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In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then return 'None'.
I... | Output: None
| task1545_conll2002_person_name_extraction_answer_generation | 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 will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initi... | None | task1545_conll2002_person_name_extraction_answer_generation | NIv2 | fs_opt | 0 | train |
Q: In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then return 'None'.... | Jerry: B-PER, Springer: I-PER, Carlo: B-PER, Gepts: I-PER | task1545_conll2002_person_name_extraction_answer_generation | NIv2 | zs_opt | 7 | train |
Instructions: In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then ret... | Pinochet: B-PER | task1545_conll2002_person_name_extraction_answer_generation | NIv2 | zs_opt | 3 | train |
instruction:
In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then retu... | Jaap: B-PER, Stam: I-PER
| task1545_conll2002_person_name_extraction_answer_generation | NIv2 | fs_opt | 9 | train |
Q: In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then return 'None'.... | None | task1545_conll2002_person_name_extraction_answer_generation | NIv2 | zs_opt | 7 | train |
Instructions: In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then ret... | Branko: B-PER | task1545_conll2002_person_name_extraction_answer_generation | NIv2 | zs_opt | 3 | train |
TASK DEFINITION: In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then ... | Johan: B-PER, Cruijff: I-PER
| task1545_conll2002_person_name_extraction_answer_generation | NIv2 | fs_opt | 8 | train |
Detailed Instructions: In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity,... | None | task1545_conll2002_person_name_extraction_answer_generation | NIv2 | fs_opt | 4 | test |
In this task, you will be presented with a question in Dutch language, and you have to write the person names from the question if present. B denotes the first item of a phrase and an I any non-initial word. Phrase used for the person name - PER. There can be instances with no person name entity, then return 'None'.
Q:... | None | task1545_conll2002_person_name_extraction_answer_generation | NIv2 | zs_opt | 4 | validation |
You will be given a definition of a task first, then some input of the task.
Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If t... | 2 | task1366_healthfact_classification | NIv2 | zs_opt | 1 | train |
Instructions: Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporting evidence and the paragraph is in overal... | 2 | task1366_healthfact_classification | NIv2 | zs_opt | 3 | train |
Definition: Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporting evidence and the paragraph is in overall ... | 2 | task1366_healthfact_classification | NIv2 | zs_opt | 2 | train |
Teacher:Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporting evidence and the paragraph is in overall favo... | 0 | task1366_healthfact_classification | NIv2 | zs_opt | 6 | train |
Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporting evidence and the paragraph is in overall favor of the... | 2
| task1366_healthfact_classification | NIv2 | fs_opt | 6 | train |
Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporting evidence and the paragraph is in overall favor of the... | 2
| task1366_healthfact_classification | NIv2 | fs_opt | 7 | train |
Given the task definition and input, reply with output. Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporti... | 0 | task1366_healthfact_classification | NIv2 | zs_opt | 5 | train |
Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporting evidence and the paragraph is in overall favor of the... | Solution: 2 | task1366_healthfact_classification | NIv2 | fs_opt | 5 | train |
You will be given a definition of a task first, then some input of the task.
Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If t... | 0 | task1366_healthfact_classification | NIv2 | zs_opt | 1 | test |
Given the task definition and input, reply with output. Given a paragraph and a claim, classify it this way: If the claim contradicts the evidence present in the paragraph, classify the claim as '0'. If the claim has multiple supporting *AND* contradicting evidences, classify the claim as '1'. If the claim has supporti... | 2 | task1366_healthfact_classification | NIv2 | zs_opt | 5 | validation |
In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to create the q... | Which state did Easterlin perform in a new production of Ariadne auf Naxos? | task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 4 | train |
In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to create the q... | What venue was the ice cream truck at? | task236_iirc_question_from_passage_answer_generation | NIv2 | fs_opt | 6 | train |
In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to create the q... | Output: Of the types of math Hirschfeld in 1979 considered necessary to learn before reading his work on Galois geometry, which is the oldest mathematical discipline?
| task236_iirc_question_from_passage_answer_generation | NIv2 | fs_opt | 2 | train |
You will be given a definition of a task first, then some input of the task.
