ceselder/lots-of-loras-task-definitions huggingface dataset:
Task: You are given a sentence in Japanese. Your job is to translate the Japanese sentence into Spanish.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

Positive examples:
input: それは個人の内面で起こる闘い悪 ､ 罪 ､ 誘惑 ､ 欲望 ､ 貪欲に対する苦闘ですそれは個人の内面で起こる闘い悪 ､ 罪 ､ 誘惑 ､ 欲望 ､ 貪欲に対する苦闘です
output: Es una lucha interna, una lucha en contra del vicio, el pecado, la tentación, el deseo, la avaricia.
explanation: The Japanese sentence is correctly translated into Spanish, because the meaning is preserved.; input: この二つが我々の世紀で起きているパワーバランスの大きな変化なのです
output: Esas dos cosas son los dos grandes cambios del poder de nuestro siglo.
explanation: The Japanese sentence is correctly translated into Spanish, because both sentences refer to two causes of power shifts.; input: 「向かってこい空の鳥野の獣の餌食にしてくれよう」と言ったときの「向かってこい」というのがヒントで彼の弱点を示しているのです
output: Cuando dice "" Acércate para que pueda alimentar con tu carne a los pájaros del cielo y a las bestias de la tierra "", la frase "" acércate "" es también una pista de su vulnerabilidad.
explanation: The Japanese sentence is correctly translated into Spanish, because the meaning is preserved.

Negative examples:
input: そのうち ３ ～ 10歳の子どもがいる ― テーブルへは戦闘態勢で臨むようになりました
output: Algunos de ellos tienen entre 5 y 10 años; ahora están listos para pelear en la mesa.
explanation: The Japanese sentence is not correctly translated into Spanish, because the age in years `3` is incorrectly translated as `5`.; input: 人々が所持金で支払うようになった事を示唆します
output: De modo que estamos empezando a pagar con dinero que tenemos.
explanation: The Japanese sentence is not correctly translated into Spanish, because the meaning is different.; input: 敵国と自分から進んで対話するなど国の機関に頼ることはできません私達がそれを実現させ尊厳を尊重させ謙虚さと強い責任感を諸外国との関係にもたらす事が必要なのです
output: Y llegué al punto que cada vez que iba a un lugar donde había un niño entre tres y diez años, estaba lista para luchar.
explanation: The Japanese sentence is not correctly translated into Spanish, because the meaning is different.
Task: Given a sentence in German, provide an equivalent paraphrased version from the original that retains the same meaning.
Categories: Paraphrasing
Domains: Wikipedia

Positive examples:
input: Die NBA-Saison 1975 - 76 war die 30. Saison der National Basketball Association.
output: Die Saison 1975 - 76 der National Basketball Association war die 30. Saison der NBA.
explanation: The paraphrase of the original sentence in German is correct and accurate because even though sentence order has been changed, the denotion that the 30th season of the NBA occurred in the years 1975 - 1976 is retained.; input: Wenn vergleichbare Flussraten aufrechterhalten werden können, sind die Ergebnisse hoch.
output: Die Ergebnisse sind hoch, wenn vergleichbare Flussraten aufrechterhalten werden können.
explanation: This is a proper paraphrase of the original sentence in German because the main idea that results are high only in the condition that comparable flow rates is maintained.

Negative examples:
input: Im Oktober 1560 traf er sich in Paris heimlich mit dem englischen Botschafter Nicolas Throckmorton und bat ihn um einen Pass, um durch Schottland nach England zurückzukehren.
output: Im Oktober 1560 traf er sich heimlich mit dem englischen Botschafter Nicolas Throckmorton in Paris und bat ihn um einen Pass, um durch England nach Schottland zurückzukehren.
explanation: The example is not considered a proper paraphrase of the original sentence in German because the through and destination locations are flipped - Scotland and England, respectively.; input: Es gibt auch spezifische Diskussionen, öffentliche Profildiskussionen und Projektdiskussionen.
output: Es gibt auch öffentliche Diskussionen, profilspezifische Diskussionen und Projektbesprechungen.
explanation: This example does not constitute a proper paraphrase of the original sentence in German because the descriptors of the nouns ("specific", "public") are swapped, and the sentence is missing any reference to debates as it has been replaced with discussions.
Task: In this task, you are given a sentence in the English language and your task is to convert it into the Hindi language. In translation, keep numbers as it is and make it sentence case (capitalize only the first word of each sentence and noun).
Categories: Translation
Domains: Public Places

