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 Hebrew.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

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

Negative examples:
input: Todo o mundo, sobre todo cando o brote comezou a estenderse globalmente, estaba tratando de aprender, participar, involucrarse.
output: מצאנו את האזור הכחול השני בצד השני של כדור הארץ, כ-1300 ק "" מ מדרום לטוקיו, בארכיפלג של אוקינאווה.
explanation: The Galician sentence is not correctly translated into Hebrew, because the meaning is different.; input: Tres reglas principais.
output: שלוש גישות עיקריות.
explanation: The Galician sentence is not correctly translated into Hebrew, because the meaning is different.; input: Atopamos a nosa segunda Zona Azul, do outro lado do planeta, aproximadamente 1.300 km ó sur de Tokio, no arquipélago de Okinawa.
output: כולם, בעיקר כשההתפרצות התחילה להתפשט גלובלית, פנו אלינו בכדי ללמוד, להשתתף, לתרום.
explanation: The Galician sentence is not correctly translated into Hebrew, because the meaning is different.
Task: Classify given movie review into two categories: positive, or negative based on its content.
Categories: Sentiment Analysis
Domains: Reviews -> Movies
Reasoning: Reasoning on Social Interactions

Positive examples:
input: if you sometimes like to go to the movies to have fun , wasabi is a good place to start .
output: positive
explanation: The author in the review recommends starting with wasabi, which has a positive sense. So, the given review is positive.; input: this is a film well worth seeing , talking and singing heads and all .
output: positive
explanation: The review states the film as worth seeing, talking and singing. So, the given review is positive.; input: the movie quickly drags on becoming boring and predictable . i tried to read the time on my watch .
output: negative
explanation: In the review, it is mentioned that the film is boring and predictable which is a negative sense. So, the given review is negative.; input: just a bloody mess .
output: negative
explanation: The author in the review states the movie as a bloddy mess, which has a negative sense. So, the given review is negative.

Negative examples:
input: creepy but ultimately unsatisfying thriller .
output: positive
explanation: The author mentions that the movie is an unsatisfying thriller, which has a negative sense. So, the given review should be negative instead of positive.; input: the film often achieves a mesmerizing poetry .
output: negative
explanation: The author mentions that the movie achieves a mesmerizing poetry, which has a positive sense. So, the given review should be positive instead of negative.
Task: Your task is to localize given English phrase into Hindi language. When localising, follow these rules - (1) General names and concepts can be translated (2) Domain specific names can just be transliterated (3) Localised phrases can have both partial translated and transliterated parts (4) But only partial translation or only partial transliteration is not allowed (5) Copy special characters and numbers as is
Categories: Translation
Domains: Computer Science

Positive examples:
input: Displays the document relations of a document
output: दस्तावेज़ का दस्तावेज़ सम्बन्ध प्रदर्शित करता है
explanation: The sentence is accurately translated; input: Enables the use of bookmarklets
output: बुकमार्कलेटों का इस्तेमाल सक्षम करता है
explanation: The localization is correct as the domain specific names are transliterated and remaining sentence is translated correctly; input: Dither in HiColor (15/ 16bit) modes
output: उच्च रंग मोड (15/ 16बिट) में कंपन
explanation: This is accurate localization as domain specific names are transliterated, generic part is translated, and special characters and numbers are copied as is

Negative examples:
input: Show image file & name
output: छवि फ़ाइल नाम दिखाएँ
explanation: This is not a accurate localization as the special character is not copied and the 'file & name' part is translated as 'फ़ाइल नाम' meaning 'file name' which is not accurate; input: This plugin cannot auto-refresh the current part.
output: यह प्लगइन मौजूदा पार्ट को स्वचलित ताज़ा कर
explanation: The localization is wrong as the not part of the sentence is not translated and remaining sentence is not completely translated; input: Could not create the plugin, please report a bug.
output: प्लगइन बना नहीं सका, कृपया बग रपट भेजें.
explanation: The word 'report' is not accurately tranliterated. Hence, this is not completely true localization
Task: In this task, you are given a context tweet, a question and corresponding answer of given question. Your task is to classify given passage into two categories: (1) "yes" if the given context is useful in answering the question, and (2) "no" if the given context is not useful.
Categories: Answerability Classification
Domains: Social Media -> Twitter

