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're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sentence that completes the story, and the position is the nu... | 2, The got all the ingredients together and got to it.
| task299_storycloze_sentence_generation | NIv2 | fs_opt | 0 | train |
You will be given a definition of a task first, then some input of the task.
In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The mi... | 3, He grumbled but went along. | task299_storycloze_sentence_generation | NIv2 | zs_opt | 1 | train |
Detailed Instructions: In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sentence that completes the story, and... | 1, Our dog was named Cinnamon. | task299_storycloze_sentence_generation | NIv2 | zs_opt | 9 | train |
You will be given a definition of a task first, then some input of the task.
In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The mi... | 1, Quinn spent a lot of time caring for her lawn. | task299_storycloze_sentence_generation | NIv2 | zs_opt | 1 | train |
Given the task definition and input, reply with output. In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sente... | 5, Allison plans to pay bingo again in the future. | task299_storycloze_sentence_generation | NIv2 | zs_opt | 5 | train |
Definition: In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sentence that completes the story, and the positi... | 2, She had been in the kitchen and hadn't heard them in awhile. | task299_storycloze_sentence_generation | NIv2 | zs_opt | 2 | train |
In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sentence that completes the story, and the position is the nu... | 2, It's not something we normally do.
| task299_storycloze_sentence_generation | NIv2 | fs_opt | 6 | train |
TASK DEFINITION: In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sentence that completes the story, and the p... | 4, She thought the interview went pretty well.
| task299_storycloze_sentence_generation | NIv2 | fs_opt | 8 | train |
TASK DEFINITION: In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sentence that completes the story, and the p... | 5, She had left the shower running.
| task299_storycloze_sentence_generation | NIv2 | fs_opt | 8 | test |
In this task, you're given four sentences of a story written in natural language in which one part is missing. Your job is to predict the position and missing part of the story and return in the following format: position, missing part. The missing part is a sentence that completes the story, and the position is the nu... | Solution: 3, He wanted to join a church choir but thought his friends would laugh. | task299_storycloze_sentence_generation | NIv2 | fs_opt | 5 | validation |
Part 1. Definition
In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
Part 2. Example
وارن بافت، (زادهٔ ۳۰ اوت ۱۹۳۰) سرمایهگذار، اقتصاددان، مدیر اجرایی و نیکوکار آمریکایی است،... | دشمن اصلی افراسیاب چه شخصی بود؟ | task394_persianqa_question_generation | NIv2 | fs_opt | 7 | train |
Instructions: In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
Input: سازمان ملل متحد سازمانی بینالمللی است که در سال ۱۹۴۵ میلادی تأسیس و جایگزین جامعه ملل که در ده ژانویه ۱... | شورای امنیت چه جایگاهی در سازمان ملل دارد؟ | task394_persianqa_question_generation | NIv2 | zs_opt | 3 | train |
Given the task definition and input, reply with output. In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
آتَش یا تَش انرژی گرمایی و نور است که هنگام واکنش شیمیایی آزاد میشو... | رنگ شعله ای اتیش به چی ربط دارد؟ | task394_persianqa_question_generation | NIv2 | zs_opt | 5 | train |
Instructions: In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
Input: امین حیایی (زادهٔ ۱۹ خرداد ۱۳۴۹) هنرپیشه و خواننده ایرانی است.امین حیایی در ۱۹ خرداد ۱۳۴۹ در تهران در مح... | شغل پدر امین حیایی چیست؟ | task394_persianqa_question_generation | NIv2 | zs_opt | 3 | train |
Instructions: In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
Input: بابک زنجانی با نام کامل بابک مرتضی زنجانی ثروتمندترین ایرانی جهان در سال ۲۰۱۲، بازرگان و سرمایهدار، موس... | اسم واقعه و اصلی بابک زنجانی چیه؟ | task394_persianqa_question_generation | NIv2 | zs_opt | 3 | train |
In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
رودآیلند (به انگلیسی: Rhode Island) ایالتی است در شمال شرقی آمریکا و یکی از ایالتهای نیوانگلند است. این ایالت از سمت شمال و... | رودآیلند جزو کدام ایالت است؟ | task394_persianqa_question_generation | NIv2 | zs_opt | 0 | train |
In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
نوروز نخستین روز سال خورشیدی ایرانی برابر با یکم فروردین ماه، جشن آغاز سال نوی ایرانی و یکی از کهنترین جشنهای به جا مانده ... | نوروز جشن چه چیزی است؟ | task394_persianqa_question_generation | NIv2 | zs_opt | 0 | train |
In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
Input: Consider Input: سیپلاسپلاس یک زبان برنامهنویسی همهمنظوره، همگردان، سطح میانی، شیءگرا و چندرگه است که از برنامه... | Output: قالبها چطور پر میشه؟
| task394_persianqa_question_generation | NIv2 | fs_opt | 2 | train |
Given the task definition, example input & output, solve the new input case.