Positive examples:
input: Italy have defeated Portugal 31-5 in Pool C of the 2007 Rugby World Cup at Parc des Princes, Paris, France.
output: 2007 में फ़्रांस, पेरिस के पार्क डेस प्रिंसेस में हुए रग्बी विश्व कप के पूल C में इटली ने पुर्तगाल को 31-5 से हराया।
explanation: This is a correct and accurate translation from English to Hindi because translated text is just paraphrased of English sentence. Also, it preserves the numbers as it is.; input: Andrea Masi opened the scoring in the fourth minute with a try for Italy.
output: एंड्रिया मैसी ने चौथे मिनट में इटली के लिए ट्राई के द्वारा स्कोरिंग की शुरुआत करी।
explanation: The English sentence is correctly translated into Hindi because it holds the original message from the English sentence that Andrea Messi opened the scoring for Italy in the fourth minute by try.

Negative examples:
input: Portugal never gave up and David Penalva scored a try in the 33rd minute, providing their only points of the match.
output: जीत न मानते हुए, पुर्तगाल के डेविड पेनलावा ने 45 मिनट में 1 पॉइंट स्कोर किया, पूरे खेल में यह पुर्तगाल का इकलौता पॉइंट था।
explanation: This is not a proper translation from English to Hindi because the resulting translation is not accurate to the original. In the English sentence, it says that David Penalva scored a try at 33rd minute however, in the Hindi translation it says that he scored at 45th minute.; input: Italy led 16-5 at half time but were matched by Portugal for much of the second half.
output: पॉइंट स्टैंडिंग में, हार्विक और जॉनसन पहले और दूसरे स्थान पर रहे।
explanation: This translation from English to Hindi is incorrect because the resulting translation does not reflect the actual topic of the sentence which is the score of the match between Italy and Portugal.
Task: Categorize the comment on the basis of severe toxicity. If the comment is severely toxic output Yes, otherwise output No.
Categories: Toxic Language Detection
Domains: Dialogue; Social Media
Reasoning: Commonsense Reasoning -> Social Situations

Positive examples:
input: First off IDIOTS HIS HONDA IS NOT STOLEN I HAVE THE TITLE SIGNED OFF BY LAST OWNER IN MY POSSESSION.2ND THE ONLY THING HE WAS IN TROUBLE FOR BEFORE THE CHASE WAS FOR NOT CHECKING IN WITH HIS PROBATION OFFICER WHICH BRINGS THIS FACT TO LIGHT.YOU CAN DOWNLOAD DOUGLAS COUNTY PROBATION DEPARTMENT LIST OF CURRENT PEOPLE ON PROBATION AND SCROLL THROUGH THE ENTIRE LIST HIS NAME IS NOWHERE ON IT.I ALSO HAVE THAT LIST ON MY PHONE DIRECTLY FROM TTHEIR WEBSIGHT SO THEREFORE I AM GOING TO INVESTIGATE MORE AND BE FILING PAPERWORK AND IF NEEDBE I WILL GO THRU FBI TO RESOLVE THE ACTIONS OF THE CROOKED DEALINGS OF DCPD....IF YOURE GONNA WRITE A STORY ABOUT SOMEONE YOU SHOULD GET THE FACTS STRAIGHT..ALSO I WAS NOT ON PHONE WITH MY SON DURING THE CHASE.HE CALLED ME AT THE BEGINNING OF CHASE AND AFTER HE LEFT HIS CAR.THE DEPUTIES WERE RIGHT HE4E WITH ME DURING MOST OF CHASE .SO AGAIN GET YOUR STORY STRAIGHT..I HAVE NOT HEARD FROM MY SONS SINCE THEN...
output: Yes
explanation: The comment here contains all caps phrase which is interpreted as strong language in text. Also, it has word 'IDIOTS' which is meant as an derogatory attack on a subject. This should be severly toxic . Hence, it is a positive example; input: David, you ought to consider checking your facts rather than making things up all while being a bully and a troll.
output: No
explanation: The comment is toxic but it doesn't use very strong language or racial slurs or words that denote sever toxicity. It is classified as not severely toxic. Therefore, this is a positive example.