Positive examples:
input: Context: Our prayers are with the students, educators & families at Independence High School & all the first responders on the scene. #PatriotPride— Doug Ducey (@dougducey) February 12, 2016 Question: at which school were first responders on the scene for? Answer: independence high school
output: yes
explanation: Here, the generated label is 'yes' because the given context is useful in answering the question.; input: Context: Working on a new superhero film called Look At Me Man. Gonna be tight, yo. 👍😈😎💪— Shane West (@shanewest_1) May 13, 2014 Question: what superhero film was mentioned? Answer: look at me man
output: yes
explanation: Here, the generated label is 'yes' because we can answer the question using the tweet.; input: Context: @williebosshog huge love to you/your family huge respect for your business prosperities and the family values you still all behold. big fan— Liam Payne (@Real_Liam_Payne) January 18, 2014 Question: at which school were first responders on the scene for? Answer: independence high school
output: no
explanation: Here, the generated label is 'no' because the given context is not useful in answering the question.

Negative examples:
input: Context: So many bad jokes about #BrazilvsGermany right now. I can't take it an neymar.— Professor Snape (@_Snape_) July 8, 2014 Question: what can’t professor snape take anymore? Answer: bad jokes
output: no
explanation: Here, the generated label should be 'yes' because the given context is helpful in answering the question, however, the label is 'no' which is not right.; input: Context: Thank you to my loved ones and those that have been there for me, who have picked me up and helped me through everything.— Oscar Pistorius (@OscarPistorius) August 8, 2014 Question: why does zendaya look gorgeous? Answer: because she speaks her mind.
output: yes
explanation: Here, the generated label should be 'no' because this context is not helpful in answering the question. Still this sample labeled as 'yes' which is wrong.
Task: In this task, you are given an input i,A where i is an integer and A is an array. You need to find every ith element of A starting with the 1st element.
Categories: Program Execution
Domains: Code

Positive examples:
input: 3, ['a', '34', 'f', '931', '7', '3432', '13245', '762']
output: a, 931, 13245
explanation: Here, every 3rd element from array are 'a', '931', and '13245'.; input: 4, ['D', '9127', '4217', '8469', '9585', '933', '5401', 't', '1977', '7989', 'l', '455', 'r', 'K', 'p', '1755', '7391', 'h']
output: D, 9585, 1977, r, 7391
explanation: Here, every 4th element from array are 'D', '9585', '1977', 'r', and '7391'.

Negative examples:
input: 4, ['5831', 'k', 'T', 'J', '3895', 'N', 'p', 'V', '9379']
output: 5831, T, N, V
explanation: Here, the answer should have been ['5831', '3895', '9379'] as every 4th element of the array are '5831', '3895', and '9379'.
Task: In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to determine if the two sentences clearly agree/disagree with each other, or if this can't be determined. Indicate your answer as yes or no respectively.
Categories: Textual Entailment
Domains: History; Fiction; Dialogue; Law; Government and Politics
Reasoning: Textual Entailment

Positive examples:
input: Sentence 1: Next to the MGM Grand you will find M and M World. Sentence 2: The candy has many fans who love its attractions.
output: no
explanation: It is not clear that M and M world is popular.; input: Sentence 1: I've forgotten his name now, confessed Tuppence. Sentence 2: Tuppence remembered his name later.
output: no
explanation: Tuppence may or may not have later remembered the name.; input: Sentence 1: One of the first organizational realignments taking place is in the Office of the Taxpayer Advocate. Sentence 2: The office of the taxpayer advocate is having an organizational realignment.
output: yes
explanation: Both sentences refer to the reorganization of an official department and agree with each other.; input: Sentence 1: yeah i tell you what though if you go price some of those tennis shoes i can see why now you know they're getting up in the hundred dollar range. Sentence 2: The tennis shoes have only one price.
output: yes
explanation: The prices of the shoes vary in the hundred dollar range in the first sentence, so the second sentence disagrees with it as the tennis shoes can't have only one price.

