DataHammer/scimrc huggingface dataset:
Question: Based on this paragraph on Japanese bullet trains, how many cars do the longest trains have?
Evidence: Trains are up to sixteen cars long. With each car measuring 25 m (82 ft) in length, the longest trains are 400 m (1⁄4 mile) end to end. Stations are similarly long to accommodate these trains. Some of Japan's high-speed maglev trains are considered Shinkansen, while other slower maglev trains (such as the Linimo maglev train line serving local community near the city of Nagoya in Aichi, Japan) are intended as alternatives to conventional urban rapid transit systems.
https://en.wikipedia.org/wiki/Shinkansen
Answer: Based on this paragraph, the longest Japanese bullets trains have 16 cars, which is determined by dividing the length of the longes trains (400m) by the length of each car (25m).
Question: Tell me whether those are countries or cities: Paris, Amsterdam, South Africa, Australia, Vienna, Singapore, Malaysia, Brunei, Argentina
Answer: Paris, Amsterdam and Vienna are cities. South Africa, Australia, Malaysia, Brunei and Argentina are countries. Singapore is actually both a city and a country.
Question: The expression in Eq. (1) is the Lagrangian. Is the dual problem stated somewhere ?
Evidence: A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem may be linearly separable with an appropriate kernel function. Next we give the formal definition of kernel machines discussed in this paper. Given a training data set {X, y} of n training instances where {X ∈ R n×d , y ∈ R n } = {(x 1 , y 1 ), (x 2 , y 2 ), ..., (x n , y n )}, and (x i , y i ) denotes the instance x i ∈ R d with its label y i , the objective of the kernel machine training is to find an optimal ω * which minimizes the structural risk as follows.
min
where λ denotes the regularization constant and f (ω, x i ) = ω, φ(x i ) . The variable ω is defined on the reproducing kernel Hilbert space (RKHS) and •, • is the inner product on the RKHS. The function φ(•) maps the instances from their original data space to a higher dimensional feature space induced by the kernel function. Assume the loss l(•, •) is an affine function of ω. The representer theorem shows that a minimizer of the optimization problem ()
where k(x i , x j ) denotes a positive definite kernel function and k(x i , x j ) = φ(x i ), φ(x j ) . By substituting the expressions of f (ω, x i ) and ω into the Equation (1), we have the objective with respect to α below.
where α = [α 1 . . . α n ] T is an n-dimension vector, each dimension of which corresponds to the contribution of a training instance to the kernel machine.
Then, we can derive that the Hessian matrix H = [H ij ] n×n of Problem () is equal to the kernel matrix. The element in the i-th row and j-th column of the matrix H is H ij = k(x i , x j ). The derivations of the first and second derivatives can be found in the supplementary material. For clarity, we use kernel matrix H to denote the Hessian matrix of the kernel machine in the rest of the paper.
Eq(1)
Answer: We've reorganized Section 2, and now Eq. (1) is in primal form.
Question: Given a reference text about Joseph Charlton, what college football team did he play for?
Evidence: Joseph Charlton (born April 7, 1997) is an American football punter who is a free agent. He played college football at South Carolina.

College career
Charlton was a member of the South Carolina Gamecocks for five seasons, redshirting as a true freshman. He was named South Carolina's starting punter going into his redshirt sophomore year. As a redshirt junior, Charlton set a new school record by averaging 44.8 yards per punt and was named second-team All-Southeastern Conference (SEC) by the league's coaches. He broke his own record in his senior season by averaging 47.7 yards per punt and was named second-team All-SEC and was an honorable mention All-American by Pro Football Focus. Charlton finished his collegiate career averaging 45.5 yards per punt, a school record.
Answer: Joseph Charlton played for the South Carolina Gamecocks.
Question: Make an argument for making buses free to ride in cities.
Answer: Making buses free to ride would increase ridership. This benefits cities by reducing cars on the road, reducing traffic congestion and pollution. People who rely on buses often have lower income, and not charging bus fare would be disproportionately beneficial to them. Not charging a fare also avoids the need to maintain payment infrastructure on buses.
