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

Positive examples:
input: A Miña esperanza é que a beleza e propósito deste universo microscópico poida inspirar enfoques novos e creativos na investigación do cancro.
output: No final, espero que a beleza e propósito deste universo microscópico inspire novas e criativas abordagens no futuro da pesquisa do cancro.
explanation: The Galician sentence is correctly translated into Portugese, because the meaning is preserved.; input: Pero isto non ten nada que ver có feito de ser mellor profesor.
output: Mas isso não tem nada a ver com ser-se melhor professor.
explanation: The Galician sentence is correctly translated into Portugese, because the meaning is preserved.; input: Seis meses despois, creamos un disco que ten en conta a ubicación para Central Park, un parque que duplica o tamaño do National Mall, con música que abarcaba dende Sheep 's Meadow ata a camiñata á presa.
output: Seis meses depois, fizemos um álbum com consciência espacial para o Central Park, um parque que é duas vezes maior que o National Mall, com música a abranger desde Sheep 's Meadow até Ramble e até Reservoir [locais no Central Park].
explanation: The Galician sentence is correctly translated into Portugese, because the meaning is preserved.

Negative examples:
input: Xa sabedes, se ía a un partido de rugby gañaríamos nós.
output: Como as pessoas podiam tirar fotos dos seus telemóveis do que estava acontecendo nas urnas, foi impossível para o Primeiro Ministro viciar aquela eleição da maneira que ele queria.
explanation: The Galician sentence is not correctly translated into Portugese, because the meaning is different.; input: O segundo son as acústicas.
output: O segundo é a cáustica.
explanation: The Galician sentence is not correctly translated into Portugese, because the meaning is different.; input: Debido a que a xente puido tomar fotos cos seus mobiles do que estaba acontecendo nos postos electorais, foi imposible que o primer ministro arranxara as eleccións na forma que quería facelo.
output: Se ele fosse a um jogo de râguebi, a nossa equipa ganhava.
explanation: The Galician sentence is not correctly translated into Portugese, because the meaning is different.
Task: You will be given a text in the Russian language, and you should classify the given input text to one of the emotion labels from this list of emotion labels- ['joy', 'sadness', 'surprise', 'fear', 'anger']. Make sure your output label (i) is strictly present in the given list of emotion labels. (ii) is unambiguous.
Categories: Sentiment Analysis
Domains: Social Media

Positive examples:
input: Кому они так необходимы, эти страдания?» А у меня спина болит, ходить больно :(
output: sadness
explanation: The input text states the suffering of a person who is unable to walk as his back hurts. There is an emotion of 'sadness' in this text. So, the input text is labeled correctly as 'sadness'.; input: Забавно как люди в возрасте удивляются входящим звонкам на мобильник)
output: joy
explanation: The input text says that it's funny how older people are surprised by the incoming calls to their mobile phones. There is an emotion of 'joy' in this text. So, the input text is labeled correctly as 'joy'.

Negative examples:
input: Мы в ужасе от того, что на этот раз был убит человек.
output: anger
explanation: The input text states that someone was horrified as a man was killed. There is an emotion of 'fear' in this text. So, the input text is labeled incorrectly  as 'anger', and the correct label should be 'fear'.; input: Елка такая стала не красивая, ужасная:((
output: joy
explanation: The input text states that someone was feeling angry as the Christmas tree became terrible. There is an emotion of 'anger in this text. So, the input text is incorrectly labeled as 'joy', and the correct label should be 'anger'.
Task: In this task, you will be presented with a question in Dutch language, and you have to write the named entities from the question if present. B denotes the first item of a phrase and an I any non-initial word. Here is the list of terms used: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). There can be instances with no named entities, then return 'None'.
Categories: Named Entity Recognition
Domains: Miscellaneous

Positive examples:
input: En daarom wordt er in Changli nu aan een nieuwe kerk gebouwd.
output: Changli: B-LOC
explanation: In the given sentence, Changli is identified as the location entity.; input: 71 concerten tijdens Marktrock in Leuven
output: Marktrock: B-MISC, Leuven: B-LOC
explanation: In this sentence, Leuven is recognized as location and Marktrock as miscellaneous entity.; input: Dat er nog toeristen zouden komen , daar is Moenaert niet bang voor.
output: Moenaert: B-PER
explanation: In the given sentence, Moneaert is identified as the person entity.