Negative examples:
input: Sentence 1: There was an earthquake in San Fransisco. Sentence 2: The earthquake caused a lot of road damage.
output: yes
explanation: The magnitude of the earthquake may or may not have been large enough to cause damage.; input: Sentence 1: Representing yourself in court can be a tricky endeavor. Sentence 2: Lawyers are much better than self-representation.
output: yes
explanation: Even though self-representation in court is tricky, that doesn't indicate or deny that lawyers are better than self-representation. So, the output should be no.; input: Sentence 1: Anna went to the school picnic and had a busy afternoon. Sentence 2: Anna had lunch at the picnic with her classmates.
output: no
explanation: The sentences both refer to a picnic Anna attended, organized by her school and clearly agree with each other. So, the output should be yes.
Task: The provided file includes inquiries about restaurants, and we ask you to translate those to the Spanish language. Please bear in mind the following guidlines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for special placeholders. Please do the same in your translations.
Categories: Translation
Domains: Public Places

Positive examples:
input: are there any " italian " restaurants nearby with 2 star reviews ?
output: ¿hay algún restaurante " italian " cerca con opiniones de 2 estrellas?
explanation: The translation correctly preserves " italian " entity and is accurate; input: what kind of food does this restaurant serve ?
output: ¿qué tipo de comida sirve este restaurante?
explanation: The translation is fluent, formal, and accurate; input: what is the telephone number and location of the nearest " chinese " restaurant ?
output: ¿cuál es el número de teléfono y la ubicación del restaurante " chinese " más cercano?
explanation: The translation correctly preserves " chinese " entity. It's also fluent, correct, and lowercased.

Negative examples:
input: where is the closest " wendy 's " ?
output: ¿dónde está el wendy 's más cercano?
explanation: Translation contain the entity " wendy 's " but quotation marks are dropped; input: show me the closest " mcdonald 's "
output: muéstreme el " McDonald's " más cercano.
explanation: The output is not lowercased; input: display all review descriptions authored by " jennifer " .
output: mostrar todas escritas por " jennifer ".
explanation: Translation of review descriptions is omitted from the output; input: what is the phone number for " noodle nirvana " ?
output: ¿cuál es el número de teléfono de " nirvana de fideos "?
explanation: The restaurant name " noodle nirvana " is translated to " nirvana de fideos " instead of being kept intact; input: can you tell me the rating of " gold mirror " in " san francisco " ?
output: ¿puede decirme la valoración de " san francisco " en " gold mirror "?
explanation: The position of entities is switched in the output
Task: In this task, you are given a question in English, and your task is to translate it into Persian.
Categories: Translation
Domains: Web

Positive examples:
input: How do I look up my pay stubs for McDonald's?
output: چگونه می توانم خرده فروشی های خود را برای مک دونالد جستجو کنم؟
explanation: This is a good example. The above sentence is correctly translated from English to Persian.; input: How do landlords qualify tenants?
output: صاحبخانه ها چگونه مستاجران را واجد شرایط می دانند؟
explanation: This sentence is translated correctly.; input: Can an introvert become an extrovert?
output: آیا یک درونگرا می تواند برون گرا شود؟
explanation: The above sentence is correctly translated from English to Persian.

Negative examples:
input: Why companies like Rolex and blackberry they never advertising their products?
output: چرا شرکت هاهرگز محصولات خود را تبلیغ نمی کنند؟
explanation: This translation is incomplete. Hence it's not acceptable.; input: Which is the best wearable technology so far?
output: بهترین نوشیدنی کنونی کدام است؟
explanation: This translation is incorrect.
Task: In this task, you are given a sentence in the French language. Your task is to convert it to Slovak language.
Categories: Translation
Domains: Miscellaneous

Positive examples:
input: Abilify est un médicament qui contient le principe actif aripiprazole.
output: Abilify je liek, ktorého účinnou látkou je aripiprazol.
explanation: Translation of French sentence to Slovak sentence is correctly translated.; input: L'incidence des décès dans le groupe des patients traités par aripiprazole a été de 3,5% comparé à 1,7% dans le groupe placebo.
output: Výskyt smrti u pacientov liečených aripiprazolom bol 3, 5% v porovnaní s 1, 7% v placebovej skupine.
explanation: French sentence is converted to Slovak sentence with the correctly punctuation marks.

