DataHammer/scimrc huggingface dataset:
Question: You have been asked to join the School Health Advisory Council to develop a new policy that focuses on the development of a more sustainable food service. List the steps you would take to develop and implement the new policy. How would you involve staff, families, and children?
Answer: Communicate with families to list out their children’s favorite food items and eating habits. Such as allergy items and if any family requests that no milk or milk-based substitute be given to their children. Also families' dietary restrictions due to religious beliefs, personal beliefs, cultural customs and health issues.
Based on those pieces of information, develop the food-from-home policy. Restrict all allergy items to school. Meeting with all staff involves, such as teachers, kitchen staff, nutrition professionals and parents who are interested, to have a planning menu together. 
For children, we can ask about their favorite foods and talk about healthy choices to get prepared for them to enjoy the new food items. Through activities and discussions to help them experience the richness of all cultures through diversity in food and food customs. Practice emerging independence by using child-sized containers and utensils, allowing preschoolers to set tables, pour beverages, serve themselves and clear their places.
Question: Why do kids love peppa pig?
Answer: Peppa pig is loved by children because it’s “cheeky”, children can understand the scenes, and there are many rhymes and songs that children love to sing along with. As a result of being loved by children, parents may need to endure repetitive peppa pig sessions.
Question: What makes a good presentation in a workplace setting?
Answer: Effective presentations have a logical structure that is tailored to the audience. For example, a logical structure should begin by introducing the purpose of the presentation, then provide a detailed overview of the main ideas, and finish with the final thoughts. Tailoring to the audience is also important so the presenter ensures that the audience understand the basic concepts and so that the presenter can appeal to the interests of the audience.
Question: How to make a dated home look modern?
Answer: 1) Repaint your walls with a neutral color.
2) Install a peel and stick modern backsplash in your kitchen
3) Use peel and stick vinyl flooring on bathroom
4) Paint your kitchen cabinets lighter color
5) Change hardware on cabinets
6) Install Modern light fixtures
Question: Name the top four benefits and drawbacks of playing Factorio.
Answer: Factorio is a highly acclaimed factory automation video game. The top four benefits of playing Factorio are:
1. Develop logistical management skills
2. Connect with friends from around the world
3. The variety of mods keeps the game always fresh
4. Massive time commitment distracts you from committing crime
The four top drawbacks of Factorio are:
1. The rest of your game library goes unplayed
2. You are distracted from your non-game relationships
3. The open-ended nature lacks a definitive goal
4. Massive save files clutter your hard drive
Question: What are the ways you can eat an apple.
Answer: You can eat the apple raw. 
Or you can add it in custard. 
You can also have caramelised apple.
I am sure you must have heard of apple pie.
You can try apple cake, sure apple sauce too, not sure how it would taste though, never really tried it. 
How about apple juice, or apple chips. 
I think there's a whole lot way to eat apple than the regular way, but I bet eating raw is the healthiest of all.
Question: In the series A Song of Ice and Fire, who is the founder of House Baratheon?
Answer: Orys Baratheon
Question: Why did the San Francisco 49ers lose some fans in the mid-2000s?
Answer: In 2005, the San Francisco 49ers drafted Alex Smith with the #1 pick in the NFL draft. The organization selected Smith, a Quarterback from the University of Utah and originally from Bremerton, WA, over Aaron Rodgers. Aaron Rodgers, a Quarterback from the University of California originally from Chico, CA, ultimately was drafted by the Green Bay Packers with pick #35 and went on to become one of the most highly regarded quarterbacks of all time.

After passing on Rodgers, the 49ers missed the playoffs for the next 5 seasons. Since they had missed the playoffs the previous three seasons, many fans were left extremely frustrated.
Question: What are the most common sorting algorithms?
Answer: Bubble sort.
Insertion sort.
Merge sort.
Quick sort.
Heap sort.
Counting sort.
Radix sort.
Bucket sort.
Question: How old is the world's oldest dog?
Answer: In April 2023, the oldest dog in the world - officially recognised by the Guiness World Records - is Bobi: a 30-year old dog, who lives in Portugal.
Question: Based on the paragraph below, describe 2 suitable applications of noble gases.
Evidence: The noble gases (historically also the inert gases; sometimes referred to as aerogens) make up a class of chemical elements with similar properties; under standard conditions, they are all odorless, colorless, monatomic gases with very low chemical reactivity. The six naturally occurring noble gases are helium (He), neon (Ne), argon (Ar), krypton (Kr), xenon (Xe), and the radioactive radon (Rn).