Question: What are the major contents of machine comprehension datasets?
Evidence: Machine comprehension datasets consist of three main components: texts, questions and answers. In this section, we describe our data collection for these 3 components. We first describe a series of pilot studies that we conducted in order to collect commonsense inference questions (Section SECREF4 ). In Section SECREF5 , we discuss the resulting data collection of questions, texts and answers via crowdsourcing on Amazon Mechanical Turk (henceforth MTurk). Section SECREF17 gives information about some necessary postprocessing steps and the dataset validation. Lastly, Section SECREF19 gives statistics about the final dataset.
Machine comprehension datasets consist of three main components: texts, questions and answers.
Answer: Machine comprehension datasets consist of three main components: texts, questions and answers.
Question: Steps to create a chatbot
Answer: Choose the right purpose for Chatbot
Decide the platform where you want it to appear
Design a chatbot conversation on a editor
test and train your chatbot
Collect feedback on your chatbot
Question: What does the theme songs of Jaws, Star Wars, and Harry Potter have in common?
Answer: They were all composed by John Walker who reignited the use of classical soundtracks in films. He drew inspiration from previous symphonic composers of the earlier 19th century like Richard Wagner. For example Star Wars theme song was based on his Ring cycle.
Question: Who is Shigeru
Answer: Shigeru is a famous video game developer. He is from Japan and has designed popular iconic games such as Donkey Kong, Mario Kart, Legend of Zelda...etc.
Question: What is sbt in Scala?
Answer: sbt is an open-source build tool for Scala and Java projects, similar to Apache's Maven and Gradle.
Question: tell me whether these are synonyms or antonyms of love: dislike, care, like, hate, affection, harsh
Answer: Synonyms: care, like, affection
Antonyms: dislike, hate, harsh
Question: What are the results achieved from the introduced method?
Evidence: FLOAT SELECTED: Table 1: Comparison of different models.
FLOAT SELECTED: Table 1: Comparison of different models.
Answer: Their model resulted in values of 0.476, 0.672 and 0.893 for recall at position 1,2 and 5 respectively in 10 candidates.
Question: Who is the best soccer player in the world?
Answer: The best player in the world as of 2023 is Lionel Messi. He is also widely regarded as the greatest player to ever grace the game.
Question: How do I train for a marathon?
Answer: To train for a marathon, start training 16 to 20 weeks before the day of the race. Your weekly milage should already be about 30 miles per week, with a strong 10 miler long run each week.  Gradually build your mileage each week by 10% to 20%.  You can also train based on time on your feet, rather than mileage.  Your diet may need to be adjusted depending on the number of days/week you are training and your basal metabolic rate.  Avoid injury by stretching daily and cross-training at least once a week.  Run at least three 18-20 milers within the last 6 weeks of your training.  Two weeks before the race, taper your runs to be shorter, with your last 2 long runs being about 15-17 miles and 10-12 miles.
Question: Give me a list of the venues that were used for the US Open Golf tournament through the years 1986-1990
Answer: 1986 - Shinnecock Hills Golf Club
1987 - Olympic Club (Lake Course)
1988 - The Country Club (Composite Course)
1989 - Oak Hill (East Course)
1990 - Medinah Country Club (Course No. 3)
Question: given this reference text about 1891 Cypriot legislative election, What caused the non-Muslim seats to drop in 1886 to 10,030?
Evidence: The Legislative Council consisted of six official members appointed by the High Commissioner and twelve elected members, three of which were Muslims and nine of which were non-Muslims.

The island was divided into three constituencies, each formed of two districts. Each constituency elected one Muslim and three non-Muslims. Muslim voters had one vote, whilst non-Muslims could vote for up to three candidates. Due to the high levels of illiteracy, voting was not secret, with voters required to tell the polling officers their candidates of choice, often in front of agents of the candidates.

