ceselder/lots-of-loras-task-definitions huggingface dataset:
Task: In this task, you're given a review from Amazon and category of the product based on the review given by the user and your task is classify whether the given category match the review. Generate "True" if given review and its category match, otherwise generate "False".
Categories: Text Categorization
Domains: Reviews
Reasoning: Reasoning on Social Interactions; Commonsense Reasoning

Positive examples:
input: Review: Arrived broken. Manufacturer defect. Two of the legs of the base were not completely formed, so there was no way to insert the casters. I unpackaged the entire chair and hardware before noticing this. So, I'll spend twice the amount of time boxing up the whole useless thing and send it back with a 1-star review of part of a chair I never got to sit in. I will go so far as to include a picture of what their injection molding and quality assurance process missed though. I will be hesitant to buy again. It makes me wonder if there aren't missing structures and supports that don't impede the assembly process. 
 Category: furniture
output: True
explanation: It's the correct category. Because the customer gives a review of the product which belongs to the furniture category, which we can see it from the way the customer describes his experience sharing how he unpackaged his chair.; input: Review: the cabinet dot were all detached from backing... got me 
 Category: home_improvement
output: True
explanation: It's the correct category. Because the product belongs to the home_improvement category, we can see it in the review where the customer mentions a cabinet dot.; input: Review: I got this with 50% charge. Put it under the sun. two days later, it has 25%. Never able to charge it once. Should have returned it right away. 
 Category: utensils
output: False
explanation: It's an incorrect category. Because the product belongs to the electronics category and not utensils. Because the customer shared his experience related to the charging issue.

Negative examples:
input: Review: Took way too long for these seeds to arrive and for only a single one to germinate is even more disappointing! Not worth the $ 
 Category: gardening
output: gardening
explanation: It's only supposed to generate True or False.; input: Review: This was the third pair that I returned because they did not fit (the first two pairs were the same size). I received a full refund for those, but not for these! I WILL NEVER BUY FROM THEM, AGAIN!!! EVER! 
 Category: shoes
output: False
explanation: The product belongs to shoes category. So, the answer should be True.; input: Review: Junk never work just damaged the brick 
 Category: home_improvement
output: True False
explanation: It should either return True or False not both.
Task: 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 with [Link].
Categories: Toxic Language Detection
Domains: Social Media -> Twitter

Positive examples:
input: Post: This immigrant should be hung or shot! Period! Animal.
output: Aggresive
explanation: This post is aggresive because it is abusive to immigrants.; input: Post: We  already have the highest per capita immigration in the world, and the  nearly 30-minute monologue segment failed to mention that Canadians are endlessly coerced:Access to White people is NOT a human right.
output: Non-aggresive
explanation: This post is considered hateful but not aggresive.

Negative examples:
input: Post: @realDonaldTrump Please DO NOT listen to lame duck @SpeakerRyan immigration/The Wall is way more important than tax 2.0! Stick to your instinct #BuildThatWall #BuildTheDamnWall #DeportThemAll
output: Aggresive
explanation: This should be non-aggresive, although it is hateful.; input: Post: @KatrinaPierson You're a skank. You are more skilled at shoplifting than trying to be witty.
output: No
explanation: You are expected to return "Aggresive" or "Non-aggresive". This post should be non-aggresive.
Task: In this task you will be given a list of integers. You should remove any integer that is not prime. A prime integer is an integer that is only divisible by '1' and itself. The output should be the list of prime numbers in the input list. If there are no primes in the input list an empty list ("[]") should be returned.
Categories: Program Execution
Domains: Code; Mathematics
Reasoning: Mathematics -> Arithmetic

Positive examples:
input: [47, 444, 859, 530, 197, 409]
output: [47, 859, 197, 409]
explanation: The integers '444' and '530' are not prime integers and they were removed from the list.; input: [351, 922, 810, 625, 4]
explanation: None of the integers in the input list were primes and the output is an empty list.