In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
Example: وارن بافت، (زادهٔ ۳۰ اوت ۱۹۳۰) سرمایهگ... | دبیر اولین بار در کجا وارد کشتی شد؟ | task394_persianqa_question_generation | NIv2 | fs_opt | 1 | test |
In this task, you will be shown a Persian passage. You need to write a Persian question for the passage. Your question should be answerable based on the passage and only have one correct answer.
One example is below.
Q: وارن بافت، (زادهٔ ۳۰ اوت ۱۹۳۰) سرمایهگذار، اقتصاددان، مدیر اجرایی و نیکوکار آمریکایی است، که هماکن... | اوتیسم از چه نوع اختلال رشدی است؟ | task394_persianqa_question_generation | NIv2 | fs_opt | 9 | validation |
Definition: This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other. The input ... | SIMILAR | task1408_dart_similarity_classification | NIv2 | zs_opt | 2 | train |
This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other. The input is a list of... | DISSIMILAR
| task1408_dart_similarity_classification | NIv2 | fs_opt | 6 | train |
Given the task definition, example input & output, solve the new input case.
This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [... | SIMILAR | task1408_dart_similarity_classification | NIv2 | fs_opt | 1 | train |
Detailed Instructions: This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other.... | DISSIMILAR | task1408_dart_similarity_classification | NIv2 | zs_opt | 9 | train |
This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other. The input is a list of... | DISSIMILAR
| task1408_dart_similarity_classification | NIv2 | fs_opt | 0 | train |
Q: This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other. The input is a list... | SIMILAR | task1408_dart_similarity_classification | NIv2 | zs_opt | 7 | train |
Q: This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other. The input is a list... | DISSIMILAR | task1408_dart_similarity_classification | NIv2 | zs_opt | 7 | train |
instruction:
This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other. The input... | DISSIMILAR
| task1408_dart_similarity_classification | NIv2 | fs_opt | 9 | train |
Detailed Instructions: This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other.... | SIMILAR | task1408_dart_similarity_classification | NIv2 | zs_opt | 9 | test |
Q: This task is about classifying the similarity of two sentences. The sentences can be classified as (a) SIMILAR - similar to each other, and (b) DISSIMILAR - not similar to each other. Sentences that have the same RDF relationship in terms of [subject, predicate, object] are similar to each other. The input is a list... | SIMILAR | task1408_dart_similarity_classification | NIv2 | zs_opt | 7 | validation |
Detailed Instructions: Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
Problem:À la mort de Roger, son fils - William de Roumare, comte de Lincoln - hérite du manoir.
Solution: | ロジャーの死後、彼の息子は継承されました - ウィリアム・ド・ルーマール、リンカーン伯爵 - 邸宅。 | task794_pawsx_french_japanese_translation | NIv2 | zs_opt | 8 | train |
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
Example Input: Brockton est à environ 25 milles au nord-est de Providence, dans le Rhode Island, et à 30 milles au sud de Boston.