In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You sh... | When was the Great Fire of London?
| task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 1 | train |
Definition: In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to ... | Which MLB team that Cox played for has the longest history? | task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 2 | train |
Detailed Instructions: In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link info... | Who won the season during which the Phillies wore HK patches over their hearts? | task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 8 | train |
Teacher:In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to crea... | Who was the president of University of Tennessee when Mike Smithson was a student? | task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 6 | train |
Instructions: In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information t... | How old was Ferenc Szálasi when he was appointed "Leader of the Nation"? | task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 3 | train |
Definition: In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to ... | Which club that Parish was loaned out to in 2011 was found first? | task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 2 | test |
In this task, you're given a passage, further information available on a particular linked term from the statement, and an answer term. Your job is to generate a question that can use the information provided to obtain the given answer. You should use the information on both passage and link information to create the q... | How long had Milo Aukerman been with the Descendent's before quitting? | task236_iirc_question_from_passage_answer_generation | NIv2 | zs_opt | 0 | validation |
Detailed Instructions: Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
See one example below:
Problem: Then, the camel stops and the woman gets down from the camel.
Solution: A woman rides a camel holding a rode with her right han... | A man and woman are standing in a dance position. | task455_swag_context_generation | NIv2 | fs_opt | 4 | train |
Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
Q: Later someone sits at the dining room table.
A: | He peers down at his dark haired gardener. | task455_swag_context_generation | NIv2 | zs_opt | 4 | train |
Teacher:Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
Teacher: Now, understand the problem? Solve this instance: Someone looks down and away from someone.
Student: | Someone glares at his father, who shakes his head. | task455_swag_context_generation | NIv2 | zs_opt | 6 | train |
Detailed Instructions: Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
See one example below:
Problem: Then, the camel stops and the woman gets down from the camel.
Solution: A woman rides a camel holding a rode with her right han... | She stops in her tracks and grins. | task455_swag_context_generation | NIv2 | fs_opt | 4 | train |
Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
[EX Q]: Someone stands up and swoops in close.
[EX A]: A huge, gray, spiky dragon lies chained on the ground.
[EX Q]: Someone fumbles with the machine.
[EX A]: Someone closes his e... | A man lies on the ground, his chest heaving.
| task455_swag_context_generation | NIv2 | fs_opt | 6 | train |
Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
Q: Medical workers tend to their patients.
A: | With his eyes closed, someone's head hangs to the side. | task455_swag_context_generation | NIv2 | zs_opt | 4 | train |
Detailed Instructions: Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
See one example below:
Problem: Then, the camel stops and the woman gets down from the camel.
Solution: A woman rides a camel holding a rode with her right han... | Someone touches her forehead reassuringly, then turns off the light. | task455_swag_context_generation | NIv2 | fs_opt | 4 | train |
Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
Q: She is raising and lowering her arms, changing to different positions.
A: | A woman is standing on a wooden floor. | task455_swag_context_generation | NIv2 | zs_opt | 4 | train |
Part 1. Definition
Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
Part 2. Example
Then, the camel stops and the woman gets down from the camel.
Answer: A woman rides a camel holding a rode with her right hand while a man pulls th... | Meanwhile, someone descends the E - deck stairs. | task455_swag_context_generation | NIv2 | fs_opt | 7 | test |
Q: Given a sentence, generate a most likely context or previous statement. The previous statement should be relevant to the given statement.
The bullet hits the tree, then the guard runs to the boat.
A: | As he leans against a tree, a guard comes at him. | task455_swag_context_generation | NIv2 | zs_opt | 7 | validation |
In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this corpus is 'ADJ'... | SCONJ
| task583_udeps_eng_coarse_pos_tagging | NIv2 | fs_opt | 0 | train |
instruction:
In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this co... | PRON
| task583_udeps_eng_coarse_pos_tagging | NIv2 | fs_opt | 9 | train |
In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this corpus is 'ADJ'... | AUX | task583_udeps_eng_coarse_pos_tagging | NIv2 | zs_opt | 0 | train |
Given the task definition and input, reply with output. In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of... | PUNCT | task583_udeps_eng_coarse_pos_tagging | NIv2 | zs_opt | 5 | train |
Instructions: In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this c... | DET | task583_udeps_eng_coarse_pos_tagging | NIv2 | zs_opt | 3 | train |
Instructions: In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this c... | SCONJ | task583_udeps_eng_coarse_pos_tagging | NIv2 | zs_opt | 3 | train |
Part 1. Definition
In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of t... | NUM | task583_udeps_eng_coarse_pos_tagging | NIv2 | fs_opt | 7 | train |
In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this corpus is 'ADJ'... | Solution: AUX | task583_udeps_eng_coarse_pos_tagging | NIv2 | fs_opt | 5 | train |
In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this corpus is 'ADJ'... | PUNCT | task583_udeps_eng_coarse_pos_tagging | NIv2 | fs_opt | 8 | test |
In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e. tagset of this corpus is 'ADJ'... | INTJ | task583_udeps_eng_coarse_pos_tagging | NIv2 | fs_opt | 8 | validation |
Detailed Instructions: An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Q: Answer: white house (plantation)
A: | where was george washingtons wedding? | task443_com_qa_ans_question_generation | NIv2 | zs_opt | 9 | train |
An answer is given. Generate a question for which the given answer is either a direct or partial answer.