Negative examples:
input: Het graf van Pu shenfu ziet er niet uit alsof het ruim vijftig jaar oud is.
output: Pu: B-PER, shenfu: I-LOC
explanation: In the given sentence, shenfu is identified as the location instead of the person entity.; input: Galatasaray heeft op de transfermarkt enkele kleppers gestrikt.
output: Galatasaray: B-LOC
explanation: In the given sentence, Galatasaray is recognized as the location instead of organization.; input: Tadzjikistan , 1999 , kleur , 106 min .
output: Tadzjikistan: B-ORG
explanation: In the given sentence, Tadzjikstan is identified as the organization instead of location entity, which is incorrect.
Task: You are given a sentence in Hebrew. Your job is to translate the Hebrew sentence into Japanese.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

Positive examples:
input: מה מייחד את המוח האנושי?
output: 人の脳の何がそんなに特別なのでしょうか ？
explanation: The Hebrew sentence is correctly translated into Japanese. They both refer to the human brain.; input: .וזה הסוף
output: 以上で終わりです
explanation: The Hebrew sentence is correctly translated into Japanese, because the meaning is preserved.; input: ואם אני לא זקוק לה, אני יכול להמיר אותה בחזרה לחלון לאנרגיה, אור, ולהקרין אותה, למרחק ראייה, לבית שלכם.
output: 必要なければ窓で光に再変換して直線上にある他の人のところへビームできます
explanation: The Hebrew sentence is correctly translated into Japanese, because the meaning is preserved.

Negative examples:
input: מה אבא שלך?
output: 「何よママ ？ 」
explanation: The Hebrew sentence is not correctly translated into Japanese. The translation refers to `mom` but the original is about `dad`; input: כל המכוניות חזרו, אתם חייבים להעריץ את נהגי המכוניות. הם מסתגלים מאוד מהר.
output: すべての車が再び戻ってきていてすぐに適応したドライバーは賞賛に値します本当に早く適応しています
explanation: The Hebrew sentence is not correctly translated into Japanese, because the meaning is different.; input: "מה, אמא?"
output: 株式の保有によってうまくいくかもしれません
explanation: The Hebrew sentence is not correctly translated into Japanese, because the meaning is different.
Task: In this task you will be given a list of numbers. You should remove any number that is not an integer (whole number). If every number is not an whole number then an empty list ("[]") should be returned. Otherwise, answer with the list of whole numbers separated by comma inside brackets.
Categories: Program Execution
Domains: Code; Mathematics
Reasoning: Mathematics -> Arithmetic

Positive examples:
input: [73.059, 7, 10.659, 18.459, 11]
output: [7, 11]
explanation: 7 and 11 are the only whole numbers in the list.; input: [-49.103, 58.234, 4.3, -43.473]
explanation: Every value in the input list is not an integer, so an empty list is returned.

Negative examples:
input: [2, 0, 3.667, 9, -0.527]
output: [3.667, -0.527]
explanation: The output contains only numbers that are not integers.; input: [14.829, 6.5, -77.181, -52.664]
output: [15, 7, -77, -53]
explanation: The output rounded all of the non integers to the closest integer.; input: [-49.103, 3, 58.234, 4.3, 16, -43.473]
output: [[3, 16], [-49.103, 58.234, 4.3, -43.473]]
explanation: The output split the integers and non-integers into separate lists.; input: [1.2, 3, 4.5, 7.4, 5]
output: (3, 5)
explanation: Your answer should be in brackets.
Task: You are given a sentence in Italian. Your job is to translate the Italian sentence into Farsi.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

Positive examples:
input: Così diventa un igloo.
output: این یک خانه ی اسکیمو می شود.
explanation: The Italian sentence is correctly translated into Farsi, because the meaning is preserved.; input: Questi dati ci permettono anche di controllare statisticamente un numero di cose che sapevamo legate all'orientamento politico e alla sensibilità al disgusto.
output: همچنین ، این مجموعه داده ها به ما اجازه کنترل آماری میده تعدادی از چیزهایی که ما میدونستیم مربوط بودند به هر دو: گرایشات سیاسی و حساسیت انزجار
explanation: The Italian sentence is correctly translated into Farsi, because the meaning is preserved.; input: Fermi. Fermi. Come vi sentite?
output: صبر کنید ، صبر کنید ، حس تون چیه ؟
explanation: The Italian sentence is correctly translated into Farsi, because the meaning is preserved.