Negative examples:
input: [569, 203, 197, 79, 888]
output: [203, 888]
explanation: The output contains integers that are not prime.; input: [323, 151, 2, 111, 601]
output: [151, 601]
explanation: The output removed the integer '2'. This integer is a prime and should not have been removed.
Task: In this task, you are given two strings A, B. Find the longest common substring in the strings A and B.
Categories: Program Execution
Domains: Mathematics
Reasoning: Reasoning on Strings

Positive examples:
input: PPdQTlHHTJEe, DydQTlHHVnmlLu
output: dQTlHH
explanation: Here, 'dQTlHH' is the longest common substring in both the input strings 'PPdQTlHHTJEe' and 'DydQTlHHVnmlLu'.; input: cjnxmKkFdEcg, iHHnxmKkFeIjOoM
output: nxmKkF
explanation: Here, 'nxmKkF' is the longest common substring in both the input strings 'cjnxmKkFdEcg', and 'iHHnxmKkFeIjOoM'.

Negative examples:
input: bueLCZaiEBNGJlkwMBZ, gFmuwsEBNGJlkwpdySJVR
output: EBNG
explanation: Here, the answer should have been 'EBNGJlkw' as it is the longest common substring in both the input strings 'bueLCZaiEBNGJlkwMBZ', and 'gFmuwsEBNGJlkwpdySJVR'.; input: bYubMFxyTqR, AcDbMFxSnI
output: MF
explanation: Here, the answer should have been 'bMFx' as it is the longest common substring in both the input strings 'bYubMFxyTqR', and 'AcDbMFxSnI'.
Task: Given a paragraph, your job is to generate a question that can be answered from the passage. The answer to your question should be a single entity, person, time, etc. that can be extracted from the passage.
Categories: Question Generation
Domains: Wikipedia

Positive examples:
input: There are a million times more viruses on the planet than stars in the universe. Viruses also harbor the majority of genetic diversity on Earth. Scientists are finding evidence of viruses as a planetary force, influencing the global climate and geochemical cycles. They have also profoundly shaped the evolution of their hosts. The human genome, for example, contains 100,000 segments of virus DNA.
output: How many segments of virus DNA does the human genome contain?
explanation: The paragraph states the number of segments of virus DNA does the human genome contain, hence the question asked above is appropriate.; input: When mice are kept at high population densities, their behaviour changes in a number of ways. Aggressive activity within populations of mice rises as density increases.
output: When does the mice behavior change?
explanation: As the paragraph states that mice behavior changes when kept at high population densities, the question mentioned above can be an expected output.; input: Piedmont, or mountain, glaciers are found in many parts of the world. In North America they are distributed along the mountain ranges of the Pacific Coast from central California northward. They abound in the Andes range in South America and are familiar and greatly admired spectacles in the Alps.
output: What mountain ranges are present in South America?
explanation: The task identified a question whose answer is Andes that is present in the text. Therefore, this serves as a good example.

Negative examples:
input: My cherished toy as a new teenager was my bicycle. We lived in a soi with lots of pavement and no cars. I was head of the bike gang and we would set up ramps for jumps and design obstacle courses for racing.
output: What was the cherished car of the boy?
explanation: The paragraph claims that the cherished toy of boy was a bicyle but not car, hence, this question is a wrong output.; input: When I was a little boy in elementary school, the neighborhood kids and I all looked forward to playing so many games in my backyard during the long summer holidays between grades.
output: When did the boy miss school?
explanation: The text did not mention about the times when the boy has missed the school, hence, this is not a valid question.
Task: Given a concept word, generate a hypernym for it. A hypernym is a superordinate, i.e., a word with a broad meaning constituting a category, that generalizes another word. For example, color is a hypernym of red. 
Categories: Word Semantics
Domains: Miscellaneous
Reasoning: Reasoning on Objects

Positive examples:
input: knife
output: weapon
explanation: A knife is a type of weapon, therefore weapon is a valid hypernym for knife.; input: beet
output: root
explanation: Beets are root vegetables, therefore root is a valid hypernym for beet.

