source stringlengths 17 501k | target stringlengths 0 8.23k | task_name stringlengths 8 85 | task_source stringclasses 4
values | template_type stringclasses 4
values | template_idx int64 0 17 | split stringclasses 3
values |
|---|---|---|---|---|---|---|
In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to determ... | Yes
| task1204_atomic_classification_hinderedby | NIv2 | fs_opt | 5 | 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 phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been re... | Yes | task1204_atomic_classification_hinderedby | NIv2 | fs_opt | 0 | train |
Part 1. Definition
In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event.... | No | task1204_atomic_classification_hinderedby | NIv2 | fs_opt | 7 | train |
In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to determ... | No | task1204_atomic_classification_hinderedby | NIv2 | fs_opt | 9 | train |
Teacher:In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have t... | No | task1204_atomic_classification_hinderedby | NIv2 | zs_opt | 6 | train |
In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to determ... | No
| task1204_atomic_classification_hinderedby | NIv2 | fs_opt | 7 | train |
Definition: In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You ha... | Yes | task1204_atomic_classification_hinderedby | NIv2 | zs_opt | 2 | train |
Q: In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to det... | No | task1204_atomic_classification_hinderedby | NIv2 | zs_opt | 7 | train |
In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to determ... | No | task1204_atomic_classification_hinderedby | NIv2 | fs_opt | 9 | test |
In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to determ... | No
****
| task1204_atomic_classification_hinderedby | NIv2 | fs_opt | 4 | validation |
Definition: In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Input: Article: Linda and L... | No | task310_race_classification | NIv2 | zs_opt | 2 | train |
Definition: In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Input: Article: When I left... | Yes | task310_race_classification | NIv2 | zs_opt | 2 | train |
Definition: In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Input: Article: Japan's All... | Yes | task310_race_classification | NIv2 | zs_opt | 2 | train |
Q: In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Article: In 2003, Bethany Hamilton,1... | Yes | task310_race_classification | NIv2 | zs_opt | 7 | train |
Detailed Instructions: In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Problem:Article:... | No | task310_race_classification | NIv2 | zs_opt | 8 | train |
In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
--------
Question: Article: Dreams can ... | Yes
| task310_race_classification | NIv2 | fs_opt | 7 | train |
In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Q: Article: The First Rule of Finance i... | No | task310_race_classification | 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're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the... | Yes | task310_race_classification | NIv2 | fs_opt | 0 | train |
Definition: In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Input: Article: Every year ... | Yes | task310_race_classification | NIv2 | zs_opt | 2 | test |
In this task, you're given an article, a question which often contains a blank, four options (associated with "A", "B", "C", "D") and the answer to that question. Your task is to classify whether the given answer is correct or not by providing "Yes" or "No", based on the article.
Let me give you an example: Article: H... | Yes | task310_race_classification | NIv2 | fs_opt | 8 | validation |
Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
--------
Question: Question: Different media affect what property of light? Answer: speed
Answer: The speed of light is different in different m... | Figure 7.14 Lactic acid fermentation is common in muscle cells that have run out of oxygen.
| task593_sciq_explanation_generation | NIv2 | fs_opt | 7 | train |
Given the task definition and input, reply with output. Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Question: During a what type of reaction do chemical changes take place? Answer: chemica... | A: During a chemical reaction, chemical changes take place. Some chemical bonds break and new chemical bonds form. | task593_sciq_explanation_generation | NIv2 | zs_opt | 5 | train |
You will be given a definition of a task first, then some input of the task.
Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Question: What cell structures are like storage centers and tend to... | The vacuoles are like storage centers. Plant cells have larger vacuoles than animal cells. Plants store water and nutrients in their large central vacuoles. | task593_sciq_explanation_generation | NIv2 | zs_opt | 1 | train |
Q: Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Question: What is the modern periodic table based on? Answer: atomic number
A: | The modern periodic table is based on atomic number. Elements in each period go from metals on the left to metalloids and then nonmetals on the right. Within groups, elements have similar properties. | task593_sciq_explanation_generation | NIv2 | zs_opt | 7 | train |
Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
One example: Question: What term refers to a list of the elements that will replace the ones below them in single-replacement reactions? Answer: ... | Respiration begins with ventilation . This is the process of moving air in and out of the lungs. The lungs are the organs in which gas exchange takes place between blood and air. | task593_sciq_explanation_generation | NIv2 | fs_opt | 6 | train |
Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Q: Question: Isobaric expansion is a process occurring without a change in what? Answer: pressure
A: | isobaric expansion is a process occurring without a change in pressure. | task593_sciq_explanation_generation | NIv2 | zs_opt | 4 | train |
Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Question: What unit of the nervous system consists of a cell body, dendrites, and axon? Answer: neuron | Neurons are the structural and functional units of the nervous system. They consist of a cell body, dendrites, and axon. | task593_sciq_explanation_generation | NIv2 | zs_opt | 0 | train |
Detailed Instructions: Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Problem:Question: In a heat map, what color represents the hottest temperatures? Answer: bright yellow
Solution: | A heat wave over the United States as indicated by heat radiated from the ground. The bright yellow areas are the hottest, and the blue and white are coolest. | task593_sciq_explanation_generation | NIv2 | zs_opt | 8 | train |
Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Q: Question: What is made of gases that are essential for photosynthesis and other life activities? Answer: atmosphere
A: The atmosphere is mad... | Human organ systems work together to carry out many of their functions. The skeletal and muscular systems are no exception.
****
| task593_sciq_explanation_generation | NIv2 | fs_opt | 4 | test |
Given a scientific question and its correct answer, generate supporting facts for the answer. This supporting fact can be an explanation for the answer to the given question.
Ex Input:
Question: What element can form two different compounds with oxygen? Answer: carbon
Ex Output:
Carbon can form two different compound... | Many animal behaviors occur in repeated cycles. Migration and hibernation are examples of behaviors with annual cycles. Sleeping and waking are examples of behaviors with daily cycles.
| task593_sciq_explanation_generation | NIv2 | fs_opt | 1 | validation |
Q: A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
ଉଭୟ ପକ୍ଷ ପ୍ରାକୃତିକ ବାଷ୍ପ ସହିତ ରୁଷର ଶକ୍ତି ସମ୍ପତି ଓ ଅକ୍ଷୟଶକ୍ତି ସ୍ରୋତ କ୍ଷେତ୍ରରେ ସମ୍ଭାବ୍ୟ ସଂଯୁକ୍ତ ପରିଯୋଜନାଗୁଡ଼ିକୁ କାର୍ଯ୍ୟକାରୀ କରିବାରେ ଭାରତର ଆଗ୍ରହକୁ ଦେଖି ଭାରତ ଓ ... | இந்திய தொழில் நிறுவனங்களின் பெருங்கூட்டமைப்பும் ஸ்கோல்கோவோ ஃபவுண்டேஷனும் இணைந்து 2018 டிசம்பரில் முதல் முறையாக புதிய தொழில்களை துவங்குவதற்கான இந்திய-ரஷிய உச்சிமாநாட்டை நடத்துவது என்ற முடிவையும் அவை பெரிதும் பாராட்டின. | task1073_pib_translation_oriya_tamil | NIv2 | zs_opt | 7 | train |
Teacher: A text is given in Oriya. Translate it from the Oriya language to the Tamil 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:
“ନୂତନ ଦିଗନ୍ତ ଆବିଷ୍କାର କରିବାର ରହିଛି ଏବଂ ନୂତନ ସ୍ଥାନକୁ ଯିବାର ... | பின்னணி: செலவு மேலாண்மை ஆணையத்தின் பரிந்துரையின் அடிப்படையில், சுகாதாரம் மற்றும் குடும்பநலத் துறையின் கீழ், சங்கங்கள் பதிவு சட்டம் 1860-ன் அடிப்படையில் உருவாக்கப்பட்ட 19 தன்னாட்சி அமைப்புகளை நித்தி ஆயோக் அமைப்பு மறுஆய்வு செய்தது. தன்னாட்சி அமைப்புகளை மறுஆய்வுசெய்து, அதனை சீரமைக்கலாம் என்ற பரிந்துரையுடன் இடைக்கால அறிக்க... | task1073_pib_translation_oriya_tamil | NIv2 | fs_opt | 2 | train |
Teacher: A text is given in Oriya. Translate it from the Oriya language to the Tamil 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:
“ନୂତନ ଦିଗନ୍ତ ଆବିଷ୍କାର କରିବାର ରହିଛି ଏବଂ ନୂତନ ସ୍ଥାନକୁ ଯିବାର ... | 7 (112 (மாதங்களில் முதிர்வடையும்) | task1073_pib_translation_oriya_tamil | NIv2 | fs_opt | 2 | train |
A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
One example is below.
