DataHammer/scimrc huggingface dataset:
Question: Given this paragraph about time travel, how practical is it
Evidence: Time travel is the concept of movement between certain points in time, analogous to movement between different points in space by an object or a person, typically with the use of a hypothetical device known as a time machine. Time travel is a widely recognized concept in philosophy and fiction, particularly science fiction. The idea of a time machine was popularized by H. G. Wells' 1895 novel The Time Machine.
Answer: Yes, time travel is indeed a real thing. But it's not quite what you've probably seen in the movies. Under certain conditions, it is possible to experience time passing at a different rate than 1 second per second. And there are important reasons why we need to understand this real-world form of time travel.
Question: How many models are tested?
Evidence: We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task BIBREF0 . We note that two of the systems are supervised and rely on the training set to build their classification models. Document frequency counts are also computed on the training set. Stemming is applied to allow more robust matching. The different keyphrase extraction models are briefly described below:
Each model uses a distinct keyphrase candidate selection method that provides a trade-off between the highest attainable recall and the size of set of candidates. Table summarizes these numbers for each model. Syntax-based selection heuristics, as used by TopicRank and WINGNUS, are better suited to prune candidates that are unlikely to be keyphrases. As for KP-miner, removing infrequent candidates may seem rather blunt, but it turns out to be a simple yet effective pruning method when dealing with long documents. For details on how candidate selection methods affect keyphrase extraction, please refer to BIBREF16 .
Apart from TopicRank that groups similar candidates into topics, the other models do not have any redundancy control mechanism. Yet, recent work has shown that up to 12% of the overall error made by state-of-the-art keyphrase extraction systems were due to redundancy BIBREF6 , BIBREF17 . Therefore as a post-ranking step, we remove redundant keyphrases from the ranked lists generated by all models. A keyphrase is considered redundant if it is included in another keyphrase that is ranked higher in the list.
We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task (Kim et al., 2010).
Answer: Five.
Question: you are riding your bicycle to the store and your bicycle has a basket. which of the following items can you take back home? a toothbrush, a TV, a bar of soap, a pair of pants, a pair of skis, a loaf of bread, a tiger, a phone charger
Answer: you can take back, the tooth brush, the bar of soap, a pair of pants, a loaf of bread and a phone charger
Question: What is the source of the visual data? 
Evidence: The recent advancements in deep neural networks, specifically for image analysis task, can lead to determining demographic features such as age and gender BIBREF13 . We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.
We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.
Answer: Profile pictures from the Twitter users' profiles.
Question: Who is the author of the most relevant related work?
Evidence: Propaganda has been tackled mostly at the article level. BIBREF3 created a corpus of news articles labelled as propaganda, trusted, hoax, or satire. BIBREF4 experimented with a binarized version of that corpus: propaganda vs. the other three categories. BIBREF5 annotated a large binary corpus of propagandist vs. non-propagandist articles and proposed a feature-based system for discriminating between them. In all these cases, the labels were obtained using distant supervision, assuming that all articles from a given news outlet share the label of that outlet, which inevitably introduces noise BIBREF6.
A related field is that of computational argumentation which, among others, deals with some logical fallacies related to propaganda. BIBREF7 presented a corpus of Web forum discussions with instances of ad hominem fallacy. BIBREF8, BIBREF9 introduced Argotario, a game to educate people to recognize and create fallacies, a by-product of which is a corpus with $1.3k$ arguments annotated with five fallacies such as ad hominem, red herring and irrelevant authority, which directly relate to propaganda.
Unlike BIBREF8, BIBREF9, BIBREF7, our corpus uses 18 techniques annotated on the same set of news articles. Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 BIBREF10 and describes a corpus that is a subset of the one used for this shared task.