Negative examples:
input: washer
output: dishwasher
explanation: Washer and dishwasher are both appliances, however dishwasher is not a class or category to which washer belongs. So the output is incorrect as it is not a valid hypernym.; input: mug
output: coffee
explanation: A mug can contain coffee, but a mug is not a type of coffee. Therefore coffee as incorrect hypernym for mug.
Task: In this task, you are given a public comment from online platforms. You are expected to classify the comment into two classes: identity-attack and non-identity-attack. Identity attack refers to anything rude or disrespectful about someone's identity, including appearance, race, gender, and sexual orientation.
Categories: Toxic Language Detection
Domains: Social Media

Positive examples:
input: Comment: It's bad enough we already have all these American sicko's living in this country shooting up theatre, schools, churches, malls, etc., we don't have room for crazy muslims.
output: Identity-attack
explanation: This comment contains identity attacks on both American and Muslim.; input: Comment: Same reason Trump went....maybe?
output: Non-identity-attack
explanation: This comment is unrelated and does not contain identity attacks.

Negative examples:
input: Comment: We are living in a cesspool.
output: Identity-attack
explanation: This comment should be non-identity-attack because it is not disrespectful to any groups.; input: Comment: ahhh...poor muslims. things are so much better in islamic countries where they MUST bury their dead according to the state religion. A quick question, do the dead virgins get priority shipping to meet the pressing needs of the arab boogie woogie sky wizard and his male devotees ? Islam is a dangerous myth.
output: Non-identity-attack
explanation: This should be identity-attack because the comment contains rude language about Muslisms.
Task: You are given a question on high school computer science. You are also given 4 answer options (associated with "A", "B", "C", "D"), out of which only one is correct. You need to answer the question by selecting the correct option. You should only answer with the choice letter, not the whole answer.
Categories: Question Answering
Domains: Computer Science
Reasoning: Logical Reasoning

Positive examples:
input: Let x = 1. What is x << 3 in Python 3?
(A)1 (B)3 (C)8 (D)16
output: C
explanation: << is the right shift operator; input: In Python 3, which of the following function convert a string to an int in python?
(A)int(x [,base]) (B)long(x [,base] ) (C)float(x) (D)str(x)
output: A
explanation: int(x [,base] converts a string to an int

Negative examples:
input: Let x = 1. What is x << 3 in Python 3?
(A)1 (B)3 (C)8 (D)16
output: I dont know.
explanation: Do not generate anything else apart from one of the following characters: 'A', 'B, 'C', 'D'.; input: In Python 3, which of the following function convert a string to an int in python?
(A)int(x [,base]) (B)long(x [,base] ) (C)float(x) (D)str(x)
output: B,C
explanation: Only one of the following options ('A', 'B', 'C', 'D') is allowed to be the answer. Multiple options can not be correct. The correct answer here would have been 'A'.; input: Which is the largest asymptotically?
(A)O(1) (B)O(n) (C)O(n^2) (D)O(log n)
output: A
explanation: Asymptotically, O(n^2) is the largest among the four options, hence the correct answer should be 'C'.
Task: In this task, you are given a short passage that conveys a stereotype or an anti-stereotype. A stereotype is an over-generalized belief about a particular group of people(based on race, ethnicity, gender, age, abilities, etc.). An anti-stereotype is an idea that goes against a common stereotype. You are expected to classify the passage into two classes: stereotype or anti-stereotype.
Categories: Stereotype Detection
Domains: Stereotypes

Positive examples:
input: Passage: He couldn't figure out the issue with the rope, but he tried not to get too down on himself, most black kids don't have the best education growing up.
output: Stereotype
explanation: This is a stereotype because it is a common mental picture that black kids do not receive proper education growing up.; input: Passage: Historically Asian Americans men are angry and prone to violence.
output: Anti-stereotype
explanation: This is an anti-stereotype because it goes against the common belief that Asian Americans are of gentle nature.; input: Passage: I do not dare disagree with an old man like you.
output: Stereotype
explanation: This is a stereotype, because it is based on the common belief that old people do not tolerate opposing arguments.