Q: “ନୂତନ ଦିଗନ୍ତ ଆବିଷ୍କାର କରିବାର ରହିଛି ଏବଂ ନୂତନ ସ୍ଥାନକୁ ଯିବାର ଅଛି । ଆମେ ଏଥିପାଇଁ ନିଜକୁ ପ୍ରସ୍ତୁତ କରିବାର ଅଛି ଏବଂ ସଫଳତାର ନୂତନ ସୋପାନ ଆରୋହଣ କରିବ... | இந்திய - ஆப்பிரிக்க கூட்டமைப்பு உச்சி மாநாட்டின் முடிவுகளை செயல்படுத்த ஆப்பிரிக்காவில் தூதரக அலுவலகங்களைத் திறக்க மத்திய அமைச்சரவை ஒப்புதல்! | task1073_pib_translation_oriya_tamil | NIv2 | fs_opt | 9 | train |
A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
Example Input: ବାଣିଜ୍ୟ, ପ୍ରଯୁକ୍ତି ଏବଂ ନିବେଶ କ୍ଷେତ୍ରରେ ଜର୍ମାନୀ ଆମର ଅଗ୍ରଣୀ ସହଯୋଗୀ । ବର୍ଲିନ୍ ଠାରେ ଚାନ୍ସେଲର ମର୍କେଲ୍ ଏବଂ ମୁଁ ଉଭୟ ଦେଶର ଶୀର୍ଷ ବ୍ୟବସାୟିକ ପ୍ରତିନିଧିମଣ୍ଡ... | ஒரு வகையில் அது சக்தியை அதிகரிக்கச் செய்கிறது.
| task1073_pib_translation_oriya_tamil | NIv2 | fs_opt | 3 | train |
Detailed Instructions: A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
Q: ପାଞ୍ଚଟି ପୁଲିସ ପଦକ ପ୍ରତିଷ୍ଠା କଲେ କେନ୍ଦ୍ର ସ୍ୱରାଷ୍ଟ୍ର ମନ୍ତ୍ରୀ
A: | உள்ளூர் தொல்லியல் மூலம் கிடைக்க வேண்டிய பாடங்கள் நமது பள்ளிக்கூடப் பாடத்திட்டங்களில் இடம்பெறச் செய்யலாம்” என்று கேட்டுக் கொண்டார். | task1073_pib_translation_oriya_tamil | NIv2 | zs_opt | 9 | train |
A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
[EX Q]: ପ୍ରଧାନମନ୍ତ୍ରୀ ନବଜାତ ଯତ୍ନ କାର୍ଯ୍ୟକ୍ରମର ସଫଳତାକୁ ପ୍ରଶଂସା କରିଥିଲେ । ଏହା ଦ୍ୱାରା ପ୍ରତିବର୍ଷ ଦେଶର 1.
[EX A]: ஏப்ரல் 23, 2012 அன்று உச்ச நீதிமன்ற தலைமை நீதிபதி... | இந்திய இளைஞர்களின் விருப்பத்தைப் பூர்த்தி செய்யும் வகையில், இந்தியாவில் உலகத் தரம் வாயந்த கல்விநிறுவனங்களை அமைப்பது எனது அரசின் நோக்கங்களில் ஒன்றாகும்.
| task1073_pib_translation_oriya_tamil | NIv2 | fs_opt | 6 | train |
A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
Ex Input:
ପୁରସ୍କାର ଉପରେ ବିସ୍ତୃତ ଭାବେ ବର୍ଣ୍ଣନା କରି କେନ୍ଦ୍ର ମହିଳା ଓ ଶିଶୁ ବିକାଶ ମନ୍ତ୍ରୀ ଶ୍ରୀମତୀ ମେନକା ସଂଜୟ ଗାନ୍ଧୀ କହିଥିଲେ ଯେ ରାଷ୍ଟ୍ରୀୟ ଶିଶୁ ପୁରସ୍କାର ଆମ ପିଲାମାନଙ୍... | “ஈஸ்டர் திருநாள் வாழ்த்துகள்!
| task1073_pib_translation_oriya_tamil | NIv2 | fs_opt | 1 | train |
You will be given a definition of a task first, then some input of the task.