The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 (Da San Martino et al., 2019) and describes a corpus that is a subset of the one used for this shared task.
Answer: The authors themselves.
Question: How many parameters does the model have?
Evidence: Instead of reading in the tokenized input text, our model reads raw utf-8 bytes. For English text in the ASCII range, this is equivalent to processing characters as individual tokens. Non-ASCII characters (e.g. accented characters, or non-Latin scripts) are typically two or three utf-8 bytes. We use a standard “transformer decoder” (a stack of transformer layers with a causal attention mask) to process the sequence $x_{0:i-1}$ and predict the following byte $x_i$. The model's prediction is an estimate of the probability distribution over all possible 256 byte values. Our input byte embedding matrix has dimensionality 256. Our byte-level transformer model has 40 standard transformer layers with hidden size 1024, filter size 8192, and 16 heads. The model has around 836M parameters, of which only 66K are byte embeddings.
The model has around 836M parameters, of which only 66K are byte embeddings.
Answer: The answers are shown as follows:
* model has around 836M parameters
Question: From the passage provided, summarise the key phases of sunset.
Evidence: Sunset, also known as sundown, is the daily disappearance of the Sun below the horizon due to Earth's rotation. As viewed from everywhere on Earth (except the North and South poles), the equinox Sun sets due west at the moment of both the spring and autumn equinoxes. As viewed from the Northern Hemisphere, the Sun sets to the northwest (or not at all) in the spring and summer, and to the southwest in the autumn and winter; these seasons are reversed for the Southern Hemisphere.
The time of sunset is defined in astronomy as the moment when the upper limb of the Sun disappears below the horizon. Near the horizon, atmospheric refraction causes sunlight rays to be distorted to such an extent that geometrically the solar disk is already about one diameter below the horizon when a sunset is observed.
Sunset is distinct from twilight, which is divided into three stages. The first one is civil twilight, which begins once the Sun has disappeared below the horizon, and continues until it descends to 6 degrees below the horizon. The second phase is nautical twilight, between 6 and 12 degrees below the horizon. The third phase is astronomical twilight, which is the period when the Sun is between 12 and 18 degrees below the horizon. Dusk is at the very end of astronomical twilight, and is the darkest moment of twilight just before night. Finally, night occurs when the Sun reaches 18 degrees below the horizon and no longer illuminates the sky.
Locations further north than the Arctic Circle and further south than the Antarctic Circle experience no full sunset or sunrise on at least one day of the year, when the polar day or the polar night persists continuously for 24 hours.
Answer: Sunset is divided into three stages. The first is twilight, which begins once the Sun has disappears below the horizon, and continues until it descends to 6 degrees below the horizon. The second phase is nautical twilight, between 6 and 12 degrees below the horizon. The third phase is astronomical twilight, which is the period when the Sun is between 12 and 18 degrees below the horizon.
Question: What is Sir Charles Lyell best known for?
Evidence: Sir Charles Lyell, 1st Baronet, FRS (14 November 1797 – 22 February 1875) was a Scottish geologist who demonstrated the power of known natural causes in explaining the earth's history. He is best known as the author of Principles of Geology (1830–33), which presented to a wide public audience the idea that the earth was shaped by the same natural processes still in operation today, operating at similar intensities. The philosopher William Whewell termed this gradualistic view "uniformitarianism" and contrasted it with catastrophism, which had been championed by Georges Cuvier and was better accepted in Europe. The combination of evidence and eloquence in Principles convinced a wide range of readers of the significance of "deep time" for understanding the earth and environment.
Answer: Sir Charles Lyell is best known as the author of Principles of Geology (1830-1833)
Question: Tell me few brainstorming techniques
Answer: Mind-Mapping - is where you write down a central idea or problem and then create branches of related idea around it. You can add sub-branches and idea to each branch, creating a visual map of ideas and connections