Oganesson (Og) is a synthetically produced highly radioactive element. Although IUPAC has used the term "noble gas" interchangeably with "group 18" and thus included oganesson, it may not be significantly chemically noble and is predicted to break the trend and be reactive due to relativistic effects. Because of the extremely short 0.7 ms half-life of its only known isotope, its chemistry has not yet been investigated.
For the first six periods of the periodic table, the noble gases are exactly the members of group 18. Noble gases are typically highly unreactive except when under particular extreme conditions. The inertness of noble gases makes them very suitable in applications where reactions are not wanted. For example, argon is used in incandescent lamps to prevent the hot tungsten filament from oxidizing; also, helium is used in breathing gas by deep-sea divers to prevent oxygen, nitrogen and carbon dioxide toxicity.
The properties of the noble gases can be well explained by modern theories of atomic structure: Their outer shell of valence electrons is considered to be "full", giving them little tendency to participate in chemical reactions, and it has been possible to prepare only a few hundred noble gas compounds. The melting and boiling points for a given noble gas are close together, differing by less than 10 °C (18 °F); that is, they are liquids over only a small temperature range.
Neon, argon, krypton, and xenon are obtained from air in an air separation unit using the methods of liquefaction of gases and fractional distillation. Helium is sourced from natural gas fields that have high concentrations of helium in the natural gas, using cryogenic gas separation techniques, and radon is usually isolated from the radioactive decay of dissolved radium, thorium, or uranium compounds. Noble gases have several important applications in industries such as lighting, welding, and space exploration. A helium-oxygen breathing gas is often used by deep-sea divers at depths of seawater over 55 m (180 ft). After the risks caused by the flammability of hydrogen became apparent in the Hindenburg disaster, it was replaced with helium in blimps and balloons.
Answer: The inertness of noble gases makes them very suitable in applications where reactions are not wanted. Two suitable examples of application are: 
1. argon is used in incandescent lamps to prevent the hot tungsten filament from oxidizing,
2. helium is used in breathing gas by deep-sea divers to prevent oxygen, nitrogen and carbon dioxide toxicity.
Question: How many layers do they use in their best performing network?
Evidence: Table TABREF20 shows results for Librispeech with SpecAugment. We test both CTC and CE/hybrid systems. There are consistent gains first from iterated loss, and then from multiple feature presentation. We also run additional CTC experiments with 36 layers Transformer (total parameters $\pm $120 millions). The baseline with 36 layers has the same performance with 24 layers, but by adding the proposed methods, the 36 layer performance improved to give the best results. This shows that our proposed methods can improve even very deep models.
We also run additional CTC experiments with 36 layers Transformer (total parameters $\pm $120 millions). The baseline with 36 layers has the same performance with 24 layers, but by adding the proposed methods, the 36 layer performance improved to give the best results. 
Answer: The answers are shown as follows:
* 36
Question: Identify which car manufacturer is French or American: Alpine, Cadillac
Answer: Cadillac is American, Alpine is French
Question: Is Gloomhaven on Steam worth buying?
Evidence: Gloomhaven is a cooperative board game for one to four players designed by Isaac Childres and published by Cephalofair Games in 2017. It is a campaign-based dungeon crawl game including a narrative campaign, 95 unique playable scenarios, and 17 playable classes. Since its introduction the game has been acclaimed by reviewers, and has been described as one of the best board games ever made.
Answer: Gloomhaven on steam is not worth buying. While the gameplay is very good, it lacks the polish of other games at its cost. Players report frequent disconnects, softlocks, and other bugs that detract from the gameplay experience. Some even report certain secenarios being "unplayable" due to technical limitations in the game. Overall, IGN gives this a score of 6.5/10, as "there's something in it for everyone."
Question: Why are many plants green?
Answer: Many plants are green because of the use of a green pigment, chlorophyll, in the process of photosynthesis. Photosynthesis is the process that a plant uses to use its stored energy and carbon dioxide and water into glucose. Chlorophyll’s role is to provide this energy by absorbing sunlight and turn that sunlight into energy. Chlorophyll is green as it absorbs all collards of white light except for green. This color is reflected back which gives chlorophyll a green appearance.
Question: Which parts do people train on the upper body?
Answer: Shoulder, chest, back and arms
Question: What does the English dialogues can be divided into?
Evidence: EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. The former comes from the scripts of the Friends TV sitcom. The other is made up of Facebook messenger chats. Each subset includes $1,000$ English dialogues, and each dialogue can be further divided into a few consecutive utterances. All the utterances are annotated by five annotators on a crowd-sourcing platform (Amazon Mechanical Turk), and the labeling work is only based on the textual content. Annotator votes for one of the seven emotions, namely Ekman’s six basic emotions BIBREF1, plus the neutral. If none of the emotion gets more than three votes, the utterance will be marked as “non-neutral”.