A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
ଭ୍ରଷ୍ଟାଚାର, ପରିବାରବାଦ ଆମ ଦେଶକୁ କଳ୍ପନାତୀତ ଭାବେ କ୍ଷତିଗ୍ରସ୍ତ କରିଛି । ଆମେ ନିରନ୍ତର ପ୍ର... | இந்த வேற்றுமையை உலகத்துக்கு அறியச் செய்து, உலகச்சந்தையை பிடிப்பதற்கு நாம் முயற்சி மேற்கொண்டால், நமது நாட்டில் உள்ள இளைஞர்கள் வேலைவாய்ப்பைப் பெறுவார்கள். | task1073_pib_translation_oriya_tamil | NIv2 | zs_opt | 1 | test |
Instructions: A text is given in Oriya. Translate it from the Oriya language to the Tamil language. The translation must not omit or add information to the original sentence.
Input: “ଟାଇମଲେସ ଲକ୍ଷ୍ମଣ” ପୁସ୍ତକ ଉନ୍ମୋଚନ କଲେ ପ୍ରଧାନମନ୍ତ୍ରୀ
Output: | SDG கண்காணிப்பு சட்டகம் குறித்த பிரதேச அளவிலான பயிற்சி முகாம் | task1073_pib_translation_oriya_tamil | NIv2 | zs_opt | 3 | validation |
You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else it... | 0 | task1535_daily_dialog_uniqueness_classification | NIv2 | fs_opt | 9 | train |
Teacher: You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1... | 0 | task1535_daily_dialog_uniqueness_classification | NIv2 | fs_opt | 2 | train |
Q: You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else... | 0 | task1535_daily_dialog_uniqueness_classification | NIv2 | zs_opt | 7 | train |
You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else it... | 1 | task1535_daily_dialog_uniqueness_classification | NIv2 | zs_opt | 4 | train |
Given the task definition, example input & output, solve the new input case.
You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotion... | 1 | task1535_daily_dialog_uniqueness_classification | NIv2 | fs_opt | 1 | train |
You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else it... | 0
| task1535_daily_dialog_uniqueness_classification | NIv2 | fs_opt | 1 | train |
Q: You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else... | 1 | task1535_daily_dialog_uniqueness_classification | NIv2 | zs_opt | 7 | train |
You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else it... | Solution: 0 | task1535_daily_dialog_uniqueness_classification | NIv2 | fs_opt | 5 | train |
You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else it... | 1 | task1535_daily_dialog_uniqueness_classification | NIv2 | zs_opt | 4 | test |
You are given a conversation between two people.'Person1:' and 'Person2:' are used to separate their respective dialogues. You have to classify if there exist more than 2 unique emotions in conversation. If there are more than 2 unique emotions present in the conversation, the output should be classified as '1' else it... | 0 | task1535_daily_dialog_uniqueness_classification | NIv2 | fs_opt | 3 | validation |
Q: Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it i... | imagined | task854_hippocorpus_classification | NIv2 | zs_opt | 7 | train |
Teacher:Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold,... | recalled | task854_hippocorpus_classification | NIv2 | zs_opt | 6 | train |
Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it is a... | Solution: retold | task854_hippocorpus_classification | NIv2 | fs_opt | 5 | train |
Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it is a... | Solution: recalled | task854_hippocorpus_classification | NIv2 | fs_opt | 5 | train |
Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it is a... | retold | task854_hippocorpus_classification | NIv2 | zs_opt | 4 | train |
Teacher: Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold... | retold | task854_hippocorpus_classification | NIv2 | fs_opt | 2 | train |
Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it is a... | retold | task854_hippocorpus_classification | NIv2 | fs_opt | 6 | train |
Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it is a... | retold
| task854_hippocorpus_classification | NIv2 | fs_opt | 1 | train |
Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it is a... | imagined
| task854_hippocorpus_classification | NIv2 | fs_opt | 7 | test |
Read the given story and classify it as 'imagined', 'recalled', or 'retold'. If a story is imagined, the person who wrote the story is making it up, pretending they experienced it. If a story is recalled, the person who wrote the story really experienced it and is recalling it from memory. If a story is retold, it is a... | recalled | task854_hippocorpus_classification | NIv2 | fs_opt | 6 | validation |
Given the task definition and input, reply with output. You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". Ther... | Yes. | task050_multirc_answerability | NIv2 | zs_opt | 5 | train |
You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". There are only two types of valid responses: Yes and No.