Free-Writing - is where you set a timer for a specific amount of time and then write down idea that comes to mind, without stopping to edit or censor. Goal is to generate as many ideas as possible in a shortest amount of time

Role-Playing - is where you assign different roles or personas to participants in the session. Example, one person can play role of a customer, another of a competitor and so on. This help participants to see things in different perspective and generate new ideas
Question: How many layers do the authors use?
Evidence: MLP contains three layers - an input layer, an output layer and some hidden layers. Input layer receives the signal, the output layer gives a decision or prediction about the input and the computation of the MLP is conducted in the hidden layers. In our system, we use 100 layers. For weight optimization, we use Limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization algorithm.
SVM gives an optimal hyper-plane and it maximizes the margin between classes. We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped. For decision function shape, we use the original one-vs-one (ovo) decision function.
NBC is based on Bayes' Theorem which gives probability of an event occurrence based on some conditions related to that event. We use Multinomial Naive Bayes Classifier with smoothing parameter equals to 0.1. A zero probability cancels the effects of all the other probabilities.
Stochastic gradient descent optimizes an objective function with suitable smoothness properties BIBREF27. It selects few examples randomly instead of whole data for each iteration. We use 'L2' regularization for reduction of overfitting.
Gradient Boosting Classifier produces a prediction model consisting of weak prediction models. Gradient boosting uses decision trees. We use 100 boosting stages in this work.
K-NN is a supervised classification and regression algorithm. It uses the neighbours of the given sample to identify its class. K determines the number of neighbours needed to be considered. We set the value of K equals to 13 in this work.
RF is an ensemble learning technique. It constructs large number of decision trees during training and then predicts the majority class. We use 500 decision trees in the forest and "entropy" function to measure the quality of a split.
In our system, we use 100 layers.
Answer: 100 layers.
Question: How is the annotation experiment evaluated?
Evidence: We find that Cohen $\kappa $ agreement ranges from .84 for Uneasiness in the English data, .81 for Humor and Nostalgia, down to German Suspense (.65), Awe/Sublime (.61) and Vitality for both languages (.50 English, .63 German). Both annotators have a similar emotion frequency profile, where the ranking is almost identical, especially for German. However, for English, Annotator 2 annotates more Vitality than Uneasiness. Figure FIGREF18 shows the confusion matrices of labels between annotators as heatmaps. Notably, Beauty/Joy and Sadness are confused across annotators more often than other labels. This is topical for poetry, and therefore not surprising: One might argue that the beauty of beings and situations is only beautiful because it is not enduring and therefore not to divorce from the sadness of the vanishing of beauty BIBREF48. We also find considerable confusion of Sadness with Awe/Sublime and Vitality, while the latter is also regularly confused with Beauty/Joy.
Figure FIGREF18 shows the confusion matrices of labels between annotators as heatmaps.
Answer: The answers are shown as follows:
* confusion matrices of labels between annotators
Question: Help summarize Dallas Cowboys
Evidence: The Dallas Cowboys are a professional American football team based in the Dallas–Fort Worth metroplex. The Cowboys compete in the National Football League (NFL) as a member club of the league's National Football Conference (NFC) East division. The team is headquartered in Frisco, Texas, and has been played its home games at AT&T Stadium in Arlington, Texas, since its opening in 2009. The stadium took its current name prior to the 2013 season. In January 2020, Mike McCarthy was hired as head coach of the Cowboys. He is the ninth in the team’s history. McCarthy follows Jason Garrett, who coached the team from 2010–2019.
Answer: The Cowboys joined the NFL as an expansion team in 1960. The team's national following might best be represented by its NFL record of consecutive sell-outs. The Cowboys' streak of 190 consecutive sold-out regular and post-season games (home and away) began in 2002. The franchise has made it to the Super Bowl eight times, tying it with the Pittsburgh Steelers and Denver Broncos for second-most Super Bowl appearances in history behind the New England Patriots' record 11 appearances. The Cowboys have won eight NFC championships, the most in the conference's history. The Cowboys are the only NFL team to record 20 straight winning seasons (from 1966 to 1985) during which they missed the playoffs only twice (1974 and 1984).