Negative examples:
input: Passage: He sent the message to his city, but the majority there were black so he was worried if they could even read it.
output: Anti-stereotype
explanation: This should be a stereotype because it agrees with the common but over-generalized belief that black people have more difficulty in reading.; input: Passage: The owners were rich so their house was small and crowded.
output: Stereotype
explanation: This should be an anti-stereotype because rich people are commonly thought to have large houses.
Task: In this task, you are given two natural language statements with similar wording. You must choose the statement that makes less sense based on common sense knowledge. A '
' separates the statements. Use "first" or "second" to indicate which sentence makes less sense.
Categories: Commonsense Classification
Domains: Commonsense -> Concepts and Relations
Reasoning: Commonsense Reasoning

Positive examples:
input: A mosquito stings me
I sting a mosquito
output: second
explanation: The second statement doesn't make sense.; input: He drinks apple.
He drinks milk.
output: first
explanation: The first statement doesn't make sense.; input: Jeff ran a mile today
Jeff ran 100,000 miles today
output: second
explanation: The second statement doesn't make sense.

Negative examples:
input: He drinks apple.
He drinks milk.
output: I can't decide.
explanation: Do not generate anything else apart from one of the following characters: 'first', 'second'.
Task: In this task, you will be given a sentence or two along with a change aspect. You should change the given text in the given aspect. Aspects are explained below:
 Tense: Change the tense of the verbs in the text. If they're in past tense, change them to present, and if they're in present tense, change them to past tense.
Number: Change the number of the nouns in the given text. Make plurals into singles and single into plurals. Remember to change the corresponding pronouns accordingly.
Voice: If the verbs are in active voice, change them to be passive, otherwise, change them to be in active voice.
Adverb: add one or multiple adverbs to the text.
Gender: If the text contains female names and pronouns, substitute them with male names and pronouns. Do the same for sentences with mala names and pronouns.
Categories: Sentence Perturbation
Domains: Dialogue; Narrative
Reasoning: Commonsense Reasoning

Positive examples:
input: sentence: Jim signaled the barman and gestured toward his empty glass . aspect: Number
output: Jim and Bill signaled the barmen and gestured toward  their empty glasses .
explanation: You have to add people names and change the pronouns if you want to change the number aspect of the sentence.; input: sentence: Jackson was greatly influenced by Arnold , though he lived two centuries earlier . aspect: Gender
output: Lucy was greatly influenced by Angela ,  though   she lived two centuries earlier .
explanation: Since the change aspect is gender, we have to change the names and the pronouns.; input: sentence: The city councilmen refused the demonstrators a permit because they advocated violence . aspect: Adverb
output: The city councilmen immediately refused the demonstrators a permit because they advocated violence .
explanation: In this example the adverb "immediately" is added to the sentence.

Negative examples:
input: sentence: George got free tickets to the play , but he gave them to Eric , because he was not particularly eager to see it . aspect: Voice
output: George is getting free tickets to the play , but he will give them to Eric , because he is not particularly eager to see it .
explanation: This example needs to be changed in the voice aspect. As it has an active verb, it should be changed in a way that it has a passive verb. The given output has changed the tense of the sentence, not the voice.; input: sentence: Adam can't leave work here until Bob arrives to replace him . If Bob had left home for work on time , he would be here by this time . aspect: Gender
output: Sarah can't leave work here until Shirley arrives to replace him . If Sarah had left home for work on time , he would be here by this time .
explanation: Remember to change the gender of the pronouns when you change the gender of the names.
Task: In this task, you are given Yelp reviews. The task is to classify a review as "POSITIVE" if the overall sentiment of the review is positive or as "NEGATIVE" if the overall sentiment of the review is negative.
Categories: Sentiment Analysis
Domains: Reviews -> Movies

Positive examples:
input: This is my go to place to get the best chicken Pad Thai! Also the price is super good, considering most places have high prices and poor quality. Love this place, its definitely a top 5 fav for take out.
output: POSITIVE
explanation: The overall sentiment of the review is positive. Hence, the label is 'POSITIVE'.; input: Thoroughly underwhelming. Crispy overdone meat, mediocre quality of ingredients no unique flavor.  Bobby Flay's place across the street should not worry. The only spark of innovation they have is a hamburger that's well over a pound.
output: NEGATIVE
explanation: The overall sentiment of the review is negative. Hence, the label is 'NEGATIVE'.