[Q]: Answer: england
[A]: what nationality are peter andre's parents?
[Q]: Answer: john franklin
[A]: who is jonh franklin?
[Q]: Answer: sacha baron cohen
[A]: | who voices king julien in madagascar?
| task443_com_qa_ans_question_generation | NIv2 | fs_opt | 5 | train |
Teacher:An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Teacher: Now, understand the problem? Solve this instance: Answer: continental army
Student: | george washington was commander-in chief of what army? | task443_com_qa_ans_question_generation | NIv2 | zs_opt | 6 | train |
instruction:
An answer is given. Generate a question for which the given answer is either a direct or partial answer.
question:
Answer: mohamed amin didi
answer:
who was the first president of maldives?
question:
Answer: space shuttle columbia
answer:
what was the first space shuttle to fly to space?
question:
Answ... | what city is manchester united stadium located?
| task443_com_qa_ans_question_generation | NIv2 | fs_opt | 9 | train |
An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Answer: lech kaczy%c5%84ski | who was the president of poland in may 2009? | task443_com_qa_ans_question_generation | NIv2 | zs_opt | 0 | train |
Q: An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Answer: italy
A: | what european country was 1st winner of the new fifa world cup trophy? | task443_com_qa_ans_question_generation | NIv2 | zs_opt | 7 | train |
Detailed Instructions: An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Q: Answer: rebecca cole
A: | who were rebecca cole siblings? | task443_com_qa_ans_question_generation | NIv2 | zs_opt | 9 | train |
Detailed Instructions: An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Problem:Answer: james s. sherman
Solution: | who was the us vice president of 1910 to 1919? | task443_com_qa_ans_question_generation | NIv2 | zs_opt | 8 | train |
An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Let me give you an example: Answer: victoria woodhull
The answer to this example can be: who was the first women to run for presidency in the us?
Here is why: The given answer directly answers the generated questio... | what did the maya do for recreation? | task443_com_qa_ans_question_generation | NIv2 | fs_opt | 8 | test |
You will be given a definition of a task first, then some input of the task.
An answer is given. Generate a question for which the given answer is either a direct or partial answer.
Answer: the sex and violence family hour
Output: | what is jim carreys 1st film? | task443_com_qa_ans_question_generation | NIv2 | zs_opt | 1 | validation |
instruction:
Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable ... | Bạn có thể chào đón một con mèo trong một túi xách.
| task411_mickey_vi_sentence_perturbation_generation | NIv2 | fs_opt | 9 | train |
Q: Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probabilit... | Rất có thể bạn sẽ tìm thấy một kiệt tác trong nghệ thuật. | task411_mickey_vi_sentence_perturbation_generation | NIv2 | zs_opt | 7 | train |
Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability o... | Bạn sẽ bò trên một chiếc ghế bởi vì bạn muốn nghỉ ngơi.
| task411_mickey_vi_sentence_perturbation_generation | NIv2 | fs_opt | 0 | train |
Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability o... | Nếu bạn dừng lại để bán một bóng đá thì bạn nên sử dụng một quân đội. | task411_mickey_vi_sentence_perturbation_generation | NIv2 | fs_opt | 9 | train |
Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability o... | Solution: Cậu phải đi tìm một cái nhẫn cưới trong một bộ phim. | task411_mickey_vi_sentence_perturbation_generation | NIv2 | fs_opt | 5 | train |
Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability o... | Bạn có thể làm rối loạn sản phẩm mỹ phẩm trong một số cửa hàng.