Example Output: ブロックトンは、... | 1784年10月29日にテュークスベリーで生まれたイーストホープは、ウスターシャー州オーバーベリー出身のジョン・リーバーの娘、トーマス・イーストホープによってエリザベスの長男でした。
| task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 3 | train |
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
One example: La saison NBA 1975 - 76 était la 30e saison de la National Basketball Association.
Solution is here: 1975 - 76年の全米バスケットボール協会のシーズンは、NBAの第30シー... | Bota Mare川はルーマニアのZăbrătău川の支流です。 | task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 6 | train |
You will be given a definition of a task first, then some input of the task.
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
La recherche d'une clé binaire dans un arbre de recherche spécifique peut êtr... | バイナリキーに従って特定の探索木を探索することは、再帰的または反復的にプログラムすることができる。 | task794_pawsx_french_japanese_translation | NIv2 | zs_opt | 1 | train |
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
La rivière Little Jocko traverse le fleuve Saint-Laurent et la rivière des Outaouais jusqu'à la rivière Jocko.
Little Jocko川はSaint Lawrence川とOttawa川を経由してJ... | 市はスネーク川とオレゴン州との国境を接するグレートワイザー川の合流点にあります。
| task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 0 | train |
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
One example is below.
Q: La saison NBA 1975 - 76 était la 30e saison de la National Basketball Association.
A: 1975 - 76年の全米バスケットボール協会のシーズンは、NBAの第30シーズンで... | 「Bonne Citoyenne」は嵐の中で被害を受け、残りのフランス艦隊から切り離されたという不幸がありました。 | task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 9 | train |
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
[Q]: La rivière Sambata est un affluent de la rivière Caprei en Roumanie.
[A]: サンバタ川はルーマニアのピアトラ川の支流です。
[Q]: Né à Séville, Hidalgo a joué pour Badajoz et... | 同所性捕食者は、マウンテンライオン、アメリカのツキノワグマとハイイログマを含みます。
| task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 5 | train |
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
[EX Q]: Elle était mariée à Edmund Ashfield et Richard Glanville après sa mort.
[EX A]: 彼女はエドマンド・アッシュフィールドと結婚し、彼の死後、リチャード・グランビルと結婚した。
[EX Q]: Les conclus... | ここでは、擬似微分演算子を微分演算子の一般化と見なします。
| task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 6 | train |
Part 1. Definition
Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
Part 2. Example
La saison NBA 1975 - 76 était la 30e saison de la National Basketball Association.
Answer: 1975 - 76年の全米バスケットボール協会のシーズ... | 画期的なアルバム「Smash」（1994年）の4曲目と3曲目のシングルです。 | task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 7 | test |
TASK DEFINITION: Given a sentence in French, provide an equivalent paraphrased translation in Japanese that retains the same meaning both through the translation and the paraphrase.
PROBLEM: Le terme «sphérocytose non héréditaire est rarement, bien que parfois, utilisé.
SOLUTION: 「非遺伝性球状赤血球症」という用語は、時折ではありますが、めったに使用されま... | ロジャーの死後、彼の息子は継承されました - ウィリアム・ド・ルーマール、リンカーン伯爵 - 邸宅。
| task794_pawsx_french_japanese_translation | NIv2 | fs_opt | 8 | validation |
Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expresses no clear view tow... | Output: Positive
| task420_persent_document_sentiment_classification | NIv2 | fs_opt | 2 | train |
Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expresses no clear view tow... | Solution: Positive | task420_persent_document_sentiment_classification | NIv2 | fs_opt | 5 | train |
Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expresses no clear view tow... | Neutral | task420_persent_document_sentiment_classification | NIv2 | fs_opt | 8 | train |
Q: Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expresses no clear view ... | Neutral | task420_persent_document_sentiment_classification | NIv2 | zs_opt | 7 | train |
Given the task definition, example input & output, solve the new input case.
Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its ... | Negative | task420_persent_document_sentiment_classification | NIv2 | fs_opt | 1 | train |
Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expresses no clear view tow... | Neutral | task420_persent_document_sentiment_classification | NIv2 | fs_opt | 6 | train |
Detailed Instructions: Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expr... | Positive | task420_persent_document_sentiment_classification | NIv2 | zs_opt | 8 | train |
Detailed Instructions: Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expr... | Negative | task420_persent_document_sentiment_classification | NIv2 | zs_opt | 9 | train |
Definition: Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expresses no cl... | Positive | task420_persent_document_sentiment_classification | NIv2 | zs_opt | 2 | test |
instruction:
Given a document and an entity the task is to select the author's sentiment towards the entity. Sentiments can be Positive, Neutral and Negative. Select Positive if the article expresses a positive view towards the given entity or praises its quality or skills. Select Neutral if the document expresses no c... | Positive
| task420_persent_document_sentiment_classification | NIv2 | fs_opt | 9 | validation |
In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional inform... | strengthener
| task935_defeasible_nli_atomic_classification | NIv2 | fs_opt | 1 | train |
Q: In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional inf... | weakener | task935_defeasible_nli_atomic_classification | NIv2 | zs_opt | 7 | train |
Detailed Instructions: In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update pro... | strengthener | task935_defeasible_nli_atomic_classification | NIv2 | zs_opt | 8 | train |
In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional inform... | weakener | task935_defeasible_nli_atomic_classification | NIv2 | zs_opt | 4 | train |
In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional inform... | strengthener | task935_defeasible_nli_atomic_classification | NIv2 | zs_opt | 0 | train |
Definition: In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides addit... | weakener | task935_defeasible_nli_atomic_classification | NIv2 | zs_opt | 2 | train |
In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional inform... | weakener | task935_defeasible_nli_atomic_classification | NIv2 | fs_opt | 8 | train |
In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional inform... | weakener | task935_defeasible_nli_atomic_classification | NIv2 | zs_opt | 4 | train |
TASK DEFINITION: In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides ... | strengthener
| task935_defeasible_nli_atomic_classification | NIv2 | fs_opt | 8 | test |
Q: In this task, you are given a premise, a hypothesis, and an update. The premise sentence describes a real-world situation and is always assumed to be true. The hypothesis sentence describes an assumption or inference that you might make about that situation having read the premise. The update provides additional inf... | strengthener | task935_defeasible_nli_atomic_classification | NIv2 | zs_opt | 7 | validation |
Definition: In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the text have b... | Non-aggresive | task335_hateeval_classification_aggresive_en | NIv2 | zs_opt | 2 | train |
Q: In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the text have been repla... | Non-aggresive | task335_hateeval_classification_aggresive_en | NIv2 | zs_opt | 7 | train |
Q: In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the text have been repla... | Aggresive | task335_hateeval_classification_aggresive_en | NIv2 | zs_opt | 7 | train |
Teacher: In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the text have been... | Non-aggresive | task335_hateeval_classification_aggresive_en | NIv2 | fs_opt | 2 | train |
Given the task definition, example input & output, solve the new input case.