[E... | No.
| task050_multirc_answerability | NIv2 | fs_opt | 6 | train |
Q: You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". There are only two types of valid responses: Yes and No.
... | Yes. | task050_multirc_answerability | NIv2 | zs_opt | 7 | train |
Given the task definition, example input & output, solve the new input case.
You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labelin... | No. | task050_multirc_answerability | NIv2 | fs_opt | 1 | train |
You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". There are only two types of valid responses: Yes and No.
Ex... | Yes.
| task050_multirc_answerability | NIv2 | fs_opt | 1 | train |
Given the task definition and input, reply with output. You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". Ther... | No. | task050_multirc_answerability | NIv2 | zs_opt | 5 | train |
You will be given a definition of a task first, then some input of the task.
You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labelin... | No. | task050_multirc_answerability | NIv2 | zs_opt | 1 | train |
You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". There are only two types of valid responses: Yes and No.
Le... | Yes. | task050_multirc_answerability | NIv2 | fs_opt | 8 | train |
You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". There are only two types of valid responses: Yes and No.
Se... | Yes.
| task050_multirc_answerability | NIv2 | fs_opt | 0 | test |
Detailed Instructions: You are given a sentence and a question in the input. If the information provided in the sentence is enough to answer the question, label "Yes", otherwise label "No". Do not use any facts other than those provided in the sentence while labeling "Yes" or "No". There are only two types of valid res... | No. | task050_multirc_answerability | NIv2 | zs_opt | 8 | validation |
In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
Q: Alex took care of the children after school so that their parents could continue to work.
A: | What will Alex want to do next? | task596_mocha_question_generation | NIv2 | zs_opt | 4 | train |
Detailed Instructions: In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
Q: Riley was in pain.
A: | How would Riley feel afterwards? | task596_mocha_question_generation | NIv2 | zs_opt | 9 | train |
In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
Let me give you an example: Tracy slept awkwardly on their new bed and was having some pain, so Tracy cracked her neck.
The answer to this example can be: What did Tracy d... | What will happen to Cameron? | task596_mocha_question_generation | NIv2 | fs_opt | 8 | train |
TASK DEFINITION: In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
PROBLEM: Riley regarded Jesse with gentle eyes before asking him out.
SOLUTION: What will happen to Jesse?
PROBLEM: Jordan wanted to tell Tracy a secret,... | Why weren't there many people who knew his real name ?
| task596_mocha_question_generation | NIv2 | fs_opt | 8 | train |
You will be given a definition of a task first, then some input of the task.
In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
From the converse sneakers that Parker sported during the ceremony - to the Dolce & Gabbana he... | Why was there a hashbrown bar ? | task596_mocha_question_generation | NIv2 | zs_opt | 1 | train |
In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
Quinn was playing and froliking in the snow, making snow angels and snowballs. | How would you describe Quinn? | task596_mocha_question_generation | NIv2 | zs_opt | 0 | train |
Detailed Instructions: In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
Problem:Riley was ready to leave the house so she put on her hat.
Solution: | How would Riley feel afterwards? | task596_mocha_question_generation | NIv2 | zs_opt | 8 | train |
Teacher: In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Tracy slept awkwardly on their new bed and was having some pain, so Tra... | Why did they use tape? | task596_mocha_question_generation | NIv2 | fs_opt | 2 | train |
In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
One example is below.
Q: Tracy slept awkwardly on their new bed and was having some pain, so Tracy cracked her neck.
A: What did Tracy do with her neck?
Rationale: The ques... | How would the father feel as a result? | task596_mocha_question_generation | NIv2 | fs_opt | 9 | test |
In this task, you're given a context passage. Your job is to generate relevant questions that can be answered by directly referring to the passage.
Q: My friend was selling popcorn to raise money for the Boy Scouts. Tracy loves popcorn, and placed a large order. Tracy gave money to my friend for the popcorn.
A: What ... | What is probably true about the narrator ?