| task411_mickey_vi_sentence_perturbation_generation | NIv2 | fs_opt | 0 | train |
Definition: Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable p... | Nếu bạn muốn đi dự một buổi hòa nhạc, sau đó bạn nên gọi cho mùa giải. | task411_mickey_vi_sentence_perturbation_generation | NIv2 | zs_opt | 2 | train |
Definition: Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable p... | Những ý tưởng mới sẽ làm cho bạn muốn có một bữa tiệc. | task411_mickey_vi_sentence_perturbation_generation | NIv2 | zs_opt | 2 | train |
TASK DEFINITION: Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasona... | Một con thuyền có thể vượt qua một cơn bão trên biển.
| task411_mickey_vi_sentence_perturbation_generation | NIv2 | fs_opt | 8 | test |
Given a sentence in Vietnamese, generate a new Vietnamese sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability o... | Rất có thể bạn sẽ phải đối mặt với một nhà vệ sinh trong một bài giảng.
| task411_mickey_vi_sentence_perturbation_generation | NIv2 | fs_opt | 3 | validation |
Detailed Instructions: You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given questio... | 4, 5. | task049_multirc_questions_needed_to_answer | NIv2 | zs_opt | 8 | train |
You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given question (do not include sente... | Solution: 7, 8, 9. | task049_multirc_questions_needed_to_answer | NIv2 | fs_opt | 5 | train |
You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given question (do not include sente... | Output: 2.
| task049_multirc_questions_needed_to_answer | NIv2 | fs_opt | 2 | train |
Instructions: You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given question (do not... | 14, 15, 16. | task049_multirc_questions_needed_to_answer | NIv2 | zs_opt | 3 | train |
Given the task definition, example input & output, solve the new input case.
You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Se... | 1, 2, 3. | task049_multirc_questions_needed_to_answer | NIv2 | fs_opt | 1 | train |
You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given question (do not include sente... | 9, 10, 11, 12. | task049_multirc_questions_needed_to_answer | NIv2 | fs_opt | 6 | train |
You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given question (do not include sente... | 2, 4.
| task049_multirc_questions_needed_to_answer | NIv2 | fs_opt | 5 | train |
You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given question (do not include sente... | 1, 4. | task049_multirc_questions_needed_to_answer | NIv2 | zs_opt | 4 | train |
Detailed Instructions: You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given questio... | 1, 2. | task049_multirc_questions_needed_to_answer | NIv2 | fs_opt | 4 | test |
TASK DEFINITION: You are given a paragraph, with numbered sentences, and a question. Write the numbers of the sentences needed to answer the given question. Separate multiple sentence numbers with a comma (", "). E.g. "1, 2" would be the correct response if Sent 1 and Sent 2 are needed to answer the given question (do ... | 1, 3.
| task049_multirc_questions_needed_to_answer | NIv2 | fs_opt | 8 | validation |
You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
One example: Men have to take power back as the superior gender.
Solution is here: gender criticism
Explanation: This example correctly labels 1 which m... | gender criticism | task1606_ethos_text_classification | NIv2 | fs_opt | 6 | train |
You will be given a definition of a task first, then some input of the task.
You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
Time to nuke China or Unleash a plague.
Output: | not gender criticism | task1606_ethos_text_classification | NIv2 | zs_opt | 1 | train |
Detailed Instructions: You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
Problem:I remember once I was in charge of a disabled kid. Every night I was wishing slow death for him
Solution: | not gender criticism | task1606_ethos_text_classification | NIv2 | zs_opt | 8 | train |
You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
Example: Men have to take power back as the superior gender.
Example solution: gender criticism
Example explanation: This example correctly labels 1 whi... | Solution: not gender criticism | task1606_ethos_text_classification | NIv2 | fs_opt | 5 | train |
Given the task definition and input, reply with output. You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
The only thing more repulsive than a nigger is a nigger faggot. Self-hating whites shower image... | not gender criticism | task1606_ethos_text_classification | NIv2 | zs_opt | 5 | train |
Teacher:You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
Teacher: Now, understand the problem? Solve this instance: Teaching my kids transgenders are anything but mentally ill will get you set on fire.... | not gender criticism | task1606_ethos_text_classification | NIv2 | zs_opt | 6 | train |
You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
Well im glad that i live in Serbia, migrants and islamists are not welcome here!
not gender criticism
this was shit the guy should of boxed his head o... | not gender criticism
| task1606_ethos_text_classification | NIv2 | fs_opt | 0 | train |
Given the task definition and input, reply with output. You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
Muslims want war! So take it to them first
| not gender criticism | task1606_ethos_text_classification | NIv2 | zs_opt | 5 | train |
Given the task definition and input, reply with output. You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
I just don't like Homosexuals raising kids it's my opinion.