Negative examples:
input: I miei colleghi in Google cominciarono una campagna di ricerca per trovarmi, e gli altri manifestanti nella piazza richiesero il mio rilascio.
output: خیلی خوب ؛ به نتیجه مطلوب رسیدیم پس ، وقتی مردم در حال زندگی هستند بدون هیچ روشنایی غیرباورکردنی ، آنها دو دفعه در طی شب می خوابند
explanation: The Italian sentence is not correctly translated into Farsi, because the meaning is different.; input: Perciò possiamo avere un passaggio gratis.
output: پس می ‌ تونیم یک سواری رایگان بگیریم.
explanation: The Italian sentence is not correctly translated into Farsi, because the meaning is different.; input: Beh, sembra che quando le persone vivono senza alcun tipo di luce artificiale, dormono due volte, ogni notte.
output: همکارانم در گوگل کمپینی برای جستجوی من شروع کردند ، و دوستان معترض دیگر هم در میدان ‌ های شهرتقاضای آزاد کردن مرا داشتند.
explanation: The Italian sentence is not correctly translated into Farsi, because the meaning is different.
Task: In this task, we can are given an english sentence and the goal is to generate a text in 'turkish' language that is a faithful translation of the input sentence
Categories: Translation
Domains: News

Positive examples:
input: It turned out the polling station subjected to FEMEN protest was where PM Recep Tayyip Erdoğan would cast his vote in İstanbul.
output: FEMEN aktivistleri Erdoğan'ın İstanbul'da oy kullanacağı yerde oy zarflarını fırlattı. #ErdoğanıYasaklayın pic.twitter.com/msuCqlQnAa
explanation: The output is a faithful translation of the input english sentence into Turkish as they both mean 'the polling station where the protest took place was where the PM would cast his vote in Istanbul'; input: "FEMEN activists stormed the voting papers where PM Erdoğan would cast his vote.”
output: "FEMEN aktivistleri Erdoğan'ın İstanbul'da oy kullanacağı yerde oy zarflarını fırlattı"
explanation: The output is a faithful translation of the input english sentence into Turkish as they both mean 'FEMEN activists stormed the voting papers where PM would cast his vote'

Negative examples:
input: Previously, FEMEN Turkey protested against the twitter ban in Turkey with a FEMEN activist sharing her bare-breast photo with "#DNS" and "#DirenTwitter” (ResistTwitter) and DNS numbers written on it. (EA/BD)
output: Yüksekovahaber sitesinin haberine göre Hakkari'nin Bağlar mahallesi ile farklı noktalarda silah sesleri yükselirken kavgada Mustafa Er isimli bir kişi hayatını kaybetti, bir kişi de yaralandı. Mustafa Er'in cenazesinin bu akşam Bağlar Mahallesi'nde toprağa verilmesi bekleniyor. 43 yaşındaki Er'in 9 çocuk babası olduğu öğrenildi.
explanation: This is a negative example because the output is not a faithful translation of the input english sentence into turkish; input: Ambulances carried four people injured at different stages of the events to Van province.
output: Aşiret büyüklerinden itidal çağrısı
explanation: This is a negative example because the output is not a faithful translation of the input english sentence into turkish
Task: You are given a sentence in Portuguese. Your job is to translate the Portuguese sentence into Galician.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

Positive examples:
input: No final, espero que a beleza e propósito deste universo microscópico inspire novas e criativas abordagens no futuro da pesquisa do cancro.
output: A Miña esperanza é que a beleza e propósito deste universo microscópico poida inspirar enfoques novos e creativos na investigación do cancro.
explanation: The Portugese sentence is correctly translated into Galician, because the meaning is preserved.; input: Mas isso não tem nada a ver com ser-se melhor professor.
output: Pero isto non ten nada que ver có feito de ser mellor profesor.
explanation: The Portugese sentence is correctly translated into Galician, because the meaning is preserved.; input: Seis meses depois, fizemos um álbum com consciência espacial para o Central Park, um parque que é duas vezes maior que o National Mall, com música a abranger desde Sheep 's Meadow até Ramble e até Reservoir [locais no Central Park].
output: Seis meses despois, creamos un disco que ten en conta a ubicación para Central Park, un parque que duplica o tamaño do National Mall, con música que abarcaba dende Sheep 's Meadow ata a camiñata á presa.
explanation: The Portugese sentence is correctly translated into Galician, because the meaning is preserved.