Suffrage was limited to men aged over 21 who had been permanent residents during the five years prior to the elections, and who had paid either the verghi tax (which was levied as an annual payment of one four-thousandth of a property's value, 4% of rental income or 3% on profits or salaries) or rent for a dwelling or shop within the last year. However, of the estimated 40,000 taxpayers in the territory at the time of the elections, only those that had been able to pay their taxes on time were able to register to vote. The number of registered voters for the non-Muslim seats dropped from 15,408 in 1886 to 10,030.
Answer: Due to the requirement of paying your taxes on time in the previous year to be able to register to vote, the number of voters that were non-Muslim dropped.
Question: Give me a list of the teams that have won the English Premier League
Answer: - Manchester United (13 times, most recently 2012-13)
- Blackburn Rovers (Once, 1994-95)
- Arsenal (Three times, most recently 2003-04)
- Chelsea (Five times, most recently 2016-17)
- Manchester City (Six times, most recently 2021-22)
- Leicester City (Once, 2015-16)
- Liverpool (Once, 2019-20)
Question: What type of music is featured in the album The Great Ray Charles
Evidence: The Great Ray Charles is the second studio album by American musician Ray Charles, released in 1957 by Atlantic Records. An instrumental jazz album, it features cover art designed by Marvin Israel. Later CD re-issues of The Great Ray Charles often include six out of the eight songs from the 1961 album The Genius After Hours, as bonus tracks.
Answer: instrumental jazz
Question: How do you get dressed in the morning?
Answer: Typically, you would put your underwear on first. Then most people would put their socks on, followed by a T-shirt or undershirt, and then any type of dress shirt. After that you would put your pants on, and if you were a belt you would put a belt on, then you would put your  shoes on, and then typically people would do their hair. After that, you were ready to take on the day!
Question: what is the architecture of the baseline model?
Evidence: Our baseline model is composed of the following typical components: word embedding, input encoder, alignment, aggregation, and prediction. Below we discuss these components in more details.
word embedding, input encoder, alignment, aggregation, and prediction
Answer: The answers are shown as follows:
* word embedding, input encoder, alignment, aggregation, and prediction.
Question: Who proposed the Gini coefficient as a measure of inequality of income or wealth?
Evidence: In economics, the Gini coefficient, also known as the Gini index or Gini ratio, is a measure of statistical dispersion intended to represent the income inequality or the wealth inequality or the consumption inequality within a nation or a social group. It was developed by statistician and sociologist Corrado Gini.
The Gini coefficient measures the inequality among values of a frequency distribution, such as levels of income. A Gini coefficient of 0 reflects perfect equality, where all income or wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal inequality among values. For example, if everyone has the same income, the Gini coefficient will be 0. In contrast, a Gini coefficient of 1 indicates that within a group of people, a single individual has all the income or consumption, while all others have none.
The Gini coefficient was proposed by Corrado Gini as a measure of inequality of income or wealth.  For OECD countries, in the late 20th century, considering the effect of taxes and transfer payments, the income Gini coefficient ranged between 0.24 and 0.49, with Slovenia being the lowest and Mexico the highest. African countries had the highest pre-tax Gini coefficients in 2008–2009, with South Africa having the world's highest, estimated to be 0.63 to 0.7, although this figure drops to 0.52 after social assistance is taken into account, and drops again to 0.47 after taxation. The global income Gini coefficient in 2005 has been estimated to be between 0.61 and 0.68 by various sources.
Answer: The Gini coefficient was proposed by Corrado Gini as a measure of inequality of income or wealth.
Question: Where is New Hampshire?
Answer: Location is a relative concept, although for the sake of this response, let's assume that you are looking at a standard map of the world built using the widely accepted Mercator projection. New Hampshire is a state located in the north east corner of the United States of America, which itself is located in the Northern and Western hemispheres. New Hampshire is bordered on 4 sides by Massachusetts to the south, Vermont to the west, Maine to the east, and Canada to the north.
Question: Who first described the Euphalacra lacunata
Answer: Euphalacra lacunata is a moth in the family Drepanidae. It was described by Jeremy Daniel Holloway in 1998. It is found on Borneo and Peninsular Malaysia.