For the datasets, there are properties worth additional mentioning. Although Friends and EmotionPush share the same data format, they are quite different in nature. Friends is a speech-based dataset which is annotated dialogues from the TV sitcom. It means most of the utterances are generated by the a few main characters. The personality of a character often affects the way of speaking, and therefore “who is the speaker" might provide extra clues for emotion prediction. In contrast, EmotionPush does not have this trait due to the anonymous mechanism. In addition, features such as typo, hyperlink, and emoji that only appear in chat-based data will need some domain-specific techniques to process.
Incidentally, the objective of the challenge is to predict the emotion for each utterance. Just, according to EmotionX 2019 specification, there are only four emotions be selected as our label candidates, which are Joy, Sadness, Anger, and Neutral. These emotions will be considered during performance evaluation. The technical detail will also be introduced and discussed in following Section SECREF13 and Section SECREF26.
Each subset includes $1,000$ English dialogues, and each dialogue can be further divided into a few consecutive utterances.
Answer: Each dialogue can be further divided into a few consecutive utterances.
Question: What system do they introduce?
Evidence: We introduce an open domain question answering system based on Chinese knowledge graph in this paper. We analyze the questions and find that most of the answers to the questions are within two hops. Therefore, we only solve the problem within two hops to reduce the complexity of the system. The system consists of a topic entity selection module, a relationship recognition module and an answer selection module. Firstly, we construct a scoring mechanism to select the core entity of the question; Then we score the relationship in the two-hop subgraph of the topic entity; Finally, we build a classier to judge whether a question is simple or complicated, so that we can choose the final relationship and generate sparql query.
We introduce an open domain question answering system based on Chinese knowledge graph in this paper.
Answer: An open domain question answering system based on Chinese knowledge graph.
Question: What is a Pythagorean triple?
Answer: In mathematics, a Pythagorean triple consists of three positive integers, a, b, c, such that a² + b² = c². These integers can form the sides of a right triangle, with c as the hypotenuse. For example, (3, 4, 5) is a Pythagorean triple because 3² + 4² = 5².
Question: Give me a list of the top 5 Colleen Hoover books
Answer: -It Ends with Us
-It Starts with Us
-Verity
-Ugly Love
-Reminders of Him
Question: What kind of task can Tweet2Vec be used for?
Evidence: In recent years, the micro-blogging site Twitter has become a major social media platform with hundreds of millions of users. The short (140 character limit), noisy and idiosyncratic nature of tweets make standard information retrieval and data mining methods ill-suited to Twitter. Consequently, there has been an ever growing body of IR and data mining literature focusing on Twitter. However, most of these works employ extensive feature engineering to create task-specific, hand-crafted features. This is time consuming and inefficient as new features need to be engineered for every task.
In this paper, we present Tweet2Vec, a method for generating general-purpose vector representation of tweets that can be used for any classification task. Tweet2Vec removes the need for expansive feature engineering and can be used to train any standard off-the-shelf classifier (e.g., logistic regression, svm, etc). Tweet2Vec uses a CNN-LSTM encoder-decoder model that operates at the character level to learn and generate vector representation of tweets. Our method is especially useful for natural language processing tasks on Twitter where it is particularly difficult to engineer features, such as speech-act classification and stance detection (as shown in our previous works on these topics BIBREF0 , BIBREF1 ).
There has been several works on generating embeddings for words, most famously Word2Vec by Mikolov et al. BIBREF2 ). There has also been a number of different works that use encoder-decoder models based on long short-term memory (LSTM) BIBREF3 , and gated recurrent neural networks (GRU) BIBREF4 . These methods have been used mostly in the context of machine translation. The encoder maps the sentence from the source language to a vector representation, while the decoder conditions on this encoded vector for translating it to the target language. Perhaps the work most related to ours is the work of Le and Mikolov le2014distributed, where they extended the Word2Vec model to generate representations for sentences (called ParagraphVec). However, these models all function at the word level, making them ill-suited to the extremely noisy and idiosyncratic nature of tweets. Our character-level model, on the other hand, can better deal with the noise and idiosyncrasies in tweets. We plan to make our model and the data used to train it publicly available to be used by other researchers that work with tweets.
In this paper, we present Tweet2Vec, a method for generating general-purpose vector representation of tweets that can be used for any classification task.
Answer: Classification task.
