source stringlengths 17 501k | target stringlengths 0 8.23k | task_name stringlengths 8 85 | task_source stringclasses 4
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Use reasoning to lead to the answer of the following question:
What can sex often be?
Options:
- nice
- good
- dirty
- great fun
- eventful
Reasoning process: | Sex is usually fun. Sex leads to enjoyment hence it is often great fun. Therefore, the final answer is great fun. | cot_ecqa | CoT | zs_opt | 5 | train |
Use reasoning to lead to the answer of the following question:
Subway stations are generally found in a?
Options:
- large city
- london
- big city
- toronto
- chicago
Reasoning process: | Subway stations are generally found in a big city. Therefore, the final answer is big city. | cot_ecqa | CoT | zs_opt | 5 | train |
What could happen to guns if they are wet?
Options:
- misfire
- explode
- jam
- break
- take life
Let's solve it slow. Misfire means fail to discharge or fire properly. The gunpowder in gun gets wet when guns are wet. Wet gunpowder fails to fire properly.... So the answer is misfire.
Minerals can be found on what thir... | Let's solve it slow. People closest to a person is called his family. When one is supported by closest people, he has a nice family.... So the answer is nice family. | cot_ecqa | CoT | fs_opt | 7 | train |
QUESTION: What do parents tell their kids to stop doing when they want them to go to sleep?
Options:
- get in bed
- get dance
- get into bed
- stop talking
- lay down
ME: The answer is stop talking
CoT: Kids generally keep chit chatting in bed without trying to sleep. Chit chatting means talking continuously. When some... | The answer is state
CoT: A town makes up a small part of state.. | cot_ecqa | CoT | fs_opt | 0 | train |
q: Why does a bodybuilder eat so much?
Options:
- pleasant conversation
- store food for winter
- satisfy hunger
- sounds of digestion
- gain weight
a: Body builder needs to gain muscle in bulking phase. Gainig muscle requires to eat more calories and result in gaining weight.
The answer is gain weight
QUESTION: He di... | Marmots are found on trees. Trees are high places.
The answer is tree | cot_ecqa | CoT | fs_opt | 1 | train |
QUESTION: To learn must have the right book, to work efficiently what must one have?
Options:
- improve yourself
- become knowledgeable
- have tools
- persistence
- have more knowledge
ANSWER: Have tools means to have any instrument or simple piece of equipment that you hold in your hands and use to do a particular kin... | You can make payments in a bank. Making payments is an activity that is done by a person in a bank.
The answer is make payments | cot_ecqa | CoT | fs_opt | 4 | train |
Where is a nightclub likely to be?
Options:
- manhattan
- downtown area
- beside a river
- building
- major city
Let's think carefully first. Step-by-step reasoning process: | Nightclubs are in posh areas like downtown areas. So the final answer is downtown area. | cot_ecqa | CoT | zs_opt | 9 | train |
QUESTION: Billy is an archaeologist. He looks for information about ancient human civilization. What is something that he might enjoy finding?
Options:
- school
- love
- adventure
- space shuttle
- library
ANSWER: Library means a collection of books for use or borrowing by member. Billy can enjoy finding books of ancie... | Cup makes noise when set on table. If you gentally set cup on tanle, it may not make noise.
The answer is noise | cot_ecqa | CoT | fs_opt | 0 | train |
Where could you find a laundry room that is only used by staff?
Options:
- house
- hotel
- green house
- college
- dorm
Step-by-step reasoning process below:
| Hotel is an establishment providing accommodation, meals and other services for travellers and tourists. You find a laundry room that is only used by staff at Hotel. The answer: hotel. | cot_ecqa | CoT | zs_opt | 1 | test |
q: What are you buying at the general store when you buy a box full of black and red chips?
Options:
- old west
- nachos
- salt
- shopping mall
- checkers
a: Checkers is a board game played between two people on an 8x8 checked board.
The answer is checkers
q: Where can one attend a famous bazaar?
Options:
- china
- fe... | Newspaper gets wet and torn if it gets in contact with water. People’s hands are wet with water when they are bathing.