Question: Please provide a list of best movies from Shahrukh Khan.
Answer: Not all these movies might have made big box office warnings but they brought acclaim to Shahrukh Khan.

My Name is Khan
Pathaan 
Baazigar 
Duplicate 
Kal Ho Naa Ho 
Kabhi Alvida Naa Ho
Dil Toh Pagal Hai 
Dilwaale Dulhaniya Le Jayenge 
Kuch Kuch Hota Hai 
Kabhi Khushi Kabhi Gham
Main Hoon Na 
Don 
Chakh De India 
Veer Zara 

And many others.
Question: What is the difference between the full test set and the hard test set?
Evidence: The dataset MSParS is published by NLPCC 2019 evaluation task. The whole dataset consists of 81,826 samples annotated by native English speakers. 80% of them are used as training set. 10% of them are used as validation set while the rest is used as test set. 3000 hard samples are selected from the test set. Metric for this dataset is the exactly matching accuracy on both full test set and hard test subset. Each sample is composed of the question, the logical form, the parameters(entity/value/type) and question type as the Table TABREF3 demonstrates.
The whole dataset consists of 81,826 samples annotated by native English speakers. 80% of them are used as training set. 10% of them are used as validation set while the rest is used as test set. 3000 hard samples are selected from the test set.
Answer: The answers are shown as follows:
* 3000 hard samples are selected from the test set
Question: What are the major industries in Seattle today?
Evidence: Logging was Seattle's first major industry, but by the late 19th century, the city had become a commercial and shipbuilding center as a gateway to Alaska during the Klondike Gold Rush. Growth after World War II was partially due to the local Boeing company, which established Seattle as a center for aircraft manufacturing. The Seattle area developed into a technology center from the 1980s onwards with companies like Microsoft becoming established in the region; Microsoft founder Bill Gates is a Seattleite by birth. Internet retailer Amazon was founded in Seattle in 1994, and major airline Alaska Airlines is based in SeaTac, Washington, serving Seattle's international airport, Seattle–Tacoma International Airport. The stream of new software, biotechnology, and Internet companies led to an economic revival, which increased the city's population by almost 50,000 between 1990 and 2000.
Answer: In the late 20th and early 21st century, the largest industries in Seattle were software, aviation, and biotechnology. Significant companies are Boeing, Microsoft, Alaska Airlines, and Amazon.
Question: What is it like to live in Arizona?
Answer: Arizona, the Grand Canyon State, is a unique and beautiful place to live.  Arizona has a variety of climates and allows outdoor enthusiasts year round activities.  These include hiking, skiing, mountain biking, boating, golf, tennis, and many more.  Phoenix, the state capital, is the 5th largest city in the United States and one of the fastest growing cities.  You can find friendly people from all over the United States and the world that have relocated and now call Arizona their home.  Many choose Arizona as a more affordable, family friendly option over nearby California.  People also enjoy the year round sunshine and warmth. 
 Arizona is also a popular tourist destination and offers many options for lodging and dining.  It's famous for its resorts, spas, and golf courses.  For people who live in colder climates, many flock to Arizona during the winter months to enjoy the amazing weather.
Question: Think up 4 different cities in California
Answer: Four cities in California are San Francisco, San Diego, Santa Barbara and Los Angeles
Question: What representation does ELMo-style pretraining discover?
Evidence: Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain. We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques. First, we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data. Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines. Further analysis using black-box probes confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo's embeddings. We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant.
We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant.
Answer: Representations which make additional awareness of shallow syntax redundant.
Question: Where is Dieveniskes located
Evidence: Dieveniškės (in Lithuanian literally: Place of gods; Polish: Dziewieniszki; Belarusian: Дзевянішкі Dzevyanishki) is a town in the Vilnius County of Lithuania, about 6 kilometres (3.7 mi) from the Belarusian border in the so-called Dieveniškės appendix. It is surrounded by the Dieveniškės Regional Park.