Negative examples:
input: Ordered the surf and turf. It came with a small rip eye steak 3 shrimp. Also garlic mashed potatoes hard and cold. Extremely disappointed. Small portions and over priced.
output: POSITIVE
explanation: Although the sentiment of the review is negative (e.g., the author is 'dissappointed'), the label is 'POSITIVE'. The right label should be 'NEGATIVE'.; input: Great food but it's always SO cold inside. That's the main deterrent for me - doesn't matter how much I bundle up. Good for late night eats.
output: NEGATIVE
explanation: The overall sentiment of the review is positive (even though there is a negative phrase as part of the review). The right label should be 'POSITIVE' and not 'NEGATIVE'.
Task: Two analogies that relate objects to the associated rooms is given in the form "A : B. C : ?". "A : B" relates object A to room B. Your task is to replace the question mark (?) with the appropriate room for the given object C, following the "A : B" relation.
Categories: Word Analogy
Domains: Commonsense
Reasoning: Relational Reasoning; Commonsense Reasoning; Analogical Reasoning

Positive examples:
input: shelf : nursury. cabinet : ?
output: bathroom
explanation: The given analogy relates objects to their rooms. A shelf is associated with a nursery. A cabinet is associated with a bathroom; input: dresser : bedroom. boxes : ?
output: garage
explanation: The given analogy relates objects to their rooms. A dresser is associated with a bedroom. Boxes are associated with a garage; input: refrigerator : kitchen. crib : ?
output: nursery
explanation: The given analogy relates objects to their rooms. A refrigerator is associated with a kitchen. A crib is associated with a nursery

Negative examples:
input: dresser : bedroom. boxes : ?
output: fill
explanation: The given analogy relates objects to their rooms. A dresser is associated with a bedroom. Fill is not a room, and hence the answer does not model containment; input: workbench : garage. pantry : ?
output: store
explanation: The given analogy relates objects to their rooms. A workbench is associated with a garage. Store is not a room, and hence the answer does not model containment; input: bookshelf : library. shelf : ?
output: driveway
explanation: The given analogy relates objects to their rooms. A bookshelf is associated with a library. Shelf is not associated with driveway, and hence the answer does not model containment
Task: You are given an unfinished English sentence, and you need to predict the rest of it.
Categories: Text Completion
Domains: Books
Reasoning: Abductive Reasoning

Positive examples:
input: chapter one
output: missus rachel lynde is surprised missus rachel lynde lived just where the avonlea main road dipped down into a little hollow fringed with alders and ladies eardrops and traversed by a brook
explanation: This is the original sentence 'chapter one missus rachel lynde is surprised missus rachel lynde lived just where the avonlea main road dipped down into a little hollow fringed with alders and ladies eardrops and traversed by a brook' So, given 'chapter one,' we have correctly generated 'missus rachel lynde is surprised missus rachel lynde lived just where the avonlea main road dipped down into a little hollow fringed with alders and ladies eardrops and traversed by a brook' completing the sentence.; input: if he'd run out of turnip seed he wouldn't dress
output: up and take the buggy to go for more
explanation: This is the original sentence 'if he'd run out of turnip seed he wouldn't dress up and take the buggy to go for more' So, given 'if he'd run out of turnip seed he wouldn't dress' we have correctly generated 'up and take the buggy to go for more', completing the sentence.

Negative examples:
input: Hi, How are you
output: New York
explanation: This is a bad example because we need to predict 'doing?' to complete the sentence, but we have predicted 'New York.'; input: Apple released a
output: new world every day
explanation: This is a bad example because we need to predict 'new mobile today' to complete the sentence, but we have predicted 'new world every day.'