| not gender criticism | task1606_ethos_text_classification | NIv2 | zs_opt | 5 | test |
Instructions: You are given a piece of text. You should find out if there are any gender specific criticized comments. Label the text as 'gender criticism' or 'not gender criticism'
Input: Im going to choke you with your cross necklace idiotic religious pig
Output: | not gender criticism | task1606_ethos_text_classification | NIv2 | zs_opt | 3 | validation |
Detailed Instructions: A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
Q: Vice President Greets People on Id-ul-Fitr
A: | ਪ੍ਰਧਾਨ ਮੰਤਰੀ ਨੇ ਈਦ-ਉਲ-ਫਿਤਰ 'ਤੇ ਮੁਬਾਰਕਾਂ ਦਿੱਤੀਆਂ | task1007_pib_translation_english_punjabi | NIv2 | zs_opt | 9 | train |
Detailed Instructions: A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
Q: Water Supply and Sewerage projects in Ajmer, Bhilwara, Bikaner, Hanumangarh, Sikar and Mount Abu.
A: | ਅਜਮੇਰ, ਭੀਲਵਾੜਾ, ਬੀਕਾਨੇਰ, ਹਨੂੰਮਾਨਗੜ੍ਹ, ਸੀਕਰ ਅਤੇ ਮਾਊਂਟ ਆਬੂ ਵਿੱਚ ਜਲ ਸਪਲਾਈ ਅਤੇ ਸੀਵਰੇਜ ਪ੍ਰੋਜੈਕਟ | task1007_pib_translation_english_punjabi | NIv2 | zs_opt | 9 | train |
Q: A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
the sanitation worker who makes our Republic cleaner and hygienic
A: | ਹਰ-ਇੱਕ ਸਵੱਛਤਾ ਕਰਮਚਾਰੀ, ਜੋ ਸਾਡੇ ਦੇਸ਼ ਨੂੰ ਸਾਫ਼-ਸੁਥਰਾ ਅਤੇ ਸਵੱਛ ਰੱਖਦਾ ਹੈ ; | task1007_pib_translation_english_punjabi | NIv2 | zs_opt | 7 | train |
Detailed Instructions: A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
See one example below:
Problem: Jacob Mathew, Mr
Solution: ਯਾਕੂਬ ਮੈਥਿ, ਸ਼੍ਰੀਮਾਨ
Explanation: Correct translation for given sentenc... | ਪ੍ਰਧਾਨ ਮੰਤਰੀ ਨੇ ਅਧਿਆਪਕ ਦਿਵਸ ‘ਤੇ ਅਧਿਆਪਨ ਭਾਈਚਾਰੇ ਨੂੰ ਵਧਾਈਆਂ ਦਿੱਤੀਆਂ ਹਨ ; | task1007_pib_translation_english_punjabi | NIv2 | fs_opt | 4 | train |
TASK DEFINITION: A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
PROBLEM: History and our people would expect us to make full use of these opportunities and not to let go of them.
SOLUTION: ਇਤਿਹਾਸ ਨੂੰ... | ਜੁਡੀਸ਼ੀਅਲ ਮੈਂਬਰ ਜਸਟਿਸ ਕੇਐੱਨ ਪਾਟਿਲ, ਤਕਨੀਕੀ ਮੈਂਬਰ ਸ਼੍ਰੀ ਐੱਸ.