Question: What can people do to entertain cats?
Answer: People can entertain cats in many different ways: 1. laser pointer against walls for cats to chase after 2. lean string/wand-like toys or strings 3. auto-moving balls 4. Videos with moving balls, mice, etc While different cats would be entertained by different ways, in common, cats like small and fast-moving objects that the cats can run after. Cats also like being patted but only by the people they are familiar with (their people)
Question: What are the best ways you can achieve type 2 fun?
Answer: Type 2 fun is typically described as an activity that is not enjoyable in the moment but highly rewarding once complete. 
Some examples include Marathon running, alpining or mountaineering. Unrelated to physical activities, this can also relate to new challenges that provide a strong feeling of accomplishment once completed. 
For someone looking at taking on a type 2 fun experience, it’s important to first consider how your reward system works. Start with something challenging but achievable. An activity that you can continuously build on is also a great candidate.
Question: Who were the Beatles?
Evidence: The Beatles were an English rock band, formed in Liverpool in 1960, that comprised John Lennon, Paul McCartney, George Harrison and Ringo Starr. They are regarded as the most influential band of all time and were integral to the development of 1960s counterculture and popular music's recognition as an art form. Rooted in skiffle, beat and 1950s rock 'n' roll, their sound incorporated elements of classical music and traditional pop in innovative ways; the band also explored music styles ranging from folk and Indian music to psychedelia and hard rock. As pioneers in recording, songwriting and artistic presentation, the Beatles revolutionised many aspects of the music industry and were often publicised as leaders of the era's youth and sociocultural movements.
Answer: The Beatles were a rock band comprised of 4 musicians. They were pioneers who had a huge influence on the music industry and wider sociocultural movements.
Question: Classify each of the following a muscle in the arm or leg: quadricep, bicep, tricep, forearm, calf
Answer: Quadricep: Leg
Bicep: Arm
Tricep: Arm
Forearm: Arm
Calf: Leg
Question: What are the disadvantages of the original softmax and why？
Evidence: In this work, we consider alternatives to Eq. EQREF5 . Since the softmax is strictly positive, it forces all words in the source to receive some probability mass in the resulting attention distribution, which can be wasteful. Moreover, it may happen that the decoder attends repeatedly to the same source words across time steps, causing repetitions in the generated translation, as BIBREF7 observed.
With this in mind, we replace Eq. EQREF5 by INLINEFORM0 , where INLINEFORM1 is a transformation that may depend both on the scores INLINEFORM2 and on upper bounds INLINEFORM3 that limit the amount of attention that each word can receive. We consider three alternatives to softmax, described next.
Since the softmax is strictly positive, it forces all words in the source to receive some probability mass in the resulting attention distribution, which can be wasteful.
Answer: Since the softmax is strictly positive, it forces all words in the source to receive some probability mass in the resulting attention distribution, which can be wasteful.
Question: In what way is the input restructured?
Evidence: Our approach consists of structuring input to the transformer network to use and guide the self-attention of the transformers, conditioning it on the entity. Our main mode of encoding the input, the entity-first method, is shown in Figure FIGREF4. The input sequence begins with a [START] token, then the entity under consideration, then a [SEP] token. After each sentence, a [CLS] token is used to anchor the prediction for that sentence. In this model, the transformer can always observe the entity it should be primarily “attending to” from the standpoint of building representations. We also have an entity-last variant where the entity is primarily observed just before the classification token to condition the [CLS] token's self-attention accordingly. These variants are naturally more computationally-intensive than post-conditioned models, as we need to rerun the transformer for each distinct entity we want to make a prediction for.
As an additional variation, we can either run the transformer once per document with multiple [CLS] tokens (a document-level model as shown in Figure FIGREF4) or specialize the prediction to a single timestep (a sentence-level model). In a sentence level model, we formulate each pair of entity $e$ and process step $t$ as a separate instance for our classification task. Thus, for a process with $T$ steps and $m$ entities we get $T \times m$ input sequences for fine tuning our classification task.
Our approach consists of structuring input to the transformer network to use and guide the self-attention of the transformers, conditioning it on the entity. Our main mode of encoding the input, the entity-first method, is shown in Figure FIGREF4. 
We also have an entity-last variant where the entity is primarily observed just before the classification token to condition the [CLS] token's self-attention accordingly. 
As an additional variation, we can either run the transformer once per document with multiple [CLS] tokens (a document-level model as shown in Figure FIGREF4) or specialize the prediction to a single timestep (a sentence-level model).
Answer: In four entity-centric ways - entity-first, entity-last, document-level and sentence-level