Negative examples:
input: 9 Dans trois études court terme (4 à 6 semaines) contrôlées versus placebo portant sur 1.228 patients schizophrènes présentant des symptômes positifs ou négatifs, l'aripiprazole a montré une amélioration significativement plus importante des symptômes psychotiques comparativement au placebo.
output: V troch krátkodobých (4 až 6 týždňových) placebom kontrolovaných štúdiách zahŕňajúcich 1 228 schizofrenických pacientov s pozitívnymi alebo negatívnymi symptómami sa aripiprazol spájal so štatisticky významnými väčšími zlepšeniami v psychotických symptómoch v porovnaní s placebom.
explanation: Translation has missed digit 9 from the input.; input: L'aripiprazole est bien absorbé avec un pic plasmatique atteint dans les 3– 5 heures après administration.
output: Aripiprazol podlieha minimálnemu pre- systémovému metabolizmu.
explanation: The translated Slovak sentence is wrong.; input: AUGMENTATION de la Créatinine Phosphokinase, Augmentation de la glycémie, Variation de la Glycémie, augmentation de l’ Hémoglobine glycosylée
output: zvýšenie kreatínfosfokinázy, zvýšenie krvného cukru, kolísanie glukózy v krvi, zvýšenie glykozylovaného hemoglobínu
explanation: Translated sentece has changed some of the upper case of French sentence into lower case.
Task: The task is to generate the correct answer for a given math problem.
Categories: Question Answering
Domains: Mathematics
Reasoning: Quantitative Reasoning; Scientific Reasoning; Mathematics -> Combinatorics; Mathematics -> Arithmetic

Positive examples:
input: a multiple choice test consists of 4 questions, and each question has 5 answer choices. in how many r ways can the test be completed if every question is unanswered ?
output: 625
explanation: Total r of 5 * 5 * 5 * 5 = 5 ^ 4 = 625 ways to answer all of them. Therefore, the answer is 625.; input: a 3 - digit positive integer is chosen at random. what is the probability that the product of its digits is even ?
output: 31 / 36
explanation: p(three odd digits) = 5 / 9 * 5 / 10 * 5 / 10 = 5 / 36 (first digit can not be 0, so we have a total of 9 digits , 4 even and 5 odd). therefore, p ( at least one even digit ) = 1 - 5 / 36 = 31 / 36. Therefore, the answer is 31 / 36.

Negative examples:
input: if x and y are positive integers and 7 + x + y + xy = 21, what is the value of x + y ?
output: 9
explanation: For a: 7 + 3 + xy = 21 ; xy = 11 ( impossible , 11 prime number . 1 + 11 does n ' t equal 3 ) for b : 7 + 5 + xy = 21 ; xy = 9 ( no combination of xy = 9 and x + y = 5 ) for c : 7 + 6 + xy = 21 ; xy = 8 ( x + y = 6 ; x = 2 , y = 4 or x = 4 , y = 2 ) for d : 7 + 8 + xy = 21 ; xy = 6 ( no combination of xy = 6 and x + y = 8 ) for e : 7 + 9 + xy = 21 ; xy = 5 ( impossible , 5 prime number . 1 + 5 does n ' t equal 9 ). Therefore, the answer is not 9.; input: the hcf and lcm of two numbers m and n are respectively 6 and 210. if m + n = 72, then 1 / m + 1 / n is equal to
output: 2
explanation: Answer we have , m x n = 6 x 210 = 1260 ∴ 1 / m + 1 / n = ( m + n ) / mn = 72 / 1260 = 4 / 70 = 2 / 35. Therefore, the answer is not 2.
Task: Given the following Catalan sentence, indicate the name entities (i.e., the real-world objects such as a person, location, organization, etc. that can be denoted with a proper name) such as 'New York Times'.  For each words of a named-entity, indicate their type 'LOC' or 'ORG' or 'PER', where 'LOC' indicates a location entity (such as 'New York City', 'ORG' indicates the token of an organization (such as 'Amazon'), 'PER' indicates the tokens of a person entity (such as 'Jeff Bezos'). Named entities with other types should be indicated with 'MISC'. To indicate boundaries of an entity, use IOB (Inside-Output-Begin) prefixes. The B- prefix before a tag indicates that the word is the beginning of a named entity. The I- prefix indicates that the word is inside a bigger chunk. For example, you can break 'New York' to 'New' and 'York.' and tag them as 'B-LOC' and 'I-LOC'. Any token that doesn't belong to a named entity must be tagged with 'O'. 
Categories: Named Entity Recognition
Domains: News