The answer is reading newspaper | cot_ecqa | CoT | fs_opt | 6 | validation |
Teacher: A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? If you are still confused, see the following example:
ഇന്ന് ഒരു വിദൂരസ്ഥ ഗ്രാമത്തിലെ പാവപ്പെട്ട പൗരന് തന്... | 6 لاکھ کروڑ روپے سالانہ ہو ں تو سبھی منظور شدہ پروجیکٹوں کی تکمیل میں کئی دہائیاں لگ جائیں گی۔ | task1059_pib_translation_malayalam_urdu | NIv2 | fs_opt | 2 | train |
A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
Let me give you an example: ഇന്ന് ഒരു വിദൂരസ്ഥ ഗ്രാമത്തിലെ പാവപ്പെട്ട പൗരന് തന്റെ അവകാശങ്ങള് നേടുന്നതിനായി ദീര്ഘദൂരം യാത്ര ചെയ്യേണ്ടതായോ ഇടനിലക്കാര്... | 19-2018 کے بجٹ کے مقابلے میں 20-2019 کے بجٹ میں 21 فی صد کا اضافہ کرکے 58166 کروڑ روپے مختص کئے گئے۔ | task1059_pib_translation_malayalam_urdu | NIv2 | fs_opt | 8 | train |
A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
Example: ഇന്ന് ഒരു വിദൂരസ്ഥ ഗ്രാമത്തിലെ പാവപ്പെട്ട പൗരന് തന്റെ അവകാശങ്ങള് നേടുന്നതിനായി ദീര്ഘദൂരം യാത്ര ചെയ്യേണ്ടതായോ ഇടനിലക്കാര്ക്ക് കൈക്കൂലി കൊടുക്... | Solution: جناب کل راج مشرا کو ہماچل پردیش کا گورنر مقرر کیاگیا ہے۔ | task1059_pib_translation_malayalam_urdu | NIv2 | fs_opt | 5 | train |
Teacher: A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? If you are still confused, see the following example:
ഇന്ന് ഒരു വിദൂരസ്ഥ ഗ്രാമത്തിലെ പാവപ്പെട്ട പൗരന് തന്... | کیرالامیں اگست 2018میں زبردست بارش کے بارے میں جاری کی گئی موسم سے متعلق وارننگ | task1059_pib_translation_malayalam_urdu | NIv2 | fs_opt | 2 | train |
Teacher:A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? Solve this instance: ഭഗത് സിംഗ്, രാജ്ഗുരു, സുഖ്ദേവ് എന്നിവരുടെ രക്തസാക്ഷി ദിനത്തില് പ്രധാനമന്ത്രിയുടെ ശ്... | بھگت سنگھ، راج گورو اور سکھ دیو کو ان کے یوم شہادت پر وزیراعظم کا خراج عقیدت | task1059_pib_translation_malayalam_urdu | NIv2 | zs_opt | 6 | train |
A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
Q: യോന്സി സര്വകലാശാലയില് ഗാന്ധിജിയുടെ അര്ദ്ധകായ പ്രതിമ അനാച്ഛാദനം ചെയ്യാന് സാധിച്ചത് ഒരു ബഹുമതിയാണെന്ന് തദവസരത്തില് പ്രധാനമന്ത്രി പറഞ്ഞു.
A: | اس موقع پر اپنی تقریر میں وزیراعظم نے یونسائی یونیورسٹی میں بابو کے نیم قد مجسمہ کی نقاب کشائی کو اپنے لئے ایک اعزاز قرار دیا۔ | task1059_pib_translation_malayalam_urdu | NIv2 | zs_opt | 4 | train |
A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
അവരുടെ പ്രവര്ത്തനക്ഷമതയില് തനിക്കു പൂര്ണ വിശ്വാസമുണ്ടെന്നും അദ്ദേഹം വ്യക്തമാക്കി. | م ن۔م ع ۔ ن ا۔ (06-04-17) | task1059_pib_translation_malayalam_urdu | NIv2 | zs_opt | 0 | train |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
ഇന്ന് ഒരു വിദൂരസ്ഥ ഗ്രാമത്തിലെ... | جناب نتیانند رائے | task1059_pib_translation_malayalam_urdu | NIv2 | fs_opt | 0 | train |
A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
[Q]: തപാല് അദാലത്ത് ഈ മാസം 21 ന്
[A]: الیکٹرانکس اور اطلاعات تکنالوجی کی وزارت
[Q]: സിക്കിമിലെ നാംച്ചി, ഗാംഗ്ടോക്ക്, അരുണാചല് പ്രദേശിലെ ഇറ്റാനഗര്... | مالیاتی شمولیت بہت چھوٹے ، چھوٹے اور درمیانہ درجے کے کاروبار کی توسیع میں بھی مدد حاصل ہوتی ہے ۔
| task1059_pib_translation_malayalam_urdu | NIv2 | fs_opt | 5 | test |
A text is given in Malayalam. Translate it from the Malayalam language to the Urdu language. The translation must not omit or add information to the original sentence.