| task1007_pib_translation_english_punjabi | NIv2 | fs_opt | 8 | train |
Detailed Instructions: A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
Q: Shri Nitin Gadkari to lay foundation stone and dedicate to nation projects worth Rs 4239 crore in Chhattisgarh
A: | ਸ਼੍ਰੀ ਨਿਤਿਨ ਗਡਕਰੀ ਛੱਤੀਸਗੜ੍ਹ ਵਿੱਚ 4239 ਕਰੋੜ ਰੁਪਏ ਲਾਗਤ ਦੇ ਪ੍ਰੋਜੈਕਟਾਂ ਦਾ ਨੀਂਹ ਪੱਥਰ ਰੱਖਣਗੇ ਅਤੇ ਰਾਸ਼ਟਰ ਨੂੰ ਸਮਰਪਿਤ ਕਰਨਗੇ | task1007_pib_translation_english_punjabi | NIv2 | zs_opt | 9 | train |
Teacher:A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? Solve this instance: The Prime Minister said one reason for this decline in Doing Business ranking, was cor... | ਉਨ੍ਹਾਂ ਕਿਹਾ ਕਿ ਕਾਰੋਬਾਰੀ ਸੁਗਮਤਾ ਸਬੰਧੀ ਰੈਂਕਿੰਗ ਵਿੱਚ ਗਿਰਾਵਟ ਦਾ ਇੱਕ ਕਾਰਨ ਭ੍ਰਿਸ਼ਟਾਚਾਰ ਸੀ । ਉਨ੍ਹਾਂ ਨੇ ਇਸ ਸੰਦਰਭ ਵਿੱਚ ਕੋਲਾ, ਕਾਮਨਵੈਲਥ ਗੇਮ, ਸਪੈਕਟ੍ਰਮ ਆਦਿ ਅਨੇਕ ਘੁਟਾਲਿਆਂ ਬਾਰੇ ਦੱਸਿਆ, ਜੋ ਉਸ ਸਮੇਂ ਖ਼ਬਰਾਂ ਦੀਆਂ ਸੁਰਖੀਆਂ ਬਣੇ ਸਨ। | task1007_pib_translation_english_punjabi | NIv2 | zs_opt | 6 | train |
A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
Example input: Jacob Mathew, Mr
Example output: ਯਾਕੂਬ ਮੈਥਿ, ਸ਼੍ਰੀਮਾਨ
Example explanation: Correct translation for given sentence. Input sentence means '... | ਮੈਨੂੰ ਦੱਸਿਆ ਗਿਆ ਕਿ ਇਹ ਰੇਡੀਓ ਸਟੇਸ਼ਨ ਹਫ਼ਤਾਵਾਰ ਸਮਾਚਾਰ ਬੁਲੇਟਿਨ ਵੀ ਪ੍ਰਸਾਰਿਤ ਕਰਦਾ ਸੀ ਜੋ ਅੰਗਰੇਜ਼ੀ, ਹਿੰਦੀ, ਤਮਿਲ, ਬਾਂਗਲਾ, ਮਰਾਠੀ, ਪੰਜਾਬੀ, ਪਸ਼ਤੋ ਅਤੇ ਉਰਦੂ ਆਦਿ ਭਾਸ਼ਾਵਾਂ ਵਿੱਚ ਹੁੰਦੇ ਸਨ। ਇਸ ਰੇਡੀਓ ਸਟੇਸ਼ਨ ਦੇ ਸੰਚਾਲਨ ਵਿੱਚ ਗੁਜਰਾਤ ਦੇ ਰਹਿਣ ਵਾਲੇ ਐੱਮ. | task1007_pib_translation_english_punjabi | NIv2 | fs_opt | 3 | train |
Teacher:A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? Solve this instance: The Prime Minister was given an overview of the extent of damage caused by floods, and... | ਪ੍ਰਧਾਨ ਮੰਤਰੀ ਨੂੰ ਹੜ੍ਹ ਕਾਰਨ ਹੋਏ ਨੁਕਸਾਨ ਅਤੇ ਸ਼ੁਰੂ ਕੀਤੇ ਰਾਹਤ ਕਾਰਜਾਂ ਬਾਰੇ ਦੱਸਿਆ ਗਿਆ। | task1007_pib_translation_english_punjabi | NIv2 | zs_opt | 6 | test |
Part 1. Definition
A text is given in English. Translate it from the English language to the Panjabi language. The translation must not omit or add information to the original sentence.
Part 2. Example
Jacob Mathew, Mr
Answer: ਯਾਕੂਬ ਮੈਥਿ, ਸ਼੍ਰੀਮਾਨ
Explanation: Correct translation for given sentence. Input sentence mean... | ਸਰਬਵਿਆਪੀ ਆਰਥਕ ਵਸੂਲੀ ਲਈ ਬ੍ਰਿਕਸ ਭਾਈਵਾਲੀ, ਵਿੱਤ ਅਤੇ ਆਰਥਕ ਸਰਬਵਿਆਪੀ ਪ੍ਰਬੰਧਨ ਸੰਸਥਾਵਾਂ ਵਿੱਚ ਸੁਧਾਰ ਅਤੇ ਚੌਥੀ ਸਨਅਤੀ ਕ੍ਰਾਂਤੀ | task1007_pib_translation_english_punjabi | NIv2 | fs_opt | 7 | validation |
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