DataHammer/scimrc huggingface dataset:
Question: Who is Harry Potter?
Answer: Harry Potter is a series of seven fantasy novels written by British author J. K. Rowling. The novels chronicle the lives of a young wizard, Harry Potter, and his friends Hermione Granger and Ron Weasley, all of whom are students at Hogwarts School of Witchcraft and Wizardry. The main story arc concerns Harry's conflict with Lord Voldemort, a dark wizard who intends to become immortal, overthrow the wizard governing body known as the Ministry of Magic and subjugate all wizards and Muggles (non-magical people). 
The series follows the life of a boy named Harry Potter. In the first book, Harry Potter and the Philosopher's Stone, Harry lives in a cupboard under the stairs in the house of the Dursleys, his aunt, uncle and cousin, Dudley. The Dursleys consider themselves perfectly normal, but at the age of 11, Harry discovers that he is a wizard. He meets a half-giant named Hagrid who invites him to attend the Hogwarts School of Witchcraft and Wizardry. Harry learns that as a baby, his parents were murdered by the dark wizard Lord Voldemort. When Voldemort attempted to kill Harry, his curse rebounded and Harry survived with a lightning-shaped scar on his forehead.
Harry becomes a student at Hogwarts and is sorted into Gryffindor House. He gains the friendship of Ron Weasley, a member of a large but poor wizarding family, and Hermione Granger, a witch of non-magical, or Muggle, parentage. Harry encounters the school's potions master, Severus Snape, who displays a dislike for him; the rich pure-blood Draco Malfoy whom he develops an enmity with; and the Defence Against the Dark Arts teacher, Quirinus Quirrell, who turns out to be allied with Lord Voldemort. The first book concludes with Harry's confrontation with Voldemort, who, in his quest to regain a body, yearns to gain the power of the Philosopher's Stone, a substance that bestows everlasting life.
Question: With reference to given text, Please provide a list of dangers associated with Smoking
Evidence: Smoking is one of the leading preventable causes of deaths globally and is the cause of over 8 million deaths annually, 1.2 million of which are non-smokers who die due to second-hand smoke.In the United States, about 500,000 deaths per year are attributed to smoking-related diseases and a recent study estimated that as much as 1/3 of China's male population will have significantly shortened life-spans due to smoking. Male and female smokers lose an average of 13.2 and 14.5 years of life, respectively. At least half of all lifelong smokers die earlier as a result of smoking.
Answer: 1. Smoking is the cause of more than 8 million deaths per year out of which 1.2 million are non-smokers who simply dies because of inhaling second-hand smoke.
2. In United states nearly 500,000 people dies annually because of smoking related diseases.
3. A recent study indicates that close to 1/3rd of China's male population have their life-spans shortened significantly because of smoking.
4. Among smokers males have average of 13.2 years less life span compared to non smoking males similarly females have 14.5 years less life span compared to non smoking females.
5 At least 50% of smokers die earlier because of smoking.
Question: What does the paper explore?
Evidence: Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting.
In this paper, we systematically explore zeroshot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus.
Answer: The cross-lingual transferring ability of multi-BERT on reading comprehension (RC) tasks.
Question: Classify each of the following among a country, a continent, both, none:  america, north america, australia, london, india, china
Answer: america : none, north america : continent, australia: both, london: none, india : country, china : country, nile : none, vatican city : country, europe : continent, arctic : none
Question: Why is the sky blue?
Answer: Blue light travels in shorter waves, thus it it scattered more than any other color
Question: Name some NHL hockey players that were born in Arizona.
Answer: Matthew Tkachuk; Sean Couturier; Brady Tkachuk; Tage Thompson; Jesse Ylönen
Question: What are some ways to improve the value of your home?
Answer: There are several things you do can to increase the value of your home. First, you can focus on what is known as "curb appeal", essentially making the outside and surrounding of your house attractive. Ensure that the landscaping is well maintained, fix any broken doors or windows, and painting the outside so that the house does not look dull. In the interior of your house, the best places to focus will be in your bathrooms and kitchen. Further, you can replace major appliances and components such as water heaters, furnaces, or the roof to increase the value. Another way to increase the value of your home is by making it bigger. Often, pricing for homes is done by looking at the area of the house, so by adding on new rooms or extra space, you can also increase the overall value of the house.
Question: Why mobile is bad for human
Answer: We are always engaged one phone which is not good.
Question: What is the most common error type?
Evidence: We also looked at where there was complete agreement by all annotators that a triple extraction was incorrect. In total there were 138 of these triples originating from 76 unique sentences. There were several patterns that appeared in these sentences.