Negative examples:
input: FFFFUUUUUUUUUUUUUUU
output: No
explanation: The comment is abbreviation of 'Fuck You' which is toxic, but classified as not severly toxic. Therefore, this is a negative example.; input: Yeah, that man would look pretty ridiculous, given that 80% of congress, 95% of Fortune 500 CEOs, and 100% of American presidents are male. Why, do you suppose, do those women make an issue of that?
output: Yes
explanation: The comment does not contain any derogatory words or attacks on any subject that would cause it to be severely toxic, however it is classified as severely toxic. This is a negative example
Task: In this task, you will be given a list.  The list is several integers and letters separated with a comma, written within a []. You can create new lists by dropping one of the items in the input list. Your task is to write a list containing all the possible lists you can make by dropping one item from the input list. For example, if the input list contains two items, you can drop one of the items each time. So the output should be a list comprising two inner lists that you have created by decreasing the items.
Categories: Program Execution
Domains: Mathematics
Reasoning: Numerical Reasoning; Mathematics -> Combinatorics

Positive examples:
input: 3; u; W; 8; j
output: [['3', 'u', 'W', '8'], ['3', 'u', 'W', 'j'], ['3', 'u', '8', 'j'], ['3', 'W', '8', 'j'], ['u', 'W', '8', 'j']]
explanation: The output is a list of length 5, containing lists of length 4. Each inner list is created by dropping one item if the input list. So this is a good example.; input: O; j; 8; j
output: [['O', 'j', '8'], ['O', 'j', 'j'], ['O', '8', 'j'], ['j', '8', 'j']]
explanation: The character 'j' appeared in the input list twice, therefore it appears twice in an inner list in the output. The number of the inner lists should be the same as the items in the input list. So this is a good example.

Negative examples:
input: j; I; U
output: [['j'], ['I'], ['U']]
explanation: In this example, the inner lists of the output have been created by dropping two elements from the input list. If we drop an item from the input list, we will have a list with two items each time. So this is a bad example.; input: 8; F; 8; j
output: [['8', 'F', '8']]
explanation: The output only contains one possible list. However, we can create four lists by dropping one of the items. So this is a bad example.; input: 5; l; p; m; 5
output: [['5', 'l', 'p', 'm'], ['l', 'p', 'm', '5']]
explanation: The output only contains two possible lists. However, we can create five lists by dropping one of the items. So this is a bad example.
Task: In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
Categories: Language Identification
Domains: Conference

Positive examples:
input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
output: es
explanation: This review is written in spanish. Hence, the label is 'es'.; input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
output: en
explanation: This review is written in english. Hence, the label is 'en'.

Negative examples:
input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
output: en
explanation: This review is written in spanish. But the label is 'en' which is wrong.; input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
output: es
explanation: This review is written in english. But the label is 'es' which is wrong.
Task: You are given a sentence in Hebrew. Your job is to translate the Hebrew sentence into Galician.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

Positive examples:
input: "אז," אמרתי, "זה כמו חלום?" והיא אמרה, "" לא, זה לא כמו חלום. זה כמו סרט. "" היא אמרה, "" יש בו צבעים. יש בו תנועה.
output: Pregunteille: "" É coma un soño? "" E dixo: "" Non, non é coma un soño. É coma unha película. Ten cor. Ten movemento.
explanation: The Hebrew sentence is correctly translated into Galician, because the meaning is preserved.; input: והן מהוות מחסום לשיתוף פעולה.
output: E impoñen unha barreira á cooperación.
explanation: The Hebrew sentence is correctly translated into Galician, because the meaning is preserved.; input: ובכן הנה מגיע הארגז הראשון.
output: Aquí chega a primeira caixa.
explanation: The Hebrew sentence is correctly translated into Galician, because the meaning is preserved.