In 2015, the Dallas Cowboys became the first sports team to be valued at $4 billion, making it the most valuable sports team in the world, according to Forbes. The Cowboys also generated $620 million in revenue in 2014, a record for a U.S. sports team. In 2018, they also became the first NFL franchise to be valued at $5 billion and making Forbes' list as the most valued NFL team for the 12th straight year.
Question: What is the Shy Plant?
Answer: The scientific name of the shy plant also known as the shame plant is Mimosa pudica, which means shy, bashful, and sensitive. The structures of the shy plant are interesting; it has an herbaceous stem. As well as a dicot on a monocot stem. It has beautiful flowers and leaves that are green, and they are thigmotropism negative. When the shy plant's leaves are touched, they curl up. The Shy plant needs direct sunlight, and 70 to 80 degrees indoors is a good place for it. When the need to water comes, the soil must be 50% dry. The medical value of the Shy plant is great. It helps with Hemorrhoids, infections, sores, and sinuses.
Question: What kind of newspaper is the Secolo d'Italia
Evidence: Secolo d'Italia ([ˈsɛːkolo diˈtaːlja]; "Century of Italy") is a daily, conservative, online newspaper in Italy, published since 1952, formerly supporting neo-fascism. In 2012, it ceased its print edition and continued as an online-only publication.
Answer: The Secolo d'Italia, Century of Italy, is a daily, conservative, online newspaper in Italy, published since 1952, formerly supporting neo-fascism. In 2012, it ceased its print edition and continued as an online-only publication.
Question: How to reverse Type 2 diabetes naturally?
Answer: Intermittent fasting is recently been found to be very effective in reversing Type 2 diabetes 
Especially healthy eating habits during in eating window and healthy lifestyle accelerates the time taken to reverse the Type 2 diabetes i.e blood sugar levels comes to normal range.
There’s a lot of research going on in this space, lots of people are seeing the results. Doctors and dietician are finding this revolutionary and out of the standard medical procedure of treating Type 2 diabetes with drugs like Metformin.”
Low carb high protein and high fat diet, keto diet, right exercise routine have found to improve the results. 
The effect of this approach depends on various factors like Age, current food habits and health lifestyle, severity of the condition, discipline, will power and lots more
Question: How do you make soap?
Answer: Soap is made with a mixture of natural or organic oils, sodium hydroxide and distilled water.  When making soap you need to mix a combination of light and heavy oils like olive and coconut oil and then mix it with sodium hydroxide and distilled water.  After this mixture has been mixed well you poor it into soap molds and let it hard over the course of many weeks.  This process called saponification is the time it takes for the sodium hydroxide to evaporate from the mixture which causes the soap to harden in he soap molds.  Other options you have when making soap are to add essential oils, like lavender and grapefruit to give a nice scent to the soap.  This has to be added prior to saponification.
Question: What are all the dates that the Borneo visited Bencoolen written in the format Month Day Year?
Evidence: EIC voyage #1 (1713–1716): Captain Thomas Lewis sailed from The Downs on 2 October 1713, bound for Banjarmasin and Bencoolen. Borneo was at the Cape of Good Hope on 30 January 1714 and reached Batavia on 7 May. She was at Banjarmasin on 1 July and Bencoolen on 26 December. She was then at Bantal on 23 January 1715 before returning to Bencoolen on 9 February. She returned to Bantal on 4 March and Bencoolen on 29 March. She then was at Batavia on 13 June and Bencoolen yet again on 24 August. She stopped at Madras on 21 February 1716, and visited Bencoolen for the last time on 30 May. Her voyage ended on 1 August 1717.
Answer: December 26th 1714, February 9th 1715, March 29th 1715, August 24th 1715, May 30th 1716.
Question: Given this paragraph about muscle strains and that my symptoms include severe and acute pain with movement limitations, how bad is my strain?
Evidence: A strain can occur as a result of improper body mechanics with any activity (e.g., contact sports, lifting heavy objects) that can induce mechanical trauma or injury. Generally, the muscle or tendon overstretches and is placed under more physical stress than it can withstand. Strains commonly result in a partial or complete tear of a tendon or muscle, or they can be severe in the form of a complete tendon rupture. Strains most commonly occur in the foot, leg, or back. Acute strains are more closely associated with recent mechanical trauma or injury. Chronic strains typically result from repetitive movement of the muscles and tendons over a long period of time.

Degrees of Injury (as classified by the American College of Sports Medicine):

First degree (mildest) – little tissue tearing; mild tenderness; pain with full range of motion.
Second degree – torn muscle or tendon tissues; painful, limited motion; possibly some swelling or depression at the spot of the injury.
Third degree (most severe) – limited or no movement; severe acute pain, though sometimes painless straight after the initial injury
Answer: You likely have a Grade 3 strain, often considered amongst the most severe types
Question: Why does my chicken create so much liquid when cooked in the skillet?
Answer: Once you dice up your chicken and pop it into the skillet, try to leave it undisturbed for 3-4 minutes while it cooks. Once browned on that side, flip it and do not disturb it again until the other side is browned. This will help reduce the amount of liquid created in your skillet. If you do end up with too much liquid, and you want to reduce it due to seasonings and flavor that may be present, you can take the chicken out and put it on a plate. This will allow you not to overcook the chicken but also reduce the liquid left in the skillet and intensify the flavor that is left.
Question: How do I decline an invitation to my Aunt's Birthday party?
Answer: Dear Aunt Ruth,

I was so excited when I received the invitation to your birthday party next Saturday.  Celebrating you and your birthday is so important to me.  You have always been one of those people in my life that has cheered me on.  I've always know I have an ally in my corner with you in my life.

My excitement turned to sadness when I tried to find a replacement at work for next Saturday.  No one was able to cover for me.  I will have to miss being with you physically.  Count on me joining you with my heart!  I will be wishing you an awesome birthday and hoping for a year full of happiness and joy.

I love you Aunt Ruth.  Thank you for the invitation.  Please don't forget to invite me next year!  I can wait for the next time we see each other.

Love,
Your Nephew
Question: What are the primary causes of inflation?
Answer: In an economy with too much money (the money supply), the currency value decreases when the total amount of money increases.
Prices of goods/services increase (price inflation) when the money supply increases (monetary inflation) over a longer period of time.
Economists disagree on the causes over shorter periods of time.