We also looked at where there was complete agreement by all annotators that a triple extraction was incorrect. In total there were 138 of these triples originating from 76 unique sentences. 
Answer: The answers are shown as follows:
* all annotators that a triple extraction was incorrect
Question: What dataset do they use?
Evidence: The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type. To make this diversity, after cleaning raw text input, we use multiple types of word tokenizers. Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7. Each sentence has a set of words corresponding to a set of word vectors, and that set of word vectors is a representation of a sentence. We also make a sentence embedding by using RoBERTa architecture BIBREF8. CBOW and RoBERTa models trained on text from some resources including VLSP 2016 Sentiment Analysis, VLSP 2018 Sentiment Analysis, VLSP 2019 HSD and text crawled from Facebook. After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. With the multiply output results, we will use an ensemble method to combine them and output the final result. Ensemble method we use here is Stacking method will be introduced in the section SECREF16.
The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. To make model being able to learn with this imbalance data, we inject class weight to the loss function with the corresponding ratio (clean, offensive, hate) is $(0.09, 0.95, 0.96)$. Formular DISPLAY_FORM17 is the loss function apply for all models in our system. $w_i$ is the class weight, $y_i$ is the ground truth and $\hat{y}_i$ is the output of the model. If the class weight is not set, we find that model cannot adjust parameters. The model tends to output all clean classes.
Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7
The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%.
Answer: They used Wiki Vietnamese language and Vietnamese newspapers to pretrain embeddings and dataset provided in HSD task to train model (details not mentioned in paper).
Question: Explain the expression "the days are long, and the years are short" as it relates to raising children.
Answer: Every day you're exhausted and pushed to the limits.  This can be from lack of sleep, because your child is waking up every few hours.  Or it can be because whatever it is you're asking of your child,  they don't want to do it.  Or it can be because the strain is affecting your own ability to be kind and loving to both yourself, your spouse, and your children.  Whatever the reasons,  the feeling at the end of every night is one of utter exhaustion, where presumably this feeling is now your life, and will be so forever.  But, in that exhaustion, it can be hard to either notice or truly appreciate beautiful little moments simultaneously occurring. For example, you might not pause to appreciate when they laugh at something you say or funny face you make.  Or maybe you miss the beauty in witnessing their frustration and struggle as they persistently try to fit that round peg in the square hole.  Or maybe the way they mispronounce certain words sounds so endearing, though you're more focused on helping them pronounce it correctly.  These are all brief moments where you are witnessing the growth of your child.  These moments are fleeting.  They don't exist forever, because eventually they grow out of them.  They don't find the same jokes or silly faces funny, they figure out the round peg fits in the round hole, and their word pronunciation improves.  And when you notice those are past, you feel nostalgic.  You wish you could relive those moments.  In that nostalgia, you forget about all the exhaustion that came with the territory.  Maybe the expression is best described as analogous to another expression "the grass is greener on the other side"
Question: Should I pursue a PhD in STEM?
Answer: Whether or not to pursue a PhD will depend on a number of factors. These days, many professions require more and more certification and proof of education. However, a PhD is usually only require in the STEM field for positions that are either focussed on research, such as a research scientist position, or on a specialized field, such as many 'wet labs' in biological research, or more specialized knowledge jobs. Other than these roles, a Master's or other accreditation like professional certificates/MBAs etc. can be used for career advancement. For those interested in an academic career, a PhD is still the fundamental requirement. 

If you do choose to do a PhD, where you do a PhD will influence the amount and type of work that you do. For example, in the EU, the UK, Australia, and some other places, a PhD is usually three years of research with no or minimal extra course work. In the US, though, a PhD typically takes over 4 years and requires the student to take several classes at a graduate level. 
While in your program, you will need to spend a lot of time focusing on publication pressures. PhD students, these days, are required to produced 1-3 papers during their PhD in high impact journals. In many institutions, this is a high stress environment and can lead to increased mental health stress. If you are passionate and build a strong social structure, this can be an enjoyable experience, albeit a trying one.
Question: Does extended screen time cause speech delay in children?
Evidence: The many other causes of speech delay include bilingual children with phonological disorders, autism spectrum conditions, childhood apraxia, auditory processing disorder, prematurity, cognitive impairment and hearing loss. In addition, when children are addicted to screens, they aren't stimulated to be involved in conversations, causing speech delays.[unreliable source?] Broomfield and Dodd's (2004a)[full citation needed] found out after survey that 6.4% of children who are perfectly normal showed speech difficulty while they lacked these disorders will often show early signs and are at times identified as "at risk" when the speech delay is diagnosed. However, a study done in Saudi Arabia showed no relationship between smart device use and speech delay, although 64.8% of the parents believed that the smart devices are a "problem". A review study from Indonesia points out the existence of contradicting results on that issue.