Negative examples:
input: כולם, בעיקר כשההתפרצות התחילה להתפשט גלובלית, פנו אלינו בכדי ללמוד, להשתתף, לתרום.
output: Atopamos a nosa segunda Zona Azul, do outro lado do planeta, aproximadamente 1.300 km ó sur de Tokio, no arquipélago de Okinawa.
explanation: The Hebrew sentence is not correctly translated into Galician, because the meaning is different.; input: שלוש גישות עיקריות.
output: Tres reglas principais.
explanation: The Hebrew sentence is not correctly translated into Galician, because the meaning is different.; input: מצאנו את האזור הכחול השני בצד השני של כדור הארץ, כ-1300 ק "" מ מדרום לטוקיו, בארכיפלג של אוקינאווה.
output: Todo o mundo, sobre todo cando o brote comezou a estenderse globalmente, estaba tratando de aprender, participar, involucrarse.
explanation: The Hebrew sentence is not correctly translated into Galician, because the meaning is different.
Task: Given a sentence in German, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
Categories: Translation
Domains: Wikipedia

Positive examples:
input: Die NBA-Saison 1975 - 76 war die 30. Saison der National Basketball Association.
output: The 1975 -- 76 season of the National Basketball Association was the 30th season of the NBA .
explanation: This is a correct and accurate translation from German to English because the translated paraphrase retains the main message that between the years 1975-1976, the 30th NBA season occurred.; input: Wenn vergleichbare Flussraten aufrechterhalten werden können, sind die Ergebnisse hoch.
output: The results are high when comparable flow rates can be maintained .
explanation: The resulting paraphrased translation from German to English is correct because the resulting translation holds the message that results are high when comparable rates of flow are maintained.

Negative examples:
input: Im Oktober 1560 traf er sich in Paris heimlich mit dem englischen Botschafter Nicolas Throckmorton und bat ihn um einen Pass, um durch Schottland nach England zurückzukehren.
output: In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .
explanation: This is not a proper translation from German to English because the resulting translation is not accurate to the original idea in which the locations of England and Scotland are not accurate.; input: Es gibt auch spezifische Diskussionen, öffentliche Profildiskussionen und Projektdiskussionen.
output: There are also public discussions , profile specific discussions , and project discussions .
explanation: This translated paraphrase from German to English is incorrect because the resulting translation does not reflect the actual topics of dicussion and debate relating to profiles and projects.
Task: You are given a sentence in Galician. Your job is to translate the Galician sentence into Arabic.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

Positive examples:
input: É basicamente un caldeiro que rota.
output: ويوجد أساسا مرجل دوار.
explanation: The Galician sentence is correctly translated into Arabic, because the meaning is preserved.; input: Ou queiman os plásticos para chegar aos metais en incineradoras como estas.
output: أو يقومون بإحراق البلاستيك للوصول إلى المعادن في منازل حرق كما ترون هنا
explanation: The Galician sentence is correctly translated into Arabic, because the meaning is preserved.; input: Esta rexión reacciona cando pensamos sobre o que outra persoa está pensando.
output: وهي تستجيب عندما تفكّر حول ما يفكّر فيه شخص آخر.
explanation: The Galician sentence is correctly translated into Arabic, because the meaning is preserved.

Negative examples:
input: Os imáns están aí dentro.
output: فينفعل أوديسيوس ويقول له أيها الاحمق أيها الغبي
explanation: The Galician sentence is not correctly translated into Arabic, because the meaning is different.; input: Así, como activista, comecei a traballar coa Fundación Parkinson — pdf.org — para crear un movemento importante que sitúe as mulleres no mapa do párkinson.
output: لذلك وكناشطة ، بدأت العمل مع مؤسسة مرض باركنسون وموقعها الإلكتروني هو pdf.org من أجل خلق مبادرة كبرى بغرض وضع النساء على خارطة مرض باركنسون.
explanation: The Galician sentence is not correctly translated into Arabic, because the meaning is different.; input: Así que desata a Ulises e este tolea.
output: تلك المغناطيسات بالداخل هناك.
explanation: The Galician sentence is not correctly translated into Arabic, because the meaning is different.
Task: In this task, you will be given a sentence, and you need to identify the set of coreference words. Coreferences are generally a set of words in the text that refer to the same person or thing. Your output set of coreference words must be strictly present in the given input sentence. Do not use any outside knowledge to create the set of coreference words. If there is a 'the' article before the person or thing you should include 'the' as well. Please separate the coreference words by commas in the output.
Categories: Coreference Resolution
Domains: Professions; Commonsense