[EX Q]: പരിഷ്ക്കാരങ്ങള് റെയില്വേയുടെ പ്രവര്ത്തനം മെച്ചപ്പെടുത്തുന്നതിന് ഗണ്യമായ സാമ്പത്തിക, ഭരണ അധികാരങ്ങള് ജനറല് മാനേജര്മാര്ക്കും ഡിവിഷണല് റെ... | بھگت سنگھ، راج گورو اور سکھ دیو کو ان کے یوم شہادت پر وزیراعظم کا خراج عقیدت
| task1059_pib_translation_malayalam_urdu | NIv2 | fs_opt | 6 | validation |
Teacher: In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, the new question... | Which cave is the longest at this national park? | task121_zest_text_modification | NIv2 | fs_opt | 2 | train |
Q: In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, the new question can b... | When were the children of this president born? | task121_zest_text_modification | NIv2 | zs_opt | 7 | train |
Part 1. Definition
In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, the ne... | Do this dog breed get large enough to be used as a watch dog for farm animals? | task121_zest_text_modification | NIv2 | fs_opt | 7 | train |
In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, the new question can be a... | Does this dog breed have spots of two different colors?
| task121_zest_text_modification | NIv2 | fs_opt | 5 | train |
In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, the new question can be a... | What colors is the tail of this dog breed? | task121_zest_text_modification | NIv2 | zs_opt | 4 | train |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is a... | Did this president major in economics or business? | task121_zest_text_modification | NIv2 | fs_opt | 0 | train |
In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, the new question can be a... | Is white the only acceptable color for this dog breed?
****
| task121_zest_text_modification | NIv2 | fs_opt | 4 | train |
In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, the new question can be a... | Who did this president inherit the budget deficit from?
| task121_zest_text_modification | NIv2 | fs_opt | 6 | train |
Detailed Instructions: In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is about the tallness of the president and the second question is about his performance at college, th... | Are the docked tails of this dog breed longer than an inch? | task121_zest_text_modification | NIv2 | fs_opt | 4 | test |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you are given two questions about a domain. Your task is to combine the main subjects of the questions to write a new, natural-sounding question. For example, if the first question is a... | Are there more than two places to eat in this national park? | task121_zest_text_modification | NIv2 | fs_opt | 0 | validation |
Generate a correct and concise answer for the question based on the words in the context.
One example is below.
Q: Context : Isaac Nicola Romero (1916 in Havana, Cuba -- 1997) was a prominent Cuban guitarist and one of the founders of the modern Cuban Guitar School.
Question : What was Isaac Nicola's city of birth?
A:... | murder | task1327_qa_zre_answer_generation_from_question | NIv2 | fs_opt | 9 | train |
Detailed Instructions: Generate a correct and concise answer for the question based on the words in the context.
Problem:Context : Blind Guys (Hungarian: Vakvagányok) is a 2001 Hungarian drama film directed by Péter Tímár.
Question : Who served as director for Blind Guys?
Solution: | Péter Tímár | task1327_qa_zre_answer_generation_from_question | NIv2 | zs_opt | 8 | train |
Instructions: Generate a correct and concise answer for the question based on the words in the context.
Input: Context : Brandy Fisher (born October 28, 1975) played ice hockey for the New Hampshire Wildcats women's ice hockey program and was the first ever winner of the Patty Kazmaier Award, awarded to the top female ... | female | task1327_qa_zre_answer_generation_from_question | NIv2 | zs_opt | 3 | train |
Instructions: Generate a correct and concise answer for the question based on the words in the context.
Input: Context : Rhythm Is It! is a 2004 German documentary film directed by Thomas Grube and Enrique Sánchez Lansch.
Question : Under whose direction was Rhythm Is It! produced?