In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way an... | Non-aggresive | task335_hateeval_classification_aggresive_en | NIv2 | fs_opt | 1 | train |
In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the text have been replaced... | Non-aggresive
| task335_hateeval_classification_aggresive_en | NIv2 | fs_opt | 3 | train |
In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the text have been replaced... | Aggresive
| task335_hateeval_classification_aggresive_en | NIv2 | fs_opt | 7 | train |
Detailed Instructions: In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the ... | Aggresive | task335_hateeval_classification_aggresive_en | NIv2 | zs_opt | 9 | train |
Q: In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the text have been repla... | Non-aggresive | task335_hateeval_classification_aggresive_en | NIv2 | zs_opt | 7 | test |
Detailed Instructions: In this task, you are given a hateful post in English from online platforms. You are expected to classify the post into two classes: aggresive or non-aggresive. An aggresive post is one that expresses feelings and opinions in an abusive way and tries to dominate others. Note that the URLs in the ... | Non-aggresive | task335_hateeval_classification_aggresive_en | NIv2 | zs_opt | 9 | validation |
Definition: In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Inpu... | Bert Rigby is a miner in a small dying town of Langmore in northern England, with aspirations to show business. He tells the story in flashback, while sitting in a bar. He lives with his mother, a musical fan, and next door to his sweetheart, Laurel Pennington. She lives above the pub where she works, and they have a b... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 2 | train |
In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Fact1: Matt Cve... | Matt Cvetic Frank Lovejoy, who works in a Pittsburgh steel mill, has been infiltrating the Communist Party for the FBI in Pittsburgh for nine years. During this time he has been unable to tell his family about his dual role, so they believe he is a Communist and despise him. He becomes emotionally involved with a Commu... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 0 | train |
In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Input: Consider... | Output: Yeongjin is a student who has become mentally ill after being imprisoned and tortured by the Japanese for his involvement in the March 1, 1919 protest against the Japanese occupation of Korea. After his release, he returns home to live with his father and sister, Yeonghui, in their village home. His old friend ... | task103_facts2story_long_text_generation | NIv2 | fs_opt | 2 | train |
In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Fact1: vice LVM... | In Las Vegas, vice LVMPD policemen Vincent Downs and Sean Cass rob a shipment of cocaine belonging to entrepreneur Stanley Rubino, who intended to sell it to mobster Rob Novak, the son of a powerful mob boss. They volunteer to investigate the robbery in order to cover up their involvement, clashing with Internal Affair... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 0 | train |
In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Q: Fact1: late f... | In Punjab, Aditi Singh Wamiqa Gabbi is a Punjab University wrestling champion. Her late father had encouraged her to take up wrestling from a young age but her older brother restricts her, sometimes through violent means. In a small village in Kerala, wrestling used to be a passion for the old generations but the young... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 4 | train |
In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Q: Fact1: Julie ... | Julie is the eldest daughter of Morris, an AngloIndian enginedriver. Usha her close friend is from an orthodox Hindu Warrier family. At Ushas residence she Julie meets her brother Sasi who is studying at a different place. Julie soon falls in love with Sasi. The relationship gets more cozy and Julie gets pregnant. The ... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 4 | train |
Teacher:In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Teacher:... | Lizette acquires four frontrow tickets to the Dan Hill concert in Toronto, Ontario one for her, one for her husband, Terry Scott Renderer, one for Simon Andrew Miller, and one for Simons date. At the very last minute, Simons date cancels and the group decides to invite Cheryl Ann to fill the seat. Cheryl Ann becomes a... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 6 | train |
Definition: In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Inpu... | In 1917, France is embroiled in World War I. Dubois, head of the French spy bureau, offers to spare the life of a captured agent an uncredited Mischa Auer if he will reveal who he is protecting. Dubois suspects it is Mata Hari, a celebrated exotic dancer, but the prisoner chooses execution by firing squad. Lieutenant A... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 2 | train |
In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Fact1: young bo... | At the time of was Sengoku in Mount Corel, a young boy named Kotaro escapes of mysterious persecutors with its dog, Tobimaru. The monk guarding Kotaro, Shouan, asks him to seek help from Master Zekkai at Mangaku Temple, Akaike Corel Province. Meanwhile, a group of foreign Ming under the command of the elderly Lord BaiL... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 0 | test |
Teacher:In this task, five ordered key facts are given. All the given facts are expressed in natural language. Your job is to generate a story 100 to 1000 words long, that includes all the facts given as input in their order of appearance while expanding upon them to produce a broader, yet coherent, narrative.