****
| task596_mocha_question_generation | NIv2 | fs_opt | 4 | validation |
Teacher: In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
Teacher: Now, understand the problem? If you are still con... | Polar Expressing | task219_rocstories_title_answer_generation | NIv2 | fs_opt | 2 | train |
In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
Q: Sentence 1: Tom had worked at his job for decades. Sentence 2: H... | Retirement | task219_rocstories_title_answer_generation | NIv2 | zs_opt | 4 | train |
In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
Sentence 1: Ted had printed some shirts. Sentence 2: They had a co... | Stolen Image | task219_rocstories_title_answer_generation | NIv2 | zs_opt | 0 | train |
In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
--------
Question: Sentence 1: Dan wanted a new tv. Sentence 2: He ... | The Swing Set
| task219_rocstories_title_answer_generation | NIv2 | fs_opt | 7 | train |
Detailed Instructions: In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
Q: Sentence 1: I was watching the storm yest... | Lighting storm. | task219_rocstories_title_answer_generation | NIv2 | zs_opt | 9 | train |
TASK DEFINITION: In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
PROBLEM: Sentence 1: Barry was confused as to what... | Limitless Embarrassment
| task219_rocstories_title_answer_generation | NIv2 | fs_opt | 8 | 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 five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simp... | Lost Debit Card | task219_rocstories_title_answer_generation | NIv2 | fs_opt | 0 | train |
In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
Ex Input:
Sentence 1: I used to wash my car every weekend. Sentenc... | Harp
| task219_rocstories_title_answer_generation | NIv2 | fs_opt | 1 | train |
In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
Q: Sentence 1: After their first baby the Wilson's were unable to h... | The adoption | task219_rocstories_title_answer_generation | NIv2 | zs_opt | 4 | test |
instruction:
In this task, you're given five sentences, numbered 1 through 5. Your job is to generate a title for the story that makes complete sense. The title must be short, with less than three words, use simple language, and include the main topic of the story.
question:
Sentence 1: Anna agreed to a blind date. Sen... | flying
| task219_rocstories_title_answer_generation | NIv2 | fs_opt | 9 | validation |
Given the task definition, example input & output, solve the new input case.
In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators... | select the rows whose tournament mvp record fuzzily matches to sean elliott , arizona . the number of such rows is 2 . | task110_logic2text_sentence_generation | NIv2 | fs_opt | 1 | train |
Instructions: In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. only:... | select the rows whose competition record fuzzily matches to friendly match . there is only one such row in the table . the date record of this unqiue row is 23 march 1993 . | task110_logic2text_sentence_generation | NIv2 | zs_opt | 3 | train |
Given the task definition and input, reply with output. In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns... | the sum of the us viewers million record of all rows is 7.9 . | task110_logic2text_sentence_generation | NIv2 | zs_opt | 5 | train |
Q: In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. only: returns wh... | the sum of the canadian chapters record of all rows is 48 . | task110_logic2text_sentence_generation | NIv2 | zs_opt | 7 | train |
In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. only: returns wheth... | select the rows whose ties record is equal to 3 . there is only one such row in the table . the season record of this unqiue row is 2008 .
| task110_logic2text_sentence_generation | NIv2 | fs_opt | 0 | train |
In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. only: returns wheth... | the sum of the crowd record of all rows is 80962 . | task110_logic2text_sentence_generation | NIv2 | zs_opt | 4 | train |
TASK DEFINITION: In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. on... | for the most spoken language records of all rows , most of them fuzzily match to sotho .
| task110_logic2text_sentence_generation | NIv2 | fs_opt | 8 | train |
In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. only: returns wheth... | select the row whose date record of all rows is 2nd maximum . the tournament record of this row is augusta , united states . | task110_logic2text_sentence_generation | NIv2 | fs_opt | 3 | train |
Teacher: In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators:
1. count: returns the number of rows in the view
2. only: retu... | select the row whose record record of all rows is 2nd minimum . the nationality record of this row is russia . | task110_logic2text_sentence_generation | NIv2 | fs_opt | 2 | test |
You will be given a definition of a task first, then some input of the task.
In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command.
Here are the definitions of logical operators... | select the rows whose release price usd record is equal to 999 . the number of such rows is 2 . | task110_logic2text_sentence_generation | NIv2 | zs_opt | 1 | validation |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.