Positive examples:
input: Una setantena de treballadors de Fomento de Construcciones y Contratas a Manresa , gairebé la totalitat de la plantilla , s' han manifestat aquest migdia pels carrers de la ciutat .
output: Una (O) setantena (O) de (O) treballadors (O) de (O) Fomento (B-ORG) de (I-ORG) Construcciones (I-ORG) y (I-ORG) Contratas (I-ORG) a (O) Manresa (B-LOC) , (O) gairebé (O) la (O) totalitat (O) de (O) la (O) plantilla (O) , (O) s' (O) han (O) manifestat (O) aquest (O) migdia (O) pels (O) carrers (O) de (O) la (O) ciutat (O) . (O)
explanation: We split the sentence into single words or punctuation and then assigned correct tags to each token as per the definition. We assigned 30 correct tags to the given sentence.; input: Aquests contenidors només estaran instal.lats mentre duri la vaga .
output: Aquests (O) contenidors (O) només (O) estaran (O) instal·lats (O) mentre (O) duri (O) la (O) vaga (O) . (O)
explanation: We split the sentence into single words or punctuation and then assigned correct tags to each token as per the definition. We assigned nine correct tags to the given sentence.

Negative examples:
input: D' una banda , s' ha fet una bustiada informativa sobre els dies que hi haurà recollida de deixalles .
output: D' (I-LOC) una (O) banda (O) , (O) s' (O) ha (O) fet (O) una (O) bustiada (O) informativa (O) sobre (O) els (O) dies (O) que (O) hi (O) haurà (O) recollida (O) de (O) deixalles (O) . (O)
explanation: The output is not correct as per the definition. We had assigned the 'I-LOC' tag to the 'D'' token when it should be 'O' because it doesn't belong to any bigger chunk.; input: Els treballadors han iniciat aquesta matinada una vaga indefinida dels serveis de neteja viària i recollida d' escombraries .
output: Els (O)
explanation: This is a bad example because we need to predict tags to 19 tokens, but we have only predicted one.
Task: In this task, you will be presented with a question about part-of-speech tag of a word in the question. You should write an implausible POS tag to the question. Even though there exist multiple wrong answers, we only need a single wrong answer.  Here is the Alphabetical list of part-of-speech tags used in this task: CC: Coordinating conjunction, CD: Cardinal number, DT: Determiner, EX: Existential there, FW: Foreign word, IN: Preposition or subordinating conjunction, JJ: Adjective, JJR: Adjective, comparative, JJS: Adjective, superlative, LS: List item marker, MD: Modal, NN: Noun, singular or mass, NNS: Noun, plural, NNP: Proper noun, singular, NNPS: Proper noun, plural, PDT: Predeterminer, POS: Possessive ending, PRP: Personal pronoun, PRP$: Possessive pronoun, RB: Adverb, RBR: Adverb, comparative, RBS: Adverb, superlative, RP: Particle, SYM: Symbol, TO: to, UH: Interjection, VB: Verb, base form, VBD: Verb, past tense, VBG: Verb, gerund or present participle, VBN: Verb, past participle, VBP: Verb, non-3rd person singular present, VBZ: Verb, 3rd person singular present, WDT: Wh-determiner, WP: Wh-pronoun, WP$: Possessive wh-pronoun, WRB: Wh-adverb
Categories: Pos Tagging
Domains: Wikipedia

Positive examples:
input: What is the part-of-speech tag of the word "the" in the following question: Who were the builders of the mosque in Herat with fire temples ?
output: IN
explanation: This is a good example. POS tag of the is DT and IN is incorrect.; input: What is the part-of-speech tag of the word "number" in the following question: After what season did the number 7 competitor retire ?
output: VBD
explanation: Based on the given question, POS tag of number is NN and VBD is incorrect.

Negative examples:
input: What is the part-of-speech tag of the word "Year" in the following question: What year was the 1971-72 ECAC Hockey Player of the Year born ?
output: NN
explanation: The part-of-speech tag of the word Year in the question is NN. Note that the task is to generate an incorrect answer.; input: What is the part-of-speech tag of the word "number" in the following question: After what season did the number 7 competitor retire ?
output: N
explanation: "N" is not one of the part-of-speech tags in the list. Your answer should be one of the provided tags in the description.; input: What is the part-of-speech tag of the word "fight" in the following question: What battle did the man born on 7 December 1839 fight in ?
output: NN
explanation: The word "flight" here is a noun, so its pos tag is "NN". The answer should be something other than the correct POS tag.