Output: | Enrique Sánchez Lansch | task1327_qa_zre_answer_generation_from_question | NIv2 | zs_opt | 3 | train |
Teacher:Generate a correct and concise answer for the question based on the words in the context.
Teacher: Now, understand the problem? Solve this instance: Context : East End Hustle is a 1976 drama film directed by Frank Vitale and distributed by Troma Entertainment.
Question : The production company responsible for ... | Troma Entertainment | task1327_qa_zre_answer_generation_from_question | NIv2 | zs_opt | 6 | train |
Q: Generate a correct and concise answer for the question based on the words in the context.
Context : USS Bermuda (1861) was a large steamer captured by the Union Navy during the American Civil War.
Question : What conflict involved USS Bermuda (1861)?
A: | American Civil War | task1327_qa_zre_answer_generation_from_question | NIv2 | zs_opt | 7 | train |
Given the task definition, example input & output, solve the new input case.
Generate a correct and concise answer for the question based on the words in the context.
Example: Context : Isaac Nicola Romero (1916 in Havana, Cuba -- 1997) was a prominent Cuban guitarist and one of the founders of the modern Cuban Guitar ... | genus | task1327_qa_zre_answer_generation_from_question | NIv2 | fs_opt | 1 | train |
Detailed Instructions: Generate a correct and concise answer for the question based on the words in the context.
See one example below:
Problem: Context : Isaac Nicola Romero (1916 in Havana, Cuba -- 1997) was a prominent Cuban guitarist and one of the founders of the modern Cuban Guitar School.
Question : What was Is... | Forsyth-Edwards Notation | task1327_qa_zre_answer_generation_from_question | NIv2 | fs_opt | 4 | train |
Teacher: Generate a correct and concise answer for the question based on the words in the context.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Context : Isaac Nicola Romero (1916 in Havana, Cuba -- 1997) was a prominent Cuban guitarist and one of the founders of the moder... | Mirai Sentai Timeranger | task1327_qa_zre_answer_generation_from_question | NIv2 | fs_opt | 2 | test |
Generate a correct and concise answer for the question based on the words in the context.
Context : Tan Yuling (1920 -- 14 August 1942), born Tatara Yuling, was a concubine of China's last emperor Puyi.
Question : What is Tan Yuling's spouse's name?
Puyi
Context : Karl-Wilhelm von Schlieben (30 October 1894 -- 18 Ju... | Parks and Recreation
| task1327_qa_zre_answer_generation_from_question | NIv2 | fs_opt | 0 | validation |
You will be given a definition of a task first, then some input of the task.
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New info... | The people are at the bowling alley. | task187_snli_entailment_to_contradiction_text_modification | NIv2 | zs_opt | 1 | train |
Given the task definition, example input & output, solve the new input case.
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New info... | The female is playing basketball. | task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 1 | train |
Teacher: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subje... | A group of hockey players are sitting on the bench. | task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 2 | train |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | Two people are examining fresh fruit and vegetables. | task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 3 | train |
Instructions: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the ... | The women are not playing a game. | task187_snli_entailment_to_contradiction_text_modification | NIv2 | zs_opt | 3 | train |
Teacher: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subje... | A person is using a bobsled down the hill | task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 2 | train |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must... | the man is working in the field , picking cotton | task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 0 | train |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | Output: A boy is on a bicycle.
| task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 2 | train |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | A couple is relaxing in the park. | task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 9 | test |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that agree with each other. Your job is to alter sentence 2 so that the pair contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | Basketball flies with flies and touch players
****
| task187_snli_entailment_to_contradiction_text_modification | NIv2 | fs_opt | 4 | validation |
The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for... | find " cracker barrel "
| task254_spl_translation_fi_en | NIv2 | fs_opt | 3 | train |
The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for... | search for 10 star restaurants in the " 49503 " zip code .
| task254_spl_translation_fi_en | NIv2 | fs_opt | 0 | train |
The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for... | Output: show me a list of " vegan " restaurants with at least 4 reviews .
| task254_spl_translation_fi_en | NIv2 | fs_opt | 2 | train |
You will be given a definition of a task first, then some input of the task.
The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally writte... | search for " luby 's cafeteria " . | task254_spl_translation_fi_en | NIv2 | zs_opt | 1 | train |
The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for... | give me reviews for " jade buffet " .