Negative examples:
input: Se ele fosse a um jogo de râguebi, a nossa equipa ganhava.
output: Debido a que a xente puido tomar fotos cos seus mobiles do que estaba acontecendo nos postos electorais, foi imposible que o primer ministro arranxara as eleccións na forma que quería facelo.
explanation: The Portugese sentence is not correctly translated into Galician, because the meaning is different.; input: O segundo é a cáustica.
output: O segundo son as acústicas.
explanation: The Portugese sentence is not correctly translated into Galician, because the meaning is different.; input: Como as pessoas podiam tirar fotos dos seus telemóveis do que estava acontecendo nas urnas, foi impossível para o Primeiro Ministro viciar aquela eleição da maneira que ele queria.
output: Xa sabedes, se ía a un partido de rugby gañaríamos nós.
explanation: The Portugese sentence is not correctly translated into Galician, because the meaning is different.
Task: In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to determine whether it is plausible for the Head to desire the Tail or not. In this task, desire means desires of sentient entities. For example, doctors likely desire to cure a patient. Classify your answers into "Yes" and "No". The phrase may also contain "___", a placeholder that can be an object, a person, and/or an action.
Categories: Commonsense Classification
Domains: Sociology; Commonsense -> Concepts and Relations -> Social Commonsense
Reasoning: Relational Reasoning; Reasoning on Social Interactions; Commonsense Reasoning -> Social Situations

Positive examples:
input: Head: person<sep>Tail: complaints to addressed
output: Yes
explanation: This is a good example. The Head desires the Tail.; input: Head: PersonX accepts the offer<sep>Tail: smiles
output: No
explanation: In this example, "PersonX accepts the offer desires smiles" doesn't make sense. So the output should be"No".; input: Head: person<sep>Tail: breakdance
output: Yes
explanation: In this example, the person desires breakdance. Thus, this example should be classified as "Yes".

Negative examples:
input: Head: PersonX affords PersonY protection<sep>Tail: gets involved
output: Yes
explanation: In this example, "PersonX affords PersonY protection desires getting involved" doesn't make sense. To have a positive answer, the Head should be an entity. So the output should be "No".; input: Head: person<sep>Tail: promoted in job
output: No
explanation: The person desires being promoted in the job, and the output should be "Yes".
Task: In this task, you're given statements in native Tamil language. The statement can be written with the Tamil alphabet or the English alphabet. Your job is to evaluate if the statement is offensive or not. Label the post as "Not offensive" if the post does not contain offense or insult.  Non-offensive posts do not include any form of offense or insult. Label the post as "Offensive" if the post contains offensive language. 
Categories: Toxic Language Detection
Domains: Social Media

Positive examples:
input: Handsome hunk  keri vaa thalaivaa
output: Not offensive
explanation: The prediction of offensiveness is based on the following sentence "Handsome hunk  keri vaa thalaivaa". It evaluates the understanding that the adjective "Handsome" is not offensive to any gender or thing or place in the sentence.; input: Serupadi too Kuruma and Kunjith Kadhal nadaka group.. dae unma vanthuruchu da  ini unga nadakam la sellathu
output: Offensive
explanation: This statement is an insult from one person to another person/group stating that the first person would hit the other as their facade is brought down.; input: Crime movie kagave pakanum da...un munji pakavarala
output: Offensive
explanation: The statement is a negative comment about a person/individual's face.

Negative examples:
input: Vera level BGM .. semma  trailer. 🤞
output: Offensive
explanation: The statement is a positive comment about a movie and it background music.; input: திருப்பத்தூர் மாவட்ட வன்னியர் சார்பாக இப்படம் வெற்றி பெற வாழ்த்துகள்
output: Offensive
explanation: The statement is a positive comment on the success of a film.
Task: In this task, you are given an input list A. If the count of numbers is more than that of alphabets in the list, answer 'Numbers Win'. If the count of alphabets is more than that of numbers in the list, answer 'Alphabets Win'. If the count of numbers is same as that of alphabets in the list, answer 'Numbers and Alphabets are Tied'.
Categories: Program Execution
Domains: Mathematics
Reasoning: Mathematics -> Counting

Positive examples:
input: 238; h; 92; U; 2799
output: Numbers Win
explanation: Here, the count of alphabets is 2, and that of numbers is 3. Since there are more numbers than alphabets, hence the answer is 'Numbers Win'.; input: ['789', 'l', '2, 'E', 'n', '67']
output: Numbers and Alphabets are Tied
explanation: Here, the count of alphabets is 3, and that of numbers is 3. Since there are equal number of numbers and alphabets, hence the answer is 'Numbers and Alphabets are Tied'.; input: X; h; 3; M; 27
output: Alphabets Win
explanation: Here, the count of alphabets is 3, and that of numbers is 2. Since there are less numbers than alphabets, hence the answer is 'Alphabets Win'.

