial |
First text:
4. Nothing in this Agreement is to be construed as granting the Recipient, by implication or otherwise, any right whatsoever with respect to the Confidential Information or part thereof.
Second text:
Agreement shall not grant Receiving Party any right to Confidential Information. | choice | contract-nli-contractnli-a-seg-c5d5a7346a:train:2:choice-paired-text-format | [
"contradiction",
"entailment",
"neutral"
] | [
0,
1,
0
] | Does text_A entail text_B, contradict it, or neither? | contract-nli/contractnli_a/seg | paired_text_format | train | contract-nli-contractnli-a-seg-c5d5a7346a:train:2 | choice-paired-text-format | cc-by-nc-sa-4.0 | non-commercial |
text_A: MUTUAL NON-DISCLOSURE AGREEMENT
This Mutual Non-Disclosure Agreement (“Agreement”) is made and entered into on the date signed below by and between
_________________________________ (hereinafter “COMPANY”) and Dealer Pay, LLC (hereinafter “Dealer Pay”).
RECITALS:
WHEREAS, Dealer Pay owns and/or controls certain... | choice | contract-nli-contractnli-b-full-1b955ccb0f:train:98 | [
"contradiction",
"entailment",
"neutral"
] | [
0,
1,
0
] | Does text_A entail text_B, contradict it, or neither? | contract-nli/contractnli_b/full | direct | train | contract-nli-contractnli-b-full-1b955ccb0f:train:98 | decision | cc-by-nc-sa-4.0 | non-commercial |
text_A: MUTUAL NON-DISCLOSURE AGREEMENT
This Mutual Non-Disclosure Agreement (“Agreement”) is made and entered into on the date signed below by and between
_________________________________ (hereinafter “COMPANY”) and Dealer Pay, LLC (hereinafter “Dealer Pay”).
RECITALS:
WHEREAS, Dealer Pay owns and/or controls certain... | choice | contract-nli-contractnli-b-full-1b955ccb0f:train:98:choice-criteria-permutation | [
"entailment",
"neutral",
"contradiction"
] | [
1,
0,
0
] | Does text_A entail text_B, contradict it, or neither? | contract-nli/contractnli_b/full | criteria_permutation | train | contract-nli-contractnli-b-full-1b955ccb0f:train:98 | choice-criteria-permutation | cc-by-nc-sa-4.0 | non-commercial |
text_A: Suppose there is a closed system of 6 variables, A, B, C, D, E and F. All the statistical relations among these 6 variables are as follows: A correlates with D. A correlates with E. A correlates with F. B correlates with C. B correlates with D. B correlates with E. C correlates with D. C correlates with E. D co... | choice | corr2cause-1d700d1e0f:train:160 | [
"entailment",
"neutral",
"contradiction"
] | [
0,
0,
1
] | Does text_A entail text_B, contradict it, or neither? | corr2cause | direct | train | corr2cause-1d700d1e0f:train:160 | decision | mit | commercial |
text_A: Suppose there is a closed system of 6 variables, A, B, C, D, E and F. All the statistical relations among these 6 variables are as follows: A correlates with D. A correlates with E. A correlates with F. B correlates with C. B correlates with D. B correlates with E. C correlates with D. C correlates with E. D co... | choice | corr2cause-1d700d1e0f:train:160:choice-criteria-permutation | [
"neutral",
"contradiction",
"entailment"
] | [
0,
1,
0
] | Does text_A entail text_B, contradict it, or neither? | corr2cause | criteria_permutation | train | corr2cause-1d700d1e0f:train:160 | choice-criteria-permutation | mit | commercial |
First text:
Suppose there is a closed system of 6 variables, A, B, C, D, E and F. All the statistical relations among these 6 variables are as follows: A correlates with D. A correlates with E. A correlates with F. B correlates with C. B correlates with D. B correlates with E. C correlates with D. C correlates with E. ... | choice | corr2cause-1d700d1e0f:train:160:choice-paired-text-format | [
"entailment",
"neutral",
"contradiction"
] | [
0,
0,
1
] | Does text_A entail text_B, contradict it, or neither? | corr2cause | paired_text_format | train | corr2cause-1d700d1e0f:train:160 | choice-paired-text-format | mit | commercial |
Which group is the cover for tonight's musical program? | choice | cos-e-v1-0-c3414e3674:train:1 | [
"opera",
"television",
"concert"
] | [
0,
0,
1
] | Choose the criterion that best answers the question. | cos_e/v1.0 | direct | train | cos-e-v1-0-c3414e3674:train:1 | decision | unspecified | unspecified |
Which group is the cover for tonight's musical program? | noul | cos-e-v1-0-c3414e3674:train:1:noul-label-verification | [] | [
0
] | Is "opera" the correct answer to the question? | cos_e/v1.0 | label_verification | train | cos-e-v1-0-c3414e3674:train:1 | noul-label-verification | unspecified | unspecified |
Good Old War and person L : I saw both of these bands Wednesday night , and they both blew me away . seriously . Good Old War is acoustic and makes me smile . I really can not help but be happy when I listen to them ; I think it 's the fact that they seemed so happy themselves when they played . In the future , will th... | choice | cosmos-qa-81b1d873b1:train:0 | [
"None of the above choices .",
"This person likes music and likes to see the show , they will see other bands play .",
"This person only likes Good Old War and Person L , no other bands .",
"Other Bands is not on tour and this person can not see them ."
] | [
0,
1,
0,
0
] | Choose the most appropriate answer from the supplied options. | cosmos_qa | direct | train | cosmos-qa-81b1d873b1:train:0 | decision | cc-by-4.0 | commercial |
Item A:
This film is about a man who has been too caught up with the accepted convention of success, trying to be ever upwardly mobile, working hard so that he could be proud of owning his own home. He assumes this is all there is to life until he accidentally takes up dancing, all because he wanted to get a closer loo... | choice | counterfactually-augmented-imdb-72396a9ef6:train:pack-5acf7a6d6f46:label-B | [
"Negative",
"Positive"
] | [
1,
0
] | Each item answers: "What sentiment does the text express?"
Choose the criterion that best describes Item B. | counterfactually-augmented-imdb | packed_derived | train | counterfactually-augmented-imdb-72396a9ef6:train:pack-5acf7a6d6f46 | label-B | unspecified | unspecified |
Item A:
This film is about a man who has been too caught up with the accepted convention of success, trying to be ever upwardly mobile, working hard so that he could be proud of owning his own home. He assumes this is all there is to life until he accidentally takes up dancing, all because he wanted to get a closer loo... | noul | counterfactually-augmented-imdb-72396a9ef6:train:pack-5acf7a6d6f46:in-A-0 | [] | [
1
] | Each item answers: "What sentiment does the text express?"
Is the label of Item A "Negative"? Possible labels: "Negative", "Positive". | counterfactually-augmented-imdb | packed_derived | train | counterfactually-augmented-imdb-72396a9ef6:train:pack-5acf7a6d6f46 | in-A-0 | unspecified | unspecified |
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