****
| task254_spl_translation_fi_en | NIv2 | fs_opt | 4 | train |
Teacher: The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* lo... | find all restaurants rated higher than 4 | task254_spl_translation_fi_en | NIv2 | fs_opt | 2 | train |
The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for... | find all restaurants rated higher than 2
| task254_spl_translation_fi_en | NIv2 | fs_opt | 3 | train |
The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for... | Output: can you show me the ratings for " j dog " ?
| task254_spl_translation_fi_en | NIv2 | fs_opt | 2 | train |
Q: The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking ... | what 's the address of the " mcdonald 's " on " parker road " ? | task254_spl_translation_fi_en | NIv2 | zs_opt | 7 | test |
Instructions: The provided file includes inquiries about restaurants in Finnish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NO... | find " mexican " restaurants . | task254_spl_translation_fi_en | NIv2 | zs_opt | 3 | validation |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | Output: People are watching a race.
| task186_snli_contradiction_to_entailment_text_modification | NIv2 | fs_opt | 2 | train |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | Output: a man paints
| task186_snli_contradiction_to_entailment_text_modification | NIv2 | fs_opt | 2 | train |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | people play baseball
| task186_snli_contradiction_to_entailment_text_modification | NIv2 | fs_opt | 1 | train |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | People celebrating a on volleyball court. | task186_snli_contradiction_to_entailment_text_modification | NIv2 | fs_opt | 9 | train |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | The marching band is wearing uniforms.
****
| task186_snli_contradiction_to_entailment_text_modification | NIv2 | fs_opt | 4 | train |
Detailed Instructions: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to con... | 3 people look at the coming storm | task186_snli_contradiction_to_entailment_text_modification | NIv2 | zs_opt | 8 | train |
Detailed Instructions: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to con... | The trick is performed in the air. | task186_snli_contradiction_to_entailment_text_modification | NIv2 | fs_opt | 4 | train |
Teacher:In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subjec... | The woman is outdoors. | task186_snli_contradiction_to_entailment_text_modification | NIv2 | zs_opt | 6 | train |
Definition: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the su... | A girl is holding something. | task186_snli_contradiction_to_entailment_text_modification | NIv2 | zs_opt | 2 | test |
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that contradict each other. Your job is to alter sentence 2 so that the pair agree with each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the... | The men are touching tile. | task186_snli_contradiction_to_entailment_text_modification | NIv2 | zs_opt | 0 | validation |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Example: Sentence-1: I'm looking forward to it.<sep>Sentence-2: I did hear about the event next week .
Example solution: Yes
Example explanation: We need to chec... | Solution: Yes | task566_circa_classification | NIv2 | fs_opt | 5 | train |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Sentence-1: I try to go to the gym a few times a week.<sep>Sentence-2: I am into fitness . | Yes | task566_circa_classification | NIv2 | zs_opt | 0 | train |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Q: Sentence-1: Lollapalooza is my favorite event of the year.<sep>Sentence-2: I have ever been to a music festival .
A: | Yes | task566_circa_classification | NIv2 | zs_opt | 4 | train |
TASK DEFINITION: In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
PROBLEM: Sentence-1: I'd rather go to a concert.<sep>Sentence-2: I have an interest in movies .
SOLUTION: Yes
PROBLEM: Sentence-1: I don't car... | Yes
| task566_circa_classification | NIv2 | fs_opt | 8 | train |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Q: Sentence-1: I love to dance at live music shows.<sep>Sentence-2: I like to see live music .
A: | Yes | task566_circa_classification | NIv2 | zs_opt | 4 | train |
Given the task definition and input, reply with output. In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Sentence-1: I move often due to work.<sep>Sentence-2: This is somewhere .
| Yes | task566_circa_classification | NIv2 | zs_opt | 5 | train |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Q: Sentence-1: As long as there isn't any singing in it.<sep>Sentence-2: I would like to attend the play at the theater .
A: Yes
****
Q: Sentence-1: I'm afraid... | Yes
****
| task566_circa_classification | NIv2 | fs_opt | 4 | train |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Q: Sentence-1: If it isn't too expensive.<sep>Sentence-2: I am up for going to the museum tomorrow .