Answer: There is no definite proof that extended screen time cause speech delay in children because there are contradicting study results.
Question: Tell me whether these cities are in Texas: Austin, Houston, New York, Chicago, Miami, Dallas
Answer: Austin, Houston, Dallas
Question: What are some common data warehouse challenges?
Answer: Common data warehouses can have slow query performance especially when all the data operations happen on the same clusters.  Also, many data warehouses have closed architectures which limits choices and drives up egress fees to use the data. Additionally,  data warehouses aren’t built to handle real-time analytics which usually require the ability to handle small writes. Lastly, data warehouses often duplicate the storage of data which balloons costs and hinders the ability to have a single source of truth.
Question: Given a reference text about Patrick James Mara, tell me what he was known for.
Evidence: Patrick James Mara (1 February 1942 – 15 January 2016) was an Irish public affairs consultant and politician who served as Senator from July 1981 to October 1981 and May 1982 to December 1982. He was most renowned for being the political adviser to former Taoiseach Charles Haughey. He also served as Press Secretary to Fianna Fáil from 1983 to 1987 and Government Press Secretary from 1987 to 1992.

He was appointed the Fianna Fáil Director of Elections for the 1997, 2002, and 2007 general elections. He was a member of the board of Digicel, the company founded by Denis O'Brien to build mobile networks in the Caribbean.

He was regularly lampooned on the satirical Raidió Teilifís Éireann (RTÉ) radio sketch show Scrap Saturday, which ran from 1989 to 1991.
Answer: Patrick James Mara was most known for being a political adviser to Taoiseach Charles Haughey and serving as Press Secretary and Government Press Secretary.
Question: What is the best way to get to Philadelphia from Downingtown if you do not have a car?
Answer: The first thing you would do would be to look up where the Downingtown train station is in relation to where you are. Find the train schedule and allow the appropriate time to walk to the train station. Board the SEPTA train and ride from there into Philadelphia.
Question: Why is it good to fly direct?
Answer: When flying direct to a location, you cut down on potential negative variable while traveling. If you have a layover, there is the chance you might miss you flight if your first flight is delayed. Additionally, if you check your luggage there is the chance it is lost between flights. This doesn't always happen but the chance of it happening is there when you book a one or two stop flight. Finally, it is always faster if you are able to fly to a destination directly
Question: How do I save for retirement?
Answer: First thing is to make a plan and the key is to start saving today no matter how small that amount might be.  You will be refining this plan as you move forward to achieving your financial goals.  You should always "Pay Yourself" first by allocating a percentage of your pay checks into a company 401K retirement plan or an IRA or Roth IRA.  A certain portion of your monthly pay will be pulled out tax free and put into a retirement savings account.  You should consult with a tax professional to understand the tax implications.  Also, you can work with a professional planner to help create a comprehensive long term plan.  This retirement plan will take into account your target financial goals, desired annual spend, investments, social security, pensions, and other financial sources.
Question: What is the 80/20 rule?
Answer: The 80/20 rule can be applied in many contexts. Casually, it may refer to the observation that often 20% of the effort yields 80% of the benefit and the corollary that the last 20% of the benefit requires 80% of the effort. The 80/20 rule also describes a property that is empirically true of the frequency distribution of many sets, such as 20% of unique words accounting for 80% of words in a book or 20% of companies in a stock portfolio accounting for 80% of the return.
Question: What makes a good teacher?
Answer: Throughout history ‘what makes a good teacher?’ has constantly been questioned by both parents and children. Many consider a good teacher to be one who is caring, helpful, yet also strict when necessary. 
 One book that focuses on what makes a good teacher is Our day out, by Willy Russel. The book provided people in the 1970s with a perspective on what both children and parents were looking for, inspiring many British teachers. According to Russel, a good teacher cares for the learning of their students and demonstrates it. If a teacher does not demonstrate caring behavior, their students will sense it. Carol, one of the students says to Mr.Briggs “Don't lie, you! I know you hate me. I've seen you goin' home in your car, passin' us on the street. You hate all the kids.” (Russell 48). Carol believes that Mr. Briggs does not care about his students, even though he does as shown earlier in the book: “Briggs: What? About you? Listen ... if I didn't care, why am I here, now, trying to stop you doing something stupid.” (Russell 48). Mr Briggs believes that caring for students implies also disciplining them at times. “Listen, I'm not going to spend any more time arguing with you. You may have organized this visit, but I'm the one who's been sent by the Headmaster to supervise. Now, either you take control of the children in your charge or I'll be forced to abandon this visit and order everyone home.” Thus, even if a teacher cares about their students, they should demonstrate it. Opinions from students of different ages will help further illustrate the image of a good teacher. 