Negative examples:
input: 7475; B; 2459; m; 8349; O; q
output: Numbers and Alphabets are Tied
explanation: Here, the answer should have been 'Alphabets Win' as the count of alphabets is 4, and that of numbers is 3. Since there are less numbers than alphabets, hence the answer is 'Alphabets Win'.; input: 75; 54; J; L; 2332; Z; e; 366
output: Numbers Win
explanation: Here, the answer should have been 'Numbers and Alphabets are Tied' as the count of alphabets is 4, and that of numbers is 4. Since there are equal number of numbers and alphabets, hence the answer is 'Numbers and Alphabets are Tied'.
Task: In this task you are given a tweet and you must identify whether the tweet contains any offense or any form of (untargeted) profanity. Label the post as NOT if the post does not contain offense or profanity.  Non-offensive posts do not include any form of offense or profanity. label the post as OFFENSIVE if the post contains offensive language or a targeted (veiled or direct) offense.  Twitter user mentions were substituted by @USER and URLs have been substitute by URL.
Categories: Toxic Language Detection
Domains: Social Media -> Twitter

Positive examples:
input: @USER Someone should'veTaken" this piece of shit to a volcano. 😂"
output: OFFENSIVE
explanation: tweet contains offensive language.; input: @USER @USER Go home you’re drunk!!! @USER #MAGA #Trump2020 👊🇺🇸👊 URL
output: OFFENSIVE
explanation: tweet contains targeted offense.; input: @USER Buy more icecream!!!
output: NOT
explanation: tweet doesn't contain offensive language or profanity.

Negative examples:
input: @USER @USER Go home you’re drunk!!! @USER #MAGA #Trump2020 👊🇺🇸👊 URL
output: I can't decide.
explanation: Do not generate anything else apart from one of the following characters: 'OFFENSIVE', 'NOT'.
Task: In this task, you are given a public comment from online platforms. You are expected to classify the comment into two classes: toxic and non-toxic. Toxicity is defiend as anything that is rude, disrespectful, or unreasonable that would make someone want to leave a converation.
Categories: Toxic Language Detection
Domains: Social Media

Positive examples:
input: Comment: i am just sorry that khadr didnt get a bullet in the head before he blew the head off that innocent father because his islamic boogie skywizard told him to.
output: Toxic
explanation: This is a toxic comment because it advocates acts of violence.; input: Comment: This is excellent news.
output: Non-toxic
explanation: This is a non-toxic comment because it is not rude or desrespectful.

Negative examples:
input: Comment: "racism" -appears to be the only card in your deck!?
output: Toxic
explanation: This should be non-toxic because the comment only speaks of racism but does not disrespect any race.; input: Comment: Fat ass, go eat another do-nut.
output: Non-toxic
explanation: This should be toxic because it disrespects the people that are overweight.
Task: Given a factoid/trivia type question, generate the topic of the question. The topic is the entity the question talks about.
Categories: Question Understanding
Domains: Wikipedia

Positive examples:
input: Who was the first American President?
output: president of the united states
explanation: The question talks about the president of the USA.; input: The Dutch duo of Ray Slijngaard and Anita Dels, who had 8 top ten hits in the 90s, were better known by what name?
output: anita doth
explanation: The question is based on Anita doth. So this is the correct answer.; input: Who devised and created the Blake's Seven TV series?
output: blake's 7
explanation: The output has correctly identified the topic being discussed in the question.

Negative examples:
input: Anna Smashnova played which sport professionally until 2007?
output: sport
explanation: The question is about Anna Smashnova, but it is generalized as a sport topic. This is a bad example.; input: Rob Davis, Les Gray, Dave Mount and Ray Stiles are members of which pop group?
output: david
explanation: This output is incorrect because Rob Davis is missed out from the output. The output should contain all the entities.; input: The song Piper at the Gates of Dawn appears on the 1997 album 'The Healing Game', by which artist?
output: piper at the gates of dawn
explanation: The album 'The Healing Game' is the correct topic for this question, not the song.