Teacher:... | Professor Brainard Fred MacMurray pronounced BRAYnard is an absentminded professor of physical chemistry at Medfield College who invents a substance that gains energy when it strikes a hard surface. This discovery follows some blackboard scribbling in which he reverses a sign in the equation for enthalpy to energy plus... | task103_facts2story_long_text_generation | NIv2 | zs_opt | 6 | validation |
In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute distance.
One example: [73, ... | 98 | task1446_farthest_integers | NIv2 | fs_opt | 6 | train |
Detailed Instructions: In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute dista... | 173 | task1446_farthest_integers | NIv2 | zs_opt | 8 | train |
In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute distance.
Example: [73, 30, ... | Solution: 154 | task1446_farthest_integers | NIv2 | fs_opt | 5 | train |
Q: In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute distance.
[23, 53, 57, 23... | 153 | task1446_farthest_integers | NIv2 | zs_opt | 7 | train |
Detailed Instructions: In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute dista... | 152 | task1446_farthest_integers | NIv2 | zs_opt | 9 | train |
Detailed Instructions: In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute dista... | 177 | task1446_farthest_integers | NIv2 | zs_opt | 8 | train |
In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute distance.
Q: [-17, -50, 20, ... | 104 | task1446_farthest_integers | 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 will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtrac... | 171 | task1446_farthest_integers | NIv2 | fs_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.
In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtrac... | 195 | task1446_farthest_integers | NIv2 | fs_opt | 0 | test |
Part 1. Definition
In this task you will be given a list of integers. You should find the maximum absolute difference between 2 integers in the list. The absolute difference is the absolute value of one integer subtracted by another. The output should be a single integer which is the largest possible absolute distance.... | 170 | task1446_farthest_integers | NIv2 | fs_opt | 7 | validation |
Given the task definition and input, reply with output. Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a l... | SSL and TLS have been widely used in several open source software projects. | task111_asset_sentence_simplification | NIv2 | zs_opt | 5 | train |
Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several simpler ones. The fina... | The color might be due to organic compounds on its surface. | task111_asset_sentence_simplification | NIv2 | fs_opt | 3 | train |
Teacher:Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several simpler ones. ... | Key factors in an RTOS are a minimal interrupt latency. They are also a minimal thread switching latency. | task111_asset_sentence_simplification | NIv2 | zs_opt | 6 | train |
Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several simpler ones. The fina... | The main town is Abingdon. Other places include Faringdon and Wantage. | task111_asset_sentence_simplification | NIv2 | fs_opt | 6 | train |
Detailed Instructions: Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several... | There were two main events. | task111_asset_sentence_simplification | NIv2 | zs_opt | 8 | train |
Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several simpler ones. The fina... | Von dem Bach-Zalewski remained in command of "anti-partisan" units created by Adolf Hitler until 1943. | task111_asset_sentence_simplification | NIv2 | zs_opt | 4 | train |
Q: Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several simpler ones. The f... | The Fatimids used religious tolerance towards non-Ismaili sects of Islam and towards Jews, Maltese Christians and Coptic Christians. | task111_asset_sentence_simplification | NIv2 | zs_opt | 7 | train |
Detailed Instructions: Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several... | It was designed to be easier than other CAD programs. | task111_asset_sentence_simplification | NIv2 | zs_opt | 9 | train |
Teacher:Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several simpler ones. ... | The battle began a seven-year war. It started the Rhine as the border of the Roman Empire for the next four hundred years It went until the decline of the Roman influence in the West. | task111_asset_sentence_simplification | NIv2 | zs_opt | 6 | test |
Detailed Instructions: Rewrite each original sentence in order to make it easier to understand by non-native speakers of English. You can do so by replacing complex words with simpler synonyms (i.e. paraphrasing), deleting unimportant information (i.e. compression), and/or splitting a long complex sentence into several... | Editors released two platinum albums.They sold over 2 million copies worldwide. | task111_asset_sentence_simplification | NIv2 | zs_opt | 9 | validation |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.