Many people think that a good teacher is primarily kind and loving.  However, through multiple interviews it appeared to be only partially true. A good teacher, according to most students, is not overly kind and should apply discipline when needed. For example, 12 year old Monon Mukherjee said that he looks for guidance and ability to teach in a teacher. On the other hand, 40 year old Bilal Aslam looks for discipline and ability to teach. 42 year old Cristian Molina also agrees with the need for discipline, however he also expects teachers to feel an urge to help students no matter who they are. The book and the interviews intersect in many ways. They both highlight the importance of kindness, caring for learning, and teacher’s ability to discipline. However, they also differ in important ways. In the book, teachers were encouraged to be kind and apply discipline when necessary, but through the interviews it has been shown that providing help and guidance are also expected in a good teacher.
In conclusion, a good teacher cares about their students' learning, is prepared to help them, yet also disciplines them when necessary. A good teacher should feel an urge to teach students and guide them through the complex path of life.
Question: Classify each of the following as either pasta or bread: casarecce, gnocco fritto, gnocchi, focaccia, grissini, ciabatta, tonnarelli, orecchiette, conchiglie, piadina
Answer: Pasta = casarecce, gnocchi, tonnarelli, orecchiette, conchiglie
Bread = gnocco fritto, focaccia, grissini, ciabatta, piadina
Question: Is my understanding correct that newly collected samples are added to both the online and offline buffers?
Evidence: Next, we introduce a simple but effective online-offline replay buffer (OORB) to distinguish between near-on-policy online interaction data, and the offline data. OORB consists of two replay buffers.
One is the online buffer that collects the online interaction data. Besides, to ensure the data in the online buffer is near-on-policy, we set it to be very small, and fresh online interaction data are stored into it by following a first-in-first-out rule. The other is the offline buffer consisting of the newly generated online interaction data and the offline dataset which may come from any policies.
Data are sampled from OORB following a Bernoulli distribution, which means that with a probability p, they are sampled from the online buffer, and with probability 1 − p, they are sampled from the offline buffer. To benefit from both online and offline data in a balanced way, we empirically set p to 0.5, and its effect on the final performance is further tested via ablation studies in Section 5.4. Results show that p = 0.5 works best overall, which confirms our claim that offline data and online interaction data are both crucial for policy learning.
Next, we introduce a simple but effective online-offline replay buffer (OORB) to distinguish between near-on-policy online interaction data, and the offline data. OORB consists of two replay buffers. One is the online buffer that collects the online interaction data. Besides, to ensure the data in the online buffer is near-on-policy, we set it to be very small, and fresh online interaction data are stored into it by following a first-in-first-out rule. The other is the offline buffer consisting of the newly generated online interaction data and the offline dataset which may come from any policies. Data are sampled from OORB following a Bernoulli distribution, which means that with a probability p, they are sampled from the online buffer, and with probability 1 − p, they are sampled from the offline buffer. To benefit from both online and offline data in a balanced way, we empirically set p to 0.5, and its effect on the final performance is further tested via ablation studies in Section 5.4. Results show that p = 0.5 works best overall, which confirms our claim that offline data and online interaction data are both crucial for policy learning
Answer: Yes. As we explained in section 4.3, the online buffer store near-on-policy data for stabilizing the learning process. That is why we need to keep it relatively small size. By contrast, the offline buffers store all samples.
Question: How did Robert Baratheon die?
Answer: King Robert I Baratheon was on a boar hunting trip when he was gored by a boar. Lancel Lannister, one of his squires, provided the king with fortified wine, to ensure the king was overly drunk and would make a mistake. Had he not died from the boar, the king would have been deliberately knocked off his horse or shot with an arrow, according to Lord Varys. The king's death was arranged by his wife, Cersei Lannister, so he would not learn the truth about who actually sired his children he shared with Cersei.
Question: Classify each of the following as amphibian or mammal. Dog, Toad, Salamander, Elephant, Frog, Bear
Answer: Mammals are Dog, Elephant, Bear. Amphibians are Toad, Salamander, Frog.
Question: What are good female celebrity costumes for an 80s party?
Answer: There are many amazing 80s female costume options to choose from. See the list below for a few top choices:

- Madonna
- Jem and the Holograms
- Pamela Anderson
- Punky Brewster
- Lady Diana
- Alex Owens from Flashdance
- The Golden Girls
- She-Ra
- Tina Turner