Answer: Dieveniskes, in Lithuanian literally: Place of gods is a town in the Vilnius County of Lithuania, about 6 kilometres (3.7 mi) from the Belarusian border in the so-called Dieveniskes appendix. It is surrounded by the Dieveniskes Regional Park.
Question: Why do sheep keepers paint their sheep?
Evidence: Another trait unique to domestic sheep as compared to wild ovines is their wide variation in color. Wild sheep are largely variations of brown hues, and variation within species is extremely limited. Colors of domestic sheep range from pure white to dark chocolate brown, and even spotted or piebald. Sheep keepers also sometimes artificially paint "smit marks" onto their sheep in any pattern or color for identification. Selection for easily dyeable white fleeces began early in sheep domestication, and as white wool is a dominant trait it spread quickly. However, colored sheep do appear in many modern breeds, and may even appear as a recessive trait in white flocks. While white wool is desirable for large commercial markets, there is a niche market for colored fleeces, mostly for handspinning. The nature of the fleece varies widely among the breeds, from dense and highly crimped, to long and hairlike. There is variation of wool type and quality even among members of the same flock, so wool classing is a step in the commercial processing of the fibre.
Answer: Sheep keepers sometimes artificially paint "smit marks" onto their sheep in any pattern or color for identification.
Question: Which one among the following three words is different to the others: Noisy, Friendly, Adventurous? Why?
Answer: Noisy is different to Friendly and Adventurous, as it is in negative sentiment, while the others are positive.
Question: Why do people like cats?
Answer: Cats are very popular pets. Many people have cats. In fact, some will argue that cats are a better pet than a dog. Cats often bond with their favorite human and provide companionship and love. When a cat purrs, it releases endorphins in the cat, and that can also help calm a human and help them release endorphins, too. Cats are independent and sometimes aloof, which may be a deterrent to humans who haven't been around cats much, but they generally warm up and will be a great friend.
Question: What date was the Men's Madison Competition at the 2018 UCI Track Cycling World Championships?
Answer: The Men's madison competition at the 2018 UCI Track Cycling World Championships was held on 4 March 2018.
Question: What's your favorite baseball team?
Answer: As a Large Language Model (LLM), I don't have any favorite baseball teams. People like baseball teams for a variety of reasons which may include their association with the team as a young person, the team's current season performance, or the legacy that the team has such as a prior championship. I think that as long as everyone on the team is having fun and doing their best, they are okay in my book!
Question: How to train the LM by the authors?
Evidence: We train the LM using a 90/10 training/validation split, reporting the validation loss and accuracy of next-word prediction on the validation set. For the LM, we selected an ASGD Weight-Dropped Long Short Term Memory (AWD_LSTM, described in Merity et al. BIBREF5 ) model included in Fast.ai. We replaced the typical Long Short Term Memory (LSTM) units with Quasi Recurrent Neural Network (QRNN, described in Bradbury et al. BIBREF6 ) units. Our network has 2304 hidden-states, 3 layers and a softmax layer to predict the next-word. We tied the embedding weights BIBREF7 on the encoder and decoder for training. We performed some simple tests with LSTM units and a Transformer Language model, finding all models were similar in performance during LM training. We thus chose to use QRNN units due to improved training speed compared to the alternatives. This model has about 60 million trainable parameters.
Parameters used for training and fine-tuning are shown in Table TABREF21 . For all networks we applied a dropout multiplier which scales the dropout used throughout the network. We used the Adam optimizer with weight decay as indicated in the table.
Following the work of Smith BIBREF8 we found the largest learning-rate that we could apply and then ran a one-cycle policy for a single epoch. This largest weight is shown in Table TABREF21 under "Learning Rate." Subsequent training epochs were run with one-cycle and lower learning rates indicated in Table TABREF21 under "Continued Training."
We train the LM using a 90/10 training/validation split, reporting the validation loss and accuracy of next-word prediction on the validation set.
Answer: The authors train the LM using a 90/10 training/validation split, reporting the validation loss and accuracy of next-word prediction on the validation set.