Positive examples:
input: The developer argued with the designer because he did not like the design.
output: The, developer, he
explanation: Here the output set of coreference words 'The', 'developer, and 'he' refer to the same person, and these are coreferences. So, the output is correct.; input: The mechanic greets with the receptionist because he was in a good mood.
output: The, mechanic, he
explanation: Here the output set of coreference words 'The', 'mechanic, and 'he' refer to the same person, and these are coreferences. So, the output is correct.; input: The mechanic greets the receptionist because she was standing in front of the door.
output: the, receptionist, she
explanation: Here the output set of coreference words 'the', 'receptionist, and 'she' refer to the same person, and these are coreferences. So, the output is correct.; input: The cook is always teaching the assistant new techniques so she will one day be equal in skill.
output: the, assistant, she
explanation: Here the output set of coreference words 'the', 'assistant, and 'she' refer to the same person, and these are coreferences. So, the output is correct.

Negative examples:
input: The cook is always teaching the assistant new techniques because he likes to teach others.
output: The, assistant, others
explanation: Here the output set of coreference words 'The', 'assistant, and 'others' do not refer to the same person, and these are coreferences. So, the output is incorrect.; input: The farmer gave the teacher a bag of eggs because he was grateful.
output: The, teacher, he
explanation: Here the output set of coreference words 'The', 'teacher, and 'he' do not refer to the same person, and these are coreferences. So, the output is incorrect.; input: The farmer gave the teacher a bag of eggs because she helped many kids.
output: the, farmer, she
explanation: Here the output set of coreference words 'The', 'farmer, and 'she' do not refer to the same person, and these are coreferences. So, the output is incorrect.; input: The CEO bought the accountant a car because he is rich.
output: The, accountant, he
explanation: Here the output set of coreference words 'The', 'accountant, and 'he' do not refer to the same person, and these are coreferences. So, the output is incorrect.
Task: In this task, you need to write a topic word from the given fact. The topic word must have at least one word overlap with the given fact. The topic word often involves adding a new word from a related concept. In your topic word, use at least one word from the given fact. Topic words with two or more words work best.
Categories: Keyword Tagging
Domains: Natural Science

Positive examples:
input: Fact: pesticides cause pollution.
output: pollution harms.
explanation: This is a good topic word, because it contains one overlapping word "pollution".; input: Fact: pesticides cause pollution.
output: modern farming pesticide.
explanation: The word "pesticide" is the overlapping word.; input: Fact: a solar panel converts sunlight into electricity.
output: sunlight sun.
explanation: The word "sunlight" is the overlapping word.; input: Fact: running requires a lot of energy.
output: marathon running.
explanation: The word "running" is the overlapping word.; input: Fact: soil is formed by rocks eroding.
output: rain erode rock.
explanation: The word "erode rock" is the overlapping word.; input: Fact: a plant requires water for survival.
output: rain water.
explanation: The word "water" is the overlapping word.; input: Fact: a radio converts electrical energy into vibrations.
output: vibrations sound.
explanation: The word "vibrations" is the overlapping word.; input: Fact: habitat destruction causes animals to move to find shelter in another habitat.
output: chopping down trees habitat destruction.
explanation: The word "habitat destruction" is the overlapping word.; input: Fact: a protractor is used for measuring the angles of a triangular object.
output: prism triangular.
explanation: The word "triangular" is the overlapping word.; input: Fact: lightning can cause a forest fire.
output: forest fire harms.
explanation: The word "forest fire" is the overlapping word.

Negative examples:
input: Fact: pesticides cause pollution.
output: computer harms.
explanation: This is a bad topic word, because it has no overlapping word with the given fact.; input: Fact: pesticides cause pollution.
output: toopaste helps.
explanation: This is a bad topic word, because it has no overlapping word with the given fact.; input: Fact: a solar panel converts sunlight into electricity.
output: moon around earth.
explanation: Even thought the topic seems related, this is a bad topic word, because it has no overlapping word with the given fact.