A: Yes
****
Q: Sentence-1: I work 5 days a week.<sep>Sente... | Yes
****
| task566_circa_classification | NIv2 | fs_opt | 4 | train |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Q: Sentence-1: It's me and my family.<sep>Sentence-2: I living alone .
A: No
****
Q: Sentence-1: I'd also choose spicy food over any other type.<sep>Sentence-2... | Yes
****
| task566_circa_classification | NIv2 | fs_opt | 4 | test |
In this task, you are given two sentences. Your task is to classify the given sentences as "Yes" if they have same meaning; otherwise, classify them as "No".
Example: Sentence-1: I'm looking forward to it.<sep>Sentence-2: I did hear about the event next week .
Example solution: Yes
Example explanation: We need to chec... | Solution: Yes | task566_circa_classification | NIv2 | fs_opt | 5 | validation |
Definition: You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
Input: ASSISTANT: What kind of movies to you like,... | USER | task925_coached_conv_pref_classifier | NIv2 | zs_opt | 2 | train |
Detailed Instructions: You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
Problem:ASSISTANT: What type of movies ... | ASSISTANT | task925_coached_conv_pref_classifier | NIv2 | zs_opt | 8 | train |
Given the task definition and input, reply with output. You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
ASSIS... | ASSISTANT | task925_coached_conv_pref_classifier | NIv2 | zs_opt | 5 | train |
Detailed Instructions: You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
Q: ASSISTANT: What kind of movies do yo... | ASSISTANT | task925_coached_conv_pref_classifier | NIv2 | zs_opt | 9 | train |
instruction:
You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
question:
ASSISTANT: What kind of movies to you l... | ASSISTANT
| task925_coached_conv_pref_classifier | NIv2 | fs_opt | 9 | train |
You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
Q: ASSISTANT: What type of movies do you like? , USER: I enjo... | USER
****
| task925_coached_conv_pref_classifier | NIv2 | fs_opt | 4 | train |
You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
ASSISTANT: what kind of movies do you like? , USER: I like ho... | USER | task925_coached_conv_pref_classifier | NIv2 | zs_opt | 0 | train |
Detailed Instructions: You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
Problem:ASSISTANT: what type of movies ... | USER | task925_coached_conv_pref_classifier | NIv2 | zs_opt | 8 | train |
You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
[EX Q]: ASSISTANT: What type of movies do you enjoy? , USER: ... | ASSISTANT
| task925_coached_conv_pref_classifier | NIv2 | fs_opt | 6 | test |
Detailed Instructions: You are provided with a list of converations between a "USER" and "ASSISTANT". Figure out if the conversation provided as "classify_text" is authored by "USER" or "ASSISTANT". There are maximum of 7 conversations between "USER" and "ASSISTANT" in each input
See one example below:
Problem: ASSISTA... | ASSISTANT | task925_coached_conv_pref_classifier | NIv2 | fs_opt | 4 | validation |
Part 1. Definition
In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be emp... | task390_torque_text_span_selection | NIv2 | fs_opt | 7 | train | |
In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be empty.
Example: Passag... | Solution: urged | task390_torque_text_span_selection | NIv2 | fs_opt | 5 | train |
In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be empty.
Let me give yo... | cleared | task390_torque_text_span_selection | NIv2 | fs_opt | 8 | train |
Given the task definition, example input & output, solve the new input case.
In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or no... | lie | task390_torque_text_span_selection | NIv2 | fs_opt | 1 | train |
In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be empty.
One example is ... | tolerate | task390_torque_text_span_selection | NIv2 | fs_opt | 9 | train |
In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be empty.
Q: Passage: "T... | hampered
****
| task390_torque_text_span_selection | NIv2 | fs_opt | 4 | train |
Part 1. Definition
In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be emp... | series | task390_torque_text_span_selection | NIv2 | fs_opt | 7 | train |
Detailed Instructions: In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be... | declared | task390_torque_text_span_selection | NIv2 | zs_opt | 8 | train |
Detailed Instructions: In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be... | called | task390_torque_text_span_selection | NIv2 | zs_opt | 9 | test |
Teacher: In this task, you will be given a passage, a question and a list of single-word events identified from the passage. The goal is to point out all events from the given event list that can answer the question. If a question is unanswerable or none of the events answer the question, output should be empty.
Teache... | task390_torque_text_span_selection | NIv2 | fs_opt | 2 | validation |
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